Merge branch 'master' of https://github.com/AUTOMATIC1111/stable-diffusion-webui into Base

.gitignore

@@ -16,3 +16,4 @@ __pycache__
 /webui-user.bat
 /webui-user.sh
 /interrogate
+/user.css

README.md

@@ -51,7 +51,7 @@ Alternatively, use [Google Colab](https://colab.research.google.com/drive/1Iy-xW
 1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
 2. Install [git](https://git-scm.com/download/win).
 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
-4. Place `model.ckpt` in the base directory, alongside `webui.py`.
+4. Place `model.ckpt` in the `models` directory.
 5. _*(Optional)*_ Place `GFPGANv1.3.pth` in the base directory, alongside `webui.py`.
 6. Run `webui-user.bat` from Windows Explorer as normal, non-administrate, user.
 
@@ -81,6 +81,7 @@ The documentation was moved from this README over to the project's [wiki](https:
 - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
 - Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
 - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
+- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
 - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)

modules/devices.py

@@ -48,3 +48,13 @@ def randn(seed, shape):
     torch.manual_seed(seed)
     return torch.randn(shape, device=device)
 
+
+def randn_without_seed(shape):
+    # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
+    if device.type == 'mps':
+        generator = torch.Generator(device=cpu)
+        noise = torch.randn(shape, generator=generator, device=cpu).to(device)
+        return noise
+
+    return torch.randn(shape, device=device)
+

modules/extras.py

@@ -36,6 +36,7 @@ def run_extras(image, image_folder, gfpgan_visibility, codeformer_visibility, co
 
     outpath = opts.outdir_samples or opts.outdir_extras_samples
 
+    outputs = []
     for image in imageArr:
         existing_pnginfo = image.info or {}
 
@@ -91,7 +92,9 @@ def run_extras(image, image_folder, gfpgan_visibility, codeformer_visibility, co
 
         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)
 
-    return imageArr, plaintext_to_html(info), ''
+        outputs.append(image)
+
+    return outputs, plaintext_to_html(info), ''
 
 
 def run_pnginfo(image):
@@ -108,8 +111,9 @@ def run_pnginfo(image):
 
         items['exif comment'] = exif_comment
 
-        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif']:
-            del items[field]
+        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
+                      'loop', 'background', 'timestamp', 'duration']:
+            items.pop(field, None)
 
 
     info = ''

modules/images.py

@@ -274,7 +274,7 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = x.replace("[height]", str(p.height))
         x = x.replace("[sampler]", sd_samplers.samplers[p.sampler_index].name)
 
-    x = x.replace("[model_hash]", shared.sd_model_hash)
+    x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
     x = x.replace("[date]", datetime.date.today().isoformat())
 
     if cmd_opts.hide_ui_dir_config:
@@ -353,13 +353,12 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         })
 
     if extension.lower() in ("jpg", "jpeg", "webp"):
-        image.save(fullfn, quality=opts.jpeg_quality, exif_bytes=exif_bytes())
+        image.save(fullfn, quality=opts.jpeg_quality)
+        if opts.enable_pnginfo and info is not None:
+            piexif.insert(exif_bytes(), fullfn)
     else:
         image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo)
 
-    if extension.lower() == "webp":
-        piexif.insert(exif_bytes, fullfn)
-
     target_side_length = 4000
     oversize = image.width > target_side_length or image.height > target_side_length
     if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024):
@@ -370,7 +369,9 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         elif oversize:
             image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
 
-        image.save(fullfn_without_extension + ".jpg", quality=opts.jpeg_quality, exif_bytes=exif_bytes())
+        image.save(fullfn_without_extension + ".jpg", quality=opts.jpeg_quality)
+        if opts.enable_pnginfo and info is not None:
+            piexif.insert(exif_bytes(), fullfn_without_extension + ".jpg")
 
     if opts.save_txt and info is not None:
         with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:

