From bb57f30c2de46cfca5419ad01738a41705f96cc3 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Fri, 14 Oct 2022 10:56:41 +0200 Subject: [PATCH 001/152] init --- README.md | 1 + aesthetic_embeddings/insert_embs_here.txt | 0 modules/processing.py | 17 +++- modules/sd_hijack.py | 80 ++++++++++++++++++- modules/shared.py | 5 ++ modules/textual_inversion/dataset.py | 2 +- .../textual_inversion/textual_inversion.py | 35 +++++--- modules/txt2img.py | 11 ++- modules/ui.py | 59 +++++++++----- 9 files changed, 172 insertions(+), 38 deletions(-) create mode 100644 aesthetic_embeddings/insert_embs_here.txt diff --git a/README.md b/README.md index 859a91b6..7b8d018b 100644 --- a/README.md +++ b/README.md @@ -70,6 +70,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args) - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args) +- Aesthetic, a way to generate images with a specific aesthetic by using clip images embds (implementation of https://github.com/vicgalle/stable-diffusion-aesthetic-gradients) ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. diff --git a/aesthetic_embeddings/insert_embs_here.txt b/aesthetic_embeddings/insert_embs_here.txt new file mode 100644 index 00000000..e69de29b diff --git a/modules/processing.py b/modules/processing.py index d5172f00..9a033759 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -316,11 +316,16 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip() -def process_images(p: StableDiffusionProcessing) -> Processed: +def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, + aesthetic_imgs=None,aesthetic_slerp=False) -> Processed: """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch""" + aesthetic_lr = float(aesthetic_lr) + aesthetic_weight = float(aesthetic_weight) + aesthetic_steps = int(aesthetic_steps) + if type(p.prompt) == list: - assert(len(p.prompt) > 0) + assert (len(p.prompt) > 0) else: assert p.prompt is not None @@ -394,7 +399,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed: #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) #c = p.sd_model.get_learned_conditioning(prompts) with devices.autocast(): - uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) + if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"): + shared.sd_model.cond_stage_model.set_aesthetic_params(0, 0, 0) + uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], + p.steps) + if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"): + shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight, + aesthetic_steps, aesthetic_imgs,aesthetic_slerp) c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps) if len(model_hijack.comments) > 0: diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index c81722a0..6d5196fe 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -9,11 +9,14 @@ from torch.nn.functional import silu import modules.textual_inversion.textual_inversion from modules import prompt_parser, devices, sd_hijack_optimizations, shared -from modules.shared import opts, device, cmd_opts +from modules.shared import opts, device, cmd_opts, aesthetic_embeddings from modules.sd_hijack_optimizations import invokeAI_mps_available import ldm.modules.attention import ldm.modules.diffusionmodules.model +from transformers import CLIPVisionModel, CLIPModel +import torch.optim as optim +import copy attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity @@ -109,13 +112,29 @@ class StableDiffusionModelHijack: _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text]) return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count) +def slerp(low, high, val): + low_norm = low/torch.norm(low, dim=1, keepdim=True) + high_norm = high/torch.norm(high, dim=1, keepdim=True) + omega = torch.acos((low_norm*high_norm).sum(1)) + so = torch.sin(omega) + res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high + return res class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def __init__(self, wrapped, hijack): super().__init__() self.wrapped = wrapped + self.clipModel = CLIPModel.from_pretrained( + self.wrapped.transformer.name_or_path + ) + del self.clipModel.vision_model self.hijack: StableDiffusionModelHijack = hijack self.tokenizer = wrapped.tokenizer + # self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval() + self.image_embs_name = None + self.image_embs = None + self.load_image_embs(None) + self.token_mults = {} self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ','][0] @@ -136,6 +155,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if mult != 1.0: self.token_mults[ident] = mult + def set_aesthetic_params(self, aesthetic_lr, aesthetic_weight, aesthetic_steps, image_embs_name=None, + aesthetic_slerp=True): + self.slerp = aesthetic_slerp + self.aesthetic_lr = aesthetic_lr + self.aesthetic_weight = aesthetic_weight + self.aesthetic_steps = aesthetic_steps + self.load_image_embs(image_embs_name) + + def load_image_embs(self, image_embs_name): + if image_embs_name is None or len(image_embs_name) == 0: + image_embs_name = None + if image_embs_name is not None and self.image_embs_name != image_embs_name: + self.image_embs_name = image_embs_name + self.image_embs = torch.load(aesthetic_embeddings[self.image_embs_name], map_location=device) + self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True) + self.image_embs.requires_grad_(False) + def tokenize_line(self, line, used_custom_terms, hijack_comments): id_end = self.wrapped.tokenizer.eos_token_id @@ -333,7 +369,47 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): z1 = self.process_tokens(tokens, multipliers) z = z1 if z is None else torch.cat((z, z1), axis=-2) - + + if len(text[ + 0]) != 0 and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None: + if not opts.use_old_emphasis_implementation: + remade_batch_tokens = [ + [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in + remade_batch_tokens] + + tokens = torch.asarray(remade_batch_tokens).to(device) + with torch.enable_grad(): + model = copy.deepcopy(self.clipModel).to(device) + model.requires_grad_(True) + + # We optimize the model to maximize the similarity + optimizer = optim.Adam( + model.text_model.parameters(), lr=self.aesthetic_lr + ) + + for i in range(self.aesthetic_steps): + text_embs = model.get_text_features(input_ids=tokens) + text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True) + sim = text_embs @ self.image_embs.T + loss = -sim + optimizer.zero_grad() + loss.mean().backward() + optimizer.step() + + zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers) + if opts.CLIP_stop_at_last_layers > 1: + zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers] + zn = model.text_model.final_layer_norm(zn) + else: + zn = zn.last_hidden_state + model.cpu() + del model + + if self.slerp: + z = slerp(z, zn, self.aesthetic_weight) + else: + z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight + remade_batch_tokens = rem_tokens batch_multipliers = rem_multipliers i += 1 diff --git a/modules/shared.py b/modules/shared.py index 5901e605..cf13a10d 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -30,6 +30,8 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)") parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)") +parser.add_argument("--aesthetic_embeddings-dir", type=str, default=os.path.join(script_path, 'aesthetic_embeddings'), + help="aesthetic_embeddings directory(default: aesthetic_embeddings)") parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory") parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage") @@ -90,6 +92,9 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True) hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir) loaded_hypernetwork = None +aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in + os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} + def reload_hypernetworks(): global hypernetworks diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 67e90afe..59b2b021 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -48,7 +48,7 @@ class PersonalizedBase(Dataset): print("Preparing dataset...") for path in tqdm.tqdm(self.image_paths): try: - image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC) + image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.Resampling.BICUBIC) except Exception: continue diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index fa0e33a2..b12a8e6d 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -172,7 +172,15 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn -def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt): +def batched(dataset, total, n=1): + for ndx in range(0, total, n): + yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))] + + +def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, + create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, + preview_image_prompt, batch_size=1, + gradient_accumulation=1): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -204,7 +212,11 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, + height=training_height, + repeats=shared.opts.training_image_repeats_per_epoch, + placeholder_token=embedding_name, model=shared.sd_model, + device=devices.device, template_file=template_file) hijack = sd_hijack.model_hijack @@ -223,7 +235,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate) - pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) + pbar = tqdm.tqdm(enumerate(batched(ds, steps - ititial_step, batch_size)), total=steps - ititial_step) for i, entry in pbar: embedding.step = i + ititial_step @@ -235,17 +247,20 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini break with torch.autocast("cuda"): - c = cond_model([entry.cond_text]) + c = cond_model([e.cond_text for e in entry]) + + x = torch.stack([e.latent for e in entry]).to(devices.device) + loss = shared.sd_model(x, c)[0] - x = entry.latent.to(devices.device) - loss = shared.sd_model(x.unsqueeze(0), c)[0] del x losses[embedding.step % losses.shape[0]] = loss.item() - optimizer.zero_grad() loss.backward() - optimizer.step() + if ((i + 1) % gradient_accumulation == 0) or (i + 1 == steps - ititial_step): + optimizer.step() + optimizer.zero_grad() + epoch_num = embedding.step // len(ds) epoch_step = embedding.step - (epoch_num * len(ds)) + 1 @@ -259,7 +274,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png') - preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt + preview_text = entry[0].cond_text if preview_image_prompt == "" else preview_image_prompt p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, @@ -305,7 +320,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini

Loss: {losses.mean():.7f}
Step: {embedding.step}
-Last prompt: {html.escape(entry.cond_text)}
+Last prompt: {html.escape(entry[-1].cond_text)}
Last saved embedding: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

diff --git a/modules/txt2img.py b/modules/txt2img.py index e985242b..78342024 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -6,7 +6,14 @@ import modules.processing as processing from modules.ui import plaintext_to_html -def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, *args): +def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, + restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, + subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, + height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, + aesthetic_lr=0, + aesthetic_weight=0, aesthetic_steps=0, + aesthetic_imgs=None, + aesthetic_slerp=False, *args): p = StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, @@ -40,7 +47,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: processed = modules.scripts.scripts_txt2img.run(p, *args) if processed is None: - processed = process_images(p) + processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp) shared.total_tqdm.clear() diff --git a/modules/ui.py b/modules/ui.py index 220fb80b..d961d126 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -24,7 +24,8 @@ import gradio.routes from modules import sd_hijack from modules.paths import script_path -from modules.shared import opts, cmd_opts +from modules.shared import opts, cmd_opts,aesthetic_embeddings + if cmd_opts.deepdanbooru: from modules.deepbooru import get_deepbooru_tags import modules.shared as shared @@ -534,6 +535,14 @@ def create_ui(wrap_gradio_gpu_call): width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + with gr.Group(): + aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005") + aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.7) + aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=50) + + aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None) + aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) + with gr.Row(): restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) tiling = gr.Checkbox(label='Tiling', value=False) @@ -586,25 +595,30 @@ def create_ui(wrap_gradio_gpu_call): fn=wrap_gradio_gpu_call(modules.txt2img.txt2img), _js="submit", inputs=[ - txt2img_prompt, - txt2img_negative_prompt, - txt2img_prompt_style, - txt2img_prompt_style2, - steps, - sampler_index, - restore_faces, - tiling, - batch_count, - batch_size, - cfg_scale, - seed, - subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, - height, - width, - enable_hr, - scale_latent, - denoising_strength, - ] + custom_inputs, + txt2img_prompt, + txt2img_negative_prompt, + txt2img_prompt_style, + txt2img_prompt_style2, + steps, + sampler_index, + restore_faces, + tiling, + batch_count, + batch_size, + cfg_scale, + seed, + subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, + height, + width, + enable_hr, + scale_latent, + denoising_strength, + aesthetic_lr, + aesthetic_weight, + aesthetic_steps, + aesthetic_imgs, + aesthetic_slerp + ] + custom_inputs, outputs=[ txt2img_gallery, generation_info, @@ -1097,6 +1111,9 @@ def create_ui(wrap_gradio_gpu_call): template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + batch_size = gr.Slider(minimum=1, maximum=64, step=1, label="Batch Size", value=4) + gradient_accumulation = gr.Slider(minimum=1, maximum=256, step=1, label="Gradient accumulation", + value=1) steps = gr.Number(label='Max steps', value=100000, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) @@ -1180,6 +1197,8 @@ def create_ui(wrap_gradio_gpu_call): template_file, save_image_with_stored_embedding, preview_image_prompt, + batch_size, + gradient_accumulation ], outputs=[ ti_output, From 37d7ffb415cd8c69b3c0bb5f61844dde0b169f78 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 15:59:37 +0200 Subject: [PATCH 002/152] fix to tokens lenght, addend embs generator, add new features to edit the embedding before the generation using text --- modules/aesthetic_clip.py | 78 ++++++++++++++++++++ modules/processing.py | 148 +++++++++++++++++++++++++------------- modules/sd_hijack.py | 111 ++++++++++++++++++---------- modules/shared.py | 4 ++ modules/txt2img.py | 10 ++- modules/ui.py | 47 ++++++++++-- 6 files changed, 302 insertions(+), 96 deletions(-) create mode 100644 modules/aesthetic_clip.py diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py new file mode 100644 index 00000000..f15cfd47 --- /dev/null +++ b/modules/aesthetic_clip.py @@ -0,0 +1,78 @@ +import itertools +import os +from pathlib import Path +import html +import gc + +import gradio as gr +import torch +from PIL import Image +from modules import shared +from modules.shared import device, aesthetic_embeddings +from transformers import CLIPModel, CLIPProcessor + +from tqdm.auto import tqdm + + +def get_all_images_in_folder(folder): + return [os.path.join(folder, f) for f in os.listdir(folder) if + os.path.isfile(os.path.join(folder, f)) and check_is_valid_image_file(f)] + + +def check_is_valid_image_file(filename): + return filename.lower().endswith(('.png', '.jpg', '.jpeg')) + + +def batched(dataset, total, n=1): + for ndx in range(0, total, n): + yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))] + + +def iter_to_batched(iterable, n=1): + it = iter(iterable) + while True: + chunk = tuple(itertools.islice(it, n)) + if not chunk: + return + yield chunk + + +def generate_imgs_embd(name, folder, batch_size): + # clipModel = CLIPModel.from_pretrained( + # shared.sd_model.cond_stage_model.clipModel.name_or_path + # ) + model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path).to(device) + processor = CLIPProcessor.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path) + + with torch.no_grad(): + embs = [] + for paths in tqdm(iter_to_batched(get_all_images_in_folder(folder), batch_size), + desc=f"Generating embeddings for {name}"): + if shared.state.interrupted: + break + inputs = processor(images=[Image.open(path) for path in paths], return_tensors="pt").to(device) + outputs = model.get_image_features(**inputs).cpu() + embs.append(torch.clone(outputs)) + inputs.to("cpu") + del inputs, outputs + + embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True) + + # The generated embedding will be located here + path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt") + torch.save(embs, path) + + model = model.cpu() + del model + del processor + del embs + gc.collect() + torch.cuda.empty_cache() + res = f""" + Done generating embedding for {name}! + Hypernetwork saved to {html.escape(path)} + """ + shared.update_aesthetic_embeddings() + return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", + value=sorted(aesthetic_embeddings.keys())[0] if len( + aesthetic_embeddings) > 0 else None), res, "" diff --git a/modules/processing.py b/modules/processing.py index 9a033759..ab68d63a 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -20,7 +20,6 @@ import modules.images as images import modules.styles import logging - # some of those options should not be changed at all because they would break the model, so I removed them from options. opt_C = 4 opt_f = 8 @@ -52,8 +51,13 @@ def get_correct_sampler(p): elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img): return sd_samplers.samplers_for_img2img + class StableDiffusionProcessing: - def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None): + def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, + subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, + sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, + restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, + extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None): self.sd_model = sd_model self.outpath_samples: str = outpath_samples self.outpath_grids: str = outpath_grids @@ -104,7 +108,8 @@ class StableDiffusionProcessing: class Processed: - def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None): + def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, + all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None): self.images = images_list self.prompt = p.prompt self.negative_prompt = p.negative_prompt @@ -141,7 +146,8 @@ class Processed: self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0] self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0] self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) - self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1 + self.subseed = int( + self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1 self.all_prompts = all_prompts or [self.prompt] self.all_seeds = all_seeds or [self.seed] @@ -181,39 +187,43 @@ class Processed: return json.dumps(obj) - def infotext(self, p: StableDiffusionProcessing, index): - return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size) + def infotext(self, p: StableDiffusionProcessing, index): + return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], + position_in_batch=index % self.batch_size, iteration=index // self.batch_size) # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3 def slerp(val, low, high): - low_norm = low/torch.norm(low, dim=1, keepdim=True) - high_norm = high/torch.norm(high, dim=1, keepdim=True) - dot = (low_norm*high_norm).sum(1) + low_norm = low / torch.norm(low, dim=1, keepdim=True) + high_norm = high / torch.norm(high, dim=1, keepdim=True) + dot = (low_norm * high_norm).sum(1) if dot.mean() > 0.9995: return low * val + high * (1 - val) omega = torch.acos(dot) so = torch.sin(omega) - res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high + res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high return res -def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None): +def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, + p=None): xs = [] # if we have multiple seeds, this means we are working with batch size>1; this then # enables the generation of additional tensors with noise that the sampler will use during its processing. # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to # produce the same images as with two batches [100], [101]. - if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0): + if p is not None and p.sampler is not None and ( + len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0): sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))] else: sampler_noises = None for i, seed in enumerate(seeds): - noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8) + noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else ( + shape[0], seed_resize_from_h // 8, seed_resize_from_w // 8) subnoise = None if subseeds is not None: @@ -241,7 +251,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see dx = max(-dx, 0) dy = max(-dy, 0) - x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w] + x[:, ty:ty + h, tx:tx + w] = noise[:, dy:dy + h, dx:dx + w] noise = x if sampler_noises is not None: @@ -293,14 +303,20 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Seed": all_seeds[index], "Face restoration": (opts.face_restoration_model if p.restore_faces else None), "Size": f"{p.width}x{p.height}", - "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), - "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), - "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')), + "Model hash": getattr(p, 'sd_model_hash', + None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), + "Model": ( + None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace( + ',', '').replace(':', '')), + "Hypernet": ( + None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace( + ':', '')), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength), - "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), + "Seed resize from": ( + None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), "Denoising strength": getattr(p, 'denoising_strength', None), "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta), "Clip skip": None if clip_skip <= 1 else clip_skip, @@ -309,7 +325,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration generation_params.update(p.extra_generation_params) - generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None]) + generation_params_text = ", ".join( + [k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None]) negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else "" @@ -317,7 +334,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, - aesthetic_imgs=None,aesthetic_slerp=False) -> Processed: + aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", + aesthetic_slerp_angle=0.15, + aesthetic_text_negative=False) -> Processed: """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch""" aesthetic_lr = float(aesthetic_lr) @@ -385,7 +404,7 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh for n in range(p.n_iter): if state.skipped: state.skipped = False - + if state.interrupted: break @@ -396,16 +415,19 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh if (len(prompts) == 0): break - #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) - #c = p.sd_model.get_learned_conditioning(prompts) + # uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) + # c = p.sd_model.get_learned_conditioning(prompts) with devices.autocast(): if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"): - shared.sd_model.cond_stage_model.set_aesthetic_params(0, 0, 0) + shared.sd_model.cond_stage_model.set_aesthetic_params() uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"): shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight, - aesthetic_steps, aesthetic_imgs,aesthetic_slerp) + aesthetic_steps, aesthetic_imgs, + aesthetic_slerp, aesthetic_imgs_text, + aesthetic_slerp_angle, + aesthetic_text_negative) c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps) if len(model_hijack.comments) > 0: @@ -413,13 +435,13 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh comments[comment] = 1 if p.n_iter > 1: - shared.state.job = f"Batch {n+1} out of {p.n_iter}" + shared.state.job = f"Batch {n + 1} out of {p.n_iter}" with devices.autocast(): - samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) + samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, + subseed_strength=p.subseed_strength) if state.interrupted or state.skipped: - # if we are interrupted, sample returns just noise # use the image collected previously in sampler loop samples_ddim = shared.state.current_latent @@ -445,7 +467,9 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh if p.restore_faces: if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration: - images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration") + images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], + opts.samples_format, info=infotext(n, i), p=p, + suffix="-before-face-restoration") devices.torch_gc() @@ -456,7 +480,8 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh if p.color_corrections is not None and i < len(p.color_corrections): if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction: - images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction") + images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, + info=infotext(n, i), p=p, suffix="-before-color-correction") image = apply_color_correction(p.color_corrections[i], image) if p.overlay_images is not None and i < len(p.overlay_images): @@ -474,7 +499,8 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh image = image.convert('RGB') if opts.samples_save and not p.do_not_save_samples: - images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p) + images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, + info=infotext(n, i), p=p) text = infotext(n, i) infotexts.append(text) @@ -482,7 +508,7 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh image.info["parameters"] = text output_images.append(image) - del x_samples_ddim + del x_samples_ddim devices.torch_gc() @@ -504,10 +530,13 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh index_of_first_image = 1 if opts.grid_save: - images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True) + images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, + info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True) devices.torch_gc() - return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts) + return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), + subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, + index_of_first_image=index_of_first_image, infotexts=infotexts) class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): @@ -543,25 +572,34 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) if not self.enable_hr: - x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, + subseeds=subseeds, subseed_strength=self.subseed_strength, + seed_resize_from_h=self.seed_resize_from_h, + seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) return samples - x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, + subseeds=subseeds, subseed_strength=self.subseed_strength, + seed_resize_from_h=self.seed_resize_from_h, + seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f - samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2] + samples = samples[:, :, truncate_y // 2:samples.shape[2] - truncate_y // 2, + truncate_x // 2:samples.shape[3] - truncate_x // 2] if self.scale_latent: - samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") + samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), + mode="bilinear") else: decoded_samples = decode_first_stage(self.sd_model, samples) if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None": - decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear") + decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), + mode="bilinear") else: lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) @@ -585,13 +623,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) - noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, + subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, + seed_resize_from_w=self.seed_resize_from_w, p=self) # GC now before running the next img2img to prevent running out of memory x = None devices.torch_gc() - samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) + samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, + steps=self.steps) return samples @@ -599,7 +640,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None - def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs): + def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, + inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, + **kwargs): super().__init__(**kwargs) self.init_images = init_images @@ -607,7 +650,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.denoising_strength: float = denoising_strength self.init_latent = None self.image_mask = mask - #self.image_unblurred_mask = None + # self.image_unblurred_mask = None self.latent_mask = None self.mask_for_overlay = None self.mask_blur = mask_blur @@ -619,7 +662,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.nmask = None def init(self, all_prompts, all_seeds, all_subseeds): - self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model) + self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, + self.sd_model) crop_region = None if self.image_mask is not None: @@ -628,7 +672,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): if self.inpainting_mask_invert: self.image_mask = ImageOps.invert(self.image_mask) - #self.image_unblurred_mask = self.image_mask + # self.image_unblurred_mask = self.image_mask if self.mask_blur > 0: self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur)) @@ -642,7 +686,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): mask = mask.crop(crop_region) self.image_mask = images.resize_image(2, mask, self.width, self.height) - self.paste_to = (x1, y1, x2-x1, y2-y1) + self.paste_to = (x1, y1, x2 - x1, y2 - y1) else: self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height) np_mask = np.array(self.image_mask) @@ -665,7 +709,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): if self.image_mask is not None: image_masked = Image.new('RGBa', (image.width, image.height)) - image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L'))) + image_masked.paste(image.convert("RGBA").convert("RGBa"), + mask=ImageOps.invert(self.mask_for_overlay.convert('L'))) self.overlay_images.append(image_masked.convert('RGBA')) @@ -714,12 +759,17 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): # this needs to be fixed to be done in sample() using actual seeds for batches if self.inpainting_fill == 2: - self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask + self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], + all_seeds[ + 0:self.init_latent.shape[ + 0]]) * self.nmask elif self.inpainting_fill == 3: self.init_latent = self.init_latent * self.mask def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): - x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, + subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, + seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 6d5196fe..192883b2 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -14,7 +14,8 @@ from modules.sd_hijack_optimizations import invokeAI_mps_available import ldm.modules.attention import ldm.modules.diffusionmodules.model -from transformers import CLIPVisionModel, CLIPModel +from tqdm import trange +from transformers import CLIPVisionModel, CLIPModel, CLIPTokenizer import torch.optim as optim import copy @@ -22,21 +23,25 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward + def apply_optimizations(): undo_optimizations() ldm.modules.diffusionmodules.model.nonlinearity = silu - if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)): + if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and ( + 6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)): print("Applying xformers cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward elif cmd_opts.opt_split_attention_v1: print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 - elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()): + elif not cmd_opts.disable_opt_split_attention and ( + cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()): if not invokeAI_mps_available and shared.device.type == 'mps': - print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.") + print( + "The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.") print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 else: @@ -112,14 +117,16 @@ class StableDiffusionModelHijack: _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text]) return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count) + def slerp(low, high, val): - low_norm = low/torch.norm(low, dim=1, keepdim=True) - high_norm = high/torch.norm(high, dim=1, keepdim=True) - omega = torch.acos((low_norm*high_norm).sum(1)) + low_norm = low / torch.norm(low, dim=1, keepdim=True) + high_norm = high / torch.norm(high, dim=1, keepdim=True) + omega = torch.acos((low_norm * high_norm).sum(1)) so = torch.sin(omega) - res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high + res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high return res + class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def __init__(self, wrapped, hijack): super().__init__() @@ -128,6 +135,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): self.wrapped.transformer.name_or_path ) del self.clipModel.vision_model + self.tokenizer = CLIPTokenizer.from_pretrained(self.wrapped.transformer.name_or_path) self.hijack: StableDiffusionModelHijack = hijack self.tokenizer = wrapped.tokenizer # self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval() @@ -139,7 +147,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ','][0] - tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k] + tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if + '(' in k or ')' in k or '[' in k or ']' in k] for text, ident in tokens_with_parens: mult = 1.0 for c in text: @@ -155,8 +164,13 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if mult != 1.0: self.token_mults[ident] = mult - def set_aesthetic_params(self, aesthetic_lr, aesthetic_weight, aesthetic_steps, image_embs_name=None, - aesthetic_slerp=True): + def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None, + aesthetic_slerp=True, aesthetic_imgs_text="", + aesthetic_slerp_angle=0.15, + aesthetic_text_negative=False): + self.aesthetic_imgs_text = aesthetic_imgs_text + self.aesthetic_slerp_angle = aesthetic_slerp_angle + self.aesthetic_text_negative = aesthetic_text_negative self.slerp = aesthetic_slerp self.aesthetic_lr = aesthetic_lr self.aesthetic_weight = aesthetic_weight @@ -180,7 +194,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): else: parsed = [[line, 1.0]] - tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"] + tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)[ + "input_ids"] fixes = [] remade_tokens = [] @@ -196,18 +211,20 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if token == self.comma_token: last_comma = len(remade_tokens) - elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack: + elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), + 1) % 75 == 0 and last_comma != -1 and len( + remade_tokens) - last_comma <= opts.comma_padding_backtrack: last_comma += 1 reloc_tokens = remade_tokens[last_comma:] reloc_mults = multipliers[last_comma:] remade_tokens = remade_tokens[:last_comma] length = len(remade_tokens) - + rem = int(math.ceil(length / 75)) * 75 - length remade_tokens += [id_end] * rem + reloc_tokens multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults - + if embedding is None: remade_tokens.append(token) multipliers.append(weight) @@ -248,7 +265,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if line in cache: remade_tokens, fixes, multipliers = cache[line] else: - remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments) + remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, + hijack_comments) token_count = max(current_token_count, token_count) cache[line] = (remade_tokens, fixes, multipliers) @@ -259,7 +277,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count - def process_text_old(self, text): id_start = self.wrapped.tokenizer.bos_token_id id_end = self.wrapped.tokenizer.eos_token_id @@ -289,7 +306,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): while i < len(tokens): token = tokens[i] - embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i) + embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, + i) mult_change = self.token_mults.get(token) if opts.enable_emphasis else None if mult_change is not None: @@ -312,11 +330,12 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): ovf = remade_tokens[maxlen - 2:] overflowing_words = [vocab.get(int(x), "") for x in ovf] overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words)) - hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n") + hijack_comments.append( + f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n") token_count = len(remade_tokens) remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens)) - remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end] + remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end] cache[tuple_tokens] = (remade_tokens, fixes, multipliers) multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers)) @@ -326,23 +345,26 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): hijack_fixes.append(fixes) batch_multipliers.append(multipliers) return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count - + def forward(self, text): use_old = opts.use_old_emphasis_implementation if use_old: - batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text) + batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old( + text) else: - batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text) + batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text( + text) self.hijack.comments += hijack_comments if len(used_custom_terms) > 0: - self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) - + self.hijack.comments.append( + "Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) + if use_old: self.hijack.fixes = hijack_fixes return self.process_tokens(remade_batch_tokens, batch_multipliers) - + z = None i = 0 while max(map(len, remade_batch_tokens)) != 0: @@ -356,7 +378,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if fix[0] == i: fixes.append(fix[1]) self.hijack.fixes.append(fixes) - + tokens = [] multipliers = [] for j in range(len(remade_batch_tokens)): @@ -378,19 +400,30 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): remade_batch_tokens] tokens = torch.asarray(remade_batch_tokens).to(device) + + model = copy.deepcopy(self.clipModel).to(device) + model.requires_grad_(True) + if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0: + text_embs_2 = model.get_text_features( + **self.tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device)) + if self.aesthetic_text_negative: + text_embs_2 = self.image_embs - text_embs_2 + text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True) + img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle) + else: + img_embs = self.image_embs + with torch.enable_grad(): - model = copy.deepcopy(self.clipModel).to(device) - model.requires_grad_(True) # We optimize the model to maximize the similarity optimizer = optim.Adam( model.text_model.parameters(), lr=self.aesthetic_lr ) - for i in range(self.aesthetic_steps): + for i in trange(self.aesthetic_steps, desc="Aesthetic optimization"): text_embs = model.get_text_features(input_ids=tokens) text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True) - sim = text_embs @ self.image_embs.T + sim = text_embs @ img_embs.T loss = -sim optimizer.zero_grad() loss.mean().backward() @@ -405,6 +438,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): model.cpu() del model + zn = torch.concat([zn for i in range(z.shape[1] // 77)], 1) if self.slerp: z = slerp(z, zn, self.aesthetic_weight) else: @@ -413,15 +447,16 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): remade_batch_tokens = rem_tokens batch_multipliers = rem_multipliers i += 1 - + return z - - + def process_tokens(self, remade_batch_tokens, batch_multipliers): if not opts.use_old_emphasis_implementation: - remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens] + remade_batch_tokens = [ + [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in + remade_batch_tokens] batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers] - + tokens = torch.asarray(remade_batch_tokens).to(device) outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers) @@ -461,8 +496,8 @@ class EmbeddingsWithFixes(torch.nn.Module): for fixes, tensor in zip(batch_fixes, inputs_embeds): for offset, embedding in fixes: emb = embedding.vec - emb_len = min(tensor.shape[0]-offset-1, emb.shape[0]) - tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]]) + emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0]) + tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]]) vecs.append(tensor) diff --git a/modules/shared.py b/modules/shared.py index cf13a10d..7cd608ca 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -95,6 +95,10 @@ loaded_hypernetwork = None aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} +def update_aesthetic_embeddings(): + global aesthetic_embeddings + aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in + os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} def reload_hypernetworks(): global hypernetworks diff --git a/modules/txt2img.py b/modules/txt2img.py index 78342024..eedcdfe0 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -13,7 +13,11 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, - aesthetic_slerp=False, *args): + aesthetic_slerp=False, + aesthetic_imgs_text="", + aesthetic_slerp_angle=0.15, + aesthetic_text_negative=False, + *args): p = StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, @@ -47,7 +51,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: processed = modules.scripts.scripts_txt2img.run(p, *args) if processed is None: - processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp) + processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp,aesthetic_imgs_text, + aesthetic_slerp_angle, + aesthetic_text_negative) shared.total_tqdm.clear() diff --git a/modules/ui.py b/modules/ui.py index d961d126..e98e2113 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -41,6 +41,7 @@ from modules import prompt_parser from modules.images import save_image import modules.textual_inversion.ui import modules.hypernetworks.ui +import modules.aesthetic_clip # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() @@ -449,7 +450,7 @@ def create_toprow(is_img2img): with gr.Row(): negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2) with gr.Column(scale=1, elem_id="roll_col"): - sh = gr.Button(elem_id="sh", visible=True) + sh = gr.Button(elem_id="sh", visible=True) with gr.Column(scale=1, elem_id="style_neg_col"): prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1) @@ -536,9 +537,13 @@ def create_ui(wrap_gradio_gpu_call): height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) with gr.Group(): - aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005") - aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.7) - aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=50) + aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001") + aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) + aesthetic_steps = gr.Slider(minimum=0, maximum=256, step=1, label="Aesthetic steps", value=5) + with gr.Row(): + aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="") + aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) + aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None) aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) @@ -617,7 +622,10 @@ def create_ui(wrap_gradio_gpu_call): aesthetic_weight, aesthetic_steps, aesthetic_imgs, - aesthetic_slerp + aesthetic_slerp, + aesthetic_imgs_text, + aesthetic_slerp_angle, + aesthetic_text_negative ] + custom_inputs, outputs=[ txt2img_gallery, @@ -721,7 +729,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Row(): inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False) - inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32) + inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=1024, step=4, value=32) with gr.TabItem('Batch img2img', id='batch'): hidden = '
Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else '' @@ -1071,6 +1079,17 @@ def create_ui(wrap_gradio_gpu_call): with gr.Column(): create_embedding = gr.Button(value="Create embedding", variant='primary') + with gr.Tab(label="Create images embedding"): + new_embedding_name_ae = gr.Textbox(label="Name") + process_src_ae = gr.Textbox(label='Source directory') + batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256) + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") + + with gr.Column(): + create_embedding_ae = gr.Button(value="Create images embedding", variant='primary') + with gr.Tab(label="Create hypernetwork"): new_hypernetwork_name = gr.Textbox(label="Name") new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) @@ -1139,7 +1158,7 @@ def create_ui(wrap_gradio_gpu_call): fn=modules.textual_inversion.ui.create_embedding, inputs=[ new_embedding_name, - initialization_text, + process_src, nvpt, ], outputs=[ @@ -1149,6 +1168,20 @@ def create_ui(wrap_gradio_gpu_call): ] ) + create_embedding_ae.click( + fn=modules.aesthetic_clip.generate_imgs_embd, + inputs=[ + new_embedding_name_ae, + process_src_ae, + batch_ae + ], + outputs=[ + aesthetic_imgs, + ti_output, + ti_outcome, + ] + ) + create_hypernetwork.click( fn=modules.hypernetworks.ui.create_hypernetwork, inputs=[ From 4387e4fe6479c08f7bc7e42924c3a1093e3a1872 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 18:39:29 +0200 Subject: [PATCH 003/152] Update modules/ui.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Víctor Gallego --- modules/ui.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index d0696101..5bb961b2 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -599,7 +599,8 @@ def create_ui(wrap_gradio_gpu_call): with gr.Group(): aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001") aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) - aesthetic_steps = gr.Slider(minimum=0, maximum=256, step=1, label="Aesthetic steps", value=5) + aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) + with gr.Row(): aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="") aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) From f7df06a98180a2a8769b3ceebf7b6a35eca8ffc5 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 18:40:06 +0200 Subject: [PATCH 004/152] Update README.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Víctor Gallego --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 7b8d018b..40104833 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,8 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args) - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args) -- Aesthetic, a way to generate images with a specific aesthetic by using clip images embds (implementation of https://github.com/vicgalle/stable-diffusion-aesthetic-gradients) +- Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) + ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. From 9b7705e0573bddde26df4575c71f994d73a4d519 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 18:40:34 +0200 Subject: [PATCH 005/152] Update modules/aesthetic_clip.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Víctor Gallego --- modules/aesthetic_clip.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py index f15cfd47..bcf2b073 100644 --- a/modules/aesthetic_clip.py +++ b/modules/aesthetic_clip.py @@ -70,7 +70,7 @@ def generate_imgs_embd(name, folder, batch_size): torch.cuda.empty_cache() res = f""" Done generating embedding for {name}! - Hypernetwork saved to {html.escape(path)} + Aesthetic embedding saved to {html.escape(path)} """ shared.update_aesthetic_embeddings() return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", From 0d4f5db235357aeb4c7a8738179ba33aaf5a6b75 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 18:40:58 +0200 Subject: [PATCH 006/152] Update modules/ui.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Víctor Gallego --- modules/ui.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index 5bb961b2..25eba548 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -597,7 +597,8 @@ def create_ui(wrap_gradio_gpu_call): height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) with gr.Group(): - aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001") + aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001") + aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) From ad9bc604a8fadcfebe72be37f66cec51e7e87fb5 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 18:41:18 +0200 Subject: [PATCH 007/152] Update modules/ui.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Víctor Gallego --- modules/ui.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index 25eba548..3b28b69c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -607,7 +607,8 @@ def create_ui(wrap_gradio_gpu_call): aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) - aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None) + aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Aesthetic imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None) + aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) with gr.Row(): From 3f5c3b981e46c16bb10948d012575b25170efb3b Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 18:41:46 +0200 Subject: [PATCH 008/152] Update modules/ui.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Víctor Gallego --- modules/ui.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index 3b28b69c..1f6fcdc9 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1190,7 +1190,8 @@ def create_ui(wrap_gradio_gpu_call): with gr.Column(): create_embedding = gr.Button(value="Create embedding", variant='primary') - with gr.Tab(label="Create images embedding"): + with gr.Tab(label="Create aesthetic images embedding"): + new_embedding_name_ae = gr.Textbox(label="Name") process_src_ae = gr.Textbox(label='Source directory') batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256) From 9a33292ce41b01252cdb8ab6214a11d274e32fa0 Mon Sep 17 00:00:00 2001 From: zhengxiaoyao0716 <1499383852@qq.com> Date: Sat, 15 Oct 2022 01:04:47 +0800 Subject: [PATCH 009/152] reload javascript files when custom script bodies --- modules/ui.py | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/modules/ui.py b/modules/ui.py index b867d40f..90b8646b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -12,7 +12,7 @@ import time import traceback import platform import subprocess as sp -from functools import reduce +from functools import partial, reduce import numpy as np import torch @@ -1491,6 +1491,7 @@ Requested path was: {f} def reload_scripts(): modules.scripts.reload_script_body_only() + reload_javascript() # need to refresh the html page reload_script_bodies.click( fn=reload_scripts, @@ -1738,22 +1739,25 @@ Requested path was: {f} return demo -with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile: - javascript = f'' +def load_javascript(raw_response): + with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile: + javascript = f'' -jsdir = os.path.join(script_path, "javascript") -for filename in sorted(os.listdir(jsdir)): - with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile: - javascript += f"\n" + jsdir = os.path.join(script_path, "javascript") + for filename in sorted(os.listdir(jsdir)): + with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile: + javascript += f"\n" - -if 'gradio_routes_templates_response' not in globals(): def template_response(*args, **kwargs): - res = gradio_routes_templates_response(*args, **kwargs) - res.body = res.body.replace(b'', f'{javascript}'.encode("utf8")) + res = raw_response(*args, **kwargs) + res.body = res.body.replace( + b'', f'{javascript}'.encode("utf8")) res.init_headers() return res - gradio_routes_templates_response = gradio.routes.templates.TemplateResponse gradio.routes.templates.TemplateResponse = template_response + +reload_javascript = partial(load_javascript, + gradio.routes.templates.TemplateResponse) +reload_javascript() From 3d21684ee30ca5734126b8d08c05b3a0f513fe75 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sun, 16 Oct 2022 00:01:00 +0200 Subject: [PATCH 010/152] Add support to other img format, fixed dropbox update --- modules/aesthetic_clip.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py index bcf2b073..68264284 100644 --- a/modules/aesthetic_clip.py +++ b/modules/aesthetic_clip.py @@ -8,7 +8,7 @@ import gradio as gr import torch from PIL import Image from modules import shared -from modules.shared import device, aesthetic_embeddings +from modules.shared import device from transformers import CLIPModel, CLIPProcessor from tqdm.auto import tqdm @@ -20,7 +20,7 @@ def get_all_images_in_folder(folder): def check_is_valid_image_file(filename): - return filename.lower().endswith(('.png', '.jpg', '.jpeg')) + return filename.lower().endswith(('.png', '.jpg', '.jpeg', ".gif", ".tiff", ".webp")) def batched(dataset, total, n=1): @@ -73,6 +73,6 @@ def generate_imgs_embd(name, folder, batch_size): Aesthetic embedding saved to {html.escape(path)} """ shared.update_aesthetic_embeddings() - return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", - value=sorted(aesthetic_embeddings.keys())[0] if len( - aesthetic_embeddings) > 0 else None), res, "" + return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding", + value=sorted(shared.aesthetic_embeddings.keys())[0] if len( + shared.aesthetic_embeddings) > 0 else None), res, "" From 9325c85f780c569d1823e422eaf51b2e497e0d3e Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sun, 16 Oct 2022 00:23:47 +0200 Subject: [PATCH 011/152] fixed dropbox update --- modules/sd_hijack.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 192883b2..491312b4 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -9,7 +9,7 @@ from torch.nn.functional import silu import modules.textual_inversion.textual_inversion from modules import prompt_parser, devices, sd_hijack_optimizations, shared -from modules.shared import opts, device, cmd_opts, aesthetic_embeddings +from modules.shared import opts, device, cmd_opts from modules.sd_hijack_optimizations import invokeAI_mps_available import ldm.modules.attention @@ -182,7 +182,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): image_embs_name = None if image_embs_name is not None and self.image_embs_name != image_embs_name: self.image_embs_name = image_embs_name - self.image_embs = torch.load(aesthetic_embeddings[self.image_embs_name], map_location=device) + self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device) self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True) self.image_embs.requires_grad_(False) From 523140d7805c644700009b8a2483ff4eb4a22304 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sun, 16 Oct 2022 10:23:30 +0200 Subject: [PATCH 012/152] ui fix --- modules/aesthetic_clip.py | 3 +-- modules/sd_hijack.py | 3 +-- modules/shared.py | 2 ++ modules/ui.py | 24 ++++++++++++++---------- 4 files changed, 18 insertions(+), 14 deletions(-) diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py index 68264284..ccb35c73 100644 --- a/modules/aesthetic_clip.py +++ b/modules/aesthetic_clip.py @@ -74,5 +74,4 @@ def generate_imgs_embd(name, folder, batch_size): """ shared.update_aesthetic_embeddings() return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding", - value=sorted(shared.aesthetic_embeddings.keys())[0] if len( - shared.aesthetic_embeddings) > 0 else None), res, "" + value="None"), res, "" diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 01fcb78f..2de2eed5 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -392,8 +392,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): z1 = self.process_tokens(tokens, multipliers) z = z1 if z is None else torch.cat((z, z1), axis=-2) - if len(text[ - 0]) != 0 and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None: + if self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None: if not opts.use_old_emphasis_implementation: remade_batch_tokens = [ [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in diff --git a/modules/shared.py b/modules/shared.py index 3c5ffef1..e2c98b2d 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -96,11 +96,13 @@ loaded_hypernetwork = None aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} +aesthetic_embeddings = aesthetic_embeddings | {"None": None} def update_aesthetic_embeddings(): global aesthetic_embeddings aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} + aesthetic_embeddings = aesthetic_embeddings | {"None": None} def reload_hypernetworks(): global hypernetworks diff --git a/modules/ui.py b/modules/ui.py index 13ba3142..4069f0d2 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -594,19 +594,23 @@ def create_ui(wrap_gradio_gpu_call): height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) with gr.Group(): - aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001") + with gr.Accordion("Open for Clip Aesthetic!",open=False): + with gr.Row(): + aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) + aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) - aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) - aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) + with gr.Row(): + aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001") + aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) + aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), + label="Aesthetic imgs embedding", + value="None") - with gr.Row(): - aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="") - aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) - aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) + with gr.Row(): + aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="") + aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) + aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) - aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Aesthetic imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None) - - aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) with gr.Row(): restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) From e4f8b5f00dd33b7547cc6b76fbed26bb83b37a64 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sun, 16 Oct 2022 10:28:21 +0200 Subject: [PATCH 013/152] ui fix --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 2de2eed5..5d0590af 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -178,7 +178,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): self.load_image_embs(image_embs_name) def load_image_embs(self, image_embs_name): - if image_embs_name is None or len(image_embs_name) == 0: + if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None": image_embs_name = None if image_embs_name is not None and self.image_embs_name != image_embs_name: self.image_embs_name = image_embs_name From 9324cdaa3199d65c182858785dd1eca42b192b8e Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sun, 16 Oct 2022 17:53:56 +0200 Subject: [PATCH 014/152] ui fix, re organization of the code --- modules/aesthetic_clip.py | 154 +++++++++++++++++++++++++-- modules/img2img.py | 14 ++- modules/processing.py | 29 ++--- modules/sd_hijack.py | 102 +----------------- modules/sd_models.py | 5 +- modules/shared.py | 14 ++- modules/textual_inversion/dataset.py | 2 +- modules/txt2img.py | 18 ++-- modules/ui.py | 50 +++++---- 9 files changed, 232 insertions(+), 156 deletions(-) diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py index ccb35c73..34efa931 100644 --- a/modules/aesthetic_clip.py +++ b/modules/aesthetic_clip.py @@ -1,3 +1,4 @@ +import copy import itertools import os from pathlib import Path @@ -7,11 +8,12 @@ import gc import gradio as gr import torch from PIL import Image -from modules import shared -from modules.shared import device -from transformers import CLIPModel, CLIPProcessor +from torch import optim -from tqdm.auto import tqdm +from modules import shared +from transformers import CLIPModel, CLIPProcessor, CLIPTokenizer +from tqdm.auto import tqdm, trange +from modules.shared import opts, device def get_all_images_in_folder(folder): @@ -37,12 +39,39 @@ def iter_to_batched(iterable, n=1): yield chunk +def create_ui(): + with gr.Group(): + with gr.Accordion("Open for Clip Aesthetic!", open=False): + with gr.Row(): + aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", + value=0.9) + aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) + + with gr.Row(): + aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', + placeholder="Aesthetic learning rate", value="0.0001") + aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) + aesthetic_imgs = gr.Dropdown(sorted(shared.aesthetic_embeddings.keys()), + label="Aesthetic imgs embedding", + value="None") + + with gr.Row(): + aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', + placeholder="This text is used to rotate the feature space of the imgs embs", + value="") + aesthetic_slerp_angle = gr.Slider(label='Slerp angle', minimum=0, maximum=1, step=0.01, + value=0.1) + aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) + + return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative + + def generate_imgs_embd(name, folder, batch_size): # clipModel = CLIPModel.from_pretrained( # shared.sd_model.cond_stage_model.clipModel.name_or_path # ) - model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path).to(device) - processor = CLIPProcessor.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path) + model = shared.clip_model.to(device) + processor = CLIPProcessor.from_pretrained(model.name_or_path) with torch.no_grad(): embs = [] @@ -63,7 +92,6 @@ def generate_imgs_embd(name, folder, batch_size): torch.save(embs, path) model = model.cpu() - del model del processor del embs gc.collect() @@ -74,4 +102,114 @@ def generate_imgs_embd(name, folder, batch_size): """ shared.update_aesthetic_embeddings() return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding", - value="None"), res, "" + value="None"), \ + gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), + label="Imgs embedding", + value="None"), res, "" + + +def slerp(low, high, val): + low_norm = low / torch.norm(low, dim=1, keepdim=True) + high_norm = high / torch.norm(high, dim=1, keepdim=True) + omega = torch.acos((low_norm * high_norm).sum(1)) + so = torch.sin(omega) + res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high + return res + + +class AestheticCLIP: + def __init__(self): + self.skip = False + self.aesthetic_steps = 0 + self.aesthetic_weight = 0 + self.aesthetic_lr = 0 + self.slerp = False + self.aesthetic_text_negative = "" + self.aesthetic_slerp_angle = 0 + self.aesthetic_imgs_text = "" + + self.image_embs_name = None + self.image_embs = None + self.load_image_embs(None) + + def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None, + aesthetic_slerp=True, aesthetic_imgs_text="", + aesthetic_slerp_angle=0.15, + aesthetic_text_negative=False): + self.aesthetic_imgs_text = aesthetic_imgs_text + self.aesthetic_slerp_angle = aesthetic_slerp_angle + self.aesthetic_text_negative = aesthetic_text_negative + self.slerp = aesthetic_slerp + self.aesthetic_lr = aesthetic_lr + self.aesthetic_weight = aesthetic_weight + self.aesthetic_steps = aesthetic_steps + self.load_image_embs(image_embs_name) + + def set_skip(self, skip): + self.skip = skip + + def load_image_embs(self, image_embs_name): + if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None": + image_embs_name = None + self.image_embs_name = None + if image_embs_name is not None and self.image_embs_name != image_embs_name: + self.image_embs_name = image_embs_name + self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device) + self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True) + self.image_embs.requires_grad_(False) + + def __call__(self, z, remade_batch_tokens): + if not self.skip and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name is not None: + tokenizer = shared.sd_model.cond_stage_model.tokenizer + if not opts.use_old_emphasis_implementation: + remade_batch_tokens = [ + [tokenizer.bos_token_id] + x[:75] + [tokenizer.eos_token_id] for x in + remade_batch_tokens] + + tokens = torch.asarray(remade_batch_tokens).to(device) + + model = copy.deepcopy(shared.clip_model).to(device) + model.requires_grad_(True) + if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0: + text_embs_2 = model.get_text_features( + **tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device)) + if self.aesthetic_text_negative: + text_embs_2 = self.image_embs - text_embs_2 + text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True) + img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle) + else: + img_embs = self.image_embs + + with torch.enable_grad(): + + # We optimize the model to maximize the similarity + optimizer = optim.Adam( + model.text_model.parameters(), lr=self.aesthetic_lr + ) + + for _ in trange(self.aesthetic_steps, desc="Aesthetic optimization"): + text_embs = model.get_text_features(input_ids=tokens) + text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True) + sim = text_embs @ img_embs.T + loss = -sim + optimizer.zero_grad() + loss.mean().backward() + optimizer.step() + + zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers) + if opts.CLIP_stop_at_last_layers > 1: + zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers] + zn = model.text_model.final_layer_norm(zn) + else: + zn = zn.last_hidden_state + model.cpu() + del model + gc.collect() + torch.cuda.empty_cache() + zn = torch.concat([zn[77 * i:77 * (i + 1)] for i in range(max(z.shape[1] // 77, 1))], 1) + if self.slerp: + z = slerp(z, zn, self.aesthetic_weight) + else: + z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight + + return z diff --git a/modules/img2img.py b/modules/img2img.py index 24126774..4ed80c4b 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -56,7 +56,14 @@ def process_batch(p, input_dir, output_dir, args): processed_image.save(os.path.join(output_dir, filename)) -def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args): +def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, + aesthetic_lr=0, + aesthetic_weight=0, aesthetic_steps=0, + aesthetic_imgs=None, + aesthetic_slerp=False, + aesthetic_imgs_text="", + aesthetic_slerp_angle=0.15, + aesthetic_text_negative=False, *args): is_inpaint = mode == 1 is_batch = mode == 2 @@ -109,6 +116,11 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro inpainting_mask_invert=inpainting_mask_invert, ) + shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), + aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, + aesthetic_slerp_angle, + aesthetic_text_negative) + if shared.cmd_opts.enable_console_prompts: print(f"\nimg2img: {prompt}", file=shared.progress_print_out) diff --git a/modules/processing.py b/modules/processing.py index 1db26c3e..685f9fcd 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -146,7 +146,8 @@ class Processed: self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0] self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0] self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1 - self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1 + self.subseed = int( + self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1 self.all_prompts = all_prompts or [self.prompt] self.all_seeds = all_seeds or [self.seed] @@ -332,16 +333,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip() -def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, - aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", - aesthetic_slerp_angle=0.15, - aesthetic_text_negative=False) -> Processed: +def process_images(p: StableDiffusionProcessing) -> Processed: """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch""" - aesthetic_lr = float(aesthetic_lr) - aesthetic_weight = float(aesthetic_weight) - aesthetic_steps = int(aesthetic_steps) - if type(p.prompt) == list: assert (len(p.prompt) > 0) else: @@ -417,16 +411,10 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh # uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) # c = p.sd_model.get_learned_conditioning(prompts) with devices.autocast(): - if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"): - shared.sd_model.cond_stage_model.set_aesthetic_params() + shared.aesthetic_clip.set_skip(True) uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) - if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"): - shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight, - aesthetic_steps, aesthetic_imgs, - aesthetic_slerp, aesthetic_imgs_text, - aesthetic_slerp_angle, - aesthetic_text_negative) + shared.aesthetic_clip.set_skip(False) c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps) if len(model_hijack.comments) > 0: @@ -582,7 +570,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) @@ -600,10 +587,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) - samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] + samples = samples[:, :, self.truncate_y // 2:samples.shape[2] - self.truncate_y // 2, + self.truncate_x // 2:samples.shape[3] - self.truncate_x // 2] if opts.use_scale_latent_for_hires_fix: - samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") + samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), + mode="bilinear") else: decoded_samples = decode_first_stage(self.sd_model, samples) lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 5d0590af..227e7670 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -29,8 +29,8 @@ def apply_optimizations(): ldm.modules.diffusionmodules.model.nonlinearity = silu - - if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)): + if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and ( + 6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)): print("Applying xformers cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward @@ -118,33 +118,14 @@ class StableDiffusionModelHijack: return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count) -def slerp(low, high, val): - low_norm = low / torch.norm(low, dim=1, keepdim=True) - high_norm = high / torch.norm(high, dim=1, keepdim=True) - omega = torch.acos((low_norm * high_norm).sum(1)) - so = torch.sin(omega) - res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high - return res - - class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def __init__(self, wrapped, hijack): super().__init__() self.wrapped = wrapped - self.clipModel = CLIPModel.from_pretrained( - self.wrapped.transformer.name_or_path - ) - del self.clipModel.vision_model - self.tokenizer = CLIPTokenizer.from_pretrained(self.wrapped.transformer.name_or_path) - self.hijack: StableDiffusionModelHijack = hijack - self.tokenizer = wrapped.tokenizer - # self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval() - self.image_embs_name = None - self.image_embs = None - self.load_image_embs(None) self.token_mults = {} - + self.hijack: StableDiffusionModelHijack = hijack + self.tokenizer = wrapped.tokenizer self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ','][0] tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if @@ -164,28 +145,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if mult != 1.0: self.token_mults[ident] = mult - def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None, - aesthetic_slerp=True, aesthetic_imgs_text="", - aesthetic_slerp_angle=0.15, - aesthetic_text_negative=False): - self.aesthetic_imgs_text = aesthetic_imgs_text - self.aesthetic_slerp_angle = aesthetic_slerp_angle - self.aesthetic_text_negative = aesthetic_text_negative - self.slerp = aesthetic_slerp - self.aesthetic_lr = aesthetic_lr - self.aesthetic_weight = aesthetic_weight - self.aesthetic_steps = aesthetic_steps - self.load_image_embs(image_embs_name) - - def load_image_embs(self, image_embs_name): - if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None": - image_embs_name = None - if image_embs_name is not None and self.image_embs_name != image_embs_name: - self.image_embs_name = image_embs_name - self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device) - self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True) - self.image_embs.requires_grad_(False) - def tokenize_line(self, line, used_custom_terms, hijack_comments): id_end = self.wrapped.tokenizer.eos_token_id @@ -391,58 +350,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): z1 = self.process_tokens(tokens, multipliers) z = z1 if z is None else torch.cat((z, z1), axis=-2) - - if self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None: - if not opts.use_old_emphasis_implementation: - remade_batch_tokens = [ - [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in - remade_batch_tokens] - - tokens = torch.asarray(remade_batch_tokens).to(device) - - model = copy.deepcopy(self.clipModel).to(device) - model.requires_grad_(True) - if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0: - text_embs_2 = model.get_text_features( - **self.tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device)) - if self.aesthetic_text_negative: - text_embs_2 = self.image_embs - text_embs_2 - text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True) - img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle) - else: - img_embs = self.image_embs - - with torch.enable_grad(): - - # We optimize the model to maximize the similarity - optimizer = optim.Adam( - model.text_model.parameters(), lr=self.aesthetic_lr - ) - - for i in trange(self.aesthetic_steps, desc="Aesthetic optimization"): - text_embs = model.get_text_features(input_ids=tokens) - text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True) - sim = text_embs @ img_embs.T - loss = -sim - optimizer.zero_grad() - loss.mean().backward() - optimizer.step() - - zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers) - if opts.CLIP_stop_at_last_layers > 1: - zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers] - zn = model.text_model.final_layer_norm(zn) - else: - zn = zn.last_hidden_state - model.cpu() - del model - - zn = torch.concat([zn for i in range(z.shape[1] // 77)], 1) - if self.slerp: - z = slerp(z, zn, self.aesthetic_weight) - else: - z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight - + z = shared.aesthetic_clip(z, remade_batch_tokens) remade_batch_tokens = rem_tokens batch_multipliers = rem_multipliers i += 1 diff --git a/modules/sd_models.py b/modules/sd_models.py index 3aa21ec1..8e4ee435 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -20,7 +20,7 @@ checkpoints_loaded = collections.OrderedDict() try: # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. - from transformers import logging + from transformers import logging, CLIPModel logging.set_verbosity_error() except Exception: @@ -196,6 +196,9 @@ def load_model(): sd_hijack.model_hijack.hijack(sd_model) + if shared.clip_model is None or shared.clip_model.transformer.name_or_path != sd_model.cond_stage_model.wrapped.transformer.name_or_path: + shared.clip_model = CLIPModel.from_pretrained(sd_model.cond_stage_model.wrapped.transformer.name_or_path) + sd_model.eval() print(f"Model loaded.") diff --git a/modules/shared.py b/modules/shared.py index e2c98b2d..e19ca779 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -3,6 +3,7 @@ import datetime import json import os import sys +from collections import OrderedDict import gradio as gr import tqdm @@ -94,15 +95,15 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True) hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir) loaded_hypernetwork = None -aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in - os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} -aesthetic_embeddings = aesthetic_embeddings | {"None": None} +aesthetic_embeddings = {} def update_aesthetic_embeddings(): global aesthetic_embeddings aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} - aesthetic_embeddings = aesthetic_embeddings | {"None": None} + aesthetic_embeddings = OrderedDict(**{"None": None}, **aesthetic_embeddings) + +update_aesthetic_embeddings() def reload_hypernetworks(): global hypernetworks @@ -381,6 +382,11 @@ sd_upscalers = [] sd_model = None +clip_model = None + +from modules.aesthetic_clip import AestheticCLIP +aesthetic_clip = AestheticCLIP() + progress_print_out = sys.stdout diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 68ceffe3..23bb4b6a 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -49,7 +49,7 @@ class PersonalizedBase(Dataset): print("Preparing dataset...") for path in tqdm.tqdm(self.image_paths): try: - image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.Resampling.BICUBIC) + image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC) except Exception: continue diff --git a/modules/txt2img.py b/modules/txt2img.py index 8f394d05..6cbc50fc 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -1,12 +1,17 @@ import modules.scripts -from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images +from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \ + StableDiffusionProcessingImg2Img, process_images from modules.shared import opts, cmd_opts import modules.shared as shared import modules.processing as processing from modules.ui import plaintext_to_html -def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int,aesthetic_lr=0, +def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, + restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, + subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, + height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, + firstphase_height: int, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, @@ -41,15 +46,17 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: firstphase_height=firstphase_height if enable_hr else None, ) + shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), + aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, + aesthetic_text_negative) + if cmd_opts.enable_console_prompts: print(f"\ntxt2img: {prompt}", file=shared.progress_print_out) processed = modules.scripts.scripts_txt2img.run(p, *args) if processed is None: - processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp,aesthetic_imgs_text, - aesthetic_slerp_angle, - aesthetic_text_negative) + processed = process_images(p) shared.total_tqdm.clear() @@ -61,4 +68,3 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: processed.images = [] return processed.images, generation_info_js, plaintext_to_html(processed.info) - diff --git a/modules/ui.py b/modules/ui.py index 4069f0d2..0e5d73f0 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -43,7 +43,7 @@ from modules.images import save_image import modules.textual_inversion.ui import modules.hypernetworks.ui -import modules.aesthetic_clip +import modules.aesthetic_clip as aesthetic_clip import modules.images_history as img_his @@ -593,23 +593,25 @@ def create_ui(wrap_gradio_gpu_call): width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) - with gr.Group(): - with gr.Accordion("Open for Clip Aesthetic!",open=False): - with gr.Row(): - aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) - aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) + # with gr.Group(): + # with gr.Accordion("Open for Clip Aesthetic!",open=False): + # with gr.Row(): + # aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) + # aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) + # + # with gr.Row(): + # aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001") + # aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) + # aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), + # label="Aesthetic imgs embedding", + # value="None") + # + # with gr.Row(): + # aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="") + # aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) + # aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) - with gr.Row(): - aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001") - aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) - aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), - label="Aesthetic imgs embedding", - value="None") - - with gr.Row(): - aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="") - aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) - aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) + aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative = aesthetic_clip.create_ui() with gr.Row(): @@ -840,6 +842,9 @@ def create_ui(wrap_gradio_gpu_call): width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + aesthetic_weight_im, aesthetic_steps_im, aesthetic_lr_im, aesthetic_slerp_im, aesthetic_imgs_im, aesthetic_imgs_text_im, aesthetic_slerp_angle_im, aesthetic_text_negative_im = aesthetic_clip.create_ui() + + with gr.Row(): restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) tiling = gr.Checkbox(label='Tiling', value=False) @@ -944,6 +949,14 @@ def create_ui(wrap_gradio_gpu_call): inpainting_mask_invert, img2img_batch_input_dir, img2img_batch_output_dir, + aesthetic_lr_im, + aesthetic_weight_im, + aesthetic_steps_im, + aesthetic_imgs_im, + aesthetic_slerp_im, + aesthetic_imgs_text_im, + aesthetic_slerp_angle_im, + aesthetic_text_negative_im, ] + custom_inputs, outputs=[ img2img_gallery, @@ -1283,7 +1296,7 @@ def create_ui(wrap_gradio_gpu_call): ) create_embedding_ae.click( - fn=modules.aesthetic_clip.generate_imgs_embd, + fn=aesthetic_clip.generate_imgs_embd, inputs=[ new_embedding_name_ae, process_src_ae, @@ -1291,6 +1304,7 @@ def create_ui(wrap_gradio_gpu_call): ], outputs=[ aesthetic_imgs, + aesthetic_imgs_im, ti_output, ti_outcome, ] From 60251c9456f5472784862896c2f97e38feb42482 Mon Sep 17 00:00:00 2001 From: arcticfaded Date: Mon, 17 Oct 2022 06:58:42 +0000 Subject: [PATCH 015/152] initial prototype by borrowing contracts --- modules/api/api.py | 60 +++++++++++++++++++++++++++++++++++++ modules/processing.py | 2 +- modules/shared.py | 2 +- webui.py | 69 +++++++++++++++++++++++++------------------ 4 files changed, 102 insertions(+), 31 deletions(-) create mode 100644 modules/api/api.py diff --git a/modules/api/api.py b/modules/api/api.py new file mode 100644 index 00000000..9d7c699d --- /dev/null +++ b/modules/api/api.py @@ -0,0 +1,60 @@ +from modules.api.processing import StableDiffusionProcessingAPI +from modules.processing import StableDiffusionProcessingTxt2Img, process_images +import modules.shared as shared +import uvicorn +from fastapi import FastAPI, Body, APIRouter +from fastapi.responses import JSONResponse +from pydantic import BaseModel, Field, Json +import json +import io +import base64 + +app = FastAPI() + +class TextToImageResponse(BaseModel): + images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + parameters: Json + info: Json + + +class Api: + def __init__(self, txt2img, img2img, run_extras, run_pnginfo): + self.router = APIRouter() + app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"]) + + def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ): + print(txt2imgreq) + p = StableDiffusionProcessingTxt2Img(**vars(txt2imgreq)) + p.sd_model = shared.sd_model + print(p) + processed = process_images(p) + + b64images = [] + for i in processed.images: + buffer = io.BytesIO() + i.save(buffer, format="png") + b64images.append(base64.b64encode(buffer.getvalue())) + + response = { + "images": b64images, + "info": processed.js(), + "parameters": json.dumps(vars(txt2imgreq)) + } + + + return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info)) + + + + def img2imgendoint(self): + raise NotImplementedError + + def extrasendoint(self): + raise NotImplementedError + + def pnginfoendoint(self): + raise NotImplementedError + + def launch(self, server_name, port): + app.include_router(self.router) + uvicorn.run(app, host=server_name, port=port) \ No newline at end of file diff --git a/modules/processing.py b/modules/processing.py index deb6125e..4a7c6ccc 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -723,4 +723,4 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): del x devices.torch_gc() - return samples + return samples \ No newline at end of file diff --git a/modules/shared.py b/modules/shared.py index c2775603..6c6405fd 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -74,7 +74,7 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help= parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False) parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None) parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False) - +parser.add_argument("--api", action='store_true', help="use api=True to launch the api instead of the webui") cmd_opts = parser.parse_args() restricted_opts = [ diff --git a/webui.py b/webui.py index fe0ce321..cd8a99ea 100644 --- a/webui.py +++ b/webui.py @@ -97,40 +97,51 @@ def webui(): os._exit(0) signal.signal(signal.SIGINT, sigint_handler) + + if cmd_opts.api: + from modules.api.api import Api + api = Api(txt2img=modules.txt2img.txt2img, + img2img=modules.img2img.img2img, + run_extras=modules.extras.run_extras, + run_pnginfo=modules.extras.run_pnginfo) - while 1: - - demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call) - - app, local_url, share_url = demo.launch( - share=cmd_opts.share, - server_name="0.0.0.0" if cmd_opts.listen else None, - server_port=cmd_opts.port, - debug=cmd_opts.gradio_debug, - auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None, - inbrowser=cmd_opts.autolaunch, - prevent_thread_lock=True - ) - - app.add_middleware(GZipMiddleware, minimum_size=1000) + api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", + port=cmd_opts.port if cmd_opts.port else 7861) + else: while 1: - time.sleep(0.5) - if getattr(demo, 'do_restart', False): - time.sleep(0.5) - demo.close() - time.sleep(0.5) - break - sd_samplers.set_samplers() + demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call) - print('Reloading Custom Scripts') - modules.scripts.reload_scripts(os.path.join(script_path, "scripts")) - print('Reloading modules: modules.ui') - importlib.reload(modules.ui) - print('Refreshing Model List') - modules.sd_models.list_models() - print('Restarting Gradio') + app, local_url, share_url = demo.launch( + share=cmd_opts.share, + server_name="0.0.0.0" if cmd_opts.listen else None, + server_port=cmd_opts.port, + debug=cmd_opts.gradio_debug, + auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None, + inbrowser=cmd_opts.autolaunch, + prevent_thread_lock=True + ) + + app.add_middleware(GZipMiddleware, minimum_size=1000) + + while 1: + time.sleep(0.5) + if getattr(demo, 'do_restart', False): + time.sleep(0.5) + demo.close() + time.sleep(0.5) + break + + sd_samplers.set_samplers() + + print('Reloading Custom Scripts') + modules.scripts.reload_scripts(os.path.join(script_path, "scripts")) + print('Reloading modules: modules.ui') + importlib.reload(modules.ui) + print('Refreshing Model List') + modules.sd_models.list_models() + print('Restarting Gradio') if __name__ == "__main__": From 9e02812afd10582f00a7fbbfa63c8f9188678e26 Mon Sep 17 00:00:00 2001 From: arcticfaded Date: Mon, 17 Oct 2022 07:02:08 +0000 Subject: [PATCH 016/152] pydantic instrumentation --- modules/api/processing.py | 99 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 99 insertions(+) create mode 100644 modules/api/processing.py diff --git a/modules/api/processing.py b/modules/api/processing.py new file mode 100644 index 00000000..459a8f49 --- /dev/null +++ b/modules/api/processing.py @@ -0,0 +1,99 @@ +from inflection import underscore +from typing import Any, Dict, Optional +from pydantic import BaseModel, Field, create_model +from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images +import inspect + + +class ModelDef(BaseModel): + """Assistance Class for Pydantic Dynamic Model Generation""" + + field: str + field_alias: str + field_type: Any + field_value: Any + + +class pydanticModelGenerator: + """ + Takes source_data:Dict ( a single instance example of something like a JSON node) and self generates a pythonic data model with Alias to original source field names. This makes it easy to popuate or export to other systems yet handle the data in a pythonic way. + Being a pydantic datamodel all the richness of pydantic data validation is available and these models can easily be used in FastAPI and or a ORM + + It does not process full JSON data structures but takes simple JSON document with basic elements + + Provide a model_name, an example of JSON data and a dict of type overrides + + Example: + + source_data = {'Name': '48 Rainbow Rd', + 'GroupAddressStyle': 'ThreeLevel', + 'LastModified': '2020-12-21T07:02:51.2400232Z', + 'ProjectStart': '2020-12-03T07:36:03.324856Z', + 'Comment': '', + 'CompletionStatus': 'Editing', + 'LastUsedPuid': '955', + 'Guid': '0c85957b-c2ae-4985-9752-b300ab385b36'} + + source_overrides = {'Guid':{'type':uuid.UUID}, + 'LastModified':{'type':datetime }, + 'ProjectStart':{'type':datetime }, + } + source_optionals = {"Comment":True} + + #create Model + model_Project=pydanticModelGenerator( + model_name="Project", + source_data=source_data, + overrides=source_overrides, + optionals=source_optionals).generate_model() + + #create instance using DynamicModel + project_instance=model_Project(**project_info) + + """ + + def __init__( + self, + model_name: str = None, + source_data: str = None, + params: Dict = {}, + overrides: Dict = {}, + optionals: Dict = {}, + ): + def field_type_generator(k, v, overrides, optionals): + print(k, v) + field_type = str if not overrides.get(k) else overrides[k]["type"] + if v is None: + field_type = Any + else: + field_type = type(v) + + return Optional[field_type] + + self._model_name = model_name + self._json_data = source_data + self._model_def = [ + ModelDef( + field=underscore(k), + field_alias=k, + field_type=field_type_generator(k, v, overrides, optionals), + field_value=v + ) + for (k,v) in source_data.items() if k in params + ] + + def generate_model(self): + """ + Creates a pydantic BaseModel + from the json and overrides provided at initialization + """ + fields = { + d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def + } + DynamicModel = create_model(self._model_name, **fields) + DynamicModel.__config__.allow_population_by_field_name = True + return DynamicModel + +StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing", + StableDiffusionProcessing().__dict__, + inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model() \ No newline at end of file From f3fe487e6340b1a2db5d2e2ddf5ae885b4eef54c Mon Sep 17 00:00:00 2001 From: Jonathan Date: Mon, 17 Oct 2022 03:14:53 -0400 Subject: [PATCH 017/152] Update webui.py --- webui.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/webui.py b/webui.py index cd8a99ea..603a4ccd 100644 --- a/webui.py +++ b/webui.py @@ -100,10 +100,7 @@ def webui(): if cmd_opts.api: from modules.api.api import Api - api = Api(txt2img=modules.txt2img.txt2img, - img2img=modules.img2img.img2img, - run_extras=modules.extras.run_extras, - run_pnginfo=modules.extras.run_pnginfo) + api = Api() api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861) From 832b490e5173f78c4d3aa7ca9ca9ac794d140664 Mon Sep 17 00:00:00 2001 From: Jonathan Date: Mon, 17 Oct 2022 03:18:41 -0400 Subject: [PATCH 018/152] Update processing.py --- modules/api/processing.py | 41 +++++---------------------------------- 1 file changed, 5 insertions(+), 36 deletions(-) diff --git a/modules/api/processing.py b/modules/api/processing.py index 459a8f49..4c3d0bd0 100644 --- a/modules/api/processing.py +++ b/modules/api/processing.py @@ -16,46 +16,15 @@ class ModelDef(BaseModel): class pydanticModelGenerator: """ - Takes source_data:Dict ( a single instance example of something like a JSON node) and self generates a pythonic data model with Alias to original source field names. This makes it easy to popuate or export to other systems yet handle the data in a pythonic way. - Being a pydantic datamodel all the richness of pydantic data validation is available and these models can easily be used in FastAPI and or a ORM - - It does not process full JSON data structures but takes simple JSON document with basic elements - - Provide a model_name, an example of JSON data and a dict of type overrides - - Example: - - source_data = {'Name': '48 Rainbow Rd', - 'GroupAddressStyle': 'ThreeLevel', - 'LastModified': '2020-12-21T07:02:51.2400232Z', - 'ProjectStart': '2020-12-03T07:36:03.324856Z', - 'Comment': '', - 'CompletionStatus': 'Editing', - 'LastUsedPuid': '955', - 'Guid': '0c85957b-c2ae-4985-9752-b300ab385b36'} - - source_overrides = {'Guid':{'type':uuid.UUID}, - 'LastModified':{'type':datetime }, - 'ProjectStart':{'type':datetime }, - } - source_optionals = {"Comment":True} - - #create Model - model_Project=pydanticModelGenerator( - model_name="Project", - source_data=source_data, - overrides=source_overrides, - optionals=source_optionals).generate_model() - - #create instance using DynamicModel - project_instance=model_Project(**project_info) - + Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about: + source_data is a snapshot of the default values produced by the class + params are the names of the actual keys required by __init__ """ def __init__( self, model_name: str = None, - source_data: str = None, + source_data: {} = {}, params: Dict = {}, overrides: Dict = {}, optionals: Dict = {}, @@ -96,4 +65,4 @@ class pydanticModelGenerator: StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing", StableDiffusionProcessing().__dict__, - inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model() \ No newline at end of file + inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model() From 99013ba68a5fe1bde3621632e5539c03562a3ae8 Mon Sep 17 00:00:00 2001 From: Jonathan Date: Mon, 17 Oct 2022 03:20:17 -0400 Subject: [PATCH 019/152] Update processing.py --- modules/api/processing.py | 1 - 1 file changed, 1 deletion(-) diff --git a/modules/api/processing.py b/modules/api/processing.py index 4c3d0bd0..e4df93c5 100644 --- a/modules/api/processing.py +++ b/modules/api/processing.py @@ -30,7 +30,6 @@ class pydanticModelGenerator: optionals: Dict = {}, ): def field_type_generator(k, v, overrides, optionals): - print(k, v) field_type = str if not overrides.get(k) else overrides[k]["type"] if v is None: field_type = Any From 71d42bb44b257f3fb274c3ad5075a195281ff915 Mon Sep 17 00:00:00 2001 From: Jonathan Date: Mon, 17 Oct 2022 03:22:19 -0400 Subject: [PATCH 020/152] Update api.py --- modules/api/api.py | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/modules/api/api.py b/modules/api/api.py index 9d7c699d..4d9619a8 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -23,10 +23,8 @@ class Api: app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"]) def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ): - print(txt2imgreq) p = StableDiffusionProcessingTxt2Img(**vars(txt2imgreq)) p.sd_model = shared.sd_model - print(p) processed = process_images(p) b64images = [] @@ -34,13 +32,6 @@ class Api: buffer = io.BytesIO() i.save(buffer, format="png") b64images.append(base64.b64encode(buffer.getvalue())) - - response = { - "images": b64images, - "info": processed.js(), - "parameters": json.dumps(vars(txt2imgreq)) - } - return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info)) @@ -57,4 +48,4 @@ class Api: def launch(self, server_name, port): app.include_router(self.router) - uvicorn.run(app, host=server_name, port=port) \ No newline at end of file + uvicorn.run(app, host=server_name, port=port) From 964b63c0423a861bd67c40b59f767e7037051083 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 17 Oct 2022 11:38:32 +0300 Subject: [PATCH 021/152] add api() function to return webui() to how it was --- webui.py | 85 +++++++++++++++++++++++++++++--------------------------- 1 file changed, 44 insertions(+), 41 deletions(-) diff --git a/webui.py b/webui.py index 603a4ccd..16c862f0 100644 --- a/webui.py +++ b/webui.py @@ -87,59 +87,62 @@ def initialize(): shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork))) shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength) - -def webui(): - initialize() - # make the program just exit at ctrl+c without waiting for anything def sigint_handler(sig, frame): print(f'Interrupted with signal {sig} in {frame}') os._exit(0) signal.signal(signal.SIGINT, sigint_handler) - - if cmd_opts.api: - from modules.api.api import Api - api = Api() - api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", - port=cmd_opts.port if cmd_opts.port else 7861) - else: +def api() + initialize() + + from modules.api.api import Api + api = Api() + api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861) + + +def webui(): + initialize() + + while 1: + + demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call) + + app, local_url, share_url = demo.launch( + share=cmd_opts.share, + server_name="0.0.0.0" if cmd_opts.listen else None, + server_port=cmd_opts.port, + debug=cmd_opts.gradio_debug, + auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None, + inbrowser=cmd_opts.autolaunch, + prevent_thread_lock=True + ) + + app.add_middleware(GZipMiddleware, minimum_size=1000) + while 1: - - demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call) - - app, local_url, share_url = demo.launch( - share=cmd_opts.share, - server_name="0.0.0.0" if cmd_opts.listen else None, - server_port=cmd_opts.port, - debug=cmd_opts.gradio_debug, - auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None, - inbrowser=cmd_opts.autolaunch, - prevent_thread_lock=True - ) - - app.add_middleware(GZipMiddleware, minimum_size=1000) - - while 1: + time.sleep(0.5) + if getattr(demo, 'do_restart', False): time.sleep(0.5) - if getattr(demo, 'do_restart', False): - time.sleep(0.5) - demo.close() - time.sleep(0.5) - break + demo.close() + time.sleep(0.5) + break - sd_samplers.set_samplers() + sd_samplers.set_samplers() - print('Reloading Custom Scripts') - modules.scripts.reload_scripts(os.path.join(script_path, "scripts")) - print('Reloading modules: modules.ui') - importlib.reload(modules.ui) - print('Refreshing Model List') - modules.sd_models.list_models() - print('Restarting Gradio') + print('Reloading Custom Scripts') + modules.scripts.reload_scripts(os.path.join(script_path, "scripts")) + print('Reloading modules: modules.ui') + importlib.reload(modules.ui) + print('Refreshing Model List') + modules.sd_models.list_models() + print('Restarting Gradio') if __name__ == "__main__": - webui() + if cmd_opts.api: + api() + else: + webui() From d42125baf62880854ad06af06c15c23e7e50cca6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 17 Oct 2022 11:50:20 +0300 Subject: [PATCH 022/152] add missing requirement for api and fix some typos --- modules/api/api.py | 2 +- requirements.txt | 1 + requirements_versions.txt | 1 + webui.py | 2 +- 4 files changed, 4 insertions(+), 2 deletions(-) diff --git a/modules/api/api.py b/modules/api/api.py index 4d9619a8..fd09d352 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -18,7 +18,7 @@ class TextToImageResponse(BaseModel): class Api: - def __init__(self, txt2img, img2img, run_extras, run_pnginfo): + def __init__(self): self.router = APIRouter() app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"]) diff --git a/requirements.txt b/requirements.txt index cf583de9..da1969cf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,3 +23,4 @@ resize-right torchdiffeq kornia lark +inflection diff --git a/requirements_versions.txt b/requirements_versions.txt index abadcb58..72ccc5a3 100644 --- a/requirements_versions.txt +++ b/requirements_versions.txt @@ -22,3 +22,4 @@ resize-right==0.0.2 torchdiffeq==0.2.3 kornia==0.6.7 lark==1.1.2 +inflection==0.5.1 diff --git a/webui.py b/webui.py index 16c862f0..eeee44c3 100644 --- a/webui.py +++ b/webui.py @@ -95,7 +95,7 @@ def initialize(): signal.signal(signal.SIGINT, sigint_handler) -def api() +def api(): initialize() from modules.api.api import Api From 8c6a981d5d9ef30381ac2327460285111550acbc Mon Sep 17 00:00:00 2001 From: Michoko Date: Mon, 17 Oct 2022 11:05:05 +0200 Subject: [PATCH 023/152] Added dark mode switch Launch the UI in dark mode with the --dark-mode switch --- javascript/ui.js | 7 +++++++ modules/shared.py | 2 +- modules/ui.py | 2 ++ 3 files changed, 10 insertions(+), 1 deletion(-) diff --git a/javascript/ui.js b/javascript/ui.js index 9e1bed4c..bfa72885 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -1,5 +1,12 @@ // various functions for interation with ui.py not large enough to warrant putting them in separate files +function go_dark_mode(){ + gradioURL = window.location.href + if (!gradioURL.endsWith('?__theme=dark')) { + window.location.replace(gradioURL + '?__theme=dark'); + } +} + function selected_gallery_index(){ var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item') var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2') diff --git a/modules/shared.py b/modules/shared.py index c2775603..cbf158e4 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -69,13 +69,13 @@ parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image upload parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last") parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv')) parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False) +parser.add_argument("--dark-mode", action='store_true', help="launches the UI in dark mode", default=False) parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False) parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False) parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False) parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None) parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False) - cmd_opts = parser.parse_args() restricted_opts = [ "samples_filename_pattern", diff --git a/modules/ui.py b/modules/ui.py index 43dc88fc..a0cd052e 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1783,6 +1783,8 @@ for filename in sorted(os.listdir(jsdir)): with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile: javascript += f"\n" +if cmd_opts.dark_mode: + javascript += "\n\n" if 'gradio_routes_templates_response' not in globals(): def template_response(*args, **kwargs): From 665beebc0825a6fad410c8252f27f6f6f0bd900b Mon Sep 17 00:00:00 2001 From: Michoko Date: Mon, 17 Oct 2022 18:24:24 +0200 Subject: [PATCH 024/152] Use of a --theme argument for more flexibility Added possibility to set the theme (light or dark) --- javascript/ui.js | 6 +++--- modules/shared.py | 2 +- modules/ui.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/javascript/ui.js b/javascript/ui.js index bfa72885..cfd0dcd3 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -1,9 +1,9 @@ // various functions for interation with ui.py not large enough to warrant putting them in separate files -function go_dark_mode(){ +function set_theme(theme){ gradioURL = window.location.href - if (!gradioURL.endsWith('?__theme=dark')) { - window.location.replace(gradioURL + '?__theme=dark'); + if (!gradioURL.includes('?__theme=')) { + window.location.replace(gradioURL + '?__theme=' + theme); } } diff --git a/modules/shared.py b/modules/shared.py index cbf158e4..fa084c69 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -69,7 +69,7 @@ parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image upload parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last") parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv')) parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False) -parser.add_argument("--dark-mode", action='store_true', help="launches the UI in dark mode", default=False) +parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None) parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False) parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False) parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False) diff --git a/modules/ui.py b/modules/ui.py index a0cd052e..d41715fa 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1783,8 +1783,8 @@ for filename in sorted(os.listdir(jsdir)): with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile: javascript += f"\n" -if cmd_opts.dark_mode: - javascript += "\n\n" +if cmd_opts.theme is not None: + javascript += f"\n\n" if 'gradio_routes_templates_response' not in globals(): def template_response(*args, **kwargs): From d62ef76614624cda99d842a2900242d5b7923eda Mon Sep 17 00:00:00 2001 From: guaneec Date: Tue, 18 Oct 2022 03:09:50 +0800 Subject: [PATCH 025/152] Don't eat colons in booru tags --- modules/deepbooru.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 4ad334a1..de16b13f 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -157,8 +157,6 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o # sort by reverse by likelihood and normal for alpha, and format tag text as requested unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort)) for weight, tag in unsorted_tags_in_theshold: - # note: tag_outformat will still have a colon if include_ranks is True - tag_outformat = tag.replace(':', ' ') if use_spaces: tag_outformat = tag_outformat.replace('_', ' ') if use_escape: From f80e914ac4aa69a9783b4040813253500b34d925 Mon Sep 17 00:00:00 2001 From: arcticfaded Date: Mon, 17 Oct 2022 19:10:36 +0000 Subject: [PATCH 026/152] example API working with gradio --- modules/api/api.py | 9 +++++-- modules/api/processing.py | 56 ++++++++++++++++++++++++++------------- modules/processing.py | 22 ++++++++++----- 3 files changed, 60 insertions(+), 27 deletions(-) diff --git a/modules/api/api.py b/modules/api/api.py index fd09d352..5e86c3bf 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -23,8 +23,13 @@ class Api: app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"]) def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ): - p = StableDiffusionProcessingTxt2Img(**vars(txt2imgreq)) - p.sd_model = shared.sd_model + populate = txt2imgreq.copy(update={ # Override __init__ params + "sd_model": shared.sd_model, + "sampler_index": 0, + } + ) + p = StableDiffusionProcessingTxt2Img(**vars(populate)) + # Override object param processed = process_images(p) b64images = [] diff --git a/modules/api/processing.py b/modules/api/processing.py index e4df93c5..b6798241 100644 --- a/modules/api/processing.py +++ b/modules/api/processing.py @@ -5,6 +5,24 @@ from modules.processing import StableDiffusionProcessing, Processed, StableDiffu import inspect +API_NOT_ALLOWED = [ + "self", + "kwargs", + "sd_model", + "outpath_samples", + "outpath_grids", + "sampler_index", + "do_not_save_samples", + "do_not_save_grid", + "extra_generation_params", + "overlay_images", + "do_not_reload_embeddings", + "seed_enable_extras", + "prompt_for_display", + "sampler_noise_scheduler_override", + "ddim_discretize" +] + class ModelDef(BaseModel): """Assistance Class for Pydantic Dynamic Model Generation""" @@ -14,7 +32,7 @@ class ModelDef(BaseModel): field_value: Any -class pydanticModelGenerator: +class PydanticModelGenerator: """ Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about: source_data is a snapshot of the default values produced by the class @@ -24,30 +42,33 @@ class pydanticModelGenerator: def __init__( self, model_name: str = None, - source_data: {} = {}, - params: Dict = {}, - overrides: Dict = {}, - optionals: Dict = {}, + class_instance = None ): - def field_type_generator(k, v, overrides, optionals): - field_type = str if not overrides.get(k) else overrides[k]["type"] - if v is None: - field_type = Any - else: - field_type = type(v) + def field_type_generator(k, v): + # field_type = str if not overrides.get(k) else overrides[k]["type"] + # print(k, v.annotation, v.default) + field_type = v.annotation return Optional[field_type] + def merge_class_params(class_): + all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_))) + parameters = {} + for classes in all_classes: + parameters = {**parameters, **inspect.signature(classes.__init__).parameters} + return parameters + + self._model_name = model_name - self._json_data = source_data + self._class_data = merge_class_params(class_instance) self._model_def = [ ModelDef( field=underscore(k), field_alias=k, - field_type=field_type_generator(k, v, overrides, optionals), - field_value=v + field_type=field_type_generator(k, v), + field_value=v.default ) - for (k,v) in source_data.items() if k in params + for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED ] def generate_model(self): @@ -60,8 +81,7 @@ class pydanticModelGenerator: } DynamicModel = create_model(self._model_name, **fields) DynamicModel.__config__.allow_population_by_field_name = True + DynamicModel.__config__.allow_mutation = True return DynamicModel -StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing", - StableDiffusionProcessing().__dict__, - inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model() +StableDiffusionProcessingAPI = PydanticModelGenerator("StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img).generate_model() diff --git a/modules/processing.py b/modules/processing.py index 4a7c6ccc..024a4fc3 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -9,6 +9,7 @@ from PIL import Image, ImageFilter, ImageOps import random import cv2 from skimage import exposure +from typing import Any, Dict, List, Optional import modules.sd_hijack from modules import devices, prompt_parser, masking, sd_samplers, lowvram @@ -51,9 +52,15 @@ def get_correct_sampler(p): return sd_samplers.samplers elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img): return sd_samplers.samplers_for_img2img + elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI): + return sd_samplers.samplers -class StableDiffusionProcessing: - def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None, do_not_reload_embeddings=False): +class StableDiffusionProcessing(): + """ + The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing + + """ + def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str="", styles: List[str]=None, seed: int=-1, subseed: int=-1, subseed_strength: float=0, seed_resize_from_h: int=-1, seed_resize_from_w: int=-1, seed_enable_extras: bool=True, sampler_index: int=0, batch_size: int=1, n_iter: int=1, steps:int =50, cfg_scale:float=7.0, width:int=512, height:int=512, restore_faces:bool=False, tiling:bool=False, do_not_save_samples:bool=False, do_not_save_grid:bool=False, extra_generation_params: Dict[Any,Any]=None, overlay_images: Any=None, negative_prompt: str=None, eta: float =None, do_not_reload_embeddings: bool=False, denoising_strength: float = 0, ddim_discretize: str = "uniform", s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0): self.sd_model = sd_model self.outpath_samples: str = outpath_samples self.outpath_grids: str = outpath_grids @@ -86,10 +93,10 @@ class StableDiffusionProcessing: self.denoising_strength: float = 0 self.sampler_noise_scheduler_override = None self.ddim_discretize = opts.ddim_discretize - self.s_churn = opts.s_churn - self.s_tmin = opts.s_tmin - self.s_tmax = float('inf') # not representable as a standard ui option - self.s_noise = opts.s_noise + self.s_churn = s_churn or opts.s_churn + self.s_tmin = s_tmin or opts.s_tmin + self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option + self.s_noise = s_noise or opts.s_noise if not seed_enable_extras: self.subseed = -1 @@ -97,6 +104,7 @@ class StableDiffusionProcessing: self.seed_resize_from_h = 0 self.seed_resize_from_w = 0 + def init(self, all_prompts, all_seeds, all_subseeds): pass @@ -497,7 +505,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sampler = None - def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs): + def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs): super().__init__(**kwargs) self.enable_hr = enable_hr self.denoising_strength = denoising_strength From 2e28c841f438b2090caac2b9a54eb62ddbda837c Mon Sep 17 00:00:00 2001 From: guaneec Date: Tue, 18 Oct 2022 03:15:41 +0800 Subject: [PATCH 027/152] Oops --- modules/deepbooru.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/deepbooru.py b/modules/deepbooru.py index de16b13f..8914662d 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -157,6 +157,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o # sort by reverse by likelihood and normal for alpha, and format tag text as requested unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort)) for weight, tag in unsorted_tags_in_theshold: + tag_outformat = tag if use_spaces: tag_outformat = tag_outformat.replace('_', ' ') if use_escape: From f29b16bad19b6332a15b2ef439864d866277fffb Mon Sep 17 00:00:00 2001 From: arcticfaded Date: Mon, 17 Oct 2022 20:36:14 +0000 Subject: [PATCH 028/152] prevent API from saving --- modules/api/api.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/modules/api/api.py b/modules/api/api.py index 5e86c3bf..ce72c5ee 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -26,6 +26,8 @@ class Api: populate = txt2imgreq.copy(update={ # Override __init__ params "sd_model": shared.sd_model, "sampler_index": 0, + "do_not_save_samples": True, + "do_not_save_grid": True } ) p = StableDiffusionProcessingTxt2Img(**vars(populate)) From c3851a853d99ad35ccedcdd8dbeb6cfbe273439b Mon Sep 17 00:00:00 2001 From: Ryan Voots Date: Mon, 17 Oct 2022 12:49:33 -0400 Subject: [PATCH 029/152] Re-use webui fastapi application rather than requiring one or the other, not both. --- modules/api/api.py | 6 ++---- webui.py | 14 +++++++------- 2 files changed, 9 insertions(+), 11 deletions(-) diff --git a/modules/api/api.py b/modules/api/api.py index ce72c5ee..8781cd86 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -2,15 +2,13 @@ from modules.api.processing import StableDiffusionProcessingAPI from modules.processing import StableDiffusionProcessingTxt2Img, process_images import modules.shared as shared import uvicorn -from fastapi import FastAPI, Body, APIRouter +from fastapi import Body, APIRouter from fastapi.responses import JSONResponse from pydantic import BaseModel, Field, Json import json import io import base64 -app = FastAPI() - class TextToImageResponse(BaseModel): images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") parameters: Json @@ -18,7 +16,7 @@ class TextToImageResponse(BaseModel): class Api: - def __init__(self): + def __init__(self, app): self.router = APIRouter() app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"]) diff --git a/webui.py b/webui.py index eeee44c3..6b55fbed 100644 --- a/webui.py +++ b/webui.py @@ -96,14 +96,11 @@ def initialize(): def api(): - initialize() - from modules.api.api import Api - api = Api() - api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861) + api = Api(app) -def webui(): +def webui(launch_api=False): initialize() while 1: @@ -122,6 +119,9 @@ def webui(): app.add_middleware(GZipMiddleware, minimum_size=1000) + if (launch_api): + api(app) + while 1: time.sleep(0.5) if getattr(demo, 'do_restart', False): @@ -143,6 +143,6 @@ def webui(): if __name__ == "__main__": if cmd_opts.api: - api() + webui(True) else: - webui() + webui(False) From 247aeb3aaaf2925c7d68a9cf47c975f3e6d3dd33 Mon Sep 17 00:00:00 2001 From: Ryan Voots Date: Mon, 17 Oct 2022 12:50:45 -0400 Subject: [PATCH 030/152] Put API under /sdapi/ so that routing is simpler in the future. This means that one could allow access to /sdapi/ but not the webui. --- modules/api/api.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/api/api.py b/modules/api/api.py index 8781cd86..14613d8c 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -18,7 +18,7 @@ class TextToImageResponse(BaseModel): class Api: def __init__(self, app): self.router = APIRouter() - app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"]) + app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"]) def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ): populate = txt2imgreq.copy(update={ # Override __init__ params From 1df3ff25e6fe2e3f308e45f7a6dd37fb4f1988e6 Mon Sep 17 00:00:00 2001 From: Ryan Voots Date: Mon, 17 Oct 2022 12:58:34 -0400 Subject: [PATCH 031/152] Add --nowebui as a means of disabling the webui and run on the other port --- modules/shared.py | 3 ++- webui.py | 35 +++++++++++++++++++++++++---------- 2 files changed, 27 insertions(+), 11 deletions(-) diff --git a/modules/shared.py b/modules/shared.py index 6c6405fd..8b436970 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -74,7 +74,8 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help= parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False) parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None) parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False) -parser.add_argument("--api", action='store_true', help="use api=True to launch the api instead of the webui") +parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui") +parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui") cmd_opts = parser.parse_args() restricted_opts = [ diff --git a/webui.py b/webui.py index 6b55fbed..6212be01 100644 --- a/webui.py +++ b/webui.py @@ -95,16 +95,34 @@ def initialize(): signal.signal(signal.SIGINT, sigint_handler) -def api(): +def create_api(app): from modules.api.api import Api api = Api(app) + return api + +def wait_on_server(demo=None): + while 1: + time.sleep(0.5) + if demo and getattr(demo, 'do_restart', False): + time.sleep(0.5) + demo.close() + time.sleep(0.5) + break + +def api_only(): + initialize() + + app = FastAPI() + app.add_middleware(GZipMiddleware, minimum_size=1000) + api = create_api(app) + + api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861) def webui(launch_api=False): initialize() while 1: - demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call) app, local_url, share_url = demo.launch( @@ -120,15 +138,9 @@ def webui(launch_api=False): app.add_middleware(GZipMiddleware, minimum_size=1000) if (launch_api): - api(app) + create_api(app) - while 1: - time.sleep(0.5) - if getattr(demo, 'do_restart', False): - time.sleep(0.5) - demo.close() - time.sleep(0.5) - break + wait_on_server(demo) sd_samplers.set_samplers() @@ -142,6 +154,9 @@ def webui(launch_api=False): if __name__ == "__main__": + if not cmd_opts.nowebui: + api_only() + if cmd_opts.api: webui(True) else: From 8d5d863a9d11850464fdb6b64f34602803c15ccc Mon Sep 17 00:00:00 2001 From: arcticfaded Date: Tue, 18 Oct 2022 06:51:53 +0000 Subject: [PATCH 032/152] gradio and FastAPI --- modules/api/api.py | 13 ++++++++----- webui.py | 18 ++++++++---------- 2 files changed, 16 insertions(+), 15 deletions(-) diff --git a/modules/api/api.py b/modules/api/api.py index 14613d8c..ce98cb8c 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -16,9 +16,11 @@ class TextToImageResponse(BaseModel): class Api: - def __init__(self, app): + def __init__(self, app, queue_lock): self.router = APIRouter() - app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"]) + self.app = app + self.queue_lock = queue_lock + self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"]) def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ): populate = txt2imgreq.copy(update={ # Override __init__ params @@ -30,7 +32,8 @@ class Api: ) p = StableDiffusionProcessingTxt2Img(**vars(populate)) # Override object param - processed = process_images(p) + with self.queue_lock: + processed = process_images(p) b64images = [] for i in processed.images: @@ -52,5 +55,5 @@ class Api: raise NotImplementedError def launch(self, server_name, port): - app.include_router(self.router) - uvicorn.run(app, host=server_name, port=port) + self.app.include_router(self.router) + uvicorn.run(self.app, host=server_name, port=port) diff --git a/webui.py b/webui.py index 6212be01..71724c3b 100644 --- a/webui.py +++ b/webui.py @@ -4,7 +4,7 @@ import time import importlib import signal import threading - +from fastapi import FastAPI from fastapi.middleware.gzip import GZipMiddleware from modules.paths import script_path @@ -31,7 +31,6 @@ from modules.paths import script_path from modules.shared import cmd_opts import modules.hypernetworks.hypernetwork - queue_lock = threading.Lock() @@ -97,7 +96,7 @@ def initialize(): def create_api(app): from modules.api.api import Api - api = Api(app) + api = Api(app, queue_lock) return api def wait_on_server(demo=None): @@ -141,7 +140,7 @@ def webui(launch_api=False): create_api(app) wait_on_server(demo) - + sd_samplers.set_samplers() print('Reloading Custom Scripts') @@ -153,11 +152,10 @@ def webui(launch_api=False): print('Restarting Gradio') -if __name__ == "__main__": - if not cmd_opts.nowebui: - api_only() - if cmd_opts.api: - webui(True) +task = [] +if __name__ == "__main__": + if cmd_opts.nowebui: + api_only() else: - webui(False) + webui(cmd_opts.api) \ No newline at end of file From 786ed499226177d71e937e0342bcb9d3b1ff260f Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Mon, 17 Oct 2022 19:48:39 +0300 Subject: [PATCH 033/152] use legacy attnblock --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 984b35c4..2407a461 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -27,7 +27,7 @@ def apply_optimizations(): if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)): print("Applying xformers cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward + ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward elif cmd_opts.opt_split_attention_v1: print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 From 2043c4a231eef838bb15044f502b864b55885037 Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Mon, 17 Oct 2022 19:49:11 +0300 Subject: [PATCH 034/152] delete xformers attnblock --- modules/sd_hijack_optimizations.