modules/memmon.py

@@ -0,0 +1,77 @@
+import threading
+import time
+from collections import defaultdict
+
+import torch
+
+
+class MemUsageMonitor(threading.Thread):
+    run_flag = None
+    device = None
+    disabled = False
+    opts = None
+    data = None
+
+    def __init__(self, name, device, opts):
+        threading.Thread.__init__(self)
+        self.name = name
+        self.device = device
+        self.opts = opts
+
+        self.daemon = True
+        self.run_flag = threading.Event()
+        self.data = defaultdict(int)
+
+    def run(self):
+        if self.disabled:
+            return
+
+        while True:
+            self.run_flag.wait()
+
+            torch.cuda.reset_peak_memory_stats()
+            self.data.clear()
+
+            if self.opts.memmon_poll_rate <= 0:
+                self.run_flag.clear()
+                continue
+
+            self.data["min_free"] = torch.cuda.mem_get_info()[0]
+
+            while self.run_flag.is_set():
+                free, total = torch.cuda.mem_get_info()  # calling with self.device errors, torch bug?
+                self.data["min_free"] = min(self.data["min_free"], free)
+
+                time.sleep(1 / self.opts.memmon_poll_rate)
+
+    def dump_debug(self):
+        print(self, 'recorded data:')
+        for k, v in self.read().items():
+            print(k, -(v // -(1024 ** 2)))
+
+        print(self, 'raw torch memory stats:')
+        tm = torch.cuda.memory_stats(self.device)
+        for k, v in tm.items():
+            if 'bytes' not in k:
+                continue
+            print('\t' if 'peak' in k else '', k, -(v // -(1024 ** 2)))
+
+        print(torch.cuda.memory_summary())
+
+    def monitor(self):
+        self.run_flag.set()
+
+    def read(self):
+        free, total = torch.cuda.mem_get_info()
+        self.data["total"] = total
+
+        torch_stats = torch.cuda.memory_stats(self.device)
+        self.data["active_peak"] = torch_stats["active_bytes.all.peak"]
+        self.data["reserved_peak"] = torch_stats["reserved_bytes.all.peak"]
+        self.data["system_peak"] = total - self.data["min_free"]
+
+        return self.data
+
+    def stop(self):
+        self.run_flag.clear()
+        return self.read()

modules/processing.py

@@ -119,8 +119,18 @@ def slerp(val, low, 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):
+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-genrated tensors instead of siimple 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:
+        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)
 
@@ -155,9 +165,17 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
             x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
             noise = x
 
+        if sampler_noises is not None:
+            cnt = p.sampler.number_of_needed_noises(p)
 
+            for j in range(cnt):
+                sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
 
         xs.append(noise)
+
+    if sampler_noises is not None:
+        p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]
+
     x = torch.stack(xs).to(shared.device)
     return x
 
@@ -170,7 +188,11 @@ def fix_seed(p):
 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"""
 
-    assert p.prompt is not None
+    if type(p.prompt) == list:
+        assert(len(p.prompt) > 0)
+    else:
+        assert p.prompt is not None
+        
     devices.torch_gc()
 
     fix_seed(p)
@@ -209,7 +231,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             "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": (None if not opts.add_model_hash_to_info or not shared.sd_model_hash else shared.sd_model_hash),
+            "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),
             "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]),
@@ -247,6 +269,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
             subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
+            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 = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
@@ -257,7 +282,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                     comments[comment] = 1
 
             # we manually generate all input noises because each one should have a specific seed
-            x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
+            x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
 
             if p.n_iter > 1:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"