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 79405525..60da7459 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -292,15 +292,3 @@ def cross_attention_attnblock_forward(self, x): return h3 -def xformers_attnblock_forward(self, x): - try: - h_ = x - h_ = self.norm(h_) - q1 = self.q(h_).contiguous() - k1 = self.k(h_).contiguous() - v = self.v(h_).contiguous() - out = xformers.ops.memory_efficient_attention(q1, k1, v) - out = self.proj_out(out) - return x + out - except NotImplementedError: - return cross_attention_attnblock_forward(self, x) From 84823275e896bcc1f7cb4ce098ae3c5d05e17b9a Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Mon, 17 Oct 2022 22:18:59 +0300 Subject: [PATCH 035/152] readd xformers attnblock --- modules/sd_hijack_optimizations.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 60da7459..7ebef3f0 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -292,3 +292,18 @@ def cross_attention_attnblock_forward(self, x): return h3 +def xformers_attnblock_forward(self, x): + try: + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + b, c, h, w = q.shape + q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v)) + out = xformers.ops.memory_efficient_attention(q, k, v) + out = rearrange(out, 'b (h w) c -> b c h w', h=h) + out = self.proj_out(out) + return x + out + except NotImplementedError: + return cross_attention_attnblock_forward(self, x) From 73b5dbf72a93b64445551c74a4c0dc924986081d Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Mon, 17 Oct 2022 22:19:18 +0300 Subject: [PATCH 036/152] Update sd_hijack.py --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 2407a461..984b35c4 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -27,7 +27,7 @@ def apply_optimizations(): if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)): print("Applying xformers cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward + ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward elif cmd_opts.opt_split_attention_v1: print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 From c71008c74156635558bb2e877d1628913f6f781e Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Tue, 18 Oct 2022 00:02:50 +0300 Subject: [PATCH 037/152] Update sd_hijack_optimizations.py --- modules/sd_hijack_optimizations.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 7ebef3f0..a3345bb9 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -301,6 +301,9 @@ def xformers_attnblock_forward(self, x): v = self.v(h_) b, c, h, w = q.shape q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v)) + q = q.contiguous() + k = k.contiguous() + v = v.contiguous() out = xformers.ops.memory_efficient_attention(q, k, v) out = rearrange(out, 'b (h w) c -> b c h w', h=h) out = self.proj_out(out) From 8b02662215917d39f76f86b703a322818d5a8ad4 Mon Sep 17 00:00:00 2001 From: trufty Date: Mon, 17 Oct 2022 10:58:21 -0400 Subject: [PATCH 038/152] Disable auto weights swap with config option --- modules/shared.py | 1 + modules/ui.py | 4 ++++ 2 files changed, 5 insertions(+) diff --git a/modules/shared.py b/modules/shared.py index 9603d26e..8a1d1881 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -266,6 +266,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"), "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."), "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"), + "disable_weights_auto_swap": OptionInfo(False, "Disable auto swapping weights to match model hash in prompts"), "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }), "filter_nsfw": OptionInfo(False, "Filter NSFW content"), 'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}), diff --git a/modules/ui.py b/modules/ui.py index 1dae4a65..75eb0b0c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -542,6 +542,10 @@ def apply_setting(key, value): if value is None: return gr.update() + # dont allow model to be swapped when model hash exists in prompt + if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap: + return gr.update() + if key == "sd_model_checkpoint": ckpt_info = sd_models.get_closet_checkpoint_match(value) From d2f459c5cf9f728256775dc1c3380c7e9a7e27fb Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 18 Oct 2022 14:22:52 +0300 Subject: [PATCH 039/152] clarify the comment for the new option from #2959 and move it to UI section. --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/shared.py b/modules/shared.py index 8a1d1881..c0d87168 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -266,7 +266,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"), "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."), "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"), - "disable_weights_auto_swap": OptionInfo(False, "Disable auto swapping weights to match model hash in prompts"), "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }), "filter_nsfw": OptionInfo(False, "Filter NSFW content"), 'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}), @@ -294,6 +293,7 @@ options_templates.update(options_section(('ui', "User interface"), { "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"), "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"), "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"), + "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."), "font": OptionInfo("", "Font for image grids that have text"), "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"), "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"), From 7543787a0a7d8a45c44f2293b242e128da7c5a1d Mon Sep 17 00:00:00 2001 From: Justin Maier Date: Mon, 17 Oct 2022 17:11:44 -0600 Subject: [PATCH 040/152] Auto select attention block for editing --- javascript/edit-attention.js | 35 ++++++++++++++++++++++++++++++++--- 1 file changed, 32 insertions(+), 3 deletions(-) diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js index 67084e7a..c0d29a74 100644 --- a/javascript/edit-attention.js +++ b/javascript/edit-attention.js @@ -9,9 +9,38 @@ addEventListener('keydown', (event) => { let minus = "ArrowDown" if (event.key != plus && event.key != minus) return; - selectionStart = target.selectionStart; - selectionEnd = target.selectionEnd; - if(selectionStart == selectionEnd) return; + let selectionStart = target.selectionStart; + let selectionEnd = target.selectionEnd; + // If the user hasn't selected anything, let's select their current parenthesis block + if (selectionStart === selectionEnd) { + // Find opening parenthesis around current cursor + const before = target.value.substring(0, selectionStart); + let beforeParen = before.lastIndexOf("("); + if (beforeParen == -1) return; + let beforeParenClose = before.lastIndexOf(")"); + while (beforeParenClose !== -1 && beforeParenClose > beforeParen) { + beforeParen = before.lastIndexOf("(", beforeParen - 1); + beforeParenClose = before.lastIndexOf(")", beforeParenClose - 1); + } + + // Find closing parenthesis around current cursor + const after = target.value.substring(selectionStart); + let afterParen = after.indexOf(")"); + if (afterParen == -1) return; + let afterParenOpen = after.indexOf("("); + while (afterParenOpen !== -1 && afterParen > afterParenOpen) { + afterParen = after.indexOf(")", afterParen + 1); + afterParenOpen = after.indexOf("(", afterParenOpen + 1); + } + if (beforeParen === -1 || afterParen === -1) return; + + // Set the selection to the text between the parenthesis + const parenContent = target.value.substring(beforeParen + 1, selectionStart + afterParen); + const lastColon = parenContent.lastIndexOf(":"); + selectionStart = beforeParen + 1; + selectionEnd = selectionStart + lastColon; + target.setSelectionRange(selectionStart, selectionEnd); + } event.preventDefault(); From 428080d469a1b760d7b08a1d81fef77d3d47832a Mon Sep 17 00:00:00 2001 From: camenduru <54370274+camenduru@users.noreply.github.com> Date: Mon, 17 Oct 2022 03:20:11 +0300 Subject: [PATCH 041/152] Remove duplicate artists. --- artists.csv | 2 -- 1 file changed, 2 deletions(-) diff --git a/artists.csv b/artists.csv index 99cdbdc6..858dfcd6 100644 --- a/artists.csv +++ b/artists.csv @@ -738,7 +738,6 @@ Abraham Mignon,0.60605425,fineart Albert Bloch,0.69573116,nudity Charles Dana Gibson,0.67155975,fineart Alexandre-Évariste Fragonard,0.6507174,fineart -Alexandre-Évariste Fragonard,0.6507174,fineart Ernst Fuchs,0.6953538,nudity Alfredo Jaar,0.6952965,digipa-high-impact Judy Chicago,0.6952246,weird @@ -2411,7 +2410,6 @@ Hermann Feierabend,0.5346168,digipa-high-impact Antonio Donghi,0.4610982,digipa-low-impact Adonna Khare,0.4858036,digipa-med-impact James Stokoe,0.5015107,digipa-med-impact -Art & Language,0.5341332,digipa-high-impact Agustín Fernández,0.53403986,fineart Germán Londoño,0.5338712,fineart Emmanuelle Moureaux,0.5335641,digipa-high-impact From 97d3ba3941536215ea15431886c7f28300a9d915 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E3=81=B5=E3=81=81?= <34892635+fa0311@users.noreply.github.com> Date: Tue, 18 Oct 2022 17:29:42 +0900 Subject: [PATCH 042/152] Add scripts to ui-config,json --- modules/scripts.py | 15 +++++++++++++-- modules/ui.py | 5 +++++ 2 files changed, 18 insertions(+), 2 deletions(-) diff --git a/modules/scripts.py b/modules/scripts.py index ac66d448..3402066d 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -96,6 +96,7 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs): class ScriptRunner: def __init__(self): self.scripts = [] + self.titles = [] def setup_ui(self, is_img2img): for script_class, path in scripts_data: @@ -107,9 +108,10 @@ class ScriptRunner: self.scripts.append(script) - titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts] + self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts] - dropdown = gr.Dropdown(label="Script", choices=["None"] + titles, value="None", type="index") + dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index") + dropdown.save_to_config = True inputs = [dropdown] for script in self.scripts: @@ -139,6 +141,15 @@ class ScriptRunner: return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))] + def init_field(title): + if title == "None": + return + script_index = self.titles.index(title) + script = self.scripts[script_index] + for i in range(script.args_from, script.args_to): + inputs[i].visible = True + + dropdown.init_field = init_field dropdown.change( fn=select_script, inputs=[dropdown], diff --git a/modules/ui.py b/modules/ui.py index 75eb0b0c..39afbc4e 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1753,6 +1753,11 @@ Requested path was: {f} print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.') else: setattr(obj, field, saved_value) + if getattr(x, 'init_field', False): + try: + x.init_field(saved_value) + except Exception: + print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.') if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible: apply_field(x, 'visible') From de29ec0743fcfb141d8891a3ccbd537ea71bf5b4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E3=81=B5=E3=81=81?= <34892635+fa0311@users.noreply.github.com> Date: Tue, 18 Oct 2022 18:15:00 +0900 Subject: [PATCH 043/152] Remove exception handling --- modules/ui.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/modules/ui.py b/modules/ui.py index 39afbc4e..b38bfb3f 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1754,10 +1754,7 @@ Requested path was: {f} else: setattr(obj, field, saved_value) if getattr(x, 'init_field', False): - try: - x.init_field(saved_value) - except Exception: - print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.') + x.init_field(saved_value) if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible: apply_field(x, 'visible') From 3003438088502774628656790d83fc8074d51ab4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E3=81=B5=E3=81=81?= <34892635+fa0311@users.noreply.github.com> Date: Tue, 18 Oct 2022 18:51:57 +0900 Subject: [PATCH 044/152] Add visible for dropdown --- modules/ui.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/modules/ui.py b/modules/ui.py index b38bfb3f..fb6eb5a0 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1737,7 +1737,7 @@ Requested path was: {f} print(traceback.format_exc(), file=sys.stderr) def loadsave(path, x): - def apply_field(obj, field, condition=None): + def apply_field(obj, field, condition=None, init_field=None): key = path + "/" + field if getattr(obj,'custom_script_source',None) is not None: @@ -1753,8 +1753,8 @@ Requested path was: {f} print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.') else: setattr(obj, field, saved_value) - if getattr(x, 'init_field', False): - x.init_field(saved_value) + if init_field is not None: + init_field(saved_value) if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible: apply_field(x, 'visible') @@ -1780,7 +1780,8 @@ Requested path was: {f} # Since there are many dropdowns that shouldn't be saved, # we only mark dropdowns that should be saved. if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False): - apply_field(x, 'value', lambda val: val in x.choices) + apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None)) + apply_field(x, 'visible') visit(txt2img_interface, loadsave, "txt2img") visit(img2img_interface, loadsave, "img2img") From 02622b19191f5f5112db7633c0630e5c7df1b2f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E3=81=B5=E3=81=81?= <34892635+fa0311@users.noreply.github.com> Date: Tue, 18 Oct 2022 18:52:27 +0900 Subject: [PATCH 045/152] update scripts.py --- modules/scripts.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/scripts.py b/modules/scripts.py index 3402066d..1039fa9c 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -142,7 +142,7 @@ class ScriptRunner: return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))] def init_field(title): - if title == "None": + if title == 'None': return script_index = self.titles.index(title) script = self.scripts[script_index] From 68e83f40bf143e25639875b27e8ad3e32b382db5 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 16 Oct 2022 10:49:28 +0100 Subject: [PATCH 046/152] add update warning to launch.py --- launch.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/launch.py b/launch.py index 7520cfee..1b133633 100644 --- a/launch.py +++ b/launch.py @@ -5,6 +5,7 @@ import sys import importlib.util import shlex import platform +import requests dir_repos = "repositories" python = sys.executable @@ -126,6 +127,16 @@ def prepare_enviroment(): print(f"Python {sys.version}") print(f"Commit hash: {commit}") + try: + commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json() + if commit != "" and commits['commit']['sha'] != commit: + print("--------------------------------------------------------") + print("| You are not up to date with the most recent release. |") + print("| Consider running `git pull` to update. |") + print("--------------------------------------------------------") + except Exception as e: + pass + if not is_installed("torch") or not is_installed("torchvision"): run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch") From a647cbc618ea13ce2312f0291ebdfc3b9449e5a1 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 16 Oct 2022 10:54:09 +0100 Subject: [PATCH 047/152] move update check to after dep installation --- launch.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/launch.py b/launch.py index 1b133633..8484d7c1 100644 --- a/launch.py +++ b/launch.py @@ -5,7 +5,6 @@ import sys import importlib.util import shlex import platform -import requests dir_repos = "repositories" python = sys.executable @@ -126,16 +125,6 @@ def prepare_enviroment(): print(f"Python {sys.version}") print(f"Commit hash: {commit}") - - try: - commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json() - if commit != "" and commits['commit']['sha'] != commit: - print("--------------------------------------------------------") - print("| You are not up to date with the most recent release. |") - print("| Consider running `git pull` to update. |") - print("--------------------------------------------------------") - except Exception as e: - pass if not is_installed("torch") or not is_installed("torchvision"): run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch") @@ -176,6 +165,17 @@ def prepare_enviroment(): sys.argv += args + try: + import requests + commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json() + if commit != "" and commits['commit']['sha'] != commit: + print("--------------------------------------------------------") + print("| You are not up to date with the most recent release. |") + print("| Consider running `git pull` to update. |") + print("--------------------------------------------------------") + except Exception as e: + pass + if "--exit" in args: print("Exiting because of --exit argument") exit(0) From e511b867a9e85e715966c5c60052d67497a4f949 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 16 Oct 2022 17:04:09 +0100 Subject: [PATCH 048/152] Make update check commandline option, give response on all paths. --- launch.py | 29 +++++++++++++++++++---------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/launch.py b/launch.py index 8484d7c1..9d9dd1b6 100644 --- a/launch.py +++ b/launch.py @@ -86,7 +86,24 @@ def git_clone(url, dir, name, commithash=None): if commithash is not None: run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}") + +def version_check(commit): + try: + import requests + commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json() + if commit != "" and commits['commit']['sha'] != commit: + print("--------------------------------------------------------") + print("| You are not up to date with the most recent release. |") + print("| Consider running `git pull` to update. |") + print("--------------------------------------------------------") + elif commits['commit']['sha'] == commit: + print("You are up to date with the most recent release.") + else: + print("Not a git clone, can't perform version check.") + except Exception as e: + print("versipm check failed",e) + def prepare_enviroment(): torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113") requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt") @@ -165,16 +182,8 @@ def prepare_enviroment(): sys.argv += args - try: - import requests - commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json() - if commit != "" and commits['commit']['sha'] != commit: - print("--------------------------------------------------------") - print("| You are not up to date with the most recent release. |") - print("| Consider running `git pull` to update. |") - print("--------------------------------------------------------") - except Exception as e: - pass + if '--update-check' in args: + version_check(commit) if "--exit" in args: print("Exiting because of --exit argument") From 4c605c5174a9b211c3a88e9aff5f5be92b53fd92 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 16 Oct 2022 17:24:06 +0100 Subject: [PATCH 049/152] add shared option for update check --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/shared.py b/modules/shared.py index c0d87168..50dc46ae 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -76,6 +76,7 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help= parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False) parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None) parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False) +parser.add_argument("--update-check", action='store_true', help="enable http check to confirm that the currently running version is the most recent release.", default=False) cmd_opts = parser.parse_args() restricted_opts = [ From 433a7525c1f5eb5963340e0cc45d31038ede3f7e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 18 Oct 2022 15:18:02 +0300 Subject: [PATCH 050/152] remove shared option for update check (because it is not an argument of webui) have launch.py examine both COMMANDLINE_ARGS as well as argv for its arguments --- launch.py | 19 +++++++++---------- modules/shared.py | 1 - 2 files changed, 9 insertions(+), 11 deletions(-) diff --git a/launch.py b/launch.py index 9d9dd1b6..7b15e78e 100644 --- a/launch.py +++ b/launch.py @@ -127,13 +127,14 @@ def prepare_enviroment(): codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af") blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9") - args = shlex.split(commandline_args) + sys.argv += shlex.split(commandline_args) - args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test') - args, reinstall_xformers = extract_arg(args, '--reinstall-xformers') - xformers = '--xformers' in args - deepdanbooru = '--deepdanbooru' in args - ngrok = '--ngrok' in args + sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test') + sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers') + sys.argv, update_check = extract_arg(sys.argv, '--update-check') + xformers = '--xformers' in sys.argv + deepdanbooru = '--deepdanbooru' in sys.argv + ngrok = '--ngrok' in sys.argv try: commit = run(f"{git} rev-parse HEAD").strip() @@ -180,12 +181,10 @@ def prepare_enviroment(): run_pip(f"install -r {requirements_file}", "requirements for Web UI") - sys.argv += args - - if '--update-check' in args: + if update_check: version_check(commit) - if "--exit" in args: + if "--exit" in sys.argv: print("Exiting because of --exit argument") exit(0) diff --git a/modules/shared.py b/modules/shared.py index 50dc46ae..c0d87168 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -76,7 +76,6 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help= parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False) parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None) parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False) -parser.add_argument("--update-check", action='store_true', help="enable http check to confirm that the currently running version is the most recent release.", default=False) cmd_opts = parser.parse_args() restricted_opts = [ From 2f448d97a9427f9a7bad19cf608561b2878ab2da Mon Sep 17 00:00:00 2001 From: w-e-w <40751091+w-e-w@users.noreply.github.com> Date: Mon, 17 Oct 2022 23:18:21 +0900 Subject: [PATCH 051/152] styles.csv encoding utf8 to utf-8-sig utf-8-bom for better compatibility for some programs --- modules/styles.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/styles.py b/modules/styles.py index d44dfc1a..3bf5c5b6 100644 --- a/modules/styles.py +++ b/modules/styles.py @@ -45,7 +45,7 @@ class StyleDatabase: if not os.path.exists(path): return - with open(path, "r", encoding="utf8", newline='') as file: + with open(path, "r", encoding="utf-8-sig", newline='') as file: reader = csv.DictReader(file) for row in reader: # Support loading old CSV format with "name, text"-columns @@ -79,7 +79,7 @@ class StyleDatabase: def save_styles(self, path: str) -> None: # Write to temporary file first, so we don't nuke the file if something goes wrong fd, temp_path = tempfile.mkstemp(".csv") - with os.fdopen(fd, "w", encoding="utf8", newline='') as file: + with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file: # _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple, # and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict() writer = csv.DictWriter(file, fieldnames=PromptStyle._fields) From e20b7e30fe17744acb74ad33c87c0963525ea921 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 18 Oct 2022 15:33:24 +0300 Subject: [PATCH 052/152] fix for add difference model merging --- modules/extras.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index c908b43e..03f6085e 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -216,8 +216,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam if theta_func1: for key in tqdm.tqdm(theta_1.keys()): if 'model' in key: - t2 = theta_2.get(key, torch.zeros_like(theta_1[key])) - theta_1[key] = theta_func1(theta_1[key], t2) + if key in theta_2: + t2 = theta_2.get(key, torch.zeros_like(theta_1[key])) + theta_1[key] = theta_func1(theta_1[key], t2) + else: + theta_1[key] = 0 del theta_2, teritary_model for key in tqdm.tqdm(theta_0.keys()): From ec1924ee5789b72c31c65932b549c59ccae0cdd6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 18 Oct 2022 16:05:52 +0300 Subject: [PATCH 053/152] additional fix for difference model merging --- modules/extras.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/extras.py b/modules/extras.py index 03f6085e..b853fa5b 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -220,7 +220,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam t2 = theta_2.get(key, torch.zeros_like(theta_1[key])) theta_1[key] = theta_func1(theta_1[key], t2) else: - theta_1[key] = 0 + theta_1[key] = torch.zeros_like(theta_1[key]) del theta_2, teritary_model for key in tqdm.tqdm(theta_0.keys()): From cbf15edbf90a68a08eeab40af5df577ba4ac90b6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 18 Oct 2022 17:23:38 +0300 Subject: [PATCH 054/152] remove dependence on TQDM for sampler progress/interrupt functionality --- modules/processing.py | 6 --- modules/sd_samplers.py | 107 ++++++++++++++++++++++------------------- 2 files changed, 58 insertions(+), 55 deletions(-) diff --git a/modules/processing.py b/modules/processing.py index deb6125e..346eea88 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -402,12 +402,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed: with devices.autocast(): samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) - if state.interrupted or state.skipped: - - # if we are interrupted, sample returns just noise - # use the image collected previously in sampler loop - samples_ddim = shared.state.current_latent - samples_ddim = samples_ddim.to(devices.dtype_vae) x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 20309e06..b58e810b 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -98,25 +98,8 @@ def store_latent(decoded): shared.state.current_image = sample_to_image(decoded) - -def extended_tdqm(sequence, *args, desc=None, **kwargs): - state.sampling_steps = len(sequence) - state.sampling_step = 0 - - seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs) - - for x in seq: - if state.interrupted or state.skipped: - break - - yield x - - state.sampling_step += 1 - shared.total_tqdm.update() - - -ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs) -ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs) +class InterruptedException(BaseException): + pass class VanillaStableDiffusionSampler: @@ -128,14 +111,32 @@ class VanillaStableDiffusionSampler: self.init_latent = None self.sampler_noises = None self.step = 0 + self.stop_at = None self.eta = None self.default_eta = 0.0 self.config = None + self.last_latent = None def number_of_needed_noises(self, p): return 0 + def launch_sampling(self, steps, func): + state.sampling_steps = steps + state.sampling_step = 0 + + try: + return func() + except InterruptedException: + return self.last_latent + def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs): + if state.interrupted or state.skipped: + raise InterruptedException + + if self.stop_at is not None and self.step > self.stop_at: + raise InterruptedException + + conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) @@ -159,11 +160,16 @@ class VanillaStableDiffusionSampler: res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs) if self.mask is not None: - store_latent(self.init_latent * self.mask + self.nmask * res[1]) + self.last_latent = self.init_latent * self.mask + self.nmask * res[1] else: - store_latent(res[1]) + self.last_latent = res[1] + + store_latent(self.last_latent) self.step += 1 + state.sampling_step = self.step + shared.total_tqdm.update() + return res def initialize(self, p): @@ -192,7 +198,7 @@ class VanillaStableDiffusionSampler: self.init_latent = x self.step = 0 - samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning) + samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)) return samples @@ -206,9 +212,9 @@ class VanillaStableDiffusionSampler: # existing code fails with certain step counts, like 9 try: - samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta) + samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0]) except Exception: - samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta) + samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0]) return samples_ddim @@ -223,6 +229,9 @@ class CFGDenoiser(torch.nn.Module): self.step = 0 def forward(self, x, sigma, uncond, cond, cond_scale): + if state.interrupted or state.skipped: + raise InterruptedException + conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step) @@ -268,25 +277,6 @@ class CFGDenoiser(torch.nn.Module): return denoised -def extended_trange(sampler, count, *args, **kwargs): - state.sampling_steps = count - state.sampling_step = 0 - - seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs) - - for x in seq: - if state.interrupted or state.skipped: - break - - if sampler.stop_at is not None and x > sampler.stop_at: - break - - yield x - - state.sampling_step += 1 - shared.total_tqdm.update() - - class TorchHijack: def __init__(self, kdiff_sampler): self.kdiff_sampler = kdiff_sampler @@ -314,9 +304,28 @@ class KDiffusionSampler: self.eta = None self.default_eta = 1.0 self.config = None + self.last_latent = None def callback_state(self, d): - store_latent(d["denoised"]) + step = d['i'] + latent = d["denoised"] + store_latent(latent) + self.last_latent = latent + + if self.stop_at is not None and step > self.stop_at: + raise InterruptedException + + state.sampling_step = step + shared.total_tqdm.update() + + def launch_sampling(self, steps, func): + state.sampling_steps = steps + state.sampling_step = 0 + + try: + return func() + except InterruptedException: + return self.last_latent def number_of_needed_noises(self, p): return p.steps @@ -339,9 +348,6 @@ class KDiffusionSampler: self.sampler_noise_index = 0 self.eta = p.eta or opts.eta_ancestral - if hasattr(k_diffusion.sampling, 'trange'): - k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs) - if self.sampler_noises is not None: k_diffusion.sampling.torch = TorchHijack(self) @@ -383,8 +389,9 @@ class KDiffusionSampler: self.model_wrap_cfg.init_latent = x - return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs) + samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)) + return samples def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): steps = steps or p.steps @@ -406,6 +413,8 @@ class KDiffusionSampler: extra_params_kwargs['n'] = steps else: extra_params_kwargs['sigmas'] = sigmas - samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs) + + samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)) + return samples From bdf1a8903a38e6c29afdbfb6370c40976cdfd99a Mon Sep 17 00:00:00 2001 From: Matthew Clark Date: Tue, 4 Oct 2022 13:44:21 -0400 Subject: [PATCH 055/152] Pass arguments from bash to python --- webui.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/webui.sh b/webui.sh index 980c0aaf..a9f85d89 100755 --- a/webui.sh +++ b/webui.sh @@ -138,4 +138,4 @@ fi printf "\n%s\n" "${delimiter}" printf "Launching launch.py..." printf "\n%s\n" "${delimiter}" -"${python_cmd}" "${LAUNCH_SCRIPT}" +"${python_cmd}" "${LAUNCH_SCRIPT}" "$@" From 82589c2d5e97491b8fc741217800598ba07e2c46 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Mon, 10 Oct 2022 01:52:35 +0100 Subject: [PATCH 056/152] add windows equivalent --- webui.bat | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/webui.bat b/webui.bat index 3f1d03f6..a38a28bb 100644 --- a/webui.bat +++ b/webui.bat @@ -33,7 +33,7 @@ goto :launch :skip_venv :launch -%PYTHON% launch.py +%PYTHON% launch.py %* pause exit /b From b76c9ded4556efa8fa19bcc3e9f442dd33ed9daa Mon Sep 17 00:00:00 2001 From: supersteve3d <39339941+supersteve3d@users.noreply.github.com> Date: Tue, 18 Oct 2022 20:14:49 +0800 Subject: [PATCH 057/152] Update artists.csv --- artists.csv | 2 -- 1 file changed, 2 deletions(-) diff --git a/artists.csv b/artists.csv index 858dfcd6..c54de486 100644 --- a/artists.csv +++ b/artists.csv @@ -523,7 +523,6 @@ Affandi,0.7170285,nudity Diane Arbus,0.655138,digipa-high-impact Joseph Ducreux,0.65247905,digipa-high-impact Berthe Morisot,0.7165984,fineart -Hilma AF Klint,0.71643853,scribbles Hilma af Klint,0.71643853,scribbles Filippino Lippi,0.7163017,fineart Leonid Afremov,0.7163005,fineart @@ -1521,7 +1520,6 @@ Gertrude Harvey,0.5903887,fineart Grant Wood,0.6266253,fineart Fyodor Vasilyev,0.5234919,digipa-med-impact Cagnaccio di San Pietro,0.6261671,fineart -Cagnaccio Di San Pietro,0.6261671,fineart Doris Boulton-Maude,0.62593174,fineart Adolf Hirémy-Hirschl,0.5946784,fineart Harold von Schmidt,0.6256755,fineart From c1093b8051606f0ac90506b7114c4b55d0447c70 Mon Sep 17 00:00:00 2001 From: supersteve3d <39339941+supersteve3d@users.noreply.github.com> Date: Tue, 18 Oct 2022 20:19:12 +0800 Subject: [PATCH 058/152] Update artists.csv --- artists.csv | 3 --- 1 file changed, 3 deletions(-) diff --git a/artists.csv b/artists.csv index c54de486..1a61ed88 100644 --- a/artists.csv +++ b/artists.csv @@ -743,7 +743,6 @@ Judy Chicago,0.6952246,weird Frans van Mieris the Younger,0.6951849,fineart Aertgen van Leyden,0.6951305,fineart Emily Carr,0.69512105,fineart -Frances Macdonald,0.6950408,scribbles Frances MacDonald,0.6950408,scribbles Hannah Höch,0.69495845,scribbles Gillis Rombouts,0.58770025,fineart @@ -893,7 +892,6 @@ Richard McGuire,0.6820089,scribbles Anni Albers,0.65708244,digipa-high-impact Aleksey Savrasov,0.65207493,fineart Wayne Barlowe,0.6537874,fineart -Giorgio De Chirico,0.6815907,fineart Giorgio de Chirico,0.6815907,fineart Ernest Procter,0.6815795,fineart Adriaen Brouwer,0.6815058,fineart @@ -1239,7 +1237,6 @@ Betty Churcher,0.65387225,fineart Claes Corneliszoon Moeyaert,0.65386075,fineart David Bomberg,0.6537477,fineart Abraham Bosschaert,0.6535562,fineart -Giuseppe De Nittis,0.65354455,fineart Giuseppe de Nittis,0.65354455,fineart John La Farge,0.65342575,fineart Frits Thaulow,0.65341854,fineart From 6021f7a75f7b5208a2be15cda5526028152f922d Mon Sep 17 00:00:00 2001 From: discus0434 Date: Wed, 19 Oct 2022 00:51:36 +0900 Subject: [PATCH 059/152] add options to custom hypernetwork layer structure --- .gitignore | 1 + modules/hypernetworks/hypernetwork.py | 90 ++++++++++++++++++++------- modules/shared.py | 4 +- webui.py | 6 +- 4 files changed, 76 insertions(+), 25 deletions(-) diff --git a/.gitignore b/.gitignore index 69785b3e..4794865c 100644 --- a/.gitignore +++ b/.gitignore @@ -27,3 +27,4 @@ __pycache__ notification.mp3 /SwinIR /textual_inversion +/hypernetwork diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 4905710e..cadb9911 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -1,52 +1,98 @@ +import csv import datetime import glob import html import os import sys import traceback -import tqdm -import csv -import torch - -from ldm.util import default -from modules import devices, shared, processing, sd_models -import torch -from torch import einsum -from einops import rearrange, repeat import modules.textual_inversion.dataset +import torch +import tqdm +from einops import rearrange, repeat +from ldm.util import default +from modules import devices, processing, sd_models, shared from modules.textual_inversion import textual_inversion from modules.textual_inversion.learn_schedule import LearnRateScheduler +from torch import einsum + + +def parse_layer_structure(dim, state_dict): + i = 0 + res = [1] + while (key := "linear.{}.weight".format(i)) in state_dict: + weight = state_dict[key] + res.append(len(weight) // dim) + i += 1 + return res class HypernetworkModule(torch.nn.Module): multiplier = 1.0 + layer_structure = None + add_layer_norm = False def __init__(self, dim, state_dict=None): super().__init__() + if (state_dict is None or 'linear.0.weight' not in state_dict) and self.layer_structure is None: + layer_structure = (1, 2, 1) + else: + if self.layer_structure is not None: + assert self.layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" + assert self.layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" + layer_structure = self.layer_structure + else: + layer_structure = parse_layer_structure(dim, state_dict) - self.linear1 = torch.nn.Linear(dim, dim * 2) - self.linear2 = torch.nn.Linear(dim * 2, dim) + linears = [] + for i in range(len(layer_structure) - 1): + linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) + if self.add_layer_norm: + linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) + + self.linear = torch.nn.Sequential(*linears) if state_dict is not None: - self.load_state_dict(state_dict, strict=True) + try: + self.load_state_dict(state_dict) + except RuntimeError: + self.try_load_previous(state_dict) else: - - self.linear1.weight.data.normal_(mean=0.0, std=0.01) - self.linear1.bias.data.zero_() - self.linear2.weight.data.normal_(mean=0.0, std=0.01) - self.linear2.bias.data.zero_() + for layer in self.linear: + layer.weight.data.normal_(mean = 0.0, std = 0.01) + layer.bias.data.zero_() self.to(devices.device) + def try_load_previous(self, state_dict): + states = self.state_dict() + states['linear.0.bias'].copy_(state_dict['linear1.bias']) + states['linear.0.weight'].copy_(state_dict['linear1.weight']) + states['linear.1.bias'].copy_(state_dict['linear2.bias']) + states['linear.1.weight'].copy_(state_dict['linear2.weight']) + def forward(self, x): - return x + (self.linear2(self.linear1(x))) * self.multiplier + return x + self.linear(x) * self.multiplier + + def trainables(self): + res = [] + for layer in self.linear: + res += [layer.weight, layer.bias] + return res def apply_strength(value=None): HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength +def apply_layer_structure(value=None): + HypernetworkModule.layer_structure = value if value is not None else shared.opts.sd_hypernetwork_layer_structure + + +def apply_layer_norm(value=None): + HypernetworkModule.add_layer_norm = value if value is not None else shared.opts.sd_hypernetwork_add_layer_norm + + class Hypernetwork: filename = None name = None @@ -68,7 +114,7 @@ class Hypernetwork: for k, layers in self.layers.items(): for layer in layers: layer.train() - res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias] + res += layer.trainables() return res @@ -226,7 +272,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size) - + assert ds.length > 1, "Dataset should contain more than 1 images" if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) @@ -261,7 +307,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log with torch.autocast("cuda"): c = stack_conds([entry.cond for entry in entries]).to(devices.device) -# c = torch.vstack([entry.cond for entry in entries]).to(devices.device) + c = torch.vstack([entry.cond for entry in entries]).to(devices.device) x = torch.stack([entry.latent for entry in entries]).to(devices.device) loss = shared.sd_model(x, c)[0] del x @@ -283,7 +329,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { "loss": f"{mean_loss:.7f}", - "learn_rate": scheduler.learn_rate + "learn_rate": f"{scheduler.learn_rate:.7f}" }) if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: diff --git a/modules/shared.py b/modules/shared.py index c0d87168..c87ce70e 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -13,7 +13,7 @@ import modules.memmon import modules.sd_models import modules.styles import modules.devices as devices -from modules import sd_samplers, sd_models, localization +from modules import sd_models, sd_samplers, localization from modules.hypernetworks import hypernetwork from modules.paths import models_path, script_path, sd_path @@ -258,6 +258,8 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models), "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}), "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), + "sd_hypernetwork_layer_structure": OptionInfo(None, "Hypernetwork layer structure Default: (1,2,1).", gr.Dropdown, lambda: {"choices": [(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)]}), + "sd_hypernetwork_add_layer_norm": OptionInfo(False, "Add layer normalization to hypernetwork architecture."), "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"), diff --git a/webui.py b/webui.py index fe0ce321..86e98ad0 100644 --- a/webui.py +++ b/webui.py @@ -86,11 +86,13 @@ def initialize(): shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model))) shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork))) shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength) + shared.opts.onchange("sd_hypernetwork_layer_structure", modules.hypernetworks.hypernetwork.apply_layer_structure) + shared.opts.onchange("sd_hypernetwork_add_layer_norm", modules.hypernetworks.hypernetwork.apply_layer_norm) def webui(): initialize() - + # make the program just exit at ctrl+c without waiting for anything def sigint_handler(sig, frame): print(f'Interrupted with signal {sig} in {frame}') @@ -101,7 +103,7 @@ def webui(): while 1: demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call) - + app, local_url, share_url = demo.launch( share=cmd_opts.share, server_name="0.0.0.0" if cmd_opts.listen else None, From a5611ea5026bd8e12d8e84023384c369d0511dda Mon Sep 17 00:00:00 2001 From: discus0434 Date: Wed, 19 Oct 2022 01:00:01 +0900 Subject: [PATCH 060/152] update --- .gitignore | 1 - modules/hypernetworks/hypernetwork.py | 16 +++++++++------- 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/.gitignore b/.gitignore index 4794865c..69785b3e 100644 --- a/.gitignore +++ b/.gitignore @@ -27,4 +27,3 @@ __pycache__ notification.mp3 /SwinIR /textual_inversion -/hypernetwork diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index cadb9911..c5835bce 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -1,20 +1,22 @@ -import csv import datetime import glob import html import os import sys import traceback - -import modules.textual_inversion.dataset -import torch import tqdm -from einops import rearrange, repeat +import csv + +import torch + from ldm.util import default -from modules import devices, processing, sd_models, shared +from modules import devices, shared, processing, sd_models +import torch +from torch import einsum +from einops import rearrange, repeat +import modules.textual_inversion.dataset from modules.textual_inversion import textual_inversion from modules.textual_inversion.learn_schedule import LearnRateScheduler -from torch import einsum def parse_layer_structure(dim, state_dict): From 7f2095c6c8db82a5c9cd7c7177f6ba856a2cc676 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Wed, 19 Oct 2022 01:01:22 +0900 Subject: [PATCH 061/152] update --- modules/shared.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/shared.py b/modules/shared.py index c87ce70e..6b6d5c41 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -13,7 +13,7 @@ import modules.memmon import modules.sd_models import modules.styles import modules.devices as devices -from modules import sd_models, sd_samplers, localization +from modules import sd_samplers, sd_models, localization from modules.hypernetworks import hypernetwork from modules.paths import models_path, script_path, sd_path @@ -135,7 +135,7 @@ class State: self.job_no += 1 self.sampling_step = 0 self.current_image_sampling_step = 0 - + def get_job_timestamp(self): return datetime.datetime.now().strftime("%Y%m%d%H%M%S") # shouldn't this return job_timestamp? From e40ba281f1b419cf99552962ea01d87d699840a5 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Wed, 19 Oct 2022 01:03:58 +0900 Subject: [PATCH 062/152] update --- modules/hypernetworks/hypernetwork.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index c5835bce..082165f4 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -309,7 +309,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log with torch.autocast("cuda"): c = stack_conds([entry.cond for entry in entries]).to(devices.device) - c = torch.vstack([entry.cond for entry in entries]).to(devices.device) + # c = torch.vstack([entry.cond for entry in entries]).to(devices.device) x = torch.stack([entry.latent for entry in entries]).to(devices.device) loss = shared.sd_model(x, c)[0] del x @@ -331,7 +331,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { "loss": f"{mean_loss:.7f}", - "learn_rate": f"{scheduler.learn_rate:.7f}" + "learn_rate": scheduler.learn_rate }) if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: From e7f4808505f7a6339927c32b9a0c01bc9134bdeb Mon Sep 17 00:00:00 2001 From: arcticfaded Date: Tue, 18 Oct 2022 19:04:56 +0000 Subject: [PATCH 063/152] provide sampler by name --- modules/api/api.py | 12 ++++++++++-- modules/api/processing.py | 16 ++++++++++++++-- 2 files changed, 24 insertions(+), 4 deletions(-) diff --git a/modules/api/api.py b/modules/api/api.py index ce98cb8c..ff9df0d1 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -1,14 +1,17 @@ from modules.api.processing import StableDiffusionProcessingAPI from modules.processing import StableDiffusionProcessingTxt2Img, process_images +from modules.sd_samplers import samplers_k_diffusion import modules.shared as shared import uvicorn -from fastapi import Body, APIRouter +from fastapi import Body, APIRouter, HTTPException from fastapi.responses import JSONResponse from pydantic import BaseModel, Field, Json import json import io import base64 +sampler_to_index = lambda name: next(filter(lambda row: name in row[1][2], enumerate(samplers_k_diffusion)), None) + class TextToImageResponse(BaseModel): images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") parameters: Json @@ -23,9 +26,14 @@ class Api: self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"]) def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ): + sampler_index = sampler_to_index(txt2imgreq.sampler_index) + + if sampler_index is None: + raise HTTPException(status_code=404, detail="Sampler not found") + populate = txt2imgreq.copy(update={ # Override __init__ params "sd_model": shared.sd_model, - "sampler_index": 0, + "sampler_index": sampler_index[0], "do_not_save_samples": True, "do_not_save_grid": True } diff --git a/modules/api/processing.py b/modules/api/processing.py index b6798241..2e6483ee 100644 --- a/modules/api/processing.py +++ b/modules/api/processing.py @@ -42,7 +42,8 @@ class PydanticModelGenerator: def __init__( self, model_name: str = None, - class_instance = None + class_instance = None, + additional_fields = None, ): def field_type_generator(k, v): # field_type = str if not overrides.get(k) else overrides[k]["type"] @@ -70,6 +71,13 @@ class PydanticModelGenerator: ) for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED ] + + for fields in additional_fields: + self._model_def.append(ModelDef( + field=underscore(fields["key"]), + field_alias=fields["key"], + field_type=fields["type"], + field_value=fields["default"])) def generate_model(self): """ @@ -84,4 +92,8 @@ class PydanticModelGenerator: DynamicModel.__config__.allow_mutation = True return DynamicModel -StableDiffusionProcessingAPI = PydanticModelGenerator("StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img).generate_model() +StableDiffusionProcessingAPI = PydanticModelGenerator( + "StableDiffusionProcessingTxt2Img", + StableDiffusionProcessingTxt2Img, + [{"key": "sampler_index", "type": str, "default": "k_euler_a"}] +).generate_model() From 0f0d6ab8e06898ce066251fc769fe14e77e98ced Mon Sep 17 00:00:00 2001 From: arcticfaded Date: Wed, 19 Oct 2022 05:19:01 +0000 Subject: [PATCH 064/152] call sampler by name --- modules/api/api.py | 11 ++++++----- modules/api/processing.py | 6 +++--- 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/modules/api/api.py b/modules/api/api.py index ff9df0d1..5b0c934e 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -1,6 +1,7 @@ from modules.api.processing import StableDiffusionProcessingAPI from modules.processing import StableDiffusionProcessingTxt2Img, process_images -from modules.sd_samplers import samplers_k_diffusion +from modules.sd_samplers import all_samplers +from modules.extras import run_pnginfo import modules.shared as shared import uvicorn from fastapi import Body, APIRouter, HTTPException @@ -10,7 +11,7 @@ import json import io import base64 -sampler_to_index = lambda name: next(filter(lambda row: name in row[1][2], enumerate(samplers_k_diffusion)), None) +sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None) class TextToImageResponse(BaseModel): images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") @@ -53,13 +54,13 @@ class Api: - def img2imgendoint(self): + def img2imgapi(self): raise NotImplementedError - def extrasendoint(self): + def extrasapi(self): raise NotImplementedError - def pnginfoendoint(self): + def pnginfoapi(self): raise NotImplementedError def launch(self, server_name, port): diff --git a/modules/api/processing.py b/modules/api/processing.py index 2e6483ee..4c541241 100644 --- a/modules/api/processing.py +++ b/modules/api/processing.py @@ -1,7 +1,7 @@ from inflection import underscore from typing import Any, Dict, Optional from pydantic import BaseModel, Field, create_model -from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images +from modules.processing import StableDiffusionProcessingTxt2Img import inspect @@ -95,5 +95,5 @@ class PydanticModelGenerator: StableDiffusionProcessingAPI = PydanticModelGenerator( "StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img, - [{"key": "sampler_index", "type": str, "default": "k_euler_a"}] -).generate_model() + [{"key": "sampler_index", "type": str, "default": "Euler"}] +).generate_model() \ No newline at end of file From 10aca1ca3e81e69e08f556a500c3dc603451429b Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 19 Oct 2022 08:42:22 +0300 Subject: [PATCH 065/152] more careful loading of model weights (eliminates some issues with checkpoints that have weird cond_stage_model layer names) --- modules/sd_models.py | 28 +++++++++++++++++++++++++--- 1 file changed, 25 insertions(+), 3 deletions(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index 3aa21ec1..7ad6d474 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -122,11 +122,33 @@ def select_checkpoint(): return checkpoint_info +chckpoint_dict_replacements = { + 'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.', + 'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.', + 'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.', +} + + +def transform_checkpoint_dict_key(k): + for text, replacement in chckpoint_dict_replacements.items(): + if k.startswith(text): + k = replacement + k[len(text):] + + return k + + def get_state_dict_from_checkpoint(pl_sd): if "state_dict" in pl_sd: - return pl_sd["state_dict"] + pl_sd = pl_sd["state_dict"] - return pl_sd + sd = {} + for k, v in pl_sd.items(): + new_key = transform_checkpoint_dict_key(k) + + if new_key is not None: + sd[new_key] = v + + return sd def load_model_weights(model, checkpoint_info): @@ -141,7 +163,7 @@ def load_model_weights(model, checkpoint_info): print(f"Global Step: {pl_sd['global_step']}") sd = get_state_dict_from_checkpoint(pl_sd) - model.load_state_dict(sd, strict=False) + missing, extra = model.load_state_dict(sd, strict=False) if shared.cmd_opts.opt_channelslast: model.to(memory_format=torch.channels_last) From da72becb13e4b750fbcb3d158c3f843311ef9938 Mon Sep 17 00:00:00 2001 From: Silent <16026653+s-ilent@users.noreply.github.com> Date: Wed, 19 Oct 2022 16:14:33 +1030 Subject: [PATCH 066/152] Use training width/height when training hypernetworks. --- modules/hypernetworks/hypernetwork.py | 4 ++-- modules/ui.py | 2 ++ 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 4905710e..b8695fc1 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -196,7 +196,7 @@ def stack_conds(conds): return torch.stack(conds) -def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): +def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): assert hypernetwork_name, 'hypernetwork not selected' path = shared.hypernetworks.get(hypernetwork_name, None) @@ -225,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size) if unload: shared.sd_model.cond_stage_model.to(devices.cpu) diff --git a/modules/ui.py b/modules/ui.py index fb6eb5a0..ca46343f 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1341,6 +1341,8 @@ def create_ui(wrap_gradio_gpu_call): batch_size, dataset_directory, log_directory, + training_width, + training_height, steps, create_image_every, save_embedding_every, From 2fd7935ef4ed296db5dfd8c7fea99244816f8cf0 Mon Sep 17 00:00:00 2001 From: Cheka Date: Tue, 18 Oct 2022 20:28:28 -0300 Subject: [PATCH 067/152] Remove wrong self reference in CUDA support for invokeai --- modules/sd_hijack_optimizations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index a3345bb9..98123fbf 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -181,7 +181,7 @@ def einsum_op_cuda(q, k, v): mem_free_torch = mem_reserved - mem_active mem_free_total = mem_free_cuda + mem_free_torch # Divide factor of safety as there's copying and fragmentation - return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20)) + return einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20)) def einsum_op(q, k, v): if q.device.type == 'cuda': From bcfbb33e50a48b237d8d961cc2be038db53774d5 Mon Sep 17 00:00:00 2001 From: Anastasius Date: Mon, 17 Oct 2022 13:35:20 -0700 Subject: [PATCH 068/152] Added time left estimation --- modules/ui.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index ca46343f..9a54aa16 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -261,6 +261,15 @@ def wrap_gradio_call(func, extra_outputs=None): return f +def calc_time_left(progress): + if progress == 0: + return "N/A" + else: + time_since_start = time.time() - shared.state.time_start + eta = (time_since_start/progress) + return time.strftime('%H:%M:%S', time.gmtime(eta-time_since_start)) + + def check_progress_call(id_part): if shared.state.job_count == 0: return "", gr_show(False), gr_show(False), gr_show(False) @@ -272,11 +281,13 @@ def check_progress_call(id_part): if shared.state.sampling_steps > 0: progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps + time_left = calc_time_left( progress ) + progress = min(progress, 1) progressbar = "" if opts.show_progressbar: - progressbar = f"""
{str(int(progress*100))+"%" if progress > 0.01 else ""}
""" + progressbar = f"""
{str(int(progress*100))+"% ETA:"+time_left if progress > 0.01 else ""}
""" image = gr_show(False) preview_visibility = gr_show(False) @@ -308,6 +319,7 @@ def check_progress_call_initial(id_part): shared.state.current_latent = None shared.state.current_image = None shared.state.textinfo = None + shared.state.time_start = time.time() return check_progress_call(id_part) From 442dbedc159bb7e9cf94f0c3626f8a409e0a50eb Mon Sep 17 00:00:00 2001 From: Anastasius Date: Tue, 18 Oct 2022 10:38:07 -0700 Subject: [PATCH 069/152] Estimated time displayed if jobs take more 60 sec --- modules/ui.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/modules/ui.py b/modules/ui.py index 9a54aa16..fa54110b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -261,13 +261,17 @@ def wrap_gradio_call(func, extra_outputs=None): return f -def calc_time_left(progress): +def calc_time_left(progress, threshold, label, force_display): if progress == 0: - return "N/A" + return "" else: time_since_start = time.time() - shared.state.time_start eta = (time_since_start/progress) - return time.strftime('%H:%M:%S', time.gmtime(eta-time_since_start)) + eta_relative = eta-time_since_start + if eta_relative > threshold or force_display: + return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative)) + else: + return "" def check_progress_call(id_part): @@ -281,13 +285,15 @@ def check_progress_call(id_part): if shared.state.sampling_steps > 0: progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps - time_left = calc_time_left( progress ) + time_left = calc_time_left( progress, 60, " ETA:", shared.state.time_left_force_display ) + if time_left != "": + shared.state.time_left_force_display = True progress = min(progress, 1) progressbar = "" if opts.show_progressbar: - progressbar = f"""
{str(int(progress*100))+"% ETA:"+time_left if progress > 0.01 else ""}
""" + progressbar = f"""
{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}
""" image = gr_show(False) preview_visibility = gr_show(False) @@ -320,6 +326,7 @@ def check_progress_call_initial(id_part): shared.state.current_image = None shared.state.textinfo = None shared.state.time_start = time.time() + shared.state.time_left_force_display = False return check_progress_call(id_part) From 1d4aa376e6111e90888a30ae24d2bcd7f978ec51 Mon Sep 17 00:00:00 2001 From: Anastasius Date: Tue, 18 Oct 2022 12:42:39 -0700 Subject: [PATCH 070/152] Predictable long operation check for time estimation --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index fa54110b..38ba1138 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -268,7 +268,7 @@ def calc_time_left(progress, threshold, label, force_display): time_since_start = time.time() - shared.state.time_start eta = (time_since_start/progress) eta_relative = eta-time_since_start - if eta_relative > threshold or force_display: + if (eta_relative > threshold and progress > 0.02) or force_display: return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative)) else: return "" From 83a517eb96cc36cf1dc0915a9ebde43a7e05c7da Mon Sep 17 00:00:00 2001 From: realryo1 <60560430+realryo1@users.noreply.github.com> Date: Wed, 19 Oct 2022 04:48:00 +0900 Subject: [PATCH 071/152] Fixed performance, vram style disorder --- style.css | 5 ----- 1 file changed, 5 deletions(-) diff --git a/style.css b/style.css index 9dc4b696..9bd408cd 100644 --- a/style.css +++ b/style.css @@ -34,9 +34,6 @@ .performance { font-size: 0.85em; color: #444; - display: flex; - justify-content: space-between; - white-space: nowrap; } .performance .time { @@ -44,8 +41,6 @@ } .performance .vram { - margin-left: 0; - text-align: right; } #txt2img_generate, #img2img_generate { From bb0e7232b301d1706bbd0e09367dece3bb7ac07c Mon Sep 17 00:00:00 2001 From: Ikko Ashimine Date: Wed, 19 Oct 2022 02:18:56 +0900 Subject: [PATCH 072/152] Fix typo in prompt_parser.py assoicated -> associated --- modules/prompt_parser.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index 919d5d31..f70872c4 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -275,7 +275,7 @@ re_attention = re.compile(r""" def parse_prompt_attention(text): """ - Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight. + Parses a string with attention tokens and returns a list of pairs: text and its associated weight. Accepted tokens are: (abc) - increases attention to abc by a multiplier of 1.1 (abc:3.12) - increases attention to abc by a multiplier of 3.12 From 9931c0bd48346dc5af23864117becfac33347a7c Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 19 Oct 2022 12:01:31 +0300 Subject: [PATCH 073/152] remove the unneeded line break introduced by #3092 --- style.css | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/style.css b/style.css index 9bd408cd..26ae36a5 100644 --- a/style.css +++ b/style.css @@ -36,6 +36,10 @@ color: #444; } +.performance p{ + display: inline-block; +} + .performance .time { margin-right: 0; } From f894dd552f68bea27476f1f360ab8e79f3a65b4f Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 19 Oct 2022 12:45:30 +0300 Subject: [PATCH 074/152] fix for broken checkpoint merger --- modules/sd_models.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index 7ad6d474..eae22e87 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -148,7 +148,10 @@ def get_state_dict_from_checkpoint(pl_sd): if new_key is not None: sd[new_key] = v - return sd + pl_sd.clear() + pl_sd.update(sd) + + return pl_sd def load_model_weights(model, checkpoint_info): From 42fbda83bb9830af18187fddb50c1bedd01da502 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Wed, 19 Oct 2022 14:30:33 +0000 Subject: [PATCH 075/152] layer options moves into create hnet ui --- modules/hypernetworks/hypernetwork.py | 64 +++++++++++++-------------- modules/hypernetworks/ui.py | 9 +++- modules/shared.py | 2 - modules/ui.py | 8 +++- webui.py | 8 ++-- 5 files changed, 48 insertions(+), 43 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 583ada31..7d519cd9 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -19,37 +19,21 @@ from modules.textual_inversion import textual_inversion from modules.textual_inversion.learn_schedule import LearnRateScheduler -def parse_layer_structure(dim, state_dict): - i = 0 - res = [1] - while (key := "linear.{}.weight".format(i)) in state_dict: - weight = state_dict[key] - res.append(len(weight) // dim) - i += 1 - return res - - class HypernetworkModule(torch.nn.Module): multiplier = 1.0 - layer_structure = None - add_layer_norm = False - def __init__(self, dim, state_dict=None): + def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False): super().__init__() - if (state_dict is None or 'linear.0.weight' not in state_dict) and self.layer_structure is None: - layer_structure = (1, 2, 1) + if layer_structure is not None: + assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" + assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" else: - if self.layer_structure is not None: - assert self.layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" - assert self.layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" - layer_structure = self.layer_structure - else: - layer_structure = parse_layer_structure(dim, state_dict) + layer_structure = parse_layer_structure(dim, state_dict) linears = [] for i in range(len(layer_structure) - 1): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) - if self.add_layer_norm: + if add_layer_norm: linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) self.linear = torch.nn.Sequential(*linears) @@ -77,38 +61,47 @@ class HypernetworkModule(torch.nn.Module): return x + self.linear(x) * self.multiplier def trainables(self): - res = [] + layer_structure = [] for layer in self.linear: - res += [layer.weight, layer.bias] - return res + layer_structure += [layer.weight, layer.bias] + return layer_structure def apply_strength(value=None): HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength -def apply_layer_structure(value=None): - HypernetworkModule.layer_structure = value if value is not None else shared.opts.sd_hypernetwork_layer_structure +def parse_layer_structure(dim, state_dict): + i = 0 + layer_structure = [1] + while (key := "linear.{}.weight".format(i)) in state_dict: + weight = state_dict[key] + layer_structure.append(len(weight) // dim) + i += 1 -def apply_layer_norm(value=None): - HypernetworkModule.add_layer_norm = value if value is not None else shared.opts.sd_hypernetwork_add_layer_norm + return layer_structure class Hypernetwork: filename = None name = None - def __init__(self, name=None, enable_sizes=None): + def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False): self.filename = None self.name = name self.layers = {} self.step = 0 self.sd_checkpoint = None self.sd_checkpoint_name = None + self.layer_structure = layer_structure + self.add_layer_norm = add_layer_norm for size in enable_sizes or []: - self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size)) + self.layers[size] = ( + HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm), + HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm), + ) def weights(self): res = [] @@ -128,6 +121,8 @@ class Hypernetwork: state_dict['step'] = self.step state_dict['name'] = self.name + state_dict['layer_structure'] = self.layer_structure + state_dict['is_layer_norm'] = self.add_layer_norm state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name @@ -142,10 +137,15 @@ class Hypernetwork: for size, sd in state_dict.items(): if type(size) == int: - self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1])) + self.layers[size] = ( + HypernetworkModule(size, sd[0], state_dict["layer_structure"], state_dict["is_layer_norm"]), + HypernetworkModule(size, sd[1], state_dict["layer_structure"], state_dict["is_layer_norm"]), + ) self.name = state_dict.get('name', self.name) self.step = state_dict.get('step', 0) + self.layer_structure = state_dict.get('layer_structure', None) + self.add_layer_norm = state_dict.get('is_layer_norm', False) self.sd_checkpoint = state_dict.get('sd_checkpoint', None) self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None) diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index dfa599af..7e8ea95e 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -9,11 +9,16 @@ from modules import sd_hijack, shared, devices from modules.hypernetworks import hypernetwork -def create_hypernetwork(name, enable_sizes): +def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False): fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") assert not os.path.exists(fn), f"file {fn} already exists" - hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes]) + hypernet = modules.hypernetworks.hypernetwork.Hypernetwork( + name=name, + enable_sizes=[int(x) for x in enable_sizes], + layer_structure=layer_structure, + add_layer_norm=add_layer_norm, + ) hypernet.save(fn) shared.reload_hypernetworks() diff --git a/modules/shared.py b/modules/shared.py index 0540cae9..faede821 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -260,8 +260,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models), "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}), "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), - "sd_hypernetwork_layer_structure": OptionInfo(None, "Hypernetwork layer structure Default: (1,2,1).", gr.Dropdown, lambda: {"choices": [(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)]}), - "sd_hypernetwork_add_layer_norm": OptionInfo(False, "Add layer normalization to hypernetwork architecture."), "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"), diff --git a/modules/ui.py b/modules/ui.py index ca46343f..d9ee462f 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -458,14 +458,14 @@ def create_toprow(is_img2img): with gr.Row(): with gr.Column(scale=80): with gr.Row(): - prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2, + prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)" ) with gr.Row(): with gr.Column(scale=80): with gr.Row(): - negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, + negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)" ) @@ -1198,6 +1198,8 @@ def create_ui(wrap_gradio_gpu_call): with gr.Tab(label="Create hypernetwork"): new_hypernetwork_name = gr.Textbox(label="Name") new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) + new_hypernetwork_layer_structure = gr.Dropdown(label="Hypernetwork layer structure", choices=[(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)]) + new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") with gr.Row(): with gr.Column(scale=3): @@ -1280,6 +1282,8 @@ def create_ui(wrap_gradio_gpu_call): inputs=[ new_hypernetwork_name, new_hypernetwork_sizes, + new_hypernetwork_layer_structure, + new_hypernetwork_add_layer_norm, ], outputs=[ train_hypernetwork_name, diff --git a/webui.py b/webui.py index c7260c7a..177bef74 100644 --- a/webui.py +++ b/webui.py @@ -85,9 +85,7 @@ def initialize(): shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model))) shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork))) shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength) - shared.opts.onchange("sd_hypernetwork_layer_structure", modules.hypernetworks.hypernetwork.apply_layer_structure) - shared.opts.onchange("sd_hypernetwork_add_layer_norm", modules.hypernetworks.hypernetwork.apply_layer_norm) - + # make the program just exit at ctrl+c without waiting for anything def sigint_handler(sig, frame): print(f'Interrupted with signal {sig} in {frame}') @@ -142,7 +140,7 @@ def webui(launch_api=False): create_api(app) wait_on_server(demo) - + sd_samplers.set_samplers() print('Reloading Custom Scripts') @@ -160,4 +158,4 @@ if __name__ == "__main__": if cmd_opts.nowebui: api_only() else: - webui(cmd_opts.api) \ No newline at end of file + webui(cmd_opts.api) From 3770b8d2fa62066d472a04739c7b84bce8538832 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Wed, 19 Oct 2022 15:28:42 +0000 Subject: [PATCH 076/152] enable to write layer structure of hn himself --- modules/hypernetworks/ui.py | 4 ++++ modules/ui.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 7e8ea95e..08f75f15 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -1,5 +1,6 @@ import html import os +import re import gradio as gr @@ -13,6 +14,9 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") assert not os.path.exists(fn), f"file {fn} already exists" + if type(layer_structure) == str: + layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure))) + hypernet = modules.hypernetworks.hypernetwork.Hypernetwork( name=name, enable_sizes=[int(x) for x in enable_sizes], diff --git a/modules/ui.py b/modules/ui.py index d9ee462f..18a2add0 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1198,7 +1198,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Tab(label="Create hypernetwork"): new_hypernetwork_name = gr.Textbox(label="Name") new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) - new_hypernetwork_layer_structure = gr.Dropdown(label="Hypernetwork layer structure", choices=[(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)]) + new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'") new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") with gr.Row(): From 019a3a88f07766f2d32c32fbe8e41625f28ecb5e Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Wed, 19 Oct 2022 17:15:47 +0100 Subject: [PATCH 077/152] Update ui.py --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index d2e24880..1573ef82 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1247,7 +1247,7 @@ def create_ui(wrap_gradio_gpu_call): run_preprocess = gr.Button(value="Preprocess", variant='primary') with gr.Tab(label="Train"): - gr.HTML(value="

Train an embedding; must specify a directory with a set of 1:1 ratio images

") + gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images
Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork wiki

") with gr.Row(): train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name") From c6e9fed5003631c87d548e74d6e359678959a453 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 19 Oct 2022 19:21:16 +0300 Subject: [PATCH 078/152] fix for #3086 failing to load any previous hypernet --- modules/hypernetworks/hypernetwork.py | 60 +++++++++++++-------------- 1 file changed, 28 insertions(+), 32 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 7d519cd9..74300122 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -24,11 +24,10 @@ class HypernetworkModule(torch.nn.Module): def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False): super().__init__() - if layer_structure is not None: - assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" - assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" - else: - layer_structure = parse_layer_structure(dim, state_dict) + + assert layer_structure is not None, "layer_structure mut not be None" + assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" + assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" linears = [] for i in range(len(layer_structure) - 1): @@ -39,23 +38,30 @@ class HypernetworkModule(torch.nn.Module): self.linear = torch.nn.Sequential(*linears) if state_dict is not None: - try: - self.load_state_dict(state_dict) - except RuntimeError: - self.try_load_previous(state_dict) + self.fix_old_state_dict(state_dict) + self.load_state_dict(state_dict) else: for layer in self.linear: - layer.weight.data.normal_(mean = 0.0, std = 0.01) + layer.weight.data.normal_(mean=0.0, std=0.01) layer.bias.data.zero_() self.to(devices.device) - def try_load_previous(self, state_dict): - states = self.state_dict() - states['linear.0.bias'].copy_(state_dict['linear1.bias']) - states['linear.0.weight'].copy_(state_dict['linear1.weight']) - states['linear.1.bias'].copy_(state_dict['linear2.bias']) - states['linear.1.weight'].copy_(state_dict['linear2.weight']) + def fix_old_state_dict(self, state_dict): + changes = { + 'linear1.bias': 'linear.0.bias', + 'linear1.weight': 'linear.0.weight', + 'linear2.bias': 'linear.1.bias', + 'linear2.weight': 'linear.1.weight', + } + + for fr, to in changes.items(): + x = state_dict.get(fr, None) + if x is None: + continue + + del state_dict[fr] + state_dict[to] = x def forward(self, x): return x + self.linear(x) * self.multiplier @@ -71,18 +77,6 @@ def apply_strength(value=None): HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength -def parse_layer_structure(dim, state_dict): - i = 0 - layer_structure = [1] - - while (key := "linear.{}.weight".format(i)) in state_dict: - weight = state_dict[key] - layer_structure.append(len(weight) // dim) - i += 1 - - return layer_structure - - class Hypernetwork: filename = None name = None @@ -135,17 +129,18 @@ class Hypernetwork: state_dict = torch.load(filename, map_location='cpu') + self.layer_structure = state_dict.get('layer_structure', [1, 2, 1]) + self.add_layer_norm = state_dict.get('is_layer_norm', False) + for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( - HypernetworkModule(size, sd[0], state_dict["layer_structure"], state_dict["is_layer_norm"]), - HypernetworkModule(size, sd[1], state_dict["layer_structure"], state_dict["is_layer_norm"]), + HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm), + HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm), ) self.name = state_dict.get('name', self.name) self.step = state_dict.get('step', 0) - self.layer_structure = state_dict.get('layer_structure', None) - self.add_layer_norm = state_dict.get('is_layer_norm', False) self.sd_checkpoint = state_dict.get('sd_checkpoint', None) self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None) @@ -244,6 +239,7 @@ def stack_conds(conds): return torch.stack(conds) + def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): assert hypernetwork_name, 'hypernetwork not selected' From 2ce52d32e41fb523d1494f45073fd18496e52d35 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Wed, 19 Oct 2022 16:31:12 +0000 Subject: [PATCH 079/152] fix for #3086 failing to load any previous hypernet --- modules/hypernetworks/hypernetwork.py | 60 +++++++++++++-------------- 1 file changed, 28 insertions(+), 32 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 7d519cd9..74300122 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -24,11 +24,10 @@ class HypernetworkModule(torch.nn.Module): def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False): super().__init__() - if layer_structure is not None: - assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" - assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" - else: - layer_structure = parse_layer_structure(dim, state_dict) + + assert layer_structure is not None, "layer_structure mut not be None" + assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" + assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" linears = [] for i in range(len(layer_structure) - 1): @@ -39,23 +38,30 @@ class HypernetworkModule(torch.nn.Module): self.linear = torch.nn.Sequential(*linears) if state_dict is not None: - try: - self.load_state_dict(state_dict) - except RuntimeError: - self.try_load_previous(state_dict) + self.fix_old_state_dict(state_dict) + self.load_state_dict(state_dict) else: for layer in self.linear: - layer.weight.data.normal_(mean = 0.0, std = 0.01) + layer.weight.data.normal_(mean=0.0, std=0.01) layer.bias.data.zero_() self.to(devices.device) - def try_load_previous(self, state_dict): - states = self.state_dict() - states['linear.0.bias'].copy_(state_dict['linear1.bias']) - states['linear.0.weight'].copy_(state_dict['linear1.weight']) - states['linear.1.bias'].copy_(state_dict['linear2.bias']) - states['linear.1.weight'].copy_(state_dict['linear2.weight']) + def fix_old_state_dict(self, state_dict): + changes = { + 'linear1.bias': 'linear.0.bias', + 'linear1.weight': 'linear.0.weight', + 'linear2.bias': 'linear.1.bias', + 'linear2.weight': 'linear.1.weight', + } + + for fr, to in changes.items(): + x = state_dict.get(fr, None) + if x is None: + continue + + del state_dict[fr] + state_dict[to] = x def forward(self, x): return x + self.linear(x) * self.multiplier @@ -71,18 +77,6 @@ def apply_strength(value=None): HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength -def parse_layer_structure(dim, state_dict): - i = 0 - layer_structure = [1] - - while (key := "linear.{}.weight".format(i)) in state_dict: - weight = state_dict[key] - layer_structure.append(len(weight) // dim) - i += 1 - - return layer_structure - - class Hypernetwork: filename = None name = None @@ -135,17 +129,18 @@ class Hypernetwork: state_dict = torch.load(filename, map_location='cpu') + self.layer_structure = state_dict.get('layer_structure', [1, 2, 1]) + self.add_layer_norm = state_dict.get('is_layer_norm', False) + for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( - HypernetworkModule(size, sd[0], state_dict["layer_structure"], state_dict["is_layer_norm"]), - HypernetworkModule(size, sd[1], state_dict["layer_structure"], state_dict["is_layer_norm"]), + HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm), + HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm), ) self.name = state_dict.get('name', self.name) self.step = state_dict.get('step', 0) - self.layer_structure = state_dict.get('layer_structure', None) - self.add_layer_norm = state_dict.get('is_layer_norm', False) self.sd_checkpoint = state_dict.get('sd_checkpoint', None) self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None) @@ -244,6 +239,7 @@ def stack_conds(conds): return torch.stack(conds) + def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): assert hypernetwork_name, 'hypernetwork not selected' From 57eb1a64c85d995cacb4fa3832e87405bf6820b9 Mon Sep 17 00:00:00 2001 From: Alexandre Simard Date: Wed, 19 Oct 2022 12:28:27 -0400 Subject: [PATCH 080/152] Update ui.py --- modules/ui.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/modules/ui.py b/modules/ui.py index d2e24880..c9a923ab 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -268,8 +268,13 @@ def calc_time_left(progress, threshold, label, force_display): time_since_start = time.time() - shared.state.time_start eta = (time_since_start/progress) eta_relative = eta-time_since_start - if (eta_relative > threshold and progress > 0.02) or force_display: - return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative)) + if (eta_relative > threshold and progress > 0.02) or force_display: + if eta_relative > 3600: + return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative)) + elif eta_relative > 60: + return label + time.strftime('%M:%S', time.gmtime(eta_relative)) + else: + return label + time.strftime('%Ss', time.gmtime(eta_relative)) else: return "" @@ -285,7 +290,7 @@ def check_progress_call(id_part): if shared.state.sampling_steps > 0: progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps - time_left = calc_time_left( progress, 60, " ETA:", shared.state.time_left_force_display ) + time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display ) if time_left != "": shared.state.time_left_force_display = True @@ -293,7 +298,7 @@ def check_progress_call(id_part): progressbar = "" if opts.show_progressbar: - progressbar = f"""