modules/sd_models.py

@@ -0,0 +1,148 @@
+import glob
+import os.path
+import sys
+from collections import namedtuple
+import torch
+from omegaconf import OmegaConf
+
+
+from ldm.util import instantiate_from_config
+
+from modules import shared
+
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash'])
+checkpoints_list = {}
+
+try:
+    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
+
+    from transformers import logging
+
+    logging.set_verbosity_error()
+except Exception:
+    pass
+
+
+def list_models():
+    checkpoints_list.clear()
+
+    model_dir = os.path.abspath(shared.cmd_opts.ckpt_dir)
+
+    def modeltitle(path, h):
+        abspath = os.path.abspath(path)
+
+        if abspath.startswith(model_dir):
+            name = abspath.replace(model_dir, '')
+        else:
+            name = os.path.basename(path)
+
+        if name.startswith("\\") or name.startswith("/"):
+            name = name[1:]
+
+        return f'{name} [{h}]'
+
+    cmd_ckpt = shared.cmd_opts.ckpt
+    if os.path.exists(cmd_ckpt):
+        h = model_hash(cmd_ckpt)
+        title = modeltitle(cmd_ckpt, h)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h)
+    elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
+        print(f"Checkpoint in --ckpt argument not found: {cmd_ckpt}", file=sys.stderr)
+
+    if os.path.exists(model_dir):
+        for filename in glob.glob(model_dir + '/**/*.ckpt', recursive=True):
+            h = model_hash(filename)
+            title = modeltitle(filename, h)
+            checkpoints_list[title] = CheckpointInfo(filename, title, h)
+
+
+def model_hash(filename):
+    try:
+        with open(filename, "rb") as file:
+            import hashlib
+            m = hashlib.sha256()
+
+            file.seek(0x100000)
+            m.update(file.read(0x10000))
+            return m.hexdigest()[0:8]
+    except FileNotFoundError:
+        return 'NOFILE'
+
+
+def select_checkpoint():
+    model_checkpoint = shared.opts.sd_model_checkpoint
+    checkpoint_info = checkpoints_list.get(model_checkpoint, None)
+    if checkpoint_info is not None:
+        return checkpoint_info
+
+    if len(checkpoints_list) == 0:
+        print(f"Checkpoint {model_checkpoint} not found and no other checkpoints found", file=sys.stderr)
+        return None
+
+    checkpoint_info = next(iter(checkpoints_list.values()))
+    if model_checkpoint is not None:
+        print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)
+
+    return checkpoint_info
+
+
+def load_model_weights(model, checkpoint_file, sd_model_hash):
+    print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
+
+    pl_sd = torch.load(checkpoint_file, map_location="cpu")
+    if "global_step" in pl_sd:
+        print(f"Global Step: {pl_sd['global_step']}")
+    sd = pl_sd["state_dict"]
+
+    model.load_state_dict(sd, strict=False)
+
+    if shared.cmd_opts.opt_channelslast:
+        model.to(memory_format=torch.channels_last)
+
+    if not shared.cmd_opts.no_half:
+        model.half()
+
+    model.sd_model_hash = sd_model_hash
+    model.sd_model_checkpint = checkpoint_file
+
+
+def load_model():
+    from modules import lowvram, sd_hijack
+    checkpoint_info = select_checkpoint()
+
+    sd_config = OmegaConf.load(shared.cmd_opts.config)
+    sd_model = instantiate_from_config(sd_config.model)
+    load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
+    else:
+        sd_model.to(shared.device)
+
+    sd_hijack.model_hijack.hijack(sd_model)
+
+    sd_model.eval()
+
+    print(f"Model loaded.")
+    return sd_model
+
+
+def reload_model_weights(sd_model, info=None):
+    from modules import lowvram, devices
+    checkpoint_info = info or select_checkpoint()
+
+    if sd_model.sd_model_checkpint == checkpoint_info.filename:
+        return
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.send_everything_to_cpu()
+    else:
+        sd_model.to(devices.cpu)
+
+    load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
+
+    if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+        sd_model.to(devices.device)
+
+    print(f"Weights loaded.")
+    return sd_model

modules/sd_samplers.py

@@ -38,6 +38,17 @@ samplers = [
 samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
 
 
+def setup_img2img_steps(p):
+    if opts.img2img_fix_steps:
+        steps = int(p.steps / min(p.denoising_strength, 0.999))
+        t_enc = p.steps - 1
+    else:
+        steps = p.steps
+        t_enc = int(min(p.denoising_strength, 0.999) * steps)
+
+    return steps, t_enc
+
+
 def sample_to_image(samples):
     x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0]
     x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
@@ -80,8 +91,12 @@ class VanillaStableDiffusionSampler:
         self.mask = None
         self.nmask = None
         self.init_latent = None
+        self.sampler_noises = None
         self.step = 0
 
+    def number_of_needed_noises(self, p):
+        return 0
+
     def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
         cond = prompt_parser.reconstruct_cond_batch(cond, self.step)
         unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
@@ -101,13 +116,13 @@ class VanillaStableDiffusionSampler:
         return res
 
     def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
-        t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
+        steps, t_enc = setup_img2img_steps(p)
 
         # existing code fails with cetain step counts, like 9
         try:
-            self.sampler.make_schedule(ddim_num_steps=p.steps, verbose=False)
+            self.sampler.make_schedule(ddim_num_steps=steps, verbose=False)
         except Exception:
-            self.sampler.make_schedule(ddim_num_steps=p.steps+1, verbose=False)
+            self.sampler.make_schedule(ddim_num_steps=steps+1, verbose=False)
 
         x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
 
@@ -115,6 +130,7 @@ class VanillaStableDiffusionSampler:
         self.mask = p.mask
         self.nmask = p.nmask
         self.init_latent = p.init_latent
+        self.step = 0
 
         samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
 
@@ -127,6 +143,7 @@ class VanillaStableDiffusionSampler:
         self.mask = None
         self.nmask = None
         self.init_latent = None
+        self.step = 0
 