{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}
""" + progressbar = f"""
{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}
""" image = gr_show(False) preview_visibility = gr_show(False) From 1e4809b251d478a102fd980dcfc26e21d6d3730b Mon Sep 17 00:00:00 2001 From: Alexandre Simard Date: Wed, 19 Oct 2022 12:53:23 -0400 Subject: [PATCH 081/152] Added a bit of padding to the left --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index c9a923ab..a2dbd41e 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -298,7 +298,7 @@ def check_progress_call(id_part): progressbar = "" if opts.show_progressbar: - progressbar = f"""
{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}
""" + progressbar = f"""
{" " * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}
""" image = gr_show(False) preview_visibility = gr_show(False) From 5e012e4dfa5dcfeade0394678cf14b70682dba6c Mon Sep 17 00:00:00 2001 From: timntorres Date: Wed, 19 Oct 2022 06:17:47 -0700 Subject: [PATCH 082/152] Infotext saves more specific hypernet name. --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/processing.py b/modules/processing.py index ea926fc3..bcb0c32c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -304,7 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Size": f"{p.width}x{p.height}", "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), - "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')), + "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.filename.split('\\')[-1].split('.')[0]), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), From 46122c4ff6aadc0f96e657f88dbac7bbd9f9bf99 Mon Sep 17 00:00:00 2001 From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com> Date: Wed, 19 Oct 2022 19:18:52 +0300 Subject: [PATCH 083/152] Send empty prompts as valid generation parameter --- modules/generation_parameters_copypaste.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index c27826b6..98d24406 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -45,10 +45,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model else: prompt += ("" if prompt == "" else "\n") + line - if len(prompt) > 0: res["Prompt"] = prompt - - if len(negative_prompt) > 0: res["Negative prompt"] = negative_prompt for k, v in re_param.findall(lastline): From 13ed73bedaa3df0f3edff41bd89bf0702f1c57b5 Mon Sep 17 00:00:00 2001 From: Greg Fuller Date: Tue, 18 Oct 2022 16:24:55 -0700 Subject: [PATCH 084/152] Update Learning Rate tooltip --- javascript/hints.js | 2 ++ 1 file changed, 2 insertions(+) diff --git a/javascript/hints.js b/javascript/hints.js index b98012f5..a1fcc93b 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -91,6 +91,8 @@ titles = { "Weighted sum": "Result = A * (1 - M) + B * M", "Add difference": "Result = A + (B - C) * M", + + "Learning rate": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.", } From fd1008f1e0f067a99793c7885e1f5010811f1ac0 Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Sun, 16 Oct 2022 12:41:03 +0200 Subject: [PATCH 085/152] Better Bug report form --- .github/ISSUE_TEMPLATE/bug_report.md | 32 -------------- .github/ISSUE_TEMPLATE/bug_report.yml | 61 +++++++++++++++++++++++++++ .vscode/settings.json | 5 +++ 3 files changed, 66 insertions(+), 32 deletions(-) delete mode 100644 .github/ISSUE_TEMPLATE/bug_report.md create mode 100644 .github/ISSUE_TEMPLATE/bug_report.yml create mode 100644 .vscode/settings.json diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md deleted file mode 100644 index 50c54210..00000000 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ /dev/null @@ -1,32 +0,0 @@ ---- -name: Bug report -about: Create a report to help us improve -title: '' -labels: bug-report -assignees: '' - ---- - -**Describe the bug** -A clear and concise description of what the bug is. - -**To Reproduce** -Steps to reproduce the behavior: -1. Go to '...' -2. Click on '....' -3. Scroll down to '....' -4. See error - -**Expected behavior** -A clear and concise description of what you expected to happen. - -**Screenshots** -If applicable, add screenshots to help explain your problem. - -**Desktop (please complete the following information):** - - OS: [e.g. Windows, Linux] - - Browser [e.g. chrome, safari] - - Commit revision [looks like this: e68484500f76a33ba477d5a99340ab30451e557b; can be seen when launching webui.bat, or obtained manually by running `git rev-parse HEAD`] - -**Additional context** -Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml new file mode 100644 index 00000000..9b7e224e --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -0,0 +1,61 @@ +name: Bug Report +description: File a bug report +title: "[Bug]: " +labels: ["bug-report"] + +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + - type: textarea + id: what-did + attributes: + label: What did happened? + description: Also tell us, what did you expect to happen? + validations: + required: true + - type: textarea + id: what-should + attributes: + label: What should have happened? + description: tell what you think the normal behavior should be + validations: + required: true + - type: textarea + id: commit + attributes: + label: commit + description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI) + - type: dropdown + id: browsers + attributes: + label: What browsers do you use to run the UI ? + multiple: true + options: + - Mozilla Firefox + - Google Chrome + - Brave + - Apple Safari + - Microsoft Edge + - type: dropdown + id: os + attributes: + label: Where are you running the webui? + multiple: true + options: + - Colab/Cloud + - Windows + - Linux + - MacOS + - type: textarea + id: cmdargs + attributes: + label: Command Line Arguments + description: Are you using any launching parameters/command line arguments (modified webui-user.py) ? If yes, please write them below + render: Shell + - type: textarea + id: misc + attributes: + label: Additionnal information, context and logs + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..d6e94aad --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,5 @@ +{ + "yaml.schemas": { + "https://json.schemastore.org/github-issue-forms.json": "file:///c%3A/AI/Repos/stable-diffusion-webui-moz/.github/ISSUE_TEMPLATE/bug_report.md" + } +} From 57c48093a92e58e07d5875de98ad2a790a7ceb14 Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Sun, 16 Oct 2022 12:43:06 +0200 Subject: [PATCH 086/152] Delete .vscode directory --- .vscode/settings.json | 5 ----- 1 file changed, 5 deletions(-) delete mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index d6e94aad..00000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "yaml.schemas": { - "https://json.schemastore.org/github-issue-forms.json": "file:///c%3A/AI/Repos/stable-diffusion-webui-moz/.github/ISSUE_TEMPLATE/bug_report.md" - } -} From d0042587adf03059977181fae9c2ede019044fe0 Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Sun, 16 Oct 2022 12:46:36 +0200 Subject: [PATCH 087/152] Cleaning & improvements --- .github/ISSUE_TEMPLATE/bug_report.yml | 26 +++++++++++--------------- .gitignore | 1 + 2 files changed, 12 insertions(+), 15 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 9b7e224e..02fc994c 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -4,10 +4,6 @@ title: "[Bug]: " labels: ["bug-report"] body: - - type: markdown - attributes: - value: | - Thanks for taking the time to fill out this bug report! - type: textarea id: what-did attributes: @@ -25,8 +21,18 @@ body: - type: textarea id: commit attributes: - label: commit + label: Commit where the problem happens description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI) + - type: dropdown + id: os + attributes: + label: What OS do you use to run the webui? + multiple: true + options: + - Colab/Runpod or Cloud based + - Windows + - Linux + - MacOS - type: dropdown id: browsers attributes: @@ -38,16 +44,6 @@ body: - Brave - Apple Safari - Microsoft Edge - - type: dropdown - id: os - attributes: - label: Where are you running the webui? - multiple: true - options: - - Colab/Cloud - - Windows - - Linux - - MacOS - type: textarea id: cmdargs attributes: diff --git a/.gitignore b/.gitignore index 69785b3e..f9c3357c 100644 --- a/.gitignore +++ b/.gitignore @@ -27,3 +27,4 @@ __pycache__ notification.mp3 /SwinIR /textual_inversion +.vscode \ No newline at end of file From dd66530a63f47aa87f4a95eaece51052d45a29f0 Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Sun, 16 Oct 2022 12:53:36 +0200 Subject: [PATCH 088/152] Fixes and adding step by step --- .github/ISSUE_TEMPLATE/bug_report.yml | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 02fc994c..3243f934 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -7,8 +7,18 @@ body: - type: textarea id: what-did attributes: - label: What did happened? - description: Also tell us, what did you expect to happen? + label: What happened? + description: Tell us what happened in a very clear and simple way + validations: + required: true + - type: textarea + id: steps + attributes: + label: Steps to reproduce the problem + description: Please provide us with precise step by step information to reproduce the bug + value: 1. Go to .... | + 2. Press .... + 3. ... validations: required: true - type: textarea @@ -18,7 +28,7 @@ body: description: tell what you think the normal behavior should be validations: required: true - - type: textarea + - type: input id: commit attributes: label: Commit where the problem happens From 45f188e0d3167f36c69bdf392a1141b8e5183d21 Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Sun, 16 Oct 2022 12:54:43 +0200 Subject: [PATCH 089/152] fixing linebreak issue --- .github/ISSUE_TEMPLATE/bug_report.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 3243f934..55fb2d72 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -16,7 +16,8 @@ body: attributes: label: Steps to reproduce the problem description: Please provide us with precise step by step information to reproduce the bug - value: 1. Go to .... | + value: | + 1. Go to .... 2. Press .... 3. ... validations: From 03cf7cf32798d45ae92832bd22bea8b299e64a17 Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Sun, 16 Oct 2022 13:00:28 +0200 Subject: [PATCH 090/152] Fixes and trying to make dropdown required --- .github/ISSUE_TEMPLATE/bug_report.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 55fb2d72..201ed57d 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -15,7 +15,7 @@ body: id: steps attributes: label: Steps to reproduce the problem - description: Please provide us with precise step by step information to reproduce the bug + description: Please provide us with precise step by step information on how to reproduce the bug value: | 1. Go to .... 2. Press .... @@ -44,6 +44,7 @@ body: - Windows - Linux - MacOS + required: true - type: dropdown id: browsers attributes: @@ -55,6 +56,7 @@ body: - Brave - Apple Safari - Microsoft Edge + required: true - type: textarea id: cmdargs attributes: From ca30e67289f9e2d0aca33c9da8d915ae3bcc39cc Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Sun, 16 Oct 2022 13:03:03 +0200 Subject: [PATCH 091/152] removing the required tag as it obviously doesn't work, adding a top description --- .github/ISSUE_TEMPLATE/bug_report.yml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 201ed57d..b53db6bb 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -4,6 +4,10 @@ title: "[Bug]: " labels: ["bug-report"] body: + - type: markdown + attributes: + value: | + Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" - type: textarea id: what-did attributes: @@ -44,7 +48,6 @@ body: - Windows - Linux - MacOS - required: true - type: dropdown id: browsers attributes: @@ -56,7 +59,6 @@ body: - Brave - Apple Safari - Microsoft Edge - required: true - type: textarea id: cmdargs attributes: From 8400e854747970ee1593619c37736c47cdac7e3e Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Sun, 16 Oct 2022 13:13:22 +0200 Subject: [PATCH 092/152] Adding a confirmation checkbox that the user has checked the issues & commits before Also small fixes --- .github/ISSUE_TEMPLATE/bug_report.yml | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index b53db6bb..3e8732f9 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -1,13 +1,20 @@ name: Bug Report -description: File a bug report +description: You think somethings is broken in the UI title: "[Bug]: " -labels: ["bug-report"] +labels: ["bug"] body: - type: markdown attributes: value: | - Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" + Make sure this issue hasn't been posted already and wasn't solved in recent commits, then fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" + - type: checkboxes + attributes: + label: Is there an existing issue for this? + description: Please search to see if an issue already exists for the bug you encountered, and that it hasn't been fixed in a recent build/commit. + options: + - label: I have searched the existing issues and checked the recent builds/commits + required: true - type: textarea id: what-did attributes: From 62a1a97fe3e1b322e2a1c6a1fcc0272e9b132704 Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Sun, 16 Oct 2022 13:22:35 +0200 Subject: [PATCH 093/152] Fixed labels and created a brand new Feature Request yaml --- .github/ISSUE_TEMPLATE/bug_report.yml | 10 +++--- .github/ISSUE_TEMPLATE/feature_request.md | 20 ----------- .github/ISSUE_TEMPLATE/feature_request.yml | 40 ++++++++++++++++++++++ 3 files changed, 45 insertions(+), 25 deletions(-) delete mode 100644 .github/ISSUE_TEMPLATE/feature_request.md create mode 100644 .github/ISSUE_TEMPLATE/feature_request.yml diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 3e8732f9..04f072bc 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -1,13 +1,9 @@ name: Bug Report description: You think somethings is broken in the UI title: "[Bug]: " -labels: ["bug"] +labels: ["bug-report"] body: - - type: markdown - attributes: - value: | - Make sure this issue hasn't been posted already and wasn't solved in recent commits, then fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" - type: checkboxes attributes: label: Is there an existing issue for this? @@ -15,6 +11,10 @@ body: options: - label: I have searched the existing issues and checked the recent builds/commits required: true + - type: markdown + attributes: + value: | + Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible* - type: textarea id: what-did attributes: diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md deleted file mode 100644 index eda42fa7..00000000 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -name: Feature request -about: Suggest an idea for this project -title: '' -labels: 'suggestion' -assignees: '' - ---- - -**Is your feature request related to a problem? Please describe.** -A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] - -**Describe the solution you'd like** -A clear and concise description of what you want to happen. - -**Describe alternatives you've considered** -A clear and concise description of any alternative solutions or features you've considered. - -**Additional context** -Add any other context or screenshots about the feature request here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml new file mode 100644 index 00000000..045e15da --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.yml @@ -0,0 +1,40 @@ +name: Feature request +description: Suggest an idea for this project +title: "[Feature Request]: " +labels: ["suggestion"] + +body: + - type: checkboxes + attributes: + label: Is there an existing issue for this? + description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit. + options: + - label: I have searched the existing issues and checked the recent builds/commits + required: true + - type: markdown + attributes: + value: | + Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible + - type: textarea + id: feature + attributes: + label: What would your feature do ? + description: Tell us about your feature in a very clear and simple way, and what problem it would solve + validations: + required: true + - type: textarea + id: workflow + attributes: + label: Proposed workflow + description: Please provide us with step by step information on how you'd like the feature to be accessed and used + value: | + 1. Go to .... + 2. Press .... + 3. ... + validations: + required: true + - type: textarea + id: misc + attributes: + label: Additionnal information + description: Add any other context or screenshots about the feature request here. From 5292d1f0920186e53ef2280637845c8344518a89 Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Sun, 16 Oct 2022 13:26:05 +0200 Subject: [PATCH 094/152] Formatting the top description --- .github/ISSUE_TEMPLATE/bug_report.yml | 2 +- .github/ISSUE_TEMPLATE/feature_request.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 04f072bc..84053e76 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -14,7 +14,7 @@ body: - type: markdown attributes: value: | - Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible* + *Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible** - type: textarea id: what-did attributes: diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml index 045e15da..62133a0d 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.yml +++ b/.github/ISSUE_TEMPLATE/feature_request.yml @@ -14,7 +14,7 @@ body: - type: markdown attributes: value: | - Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible + *Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible* - type: textarea id: feature attributes: From 3e2a035ffaf6f45a18b971ee0388f5ddca312714 Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Sun, 16 Oct 2022 14:30:11 +0200 Subject: [PATCH 095/152] Removed obsolete legacy Hlky description --- .github/ISSUE_TEMPLATE/bug_report.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 84053e76..629afad3 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -76,4 +76,4 @@ body: id: misc attributes: label: Additionnal information, context and logs - description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + description: Please provide us with any relevant additional info, context or log output. From a0e50d5e70094dbdfd048efee566865ab93e4c3f Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Mon, 17 Oct 2022 19:09:06 +0200 Subject: [PATCH 096/152] Improved the OS/Platforms field --- .github/ISSUE_TEMPLATE/bug_report.yml | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 629afad3..d2edb250 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -46,19 +46,21 @@ body: label: Commit where the problem happens description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI) - type: dropdown - id: os + id: platforms attributes: - label: What OS do you use to run the webui? + label: What platforms do you use to access UI ? multiple: true options: - - Colab/Runpod or Cloud based - Windows - Linux - MacOS + - iOS + - Android + - Other/Cloud - type: dropdown id: browsers attributes: - label: What browsers do you use to run the UI ? + label: What browsers do you use to access the UI ? multiple: true options: - Mozilla Firefox From 5d9e3acd4e8fc9562d0b2972e79f6cf8597d3805 Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Wed, 19 Oct 2022 19:38:38 +0200 Subject: [PATCH 097/152] Fixed additionnnnal typo, sorry --- .github/ISSUE_TEMPLATE/bug_report.yml | 2 +- .github/ISSUE_TEMPLATE/feature_request.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index d2edb250..35802a53 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -77,5 +77,5 @@ body: - type: textarea id: misc attributes: - label: Additionnal information, context and logs + label: Additional information, context and logs description: Please provide us with any relevant additional info, context or log output. diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml index 62133a0d..8ca6e21f 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.yml +++ b/.github/ISSUE_TEMPLATE/feature_request.yml @@ -36,5 +36,5 @@ body: - type: textarea id: misc attributes: - label: Additionnal information + label: Additional information description: Add any other context or screenshots about the feature request here. From b748b583c0b9f771c1be509175a6913e3f2ad97c Mon Sep 17 00:00:00 2001 From: Mackerel Date: Wed, 19 Oct 2022 14:22:03 -0400 Subject: [PATCH 098/152] generation_parameters_copypaste.py: fix indent --- modules/generation_parameters_copypaste.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 98d24406..0f041449 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -45,8 +45,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model else: prompt += ("" if prompt == "" else "\n") + line - res["Prompt"] = prompt - res["Negative prompt"] = negative_prompt + res["Prompt"] = prompt + res["Negative prompt"] = negative_prompt for k, v in re_param.findall(lastline): m = re_imagesize.match(v) From 604620a7f08d1126a8689f9f4bec8ade0801a69b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E3=81=B5=E3=81=81?= <34892635+fa0311@users.noreply.github.com> Date: Thu, 20 Oct 2022 03:16:22 +0900 Subject: [PATCH 099/152] Add xformers message. --- launch.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/launch.py b/launch.py index 7b15e78e..333f308a 100644 --- a/launch.py +++ b/launch.py @@ -156,9 +156,15 @@ def prepare_enviroment(): if not is_installed("clip"): run_pip(f"install {clip_package}", "clip") - if (not is_installed("xformers") or reinstall_xformers) and xformers and platform.python_version().startswith("3.10"): + if (not is_installed("xformers") or reinstall_xformers) and xformers: if platform.system() == "Windows": - run_pip(f"install -U -I --no-deps {xformers_windows_package}", "xformers") + if platform.python_version().startswith("3.10"): + run_pip(f"install -U -I --no-deps {xformers_windows_package}", "xformers") + else: + print("Installation of xformers is not supported in this version of Python.") + print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness") + if not is_installed("xformers"): + exit(0) elif platform.system() == "Linux": run_pip("install xformers", "xformers") From eb7ba4b713ac2fb960ecf6365b1de0c89451e583 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Wed, 19 Oct 2022 19:50:46 +0100 Subject: [PATCH 100/152] update training header text --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index 1573ef82..93c0767c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1247,7 +1247,7 @@ def create_ui(wrap_gradio_gpu_call): run_preprocess = gr.Button(value="Preprocess", variant='primary') with gr.Tab(label="Train"): - gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images
Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork wiki

") + gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images
Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork [wiki]

") with gr.Row(): train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name") From 4d663055ded968831ec97f047dfa8e94036cf1c1 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Wed, 19 Oct 2022 20:33:18 +0100 Subject: [PATCH 101/152] update ui with extra training options --- modules/ui.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/modules/ui.py b/modules/ui.py index 93c0767c..cdb9d335 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1206,6 +1206,7 @@ def create_ui(wrap_gradio_gpu_call): new_embedding_name = gr.Textbox(label="Name") initialization_text = gr.Textbox(label="Initialization text", value="*") nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1) + overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding") with gr.Row(): with gr.Column(scale=3): @@ -1219,6 +1220,7 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'") new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") + overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork") with gr.Row(): with gr.Column(scale=3): @@ -1247,14 +1249,17 @@ def create_ui(wrap_gradio_gpu_call): run_preprocess = gr.Button(value="Preprocess", variant='primary') with gr.Tab(label="Train"): - gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images
Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork [wiki]

") + gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]

") with gr.Row(): train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name") with gr.Row(): train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()]) create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name") - learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005") + with gr.Row(): + embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005") + hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001") + batch_size = gr.Number(label='Batch size', value=1, precision=0) dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") @@ -1288,6 +1293,7 @@ def create_ui(wrap_gradio_gpu_call): new_embedding_name, initialization_text, nvpt, + overwrite_old_embedding, ], outputs=[ train_embedding_name, @@ -1303,6 +1309,7 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_sizes, new_hypernetwork_layer_structure, new_hypernetwork_add_layer_norm, + overwrite_old_hypernetwork, ], outputs=[ train_hypernetwork_name, From 8e7097d06a6a261580d34375c9d2a9e4ffc63ffa Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Wed, 19 Oct 2022 13:47:45 -0700 Subject: [PATCH 102/152] Added support for RunwayML inpainting model --- modules/processing.py | 34 +++++- modules/sd_hijack_inpainting.py | 208 ++++++++++++++++++++++++++++++++ modules/sd_models.py | 16 ++- modules/sd_samplers.py | 50 ++++++-- 4 files changed, 293 insertions(+), 15 deletions(-) create mode 100644 modules/sd_hijack_inpainting.py diff --git a/modules/processing.py b/modules/processing.py index bcb0c32c..a6c308f9 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -546,7 +546,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if not self.enable_hr: x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) + + # The "masked-image" in this case will just be all zeros since the entire image is masked. + image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device) + image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) + + # Add the fake full 1s mask to the first dimension. + image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) + image_conditioning = image_conditioning.to(x.dtype) + + samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=image_conditioning) return samples x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) @@ -714,10 +723,31 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): elif self.inpainting_fill == 3: self.init_latent = self.init_latent * self.mask + if self.image_mask is not None: + conditioning_mask = np.array(self.image_mask.convert("L")) + conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 + conditioning_mask = torch.from_numpy(conditioning_mask[None, None]) + + # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0 + conditioning_mask = torch.round(conditioning_mask) + else: + conditioning_mask = torch.ones(1, 1, *image.shape[-2:]) + + # Create another latent image, this time with a masked version of the original input. + conditioning_mask = conditioning_mask.to(image.device) + conditioning_image = image * (1.0 - conditioning_mask) + conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image)) + + # Create the concatenated conditioning tensor to be fed to `c_concat` + conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:]) + conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1) + self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1) + self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype) + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning) + samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning) if self.mask is not None: samples = samples * self.nmask + self.init_latent * self.mask diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py new file mode 100644 index 00000000..7e5670d6 --- /dev/null +++ b/modules/sd_hijack_inpainting.py @@ -0,0 +1,208 @@ +import torch +import numpy as np + +from tqdm import tqdm +from einops import rearrange, repeat +from omegaconf import ListConfig + +from types import MethodType + +import ldm.models.diffusion.ddpm +import ldm.models.diffusion.ddim + +from ldm.models.diffusion.ddpm import LatentDiffusion +from ldm.models.diffusion.ddim import DDIMSampler, noise_like + +# ================================================================================================= +# Monkey patch DDIMSampler methods from RunwayML repo directly. +# Adapted from: +# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py +# ================================================================================================= +@torch.no_grad() +def sample( + self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + ctmp = conditioning[list(conditioning.keys())[0]] + while isinstance(ctmp, list): + ctmp = elf.inpainting_fill == 2: + self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask + elif self.inpainting_fill == 3: + self.init_latent = self.init_latent * self.mask + + if self.image_mask is not None: + conditioning_mask = np.array(self.image_mask.convert("L")) + conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 + conditioning_mask = torch.from_numpy(conditioning_mask[None, None]) + + # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0 + conditioning_mask = torch.round(conditioning_mask) + else: + conditioning_mask = torch.ones(1, 1, *image.shape[-2:]) + + # Create another latent image, this time with a masked version of the original input. + conditioning_mask = conditioning_mask.to(image.device) + conditioning_image = image * (1.0 - conditioning_mask) + conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image)) + + # Create the concatenated conditioning tensor to be fed to `c_concat` + conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:]) + conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1) + self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1) + self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype) + + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): + x = create_random_tensors([opctmp[0] + cbs = ctmp.shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + print(f'Data shape for DDIM sampling is {size}, eta {eta}') + + samples, intermediates = self.ddim_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + ) + return samples, intermediates + + +@torch.no_grad() +def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None): + b, *_, device = *x.shape, x.device + + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + e_t = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + if isinstance(c, dict): + assert isinstance(unconditional_conditioning, dict) + c_in = dict() + for k in c: + if isinstance(c[k], list): + c_in[k] = [ + torch.cat([unconditional_conditioning[k][i], c[k][i]]) + for i in range(len(c[k])) + ] + else: + c_in[k] = torch.cat([unconditional_conditioning[k], c[k]]) + else: + c_in = torch.cat([unconditional_conditioning, c]) + e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) + + if score_corrector is not None: + assert self.model.parameterization == "eps" + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + +# ================================================================================================= +# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config. +# Adapted from: +# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py +# ================================================================================================= + +@torch.no_grad() +def get_unconditional_conditioning(self, batch_size, null_label=None): + if null_label is not None: + xc = null_label + if isinstance(xc, ListConfig): + xc = list(xc) + if isinstance(xc, dict) or isinstance(xc, list): + c = self.get_learned_conditioning(xc) + else: + if hasattr(xc, "to"): + xc = xc.to(self.device) + c = self.get_learned_conditioning(xc) + else: + # todo: get null label from cond_stage_model + raise NotImplementedError() + c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device) + return c + +class LatentInpaintDiffusion(LatentDiffusion): + def __init__( + self, + concat_keys=("mask", "masked_image"), + masked_image_key="masked_image", + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + self.masked_image_key = masked_image_key + assert self.masked_image_key in concat_keys + self.concat_keys = concat_keys + +def should_hijack_inpainting(checkpoint_info): + return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml") + +def do_inpainting_hijack(): + ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning + ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion + ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim + ldm.models.diffusion.ddim.DDIMSampler.sample = sample \ No newline at end of file diff --git a/modules/sd_models.py b/modules/sd_models.py index eae22e87..47836d25 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -9,6 +9,7 @@ from ldm.util import instantiate_from_config from modules import shared, modelloader, devices from modules.paths import models_path +from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(models_path, model_dir)) @@ -211,6 +212,19 @@ def load_model(): print(f"Loading config from: {checkpoint_info.config}") sd_config = OmegaConf.load(checkpoint_info.config) + + if should_hijack_inpainting(checkpoint_info): + do_inpainting_hijack() + + # Hardcoded config for now... + sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion" + sd_config.model.params.use_ema = False + sd_config.model.params.conditioning_key = "hybrid" + sd_config.model.params.unet_config.params.in_channels = 9 + + # Create a "fake" config with a different name so that we know to unload it when switching models. + checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml")) + sd_model = instantiate_from_config(sd_config.model) load_model_weights(sd_model, checkpoint_info) @@ -234,7 +248,7 @@ def reload_model_weights(sd_model, info=None): if sd_model.sd_model_checkpoint == checkpoint_info.filename: return - if sd_model.sd_checkpoint_info.config != checkpoint_info.config: + if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info): checkpoints_loaded.clear() shared.sd_model = load_model() return shared.sd_model diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index b58e810b..9d3cf289 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -136,9 +136,15 @@ class VanillaStableDiffusionSampler: if self.stop_at is not None and self.step > self.stop_at: raise InterruptedException + # Have to unwrap the inpainting conditioning here to perform pre-preocessing + image_conditioning = None + if isinstance(cond, dict): + image_conditioning = cond["c_concat"][0] + cond = cond["c_crossattn"][0] + unconditional_conditioning = unconditional_conditioning["c_crossattn"][0] conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) - unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) + unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers' cond = tensor @@ -157,6 +163,10 @@ class VanillaStableDiffusionSampler: img_orig = self.sampler.model.q_sample(self.init_latent, ts) x_dec = img_orig * self.mask + self.nmask * x_dec + if image_conditioning is not None: + cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]} + unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} + res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs) if self.mask is not None: @@ -182,7 +192,7 @@ class VanillaStableDiffusionSampler: self.mask = p.mask if hasattr(p, 'mask') else None self.nmask = p.nmask if hasattr(p, 'nmask') else None - def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): + def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): steps, t_enc = setup_img2img_steps(p, steps) self.initialize(p) @@ -202,7 +212,7 @@ class VanillaStableDiffusionSampler: return samples - def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): + def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): self.initialize(p) self.init_latent = None @@ -210,6 +220,11 @@ class VanillaStableDiffusionSampler: steps = steps or p.steps + # Wrap the conditioning models with additional image conditioning for inpainting model + if image_conditioning is not None: + conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]} + unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} + # existing code fails with certain step counts, like 9 try: samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0]) @@ -228,7 +243,7 @@ class CFGDenoiser(torch.nn.Module): self.init_latent = None self.step = 0 - def forward(self, x, sigma, uncond, cond, cond_scale): + def forward(self, x, sigma, uncond, cond, cond_scale, image_cond): if state.interrupted or state.skipped: raise InterruptedException @@ -239,28 +254,29 @@ class CFGDenoiser(torch.nn.Module): repeats = [len(conds_list[i]) for i in range(batch_size)] x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x]) + image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond]) sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma]) if tensor.shape[1] == uncond.shape[1]: cond_in = torch.cat([tensor, uncond]) if shared.batch_cond_uncond: - x_out = self.inner_model(x_in, sigma_in, cond=cond_in) + x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]}) else: x_out = torch.zeros_like(x_in) for batch_offset in range(0, x_out.shape[0], batch_size): a = batch_offset b = a + batch_size - x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b]) + x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]}) else: x_out = torch.zeros_like(x_in) batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size for batch_offset in range(0, tensor.shape[0], batch_size): a = batch_offset b = min(a + batch_size, tensor.shape[0]) - x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b]) + x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]}) - x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond) + x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]}) denoised_uncond = x_out[-uncond.shape[0]:] denoised = torch.clone(denoised_uncond) @@ -361,7 +377,7 @@ class KDiffusionSampler: return extra_params_kwargs - def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): + def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): steps, t_enc = setup_img2img_steps(p, steps) if p.sampler_noise_scheduler_override: @@ -389,11 +405,16 @@ class KDiffusionSampler: self.model_wrap_cfg.init_latent = x - samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)) + samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={ + 'cond': conditioning, + 'image_cond': image_conditioning, + 'uncond': unconditional_conditioning, + 'cond_scale': p.cfg_scale + }, disable=False, callback=self.callback_state, **extra_params_kwargs)) return samples - def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): + def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None): steps = steps or p.steps if p.sampler_noise_scheduler_override: @@ -414,7 +435,12 @@ class KDiffusionSampler: else: extra_params_kwargs['sigmas'] = sigmas - samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)) + samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={ + 'cond': conditioning, + 'image_cond': image_conditioning, + 'uncond': unconditional_conditioning, + 'cond_scale': p.cfg_scale + }, disable=False, callback=self.callback_state, **extra_params_kwargs)) return samples From 0719c10bf1b817364a498ee11b90d30d3d527344 Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Wed, 19 Oct 2022 13:56:26 -0700 Subject: [PATCH 103/152] Fixed copying mistake --- modules/sd_hijack_inpainting.py | 79 +++++++++++---------------------- 1 file changed, 25 insertions(+), 54 deletions(-) diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py index 7e5670d6..d4d28d2e 100644 --- a/modules/sd_hijack_inpainting.py +++ b/modules/sd_hijack_inpainting.py @@ -19,63 +19,35 @@ from ldm.models.diffusion.ddim import DDIMSampler, noise_like # https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py # ================================================================================================= @torch.no_grad() -def sample( - self, - S, - batch_size, - shape, - conditioning=None, - callback=None, - normals_sequence=None, - img_callback=None, - quantize_x0=False, - eta=0., - mask=None, - x0=None, - temperature=1., - noise_dropout=0., - score_corrector=None, - corrector_kwargs=None, - verbose=True, - x_T=None, - log_every_t=100, - unconditional_guidance_scale=1., - unconditional_conditioning=None, - # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... - **kwargs - ): +def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): if conditioning is not None: if isinstance(conditioning, dict): ctmp = conditioning[list(conditioning.keys())[0]] while isinstance(ctmp, list): - ctmp = elf.inpainting_fill == 2: - self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask - elif self.inpainting_fill == 3: - self.init_latent = self.init_latent * self.mask - - if self.image_mask is not None: - conditioning_mask = np.array(self.image_mask.convert("L")) - conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 - conditioning_mask = torch.from_numpy(conditioning_mask[None, None]) - - # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0 - conditioning_mask = torch.round(conditioning_mask) - else: - conditioning_mask = torch.ones(1, 1, *image.shape[-2:]) - - # Create another latent image, this time with a masked version of the original input. - conditioning_mask = conditioning_mask.to(image.device) - conditioning_image = image * (1.0 - conditioning_mask) - conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image)) - - # Create the concatenated conditioning tensor to be fed to `c_concat` - conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:]) - conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1) - self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1) - self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype) - - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): - x = create_random_tensors([opctmp[0] + ctmp = ctmp[0] cbs = ctmp.shape[0] if cbs != batch_size: print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") @@ -106,7 +78,6 @@ def sample( ) return samples, intermediates - @torch.no_grad() def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, From dde9f960727bfe151d418e43685a2881cf580a17 Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Wed, 19 Oct 2022 14:14:24 -0700 Subject: [PATCH 104/152] added support for ddim img2img --- modules/sd_samplers.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 9d3cf289..d270e4df 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -208,6 +208,12 @@ class VanillaStableDiffusionSampler: self.init_latent = x self.step = 0 + # Wrap the conditioning models with additional image conditioning for inpainting model + if image_conditioning is not None: + conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]} + unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} + + samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)) return samples From c418467c03db916c3e5312e6ac4a67365e196dbd Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Wed, 19 Oct 2022 15:09:43 -0700 Subject: [PATCH 105/152] Don't compute latent mask if were not using it. Also added support for fixed highres_fix generation. --- modules/processing.py | 70 ++++++++++++++++++++++++++---------------- modules/sd_samplers.py | 4 +++ 2 files changed, 48 insertions(+), 26 deletions(-) diff --git a/modules/processing.py b/modules/processing.py index a6c308f9..684e5833 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -541,12 +541,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): - self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) - - if not self.enable_hr: - x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - + def create_dummy_mask(self, x): + if self.sampler.conditioning_key in {'hybrid', 'concat'}: # The "masked-image" in this case will just be all zeros since the entire image is masked. image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device) image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) @@ -555,11 +551,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) image_conditioning = image_conditioning.to(x.dtype) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=image_conditioning) + else: + # Dummy zero conditioning if we're not using inpainting model. + # Still takes up a bit of memory, but no encoder call. + image_conditioning = torch.zeros(x.shape[0], 5, x.shape[-2], x.shape[-1], dtype=x.dtype, device=x.device) + + return image_conditioning + + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): + self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) + + if not self.enable_hr: + x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x)) return samples x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) + samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x)) samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] @@ -596,7 +604,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x = None devices.torch_gc() - samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) + samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=self.create_dummy_mask(samples)) return samples @@ -723,26 +731,36 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): elif self.inpainting_fill == 3: self.init_latent = self.init_latent * self.mask - if self.image_mask is not None: - conditioning_mask = np.array(self.image_mask.convert("L")) - conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 - conditioning_mask = torch.from_numpy(conditioning_mask[None, None]) + conditioning_key = self.sampler.conditioning_key - # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0 - conditioning_mask = torch.round(conditioning_mask) + if conditioning_key in {'hybrid', 'concat'}: + if self.image_mask is not None: + conditioning_mask = np.array(self.image_mask.convert("L")) + conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 + conditioning_mask = torch.from_numpy(conditioning_mask[None, None]) + + # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0 + conditioning_mask = torch.round(conditioning_mask) + else: + conditioning_mask = torch.ones(1, 1, *image.shape[-2:]) + + # Create another latent image, this time with a masked version of the original input. + conditioning_mask = conditioning_mask.to(image.device) + conditioning_image = image * (1.0 - conditioning_mask) + conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image)) + + # Create the concatenated conditioning tensor to be fed to `c_concat` + conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:]) + conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1) + self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1) + self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype) else: - conditioning_mask = torch.ones(1, 1, *image.shape[-2:]) + self.image_conditioning = torch.zeros( + self.init_latent.shape[0], 5, self.init_latent.shape[-2], self.init_latent.shape[-1], + dtype=self.init_latent.dtype, + device=self.init_latent.device + ) - # Create another latent image, this time with a masked version of the original input. - conditioning_mask = conditioning_mask.to(image.device) - conditioning_image = image * (1.0 - conditioning_mask) - conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image)) - - # Create the concatenated conditioning tensor to be fed to `c_concat` - conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:]) - conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1) - self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1) - self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype) def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index d270e4df..c21be26e 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -117,6 +117,8 @@ class VanillaStableDiffusionSampler: self.config = None self.last_latent = None + self.conditioning_key = sd_model.model.conditioning_key + def number_of_needed_noises(self, p): return 0 @@ -328,6 +330,8 @@ class KDiffusionSampler: self.config = None self.last_latent = None + self.conditioning_key = sd_model.model.conditioning_key + def callback_state(self, d): step = d['i'] latent = d["denoised"] From d6ea5841374a28f3f6deb73abc251c8f0bcb240f Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:07:57 +0100 Subject: [PATCH 106/152] change html output --- modules/hypernetworks/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 7d519cd9..73c1cb80 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -380,7 +380,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log Loss: {mean_loss:.7f}
Step: {hypernetwork.step}
Last prompt: {html.escape(entries[0].cond_text)}
-Last saved embedding: {html.escape(last_saved_file)}
+Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" From 166be3919b817cee5e702fd01c34afe9081b952c Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:09:40 +0100 Subject: [PATCH 107/152] allow overwrite old hn --- modules/hypernetworks/ui.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 08f75f15..f45345ea 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -10,9 +10,10 @@ from modules import sd_hijack, shared, devices from modules.hypernetworks import hypernetwork -def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False): +def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, add_layer_norm=False): fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") - assert not os.path.exists(fn), f"file {fn} already exists" + if not overwrite_old: + assert not os.path.exists(fn), f"file {fn} already exists" if type(layer_structure) == str: layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure))) From 0087079c2d487b67b06ffc30f36ce486a74e6318 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:10:59 +0100 Subject: [PATCH 108/152] allow overwrite old embedding --- modules/textual_inversion/textual_inversion.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 3be69562..5776778b 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -153,7 +153,7 @@ class EmbeddingDatabase: return None, None -def create_embedding(name, num_vectors_per_token, init_text='*'): +def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'): cond_model = shared.sd_model.cond_stage_model embedding_layer = cond_model.wrapped.transformer.text_model.embeddings @@ -165,7 +165,8 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token] fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt") - assert not os.path.exists(fn), f"file {fn} already exists" + if not overwrite_old: + assert not os.path.exists(fn), f"file {fn} already exists" embedding = Embedding(vec, name) embedding.step = 0 From 632e8d660293081cadb145d8062e5aff0a4a8f0d Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:19:40 +0100 Subject: [PATCH 109/152] split learn rates --- modules/ui.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/ui.py b/modules/ui.py index cdb9d335..d07184ee 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1342,7 +1342,7 @@ def create_ui(wrap_gradio_gpu_call): _js="start_training_textual_inversion", inputs=[ train_embedding_name, - learn_rate, + embedding_learn_rate, batch_size, dataset_directory, log_directory, @@ -1367,7 +1367,7 @@ def create_ui(wrap_gradio_gpu_call): _js="start_training_textual_inversion", inputs=[ train_hypernetwork_name, - learn_rate, + hypernetwork_learn_rate, batch_size, dataset_directory, log_directory, From c3835ec85cbb44fa3c46fa871c622b6fee235c89 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:24:24 +0100 Subject: [PATCH 110/152] pass overwrite old flag --- modules/textual_inversion/ui.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py index 36881e7a..e712284d 100644 --- a/modules/textual_inversion/ui.py +++ b/modules/textual_inversion/ui.py @@ -7,8 +7,8 @@ import modules.textual_inversion.preprocess from modules import sd_hijack, shared -def create_embedding(name, initialization_text, nvpt): - filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text) +def create_embedding(name, initialization_text, nvpt, overwrite_old): + filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, overwrite_old, init_text=initialization_text) sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() From 4d6b9f76a55fd0ac0f72634071032dd9c6efb409 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:27:16 +0100 Subject: [PATCH 111/152] reorder create_hypernetwork params --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index d07184ee..322c082b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1307,9 +1307,9 @@ def create_ui(wrap_gradio_gpu_call): inputs=[ new_hypernetwork_name, new_hypernetwork_sizes, + overwrite_old_hypernetwork, new_hypernetwork_layer_structure, new_hypernetwork_add_layer_norm, - overwrite_old_hypernetwork, ], outputs=[ train_hypernetwork_name, From fbcce66601994f6ed370db36d9c238840fed6bd2 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:46:54 +0100 Subject: [PATCH 112/152] add existing caption file handling --- modules/textual_inversion/preprocess.py | 32 ++++++++++++++++++------- 1 file changed, 24 insertions(+), 8 deletions(-) diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 886cf0c3..5c43fe13 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -48,7 +48,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro shared.state.textinfo = "Preprocessing..." shared.state.job_count = len(files) - def save_pic_with_caption(image, index): + def save_pic_with_caption(image, index, existing_caption=None): caption = "" if process_caption: @@ -66,17 +66,26 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro basename = f"{index:05}-{subindex[0]}-{filename_part}" image.save(os.path.join(dst, f"{basename}.png")) + if preprocess_txt_action == 'prepend' and existing_caption: + caption = existing_caption + ' ' + caption + elif preprocess_txt_action == 'append' and existing_caption: + caption = caption + ' ' + existing_caption + elif preprocess_txt_action == 'copy' and existing_caption: + caption = existing_caption + + caption = caption.strip() + if len(caption) > 0: with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file: file.write(caption) subindex[0] += 1 - def save_pic(image, index): + def save_pic(image, index, existing_caption=None): save_pic_with_caption(image, index) if process_flip: - save_pic_with_caption(ImageOps.mirror(image), index) + save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption) for index, imagefile in enumerate(tqdm.tqdm(files)): subindex = [0] @@ -86,6 +95,13 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro except Exception: continue + existing_caption = None + + try: + existing_caption = open(os.path.splitext(filename)[0] + '.txt', 'r').read() + except Exception as e: + print(e) + if shared.state.interrupted: break @@ -97,20 +113,20 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro img = img.resize((width, height * img.height // img.width)) top = img.crop((0, 0, width, height)) - save_pic(top, index) + save_pic(top, index, existing_caption=existing_caption) bot = img.crop((0, img.height - height, width, img.height)) - save_pic(bot, index) + save_pic(bot, index, existing_caption=existing_caption) elif process_split and is_wide: img = img.resize((width * img.width // img.height, height)) left = img.crop((0, 0, width, height)) - save_pic(left, index) + save_pic(left, index, existing_caption=existing_caption) right = img.crop((img.width - width, 0, img.width, height)) - save_pic(right, index) + save_pic(right, index, existing_caption=existing_caption) else: img = images.resize_image(1, img, width, height) - save_pic(img, index) + save_pic(img, index, existing_caption=existing_caption) shared.state.nextjob() From ab353b141df8eee042b0964bcb645015dabf3459 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:48:07 +0100 Subject: [PATCH 113/152] link existing txt option --- modules/ui.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/modules/ui.py b/modules/ui.py index 322c082b..7f52ac0c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1234,6 +1234,7 @@ def create_ui(wrap_gradio_gpu_call): process_dst = gr.Textbox(label='Destination directory') process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', choices=['ignore', 'copy', 'prepend', 'append']) with gr.Row(): process_flip = gr.Checkbox(label='Create flipped copies') @@ -1326,6 +1327,7 @@ def create_ui(wrap_gradio_gpu_call): process_dst, process_width, process_height, + preprocess_txt_action, process_flip, process_split, process_caption, From 9b65c4ecf4f8eb6187ee721918adebe68e9bc631 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:49:23 +0100 Subject: [PATCH 114/152] pass preprocess_txt_action param --- modules/textual_inversion/preprocess.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 5c43fe13..3713bc89 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -11,7 +11,7 @@ if cmd_opts.deepdanbooru: import modules.deepbooru as deepbooru -def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False): +def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False): try: if process_caption: shared.interrogator.load() @@ -21,7 +21,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ db_opts[deepbooru.OPT_INCLUDE_RANKS] = False deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts) - preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru) + preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru) finally: @@ -33,7 +33,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ -def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False): +def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False): width = process_width height = process_height src = os.path.abspath(process_src) From 55d8c6cce6d3aef848b9f194adad2ce53064d8b7 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:53:29 +0100 Subject: [PATCH 115/152] default to ignore existing captions --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index 7f52ac0c..bd5f1b05 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1234,7 +1234,7 @@ def create_ui(wrap_gradio_gpu_call): process_dst = gr.Textbox(label='Destination directory') process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) - preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', choices=['ignore', 'copy', 'prepend', 'append']) + preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"]) with gr.Row(): process_flip = gr.Checkbox(label='Create flipped copies') From 6f98e89486f55b0e4657e96ce640cf1c4675d187 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Thu, 20 Oct 2022 00:10:45 +0000 Subject: [PATCH 116/152] update --- modules/hypernetworks/hypernetwork.py | 29 +++++++++++------ modules/hypernetworks/ui.py | 3 +- modules/ui.py | 45 ++++++++++++++------------- 3 files changed, 45 insertions(+), 32 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 74300122..7d617680 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -22,16 +22,20 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler class HypernetworkModule(torch.nn.Module): multiplier = 1.0 - def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False): + def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False, activation_func=None): super().__init__() - assert layer_structure is not None, "layer_structure mut not be None" + assert layer_structure is not None, "layer_structure must not be None" assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" linears = [] for i in range(len(layer_structure) - 1): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) + if activation_func == "relu": + linears.append(torch.nn.ReLU()) + if activation_func == "leakyrelu": + linears.append(torch.nn.LeakyReLU()) if add_layer_norm: linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) @@ -42,8 +46,9 @@ class HypernetworkModule(torch.nn.Module): self.load_state_dict(state_dict) else: for layer in self.linear: - layer.weight.data.normal_(mean=0.0, std=0.01) - layer.bias.data.zero_() + if not "ReLU" in layer.__str__(): + layer.weight.data.normal_(mean=0.0, std=0.01) + layer.bias.data.zero_() self.to(devices.device) @@ -69,7 +74,8 @@ class HypernetworkModule(torch.nn.Module): def trainables(self): layer_structure = [] for layer in self.linear: - layer_structure += [layer.weight, layer.bias] + if not "ReLU" in layer.__str__(): + layer_structure += [layer.weight, layer.bias] return layer_structure @@ -81,7 +87,7 @@ class Hypernetwork: filename = None name = None - def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False): + def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False, activation_func=None): self.filename = None self.name = name self.layers = {} @@ -90,11 +96,12 @@ class Hypernetwork: self.sd_checkpoint_name = None self.layer_structure = layer_structure self.add_layer_norm = add_layer_norm + self.activation_func = activation_func for size in enable_sizes or []: self.layers[size] = ( - HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm), - HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm), + HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func), + HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func), ) def weights(self): @@ -117,6 +124,7 @@ class Hypernetwork: state_dict['name'] = self.name state_dict['layer_structure'] = self.layer_structure state_dict['is_layer_norm'] = self.add_layer_norm + state_dict['activation_func'] = self.activation_func state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name @@ -131,12 +139,13 @@ class Hypernetwork: self.layer_structure = state_dict.get('layer_structure', [1, 2, 1]) self.add_layer_norm = state_dict.get('is_layer_norm', False) + self.activation_func = state_dict.get('activation_func', None) for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( - HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm), - HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm), + HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm, self.activation_func), + HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm, self.activation_func), ) self.name = state_dict.get('name', self.name) diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 08f75f15..83f9547b 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -10,7 +10,7 @@ from modules import sd_hijack, shared, devices from modules.hypernetworks import hypernetwork -def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False): +def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False, activation_func=None): fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") assert not os.path.exists(fn), f"file {fn} already exists" @@ -22,6 +22,7 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm enable_sizes=[int(x) for x in enable_sizes], layer_structure=layer_structure, add_layer_norm=add_layer_norm, + activation_func=activation_func, ) hypernet.save(fn) diff --git a/modules/ui.py b/modules/ui.py index d2e24880..8751fa9c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -5,43 +5,44 @@ import json import math import mimetypes import os +import platform import random +import subprocess as sp import sys import tempfile import time import traceback -import platform -import subprocess as sp from functools import partial, reduce +import gradio as gr +import gradio.routes +import gradio.utils import numpy as np +import piexif import torch from PIL import Image, PngImagePlugin -import piexif -import gradio as gr -import gradio.utils -import gradio.routes - -from modules import sd_hijack, sd_models, localization +from modules import localization, sd_hijack, sd_models from modules.paths import script_path -from modules.shared import opts, cmd_opts, restricted_opts +from modules.shared import cmd_opts, opts, restricted_opts + if cmd_opts.deepdanbooru: from modules.deepbooru import get_deepbooru_tags -import modules.shared as shared -from modules.sd_samplers import samplers, samplers_for_img2img -from modules.sd_hijack import model_hijack -import modules.ldsr_model -import modules.scripts -import modules.gfpgan_model + import modules.codeformer_model -import modules.styles import modules.generation_parameters_copypaste -from modules import prompt_parser -from modules.images import save_image -import modules.textual_inversion.ui +import modules.gfpgan_model import modules.hypernetworks.ui import modules.images_history as img_his +import modules.ldsr_model +import modules.scripts +import modules.shared as shared +import modules.styles +import modules.textual_inversion.ui +from modules import prompt_parser +from modules.images import save_image +from modules.sd_hijack import model_hijack +from modules.sd_samplers import samplers, samplers_for_img2img # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() @@ -268,8 +269,8 @@ def calc_time_left(progress, threshold, label, force_display): time_since_start = time.time() - shared.state.time_start eta = (time_since_start/progress) eta_relative = eta-time_since_start - if (eta_relative > threshold and progress > 0.02) or force_display: - return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative)) + if (eta_relative > threshold and progress > 0.02) or force_display: + return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative)) else: return "" @@ -1219,6 +1220,7 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'") new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") + new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["relu", "leakyrelu"]) with gr.Row(): with gr.Column(scale=3): @@ -1303,6 +1305,7 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_sizes, new_hypernetwork_layer_structure, new_hypernetwork_add_layer_norm, + new_hypernetwork_activation_func, ], outputs=[ train_hypernetwork_name, From ba469343e6a1c6e23e82acf5feb65c6101dacbb2 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Thu, 20 Oct 2022 00:17:04 +0000 Subject: [PATCH 117/152] align ui.py imports with upstream --- modules/ui.py | 37 ++++++++++++++++++------------------- 1 file changed, 18 insertions(+), 19 deletions(-) diff --git a/modules/ui.py b/modules/ui.py index 987b1d7d..913b23b4 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -5,44 +5,43 @@ import json import math import mimetypes import os -import platform import random -import subprocess as sp import sys import tempfile import time import traceback +import platform +import subprocess as sp from functools import partial, reduce -import gradio as gr -import gradio.routes -import gradio.utils import numpy as np -import piexif import torch from PIL import Image, PngImagePlugin +import piexif -from modules import localization, sd_hijack, sd_models +import gradio as gr +import gradio.utils +import gradio.routes + +from modules import sd_hijack, sd_models, localization from modules.paths import script_path -from modules.shared import cmd_opts, opts, restricted_opts - +from modules.shared import opts, cmd_opts, restricted_opts if cmd_opts.deepdanbooru: from modules.deepbooru import get_deepbooru_tags - -import modules.codeformer_model -import modules.generation_parameters_copypaste -import modules.gfpgan_model -import modules.hypernetworks.ui -import modules.images_history as img_his +import modules.shared as shared +from modules.sd_samplers import samplers, samplers_for_img2img +from modules.sd_hijack import model_hijack import modules.ldsr_model import modules.scripts -import modules.shared as shared +import modules.gfpgan_model +import modules.codeformer_model import modules.styles -import modules.textual_inversion.ui +import modules.generation_parameters_copypaste from modules import prompt_parser from modules.images import save_image -from modules.sd_hijack import model_hijack -from modules.sd_samplers import samplers, samplers_for_img2img +import modules.textual_inversion.ui +import modules.hypernetworks.ui +import modules.images_history as img_his # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() From 858462f719c22ca9f24b94a41699653c34b5f4fb Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 02:57:18 +0100 Subject: [PATCH 118/152] do caption copy for both flips --- modules/textual_inversion/preprocess.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 3713bc89..6bba3852 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -82,7 +82,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre subindex[0] += 1 def save_pic(image, index, existing_caption=None): - save_pic_with_caption(image, index) + save_pic_with_caption(image, index, existing_caption=existing_caption) if process_flip: save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption) From aa7ff2a1972f3865883e10ba28c5414cdebe8e3b Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Wed, 19 Oct 2022 21:46:13 -0700 Subject: [PATCH 119/152] Fixed non-square highres fix generation --- modules/processing.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/modules/processing.py b/modules/processing.py index 684e5833..3caac25e 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -541,10 +541,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f - def create_dummy_mask(self, x): + def create_dummy_mask(self, x, first_phase: bool = False): if self.sampler.conditioning_key in {'hybrid', 'concat'}: + height = self.firstphase_height if first_phase else self.height + width = self.firstphase_width if first_phase else self.width + # The "masked-image" in this case will just be all zeros since the entire image is masked. - image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device) + image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) # Add the fake full 1s mask to the first dimension. @@ -567,7 +570,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): return samples x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x)) + samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, first_phase=True)) samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] From 930b4c64f7dbce6918894d53538003e5959fd022 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 20 Oct 2022 08:18:02 +0300 Subject: [PATCH 120/152] allow float sizes for hypernet's layer_structure --- modules/hypernetworks/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 08f75f15..e0741d08 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -15,7 +15,7 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm assert not os.path.exists(fn), f"file {fn} already exists" if type(layer_structure) == str: - layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure))) + layer_structure = [float(x.strip()) for x in layer_structure.split(",")] hypernet = modules.hypernetworks.hypernetwork.Hypernetwork( name=name, From f8733ad08be08bafb40f4299785590e11f049e96 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Thu, 20 Oct 2022 11:07:37 +0000 Subject: [PATCH 121/152] add linear as a act func (option for doin nothing) --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index 913b23b4..716f14b8 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1224,7 +1224,7 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'") new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") - new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["relu", "leakyrelu"]) + new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"]) with gr.Row(): with gr.Column(scale=3): From 92a17a7a4a13fceb3c3e25a2e854b2a7dd6eb5df Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Thu, 20 Oct 2022 09:45:03 -0700 Subject: [PATCH 122/152] Made dummy latents smaller. Minor code cleanups --- modules/processing.py | 7 ++++--- modules/sd_samplers.py | 6 ++++-- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/modules/processing.py b/modules/processing.py index 3caac25e..539cde38 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -557,7 +557,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): else: # Dummy zero conditioning if we're not using inpainting model. # Still takes up a bit of memory, but no encoder call. - image_conditioning = torch.zeros(x.shape[0], 5, x.shape[-2], x.shape[-1], dtype=x.dtype, device=x.device) + # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size. + image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) return image_conditioning @@ -759,8 +760,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype) else: self.image_conditioning = torch.zeros( - self.init_latent.shape[0], 5, self.init_latent.shape[-2], self.init_latent.shape[-1], - dtype=self.init_latent.dtype, + self.init_latent.shape[0], 5, 1, 1, + dtype=self.init_latent.dtype, device=self.init_latent.device ) diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index c21be26e..cc682593 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -138,7 +138,7 @@ class VanillaStableDiffusionSampler: if self.stop_at is not None and self.step > self.stop_at: raise InterruptedException - # Have to unwrap the inpainting conditioning here to perform pre-preocessing + # Have to unwrap the inpainting conditioning here to perform pre-processing image_conditioning = None if isinstance(cond, dict): image_conditioning = cond["c_concat"][0] @@ -146,7 +146,7 @@ class VanillaStableDiffusionSampler: unconditional_conditioning = unconditional_conditioning["c_crossattn"][0] conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) - unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) + unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers' cond = tensor @@ -165,6 +165,8 @@ class VanillaStableDiffusionSampler: img_orig = self.sampler.model.q_sample(self.init_latent, ts) x_dec = img_orig * self.mask + self.nmask * x_dec + # Wrap the image conditioning back up since the DDIM code can accept the dict directly. + # Note that they need to be lists because it just concatenates them later. if image_conditioning is not None: cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]} unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} From d1cb08bfb221cd1b0cfc6078162b4e206ea80a5c Mon Sep 17 00:00:00 2001 From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com> Date: Thu, 20 Oct 2022 22:49:06 +0300 Subject: [PATCH 123/152] fix skip and interrupt for highres. fix option --- modules/processing.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/modules/processing.py b/modules/processing.py index bcb0c32c..6324ca91 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -587,9 +587,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x = None devices.torch_gc() - samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) - - return samples + return self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) or samples class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): From 708c3a7bd8ce68cbe1aa7c268e5a4b1980affc9f Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Thu, 20 Oct 2022 13:28:43 -0700 Subject: [PATCH 124/152] Added PLMS hijack and made sure to always replace methods --- modules/sd_hijack_inpainting.py | 163 ++++++++++++++++++++++++++++++-- modules/sd_models.py | 3 +- 2 files changed, 157 insertions(+), 9 deletions(-) diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py index d4d28d2e..43938071 100644 --- a/modules/sd_hijack_inpainting.py +++ b/modules/sd_hijack_inpainting.py @@ -1,16 +1,14 @@ import torch -import numpy as np -from tqdm import tqdm -from einops import rearrange, repeat +from einops import repeat from omegaconf import ListConfig -from types import MethodType - import ldm.models.diffusion.ddpm import ldm.models.diffusion.ddim +import ldm.models.diffusion.plms from ldm.models.diffusion.ddpm import LatentDiffusion +from ldm.models.diffusion.plms import PLMSSampler from ldm.models.diffusion.ddim import DDIMSampler, noise_like # ================================================================================================= @@ -19,7 +17,7 @@ from ldm.models.diffusion.ddim import DDIMSampler, noise_like # https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py # ================================================================================================= @torch.no_grad() -def sample(self, +def sample_ddim(self, S, batch_size, shape, @@ -132,6 +130,153 @@ def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=F return x_prev, pred_x0 +# ================================================================================================= +# Monkey patch PLMSSampler methods. +# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes. +# Adapted from: +# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py +# ================================================================================================= +@torch.no_grad() +def sample_plms(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + ctmp = conditioning[list(conditioning.keys())[0]] + while isinstance(ctmp, list): + ctmp = ctmp[0] + cbs = ctmp.shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + print(f'Data shape for PLMS sampling is {size}') + + samples, intermediates = self.plms_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + ) + return samples, intermediates + + +@torch.no_grad() +def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None): + b, *_, device = *x.shape, x.device + + def get_model_output(x, t): + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + e_t = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + + if isinstance(c, dict): + assert isinstance(unconditional_conditioning, dict) + c_in = dict() + for k in c: + if isinstance(c[k], list): + c_in[k] = [ + torch.cat([unconditional_conditioning[k][i], c[k][i]]) + for i in range(len(c[k])) + ] + else: + c_in[k] = torch.cat([unconditional_conditioning[k], c[k]]) + else: + c_in = torch.cat([unconditional_conditioning, c]) + + e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) + + if score_corrector is not None: + assert self.model.parameterization == "eps" + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + return e_t + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + + def get_x_prev_and_pred_x0(e_t, index): + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + e_t = get_model_output(x, t) + if len(old_eps) == 0: + # Pseudo Improved Euler (2nd order) + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) + e_t_next = get_model_output(x_prev, t_next) + e_t_prime = (e_t + e_t_next) / 2 + elif len(old_eps) == 1: + # 2nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (3 * e_t - old_eps[-1]) / 2 + elif len(old_eps) == 2: + # 3nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 + elif len(old_eps) >= 3: + # 4nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 + + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) + + return x_prev, pred_x0, e_t + # ================================================================================================= # Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config. # Adapted from: @@ -175,5 +320,9 @@ def should_hijack_inpainting(checkpoint_info): def do_inpainting_hijack(): ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion + ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim - ldm.models.diffusion.ddim.DDIMSampler.sample = sample \ No newline at end of file + ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim + + ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms + ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms \ No newline at end of file diff --git a/modules/sd_models.py b/modules/sd_models.py index 47836d25..7072db08 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -214,8 +214,6 @@ def load_model(): sd_config = OmegaConf.load(checkpoint_info.config) if should_hijack_inpainting(checkpoint_info): - do_inpainting_hijack() - # Hardcoded config for now... sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion" sd_config.model.params.use_ema = False @@ -225,6 +223,7 @@ def load_model(): # Create a "fake" config with a different name so that we know to unload it when switching models. checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml")) + do_inpainting_hijack() sd_model = instantiate_from_config(sd_config.model) load_model_weights(sd_model, checkpoint_info) From d23a46ceaa76af2847f11172f32c92665c268b1b Mon Sep 17 00:00:00 2001 From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com> Date: Thu, 20 Oct 2022 23:49:14 +0300 Subject: [PATCH 125/152] Different approach to skip/interrupt with highres fix --- modules/processing.py | 4 +++- modules/sd_samplers.py | 4 ++++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/modules/processing.py b/modules/processing.py index 6324ca91..bcb0c32c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -587,7 +587,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x = None devices.torch_gc() - return self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) or samples + samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) + + return samples class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index b58e810b..7ff77c01 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -196,6 +196,7 @@ class VanillaStableDiffusionSampler: x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise) self.init_latent = x + self.last_latent = x self.step = 0 samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)) @@ -206,6 +207,7 @@ class VanillaStableDiffusionSampler: self.initialize(p) self.init_latent = None + self.last_latent = x self.step = 0 steps = steps or p.steps @@ -388,6 +390,7 @@ class KDiffusionSampler: extra_params_kwargs['sigmas'] = sigma_sched self.model_wrap_cfg.init_latent = x + self.last_latent = x samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)) @@ -414,6 +417,7 @@ class KDiffusionSampler: else: extra_params_kwargs['sigmas'] = sigmas + self.last_latent = x samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)) return samples From 49533eed9e3aad19e9868ee140708baec4fd44be Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Thu, 20 Oct 2022 16:01:27 -0700 Subject: [PATCH 126/152] XY grid correctly re-assignes model when config changes --- modules/sd_models.py | 6 +++--- scripts/xy_grid.py | 1 + 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index 7072db08..fea84630 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -204,9 +204,9 @@ def load_model_weights(model, checkpoint_info): model.sd_checkpoint_info = checkpoint_info -def load_model(): +def load_model(checkpoint_info=None): from modules import lowvram, sd_hijack - checkpoint_info = select_checkpoint() + checkpoint_info = checkpoint_info or select_checkpoint() if checkpoint_info.config != shared.cmd_opts.config: print(f"Loading config from: {checkpoint_info.config}") @@ -249,7 +249,7 @@ def reload_model_weights(sd_model, info=None): if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info): checkpoints_loaded.clear() - shared.sd_model = load_model() + shared.sd_model = load_model(checkpoint_info) return shared.sd_model if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py index 5cca168a..eff0c942 100644 --- a/scripts/xy_grid.py +++ b/scripts/xy_grid.py @@ -89,6 +89,7 @@ def apply_checkpoint(p, x, xs): if info is None: raise RuntimeError(f"Unknown checkpoint: {x}") modules.sd_models.reload_model_weights(shared.sd_model, info) + p.sd_model = shared.sd_model def confirm_checkpoints(p, xs): From 0110429dc4bc004ac56573fe1a6b05cb0123678e Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Thu, 20 Oct 2022 19:10:32 +0900 Subject: [PATCH 127/152] Fixed path issue while extras batch processing --- modules/extras.