         # existing code fails with cetin step counts, like 9
         try:
@@ -183,42 +200,82 @@ def extended_trange(count, *args, **kwargs):
         shared.total_tqdm.update()
 
 
+class TorchHijack:
+    def __init__(self, kdiff_sampler):
+        self.kdiff_sampler = kdiff_sampler
+
+    def __getattr__(self, item):
+        if item == 'randn_like':
+            return self.kdiff_sampler.randn_like
+
+        if hasattr(torch, item):
+            return getattr(torch, item)
+
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+
+
 class KDiffusionSampler:
     def __init__(self, funcname, sd_model):
         self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
         self.funcname = funcname
         self.func = getattr(k_diffusion.sampling, self.funcname)
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
+        self.sampler_noises = None
+        self.sampler_noise_index = 0
 
     def callback_state(self, d):
         store_latent(d["denoised"])
 
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
-        t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
-        sigmas = self.model_wrap.get_sigmas(p.steps)
+    def number_of_needed_noises(self, p):
+        return p.steps
 
-        noise = noise * sigmas[p.steps - t_enc - 1]
+    def randn_like(self, x):
+        noise = self.sampler_noises[self.sampler_noise_index] if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises) else None
+
+        if noise is not None and x.shape == noise.shape:
+            res = noise
+        else:
+            res = torch.randn_like(x)
+
+        self.sampler_noise_index += 1
+        return res
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
+        steps, t_enc = setup_img2img_steps(p)
+
+        sigmas = self.model_wrap.get_sigmas(steps)
+
+        noise = noise * sigmas[steps - t_enc - 1]
 
         xi = x + noise
 
-        sigma_sched = sigmas[p.steps - t_enc - 1:]
+        sigma_sched = sigmas[steps - t_enc - 1:]
 
         self.model_wrap_cfg.mask = p.mask
         self.model_wrap_cfg.nmask = p.nmask
         self.model_wrap_cfg.init_latent = p.init_latent
+        self.model_wrap.step = 0
 
         if hasattr(k_diffusion.sampling, 'trange'):
             k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
 
+        if self.sampler_noises is not None:
+            k_diffusion.sampling.torch = TorchHijack(self)
+
         return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
 
     def sample(self, p, x, conditioning, unconditional_conditioning):
         sigmas = self.model_wrap.get_sigmas(p.steps)
         x = x * sigmas[0]
 
+        self.model_wrap_cfg.step = 0
+
         if hasattr(k_diffusion.sampling, 'trange'):
             k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
 
+        if self.sampler_noises is not None:
+            k_diffusion.sampling.torch = TorchHijack(self)
+
         samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
         return samples_ddim
 

modules/shared.py

@@ -12,14 +12,16 @@ from modules.paths import script_path, sd_path
 from modules.devices import get_optimal_device
 import modules.styles
 import modules.interrogate
+import modules.memmon
+import modules.sd_models
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
-if not os.path.exists(sd_model_file):
-    sd_model_file = "models/ldm/stable-diffusion-v1/model.ckpt"
+default_sd_model_file = sd_model_file
 
 parser = argparse.ArgumentParser()
 parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
-parser.add_argument("--ckpt", type=str, default=os.path.join(sd_path, sd_model_file), help="path to checkpoint of model",)
+parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
+parser.add_argument("--ckpt-dir", type=str, default=os.path.join(script_path, 'models'), help="path to directory with stable diffusion checkpoints",)
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
 parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default='GFPGANv1.3.pth')
 parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
@@ -87,13 +89,17 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
 
+modules.sd_models.list_models()
+
+
 class Options:
     class OptionInfo:
-        def __init__(self, default=None, label="", component=None, component_args=None):
+        def __init__(self, default=None, label="", component=None, component_args=None, onchange=None):
             self.default = default
             self.label = label
             self.component = component
             self.component_args = component_args
+            self.onchange = onchange
 
     data = None
     hide_dirs = {"visible": False} if cmd_opts.hide_ui_dir_config else None
@@ -125,9 +131,11 @@ class Options:
         "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
         "add_model_hash_to_info": OptionInfo(False, "Add model hash to generation information"),
         "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
+        "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normaly you'd do less with less denoising)."),
         "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
         "font": OptionInfo("", "Font for image grids that have text"),
         "enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+        "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
         "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
         "ESRGAN_tile": OptionInfo(192, "Tile size for upscaling. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
         "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for upscaling. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
@@ -136,6 +144,7 @@ class Options:
         "show_progressbar": OptionInfo(True, "Show progressbar"),
         "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
         "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),
+        "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step":1}),
         "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
         "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
         "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
@@ -146,6 +155,7 @@ class Options:
         "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
         "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
         "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
+        "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": [x.title for x in modules.sd_models.checkpoints_list.values()]}),
     }
 
     def __init__(self):
@@ -176,6 +186,10 @@ class Options:
         with open(filename, "r", encoding="utf8") as file:
             self.data = json.load(file)
 
+    def onchange(self, key, func):
+        item = self.data_labels.get(key)
+        item.onchange = func
+
 
 opts = Options()
 if os.path.exists(config_filename):
@@ -184,7 +198,6 @@ if os.path.exists(config_filename):
 sd_upscalers = []
 
 sd_model = None
-sd_model_hash = ''
 
 progress_print_out = sys.stdout
 
@@ -215,3 +228,6 @@ class TotalTQDM:
 
 
 total_tqdm = TotalTQDM()
+
+mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
+mem_mon.start()

modules/ui.py

@@ -119,6 +119,7 @@ def save_files(js_data, images, index):
 
 def wrap_gradio_call(func):
     def f(*args, **kwargs):
+        shared.mem_mon.monitor()
         t = time.perf_counter()
 
         try:
@@ -135,8 +136,20 @@ def wrap_gradio_call(func):
 
         elapsed = time.perf_counter() - t
 
+        mem_stats = {k: -(v//-(1024*1024)) for k,v in shared.mem_mon.stop().items()}
+        active_peak = mem_stats['active_peak']
+        reserved_peak = mem_stats['reserved_peak']
+        sys_peak = '?' if opts.memmon_poll_rate <= 0 else mem_stats['system_peak']
+        sys_total = mem_stats['total']
+        sys_pct = '?' if opts.memmon_poll_rate <= 0 else round(sys_peak/sys_total * 100, 2)
+        vram_tooltip = "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.&#013;" \
+                       "Torch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.&#013;" \
+                       "Sys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%)."
+
+        vram_html = '' if opts.memmon_poll_rate == 0 else f"<p class='vram' title='{vram_tooltip}'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
+
         # last item is always HTML
-        res[-1] = res[-1] + f"<p class='performance'>Time taken: {elapsed:.2f}s</p>"
+        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"
 
         shared.state.interrupted = False
 
@@ -324,6 +337,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
             with gr.Column(variant='panel'):
+                progressbar = gr.HTML(elem_id="progressbar")
+
                 with gr.Group():
                     txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
                     txt2img_gallery = gr.Gallery(label='Output', elem_id='txt2img_gallery').style(grid=4)
@@ -336,8 +351,6 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                         send_to_extras = gr.Button('Send to extras')
                         interrupt = gr.Button('Interrupt')
 
-                progressbar = gr.HTML(elem_id="progressbar")
-
                 with gr.Group():
                     html_info = gr.HTML()
                     generation_info = gr.Textbox(visible=False)
@@ -461,6 +474,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
 
             with gr.Column(variant='panel'):
+                progressbar = gr.HTML(elem_id="progressbar")
+
                 with gr.Group():
                     img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
                     img2img_gallery = gr.Gallery(label='Output', elem_id='img2img_gallery').style(grid=4)
@@ -474,7 +489,6 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                         interrupt = gr.Button('Interrupt')
                         img2img_save_style = gr.Button('Save prompt as style')
 
-                progressbar = gr.HTML(elem_id="progressbar")
 
                 with gr.Group():
                     html_info = gr.HTML()
@@ -649,7 +663,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                         image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
 
                     with gr.TabItem('Batch Process'):
-                        image_batch = gr.File(label="Batch Process", file_count="multiple", source="upload", interactive=True, type="file")
+                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
 
                 upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
 
@@ -745,7 +759,12 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
             if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
                 continue
 
+            oldval = opts.data.get(key, None)
             opts.data[key] = value
+
+            if oldval != value and opts.data_labels[key].onchange is not None:
+                opts.data_labels[key].onchange()
+
             up.append(comp.update(value=value))
 
         opts.save(shared.config_filename)
@@ -782,6 +801,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
     with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
         css = file.read()
 
+    if os.path.exists(os.path.join(script_path, "user.css")):
+        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
+            usercss = file.read()
+            css += usercss
+
     if not cmd_opts.no_progressbar_hiding:
         css += css_hide_progressbar
 

script.js

@@ -66,6 +66,8 @@ titles = {
     "Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both",
     "Apply style": "Insert selected styles into prompt fields",
     "Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.",
+
+    "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.",
 }
 
 function gradioApp(){
@@ -74,6 +76,90 @@ function gradioApp(){
 
 global_progressbar = null
 
+function closeModal() {
+  gradioApp().getElementById("lightboxModal").style.display = "none";
+}
+
+function showModal(event) {
+  var source = event.target || event.srcElement;
+  gradioApp().getElementById("modalImage").src = source.src
+  var lb = gradioApp().getElementById("lightboxModal")
+  lb.style.display = "block";
+  lb.focus()
+  event.stopPropagation()
+}
+
+function negmod(n, m) {
+  return ((n % m) + m) % m;
+}
+
+function modalImageSwitch(offset){
+  var galleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all")
+
+  if(galleryButtons.length>1){
+      var currentButton  = gradioApp().querySelector(".gallery-item.transition-all.\\!ring-2")
+
+      var result = -1
+      galleryButtons.forEach(function(v, i){ if(v==currentButton) { result = i } })
+
+      if(result != -1){
+        nextButton = galleryButtons[negmod((result+offset),galleryButtons.length)]
+        nextButton.click()
+        gradioApp().getElementById("modalImage").src = nextButton.children[0].src
+        setTimeout( function(){gradioApp().getElementById("lightboxModal").focus()},10)
+      }
+  }
+
+}
+
+function modalNextImage(event){
+  modalImageSwitch(1)
+  event.stopPropagation()
+}
+
+function modalPrevImage(event){
+  modalImageSwitch(-1)  
+  event.stopPropagation()
+}
+
+function modalKeyHandler(event){
+    switch (event.key) {
+        case "ArrowLeft":
+            modalPrevImage(event)
+            break;
+        case "ArrowRight":
+            modalNextImage(event)
+            break;
+    }
+}
+
+function showGalleryImage(){
+    setTimeout(function() {
+        fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain')
+        
+        if(fullImg_preview != null){
+            fullImg_preview.forEach(function function_name(e) {
+                if(e && e.parentElement.tagName == 'DIV'){
+
+                    e.style.cursor='pointer'
+
+                    e.addEventListener('click', function (evt) {
+                      showModal(evt)
+
+                    },true);
+                }
+            });
+        }
+
+    }, 100);
+}
+
+function galleryImageHandler(e){
+    if(e && e.parentElement.tagName == 'BUTTON'){
+        e.onclick = showGalleryImage;
+    }
+}
+
 function addTitles(root){
 	root.querySelectorAll('span, button, select').forEach(function(span){
 		tooltip = titles[span.textContent];
@@ -115,13 +201,18 @@ function addTitles(root){
                 img2img_preview.style.width = img2img_gallery.clientWidth + "px"
                 img2img_preview.style.height = img2img_gallery.clientHeight + "px"
             }
-
-
+		
             window.setTimeout(requestProgress, 500)
         });
         mutationObserver.observe( progressbar, { childList:true, subtree:true })
 	}
+	
+	fullImg_preview = gradioApp().querySelectorAll('img.w-full')
 
+	    if(fullImg_preview != null){
+		fullImg_preview.forEach(galleryImageHandler);
+	}
+	
 }
 
 document.addEventListener("DOMContentLoaded", function() {
@@ -129,6 +220,49 @@ document.addEventListener("DOMContentLoaded", function() {
         addTitles(gradioApp());
     });
     mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
+	
+    const modalFragment = document.createDocumentFragment();
+    const modal = document.createElement('div')
+    modal.onclick = closeModal;
+    
+    const modalClose = document.createElement('span')
+    modalClose.className = 'modalClose cursor';
+    modalClose.innerHTML = '×'
+    modalClose.onclick = closeModal;
+    modal.id = "lightboxModal";
+    modal.tabIndex=0
+    modal.addEventListener('keydown', modalKeyHandler, true)
+    modal.appendChild(modalClose)
+
+    const modalImage = document.createElement('img')
+    modalImage.id = 'modalImage';
+    modalImage.onclick = closeModal;
+    modalImage.tabIndex=0
+    modalImage.addEventListener('keydown', modalKeyHandler, true)
+    modal.appendChild(modalImage)
+
+    const modalPrev = document.createElement('a')
+    modalPrev.className = 'modalPrev';
+    modalPrev.innerHTML = '❮'
+    modalPrev.tabIndex=0
+    modalPrev.addEventListener('click',modalPrevImage,true);
+    modalPrev.addEventListener('