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index b853fa5b..f9796624 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -118,10 +118,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ while len(cached_images) > 2: del cached_images[next(iter(cached_images.keys()))] + + if opts.use_original_name_batch and image_name != None: + basename = os.path.splitext(os.path.basename(image_name))[0] + else: + basename = '' - images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, - no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, - forced_filename=image_name if opts.use_original_name_batch else None) + images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, + no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None) if opts.enable_pnginfo: image.info = existing_pnginfo From aacc4c1ecbcba3cef421d8776dc5b4b239df9b42 Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Thu, 20 Oct 2022 21:31:29 +0900 Subject: [PATCH 128/152] Added try except to extras batch from directory --- modules/extras.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index f9796624..0d817cf9 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -41,7 +41,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ return outputs, "Please select an input directory.", '' image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)] for img in image_list: - image = Image.open(img) + try: + image = Image.open(img) + except Exception: + continue imageArr.append(image) imageNameArr.append(img) else: @@ -122,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ if opts.use_original_name_batch and image_name != None: basename = os.path.splitext(os.path.basename(image_name))[0] else: - basename = '' + basename = None - images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, - no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None) + images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, + no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename) if opts.enable_pnginfo: image.info = existing_pnginfo From bc16b135b527224545dca555a9d51edb0adcee2d Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Thu, 20 Oct 2022 21:43:27 +0900 Subject: [PATCH 129/152] Fixed path issue while extras batch processing --- modules/extras.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index 0d817cf9..ac85142c 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -125,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ if opts.use_original_name_batch and image_name != None: basename = os.path.splitext(os.path.basename(image_name))[0] else: - basename = None + basename = '' - images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, - no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename) + images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, + no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None) if opts.enable_pnginfo: image.info = existing_pnginfo From 991a595686b8d105025d68d0e833d1cbf44cb143 Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Fri, 21 Oct 2022 09:23:13 +0900 Subject: [PATCH 130/152] sort file list in alphabetical ordering in extras --- modules/extras.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/extras.py b/modules/extras.py index ac85142c..22c5a1c1 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -39,7 +39,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ if input_dir == '': return outputs, "Please select an input directory.", '' - image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)] + image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)] for img in image_list: try: image = Image.open(img) From 45872181902ada06267e2de601586d512cf5df1a Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 09:00:39 +0300 Subject: [PATCH 131/152] updated readme and some small stylistic changes to code --- README.md | 1 + modules/processing.py | 14 ++++++-------- modules/sd_hijack_inpainting.py | 3 +++ 3 files changed, 10 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 859a91b6..a98bb00b 100644 --- a/README.md +++ b/README.md @@ -70,6 +70,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args) - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args) +- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. diff --git a/modules/processing.py b/modules/processing.py index 539cde38..21786968 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -540,11 +540,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f - - def create_dummy_mask(self, x, first_phase: bool = False): + def create_dummy_mask(self, x, width=None, height=None): if self.sampler.conditioning_key in {'hybrid', 'concat'}: - height = self.firstphase_height if first_phase else self.height - width = self.firstphase_width if first_phase else self.width + height = height or self.height + width = width or self.width # The "masked-image" in this case will just be all zeros since the entire image is masked. image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) @@ -571,7 +570,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): return samples x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, first_phase=True)) + samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height)) samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] @@ -634,6 +633,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.inpainting_mask_invert = inpainting_mask_invert self.mask = None self.nmask = None + self.image_conditioning = None def init(self, all_prompts, all_seeds, all_subseeds): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model) @@ -735,9 +735,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): elif self.inpainting_fill == 3: self.init_latent = self.init_latent * self.mask - conditioning_key = self.sampler.conditioning_key - - if conditioning_key in {'hybrid', 'concat'}: + if self.sampler.conditioning_key in {'hybrid', 'concat'}: if self.image_mask is not None: conditioning_mask = np.array(self.image_mask.convert("L")) conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py index 43938071..fd92a335 100644 --- a/modules/sd_hijack_inpainting.py +++ b/modules/sd_hijack_inpainting.py @@ -301,6 +301,7 @@ def get_unconditional_conditioning(self, batch_size, null_label=None): c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device) return c + class LatentInpaintDiffusion(LatentDiffusion): def __init__( self, @@ -314,9 +315,11 @@ class LatentInpaintDiffusion(LatentDiffusion): assert self.masked_image_key in concat_keys self.concat_keys = concat_keys + def should_hijack_inpainting(checkpoint_info): return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml") + def do_inpainting_hijack(): ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion From 74088c2a06a975092806362aede22f82716cb011 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 20 Oct 2022 08:18:02 +0300 Subject: [PATCH 132/152] allow float sizes for hypernet's layer_structure --- modules/hypernetworks/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 08f75f15..e0741d08 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -15,7 +15,7 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm assert not os.path.exists(fn), f"file {fn} already exists" if type(layer_structure) == str: - layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure))) + layer_structure = [float(x.strip()) for x in layer_structure.split(",")] hypernet = modules.hypernetworks.hypernetwork.Hypernetwork( name=name, From 60872c5b404114336f9ca0c671ba88fa4a8201c9 Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Thu, 20 Oct 2022 19:10:32 +0900 Subject: [PATCH 133/152] Fixed path issue while extras batch processing --- modules/extras.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index b853fa5b..f9796624 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -118,10 +118,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ while len(cached_images) > 2: del cached_images[next(iter(cached_images.keys()))] + + if opts.use_original_name_batch and image_name != None: + basename = os.path.splitext(os.path.basename(image_name))[0] + else: + basename = '' - images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, - no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, - forced_filename=image_name if opts.use_original_name_batch else None) + images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, + no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None) if opts.enable_pnginfo: image.info = existing_pnginfo From fb5a8cf0d9ed027ea3aa2e5422c946d8e6e72efe Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Thu, 20 Oct 2022 21:31:29 +0900 Subject: [PATCH 134/152] Added try except to extras batch from directory --- modules/extras.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index f9796624..0d817cf9 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -41,7 +41,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ return outputs, "Please select an input directory.", '' image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)] for img in image_list: - image = Image.open(img) + try: + image = Image.open(img) + except Exception: + continue imageArr.append(image) imageNameArr.append(img) else: @@ -122,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ if opts.use_original_name_batch and image_name != None: basename = os.path.splitext(os.path.basename(image_name))[0] else: - basename = '' + basename = None - images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, - no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None) + images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, + no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename) if opts.enable_pnginfo: image.info = existing_pnginfo From a13c3bed3cec27afe3c015d3d62db36e25b10d1f Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Thu, 20 Oct 2022 21:43:27 +0900 Subject: [PATCH 135/152] Fixed path issue while extras batch processing --- modules/extras.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index 0d817cf9..ac85142c 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -125,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ if opts.use_original_name_batch and image_name != None: basename = os.path.splitext(os.path.basename(image_name))[0] else: - basename = None + basename = '' - images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, - no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename) + images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, + no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None) if opts.enable_pnginfo: image.info = existing_pnginfo From 9d71eef02e7395e179b8d5e61e6d91ddd8928d2e Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Fri, 21 Oct 2022 09:23:13 +0900 Subject: [PATCH 136/152] sort file list in alphabetical ordering in extras --- modules/extras.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/extras.py b/modules/extras.py index ac85142c..22c5a1c1 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -39,7 +39,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ if input_dir == '': return outputs, "Please select an input directory.", '' - image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)] + image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)] for img in image_list: try: image = Image.open(img) From c23f666dba2b484d521d2dc4be91cf9e09312647 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 09:47:43 +0300 Subject: [PATCH 137/152] a more strict check for activation type and a more reasonable check for type of layer in hypernets --- modules/hypernetworks/hypernetwork.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 7d617680..84e7e350 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -32,10 +32,16 @@ class HypernetworkModule(torch.nn.Module): linears = [] for i in range(len(layer_structure) - 1): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) + if activation_func == "relu": linears.append(torch.nn.ReLU()) - if activation_func == "leakyrelu": + elif activation_func == "leakyrelu": linears.append(torch.nn.LeakyReLU()) + elif activation_func == 'linear' or activation_func is None: + pass + else: + raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}') + if add_layer_norm: linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) @@ -46,7 +52,7 @@ class HypernetworkModule(torch.nn.Module): self.load_state_dict(state_dict) else: for layer in self.linear: - if not "ReLU" in layer.__str__(): + if type(layer) == torch.nn.Linear: layer.weight.data.normal_(mean=0.0, std=0.01) layer.bias.data.zero_() @@ -74,7 +80,7 @@ class HypernetworkModule(torch.nn.Module): def trainables(self): layer_structure = [] for layer in self.linear: - if not "ReLU" in layer.__str__(): + if type(layer) == torch.nn.Linear: layer_structure += [layer.weight, layer.bias] return layer_structure From 5f4fec307c14dd7f817244ffa92e8a4a64abed0b Mon Sep 17 00:00:00 2001 From: Stephen Date: Thu, 20 Oct 2022 11:32:17 -0400 Subject: [PATCH 138/152] [Bugfix][API] - Fix API arg in launch script --- webui.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/webui.py b/webui.py index 177bef74..87589064 100644 --- a/webui.py +++ b/webui.py @@ -118,7 +118,8 @@ def api_only(): api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861) -def webui(launch_api=False): +def webui(): + launch_api = cmd_opts.api initialize() while 1: @@ -158,4 +159,4 @@ if __name__ == "__main__": if cmd_opts.nowebui: api_only() else: - webui(cmd_opts.api) + webui() From 7157e5d064741fa57ca81a2c6432a651f21ee82f Mon Sep 17 00:00:00 2001 From: Patryk Wychowaniec Date: Thu, 20 Oct 2022 19:22:59 +0200 Subject: [PATCH 139/152] interrogate: Fix CLIP-interrogation on CPU Currently, trying to perform CLIP interrogation on a CPU fails, saying: ``` RuntimeError: "slow_conv2d_cpu" not implemented for 'Half' ``` This merge request fixes this issue by detecting whether the target device is CPU and, if so, force-enabling `--no-half` and passing `device="cpu"` to `clip.load()` (which then does some extra tricks to ensure it works correctly on CPU). --- modules/interrogate.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/modules/interrogate.py b/modules/interrogate.py index 64b91eb4..65b05d34 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -28,9 +28,11 @@ class InterrogateModels: clip_preprocess = None categories = None dtype = None + running_on_cpu = None def __init__(self, content_dir): self.categories = [] + self.running_on_cpu = devices.device_interrogate == torch.device("cpu") if os.path.exists(content_dir): for filename in os.listdir(content_dir): @@ -53,7 +55,11 @@ class InterrogateModels: def load_clip_model(self): import clip - model, preprocess = clip.load(clip_model_name) + if self.running_on_cpu: + model, preprocess = clip.load(clip_model_name, device="cpu") + else: + model, preprocess = clip.load(clip_model_name) + model.eval() model = model.to(devices.device_interrogate) @@ -62,14 +68,14 @@ class InterrogateModels: def load(self): if self.blip_model is None: self.blip_model = self.load_blip_model() - if not shared.cmd_opts.no_half: + if not shared.cmd_opts.no_half and not self.running_on_cpu: self.blip_model = self.blip_model.half() self.blip_model = self.blip_model.to(devices.device_interrogate) if self.clip_model is None: self.clip_model, self.clip_preprocess = self.load_clip_model() - if not shared.cmd_opts.no_half: + if not shared.cmd_opts.no_half and not self.running_on_cpu: self.clip_model = self.clip_model.half() self.clip_model = self.clip_model.to(devices.device_interrogate) From b69c37d25e4ffc56e8f8c247fa2c38b4648cefb7 Mon Sep 17 00:00:00 2001 From: guaneec Date: Thu, 20 Oct 2022 22:21:12 +0800 Subject: [PATCH 140/152] Allow datasets with only 1 image in TI --- modules/textual_inversion/dataset.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 23bb4b6a..5b1c5002 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -83,7 +83,7 @@ class PersonalizedBase(Dataset): self.dataset.append(entry) - assert len(self.dataset) > 1, "No images have been found in the dataset." + assert len(self.dataset) > 0, "No images have been found in the dataset." self.length = len(self.dataset) * repeats // batch_size self.initial_indexes = np.arange(len(self.dataset)) @@ -91,7 +91,7 @@ class PersonalizedBase(Dataset): self.shuffle() def shuffle(self): - self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])] + self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0]).numpy()] def create_text(self, filename_text): text = random.choice(self.lines) From 5245c7a4935f67b677da0f5a1fc2b74c074aa0e2 Mon Sep 17 00:00:00 2001 From: timntorres Date: Wed, 19 Oct 2022 12:21:32 -0700 Subject: [PATCH 141/152] Issue #2921-Give PNG info to Hypernet previews. --- modules/hypernetworks/hypernetwork.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 84e7e350..68c8f26d 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -256,6 +256,9 @@ def stack_conds(conds): def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): + # images is required here to give training previews their infotext. Importing this at the very top causes a circular dependency. + from modules import images + assert hypernetwork_name, 'hypernetwork not selected' path = shared.hypernetworks.get(hypernetwork_name, None) @@ -298,6 +301,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log last_saved_file = "" last_saved_image = "" + forced_filename = "" ititial_step = hypernetwork.step or 0 if ititial_step > steps: @@ -345,7 +349,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log }) if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: - last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') + forced_filename = f'{hypernetwork_name}-{hypernetwork.step}' + last_saved_image = os.path.join(images_dir, forced_filename) optimizer.zero_grad() shared.sd_model.cond_stage_model.to(devices.device) @@ -381,7 +386,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if image is not None: shared.state.current_image = image - image.save(last_saved_image) + last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename) last_saved_image += f", prompt: {preview_text}" shared.state.job_no = hypernetwork.step From 6014fb8afbe05c8d02fffe7a36a2e48128713bd2 Mon Sep 17 00:00:00 2001 From: timntorres Date: Wed, 19 Oct 2022 12:22:23 -0700 Subject: [PATCH 142/152] Do nothing if image file already exists. --- modules/images.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/modules/images.py b/modules/images.py index b9589563..550e53ae 100644 --- a/modules/images.py +++ b/modules/images.py @@ -416,7 +416,11 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /') path = os.path.join(path, dirname) - os.makedirs(path, exist_ok=True) + try: + os.makedirs(path, exist_ok=True) + except FileExistsError: + # If the file already exists, continue and allow said file to be overwritten. + pass if forced_filename is None: basecount = get_next_sequence_number(path, basename) From 4ff274e1e35bb642687253ce744d2cfa738ab293 Mon Sep 17 00:00:00 2001 From: timntorres Date: Wed, 19 Oct 2022 12:32:22 -0700 Subject: [PATCH 143/152] Revise comments. --- modules/hypernetworks/hypernetwork.py | 2 +- modules/images.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 68c8f26d..3f96361c 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -256,7 +256,7 @@ def stack_conds(conds): def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): - # images is required here to give training previews their infotext. Importing this at the very top causes a circular dependency. + # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images assert hypernetwork_name, 'hypernetwork not selected' diff --git a/modules/images.py b/modules/images.py index 550e53ae..b8834e3c 100644 --- a/modules/images.py +++ b/modules/images.py @@ -419,7 +419,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i try: os.makedirs(path, exist_ok=True) except FileExistsError: - # If the file already exists, continue and allow said file to be overwritten. + # If the file already exists, allow said file to be overwritten. pass if forced_filename is None: From 2273e752fb3e578f1047f6d38b96330b07bf61a9 Mon Sep 17 00:00:00 2001 From: timntorres Date: Wed, 19 Oct 2022 14:23:48 -0700 Subject: [PATCH 144/152] Remove redundant try/except. --- modules/images.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/modules/images.py b/modules/images.py index b8834e3c..b9589563 100644 --- a/modules/images.py +++ b/modules/images.py @@ -416,11 +416,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /') path = os.path.join(path, dirname) - try: - os.makedirs(path, exist_ok=True) - except FileExistsError: - # If the file already exists, allow said file to be overwritten. - pass + os.makedirs(path, exist_ok=True) if forced_filename is None: basecount = get_next_sequence_number(path, basename) From 03a1e288c4973dd2dff57a97469b40f146b6fccf Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 10:13:24 +0300 Subject: [PATCH 145/152] turns out LayerNorm also has weight and bias and needs to be pre-multiplied and trained for hypernets --- modules/hypernetworks/hypernetwork.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 3274a802..b1a5d0c7 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -52,7 +52,7 @@ class HypernetworkModule(torch.nn.Module): self.load_state_dict(state_dict) else: for layer in self.linear: - if type(layer) == torch.nn.Linear: + if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm: layer.weight.data.normal_(mean=0.0, std=0.01) layer.bias.data.zero_() @@ -80,7 +80,7 @@ class HypernetworkModule(torch.nn.Module): def trainables(self): layer_structure = [] for layer in self.linear: - if type(layer) == torch.nn.Linear: + if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm: layer_structure += [layer.weight, layer.bias] return layer_structure From bf30673f5132c8f28357b31224c54331e788d3e7 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 10:19:25 +0300 Subject: [PATCH 146/152] Fix Hypernet infotext string split bug for PR #3283 --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/processing.py b/modules/processing.py index 21786968..d1deffa9 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -304,7 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Size": f"{p.width}x{p.height}", "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), - "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.filename.split('\\')[-1].split('.')[0]), + "Hypernet": (None if shared.loaded_hypernetwork is None else os.path.splitext(os.path.basename(shared.loaded_hypernetwork.filename))[0]), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), From 1ed227b3b57f06f4152be3bfc9f83b0a839a2604 Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Fri, 21 Oct 2022 10:57:40 +0200 Subject: [PATCH 147/152] wtf is happening --- .github/ISSUE_TEMPLATE/config.yml | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/config.yml diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 00000000..f58c94a9 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,5 @@ +blank_issues_enabled: false +contact_links: + - name: WebUI Community Support + url: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions + about: Please ask and answer questions here. From df5706409386cc2e88718bd9101045587c39f8bb Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 16:10:51 +0300 Subject: [PATCH 148/152] do not load aesthetic clip model until it's needed add refresh button for aesthetic embeddings add aesthetic params to images' infotext --- modules/aesthetic_clip.py | 40 ++++++++++++++--- modules/generation_parameters_copypaste.py | 18 +++++++- modules/img2img.py | 5 +-- modules/processing.py | 4 +- modules/sd_models.py | 3 -- modules/txt2img.py | 4 +- modules/ui.py | 52 +++++++++++++++------- style.css | 2 +- 8 files changed, 89 insertions(+), 39 deletions(-) diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py index 34efa931..8c828541 100644 --- a/modules/aesthetic_clip.py +++ b/modules/aesthetic_clip.py @@ -40,6 +40,8 @@ def iter_to_batched(iterable, n=1): def create_ui(): + import modules.ui + with gr.Group(): with gr.Accordion("Open for Clip Aesthetic!", open=False): with gr.Row(): @@ -55,6 +57,8 @@ def create_ui(): label="Aesthetic imgs embedding", value="None") + modules.ui.create_refresh_button(aesthetic_imgs, shared.update_aesthetic_embeddings, lambda: {"choices": sorted(shared.aesthetic_embeddings.keys())}, "refresh_aesthetic_embeddings") + with gr.Row(): aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", @@ -66,11 +70,21 @@ def create_ui(): return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative +aesthetic_clip_model = None + + +def aesthetic_clip(): + global aesthetic_clip_model + + if aesthetic_clip_model is None or aesthetic_clip_model.name_or_path != shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path: + aesthetic_clip_model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path) + aesthetic_clip_model.cpu() + + return aesthetic_clip_model + + def generate_imgs_embd(name, folder, batch_size): - # clipModel = CLIPModel.from_pretrained( - # shared.sd_model.cond_stage_model.clipModel.name_or_path - # ) - model = shared.clip_model.to(device) + model = aesthetic_clip().to(device) processor = CLIPProcessor.from_pretrained(model.name_or_path) with torch.no_grad(): @@ -91,7 +105,7 @@ def generate_imgs_embd(name, folder, batch_size): path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt") torch.save(embs, path) - model = model.cpu() + model.cpu() del processor del embs gc.collect() @@ -132,7 +146,7 @@ class AestheticCLIP: self.image_embs = None self.load_image_embs(None) - def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None, + def set_aesthetic_params(self, p, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None, aesthetic_slerp=True, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False): @@ -145,6 +159,18 @@ class AestheticCLIP: self.aesthetic_steps = aesthetic_steps self.load_image_embs(image_embs_name) + if self.image_embs_name is not None: + p.extra_generation_params.update({ + "Aesthetic LR": aesthetic_lr, + "Aesthetic weight": aesthetic_weight, + "Aesthetic steps": aesthetic_steps, + "Aesthetic embedding": self.image_embs_name, + "Aesthetic slerp": aesthetic_slerp, + "Aesthetic text": aesthetic_imgs_text, + "Aesthetic text negative": aesthetic_text_negative, + "Aesthetic slerp angle": aesthetic_slerp_angle, + }) + def set_skip(self, skip): self.skip = skip @@ -168,7 +194,7 @@ class AestheticCLIP: tokens = torch.asarray(remade_batch_tokens).to(device) - model = copy.deepcopy(shared.clip_model).to(device) + model = copy.deepcopy(aesthetic_clip()).to(device) model.requires_grad_(True) if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0: text_embs_2 = model.get_text_features( diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 0f041449..f73647da 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -4,13 +4,22 @@ import gradio as gr from modules.shared import script_path from modules import shared -re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)" +re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)' re_param = re.compile(re_param_code) re_params = re.compile(r"^(?:" + re_param_code + "){3,}$") re_imagesize = re.compile(r"^(\d+)x(\d+)$") type_of_gr_update = type(gr.update()) +def quote(text): + if ',' not in str(text): + return text + + text = str(text) + text = text.replace('\\', '\\\\') + text = text.replace('"', '\\"') + return f'"{text}"' + def parse_generation_parameters(x: str): """parses generation parameters string, the one you see in text field under the picture in UI: ``` @@ -83,7 +92,12 @@ def connect_paste(button, paste_fields, input_comp, js=None): else: try: valtype = type(output.value) - val = valtype(v) + + if valtype == bool and v == "False": + val = False + else: + val = valtype(v) + res.append(gr.update(value=val)) except Exception: res.append(gr.update()) diff --git a/modules/img2img.py b/modules/img2img.py index bc7c66bc..eea5199b 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -109,10 +109,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro inpainting_mask_invert=inpainting_mask_invert, ) - shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), - aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, - aesthetic_slerp_angle, - aesthetic_text_negative) + shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative) if shared.cmd_opts.enable_console_prompts: print(f"\nimg2img: {prompt}", file=shared.progress_print_out) diff --git a/modules/processing.py b/modules/processing.py index d1deffa9..f0852cd5 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -12,7 +12,7 @@ from skimage import exposure from typing import Any, Dict, List, Optional import modules.sd_hijack -from modules import devices, prompt_parser, masking, sd_samplers, lowvram +from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste from modules.sd_hijack import model_hijack from modules.shared import opts, cmd_opts, state import modules.shared as shared @@ -318,7 +318,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration generation_params.update(p.extra_generation_params) - generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None]) + generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None]) negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else "" diff --git a/modules/sd_models.py b/modules/sd_models.py index 05a1df28..b1c91b0d 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -234,9 +234,6 @@ def load_model(checkpoint_info=None): sd_hijack.model_hijack.hijack(sd_model) - if shared.clip_model is None or shared.clip_model.transformer.name_or_path != sd_model.cond_stage_model.wrapped.transformer.name_or_path: - shared.clip_model = CLIPModel.from_pretrained(sd_model.cond_stage_model.wrapped.transformer.name_or_path) - sd_model.eval() print(f"Model loaded.") diff --git a/modules/txt2img.py b/modules/txt2img.py index 32ed1d8d..1761cfa2 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -36,9 +36,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: firstphase_height=firstphase_height if enable_hr else None, ) - shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), - aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, - aesthetic_text_negative) + shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative) if cmd_opts.enable_console_prompts: print(f"\ntxt2img: {prompt}", file=shared.progress_print_out) diff --git a/modules/ui.py b/modules/ui.py index 381ca925..0d020de6 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -597,27 +597,29 @@ def apply_setting(key, value): return value +def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): + def refresh(): + refresh_method() + args = refreshed_args() if callable(refreshed_args) else refreshed_args + + for k, v in args.items(): + setattr(refresh_component, k, v) + + return gr.update(**(args or {})) + + refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id) + refresh_button.click( + fn=refresh, + inputs=[], + outputs=[refresh_component] + ) + return refresh_button + + def create_ui(wrap_gradio_gpu_call): import modules.img2img import modules.txt2img - def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): - def refresh(): - refresh_method() - args = refreshed_args() if callable(refreshed_args) else refreshed_args - - for k, v in args.items(): - setattr(refresh_component, k, v) - - return gr.update(**(args or {})) - - refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id) - refresh_button.click( - fn = refresh, - inputs = [], - outputs = [refresh_component] - ) - return refresh_button with gr.Blocks(analytics_enabled=False) as txt2img_interface: txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False) @@ -802,6 +804,14 @@ def create_ui(wrap_gradio_gpu_call): (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), (firstphase_width, "First pass size-1"), (firstphase_height, "First pass size-2"), + (aesthetic_lr, "Aesthetic LR"), + (aesthetic_weight, "Aesthetic weight"), + (aesthetic_steps, "Aesthetic steps"), + (aesthetic_imgs, "Aesthetic embedding"), + (aesthetic_slerp, "Aesthetic slerp"), + (aesthetic_imgs_text, "Aesthetic text"), + (aesthetic_text_negative, "Aesthetic text negative"), + (aesthetic_slerp_angle, "Aesthetic slerp angle"), ] txt2img_preview_params = [ @@ -1077,6 +1087,14 @@ def create_ui(wrap_gradio_gpu_call): (seed_resize_from_w, "Seed resize from-1"), (seed_resize_from_h, "Seed resize from-2"), (denoising_strength, "Denoising strength"), + (aesthetic_lr_im, "Aesthetic LR"), + (aesthetic_weight_im, "Aesthetic weight"), + (aesthetic_steps_im, "Aesthetic steps"), + (aesthetic_imgs_im, "Aesthetic embedding"), + (aesthetic_slerp_im, "Aesthetic slerp"), + (aesthetic_imgs_text_im, "Aesthetic text"), + (aesthetic_text_negative_im, "Aesthetic text negative"), + (aesthetic_slerp_angle_im, "Aesthetic slerp angle"), ] token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) diff --git a/style.css b/style.css index 26ae36a5..5d2bacc9 100644 --- a/style.css +++ b/style.css @@ -477,7 +477,7 @@ input[type="range"]{ padding: 0; } -#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{ +#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization, #refresh_aesthetic_embeddings{ max-width: 2.5em; min-width: 2.5em; height: 2.4em; From 9286fe53de2eef91f13cc3ad5938ddf67ecc8413 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 16:38:06 +0300 Subject: [PATCH 149/152] make aestetic embedding ciompatible with prompts longer than 75 tokens --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 36198a3c..1f8587d1 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -332,8 +332,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): multipliers.append([1.0] * 75) z1 = self.process_tokens(tokens, multipliers) + z1 = shared.aesthetic_clip(z1, remade_batch_tokens) z = z1 if z is None else torch.cat((z, z1), axis=-2) - z = shared.aesthetic_clip(z, remade_batch_tokens) remade_batch_tokens = rem_tokens batch_multipliers = rem_multipliers From d0ea471b0cdaede163c6e7f6fae8535f5c3cd226 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Fri, 21 Oct 2022 14:04:41 +0100 Subject: [PATCH 150/152] Use opts in textual_inversion image_embedding.py for dynamic fonts --- modules/textual_inversion/image_embedding.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py index 898ce3b3..c50b1e7b 100644 --- a/modules/textual_inversion/image_embedding.py +++ b/modules/textual_inversion/image_embedding.py @@ -5,6 +5,7 @@ import zlib from PIL import Image, PngImagePlugin, ImageDraw, ImageFont from fonts.ttf import Roboto import torch +from modules.shared import opts class EmbeddingEncoder(json.JSONEncoder): From 306e2ff6ab8f4c7e94ab55f4f08ab8f94d73d287 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Fri, 21 Oct 2022 14:47:21 +0100 Subject: [PATCH 151/152] Update image_embedding.py --- modules/textual_inversion/image_embedding.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py index c50b1e7b..ea653806 100644 --- a/modules/textual_inversion/image_embedding.py +++ b/modules/textual_inversion/image_embedding.py @@ -134,7 +134,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t from math import cos image = srcimage.copy() - + fontsize = 32 if textfont is None: try: textfont = ImageFont.truetype(opts.font or Roboto, fontsize) @@ -151,7 +151,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size)) draw = ImageDraw.Draw(image) - fontsize = 32 + font = ImageFont.truetype(textfont, fontsize) padding = 10 From 85cb5918ee7c97cafafe4149880ed82f1933d919 Mon Sep 17 00:00:00 2001 From: parsec501 <105080989+parsec501@users.noreply.github.com> Date: Fri, 21 Oct 2022 14:43:23 +0200 Subject: [PATCH 152/152] Make commit hash mandatory field --- .github/ISSUE_TEMPLATE/bug_report.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 35802a53..9c2ff313 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -45,6 +45,8 @@ body: attributes: label: Commit where the problem happens description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI) + validations: + required: true - type: dropdown id: platforms attributes: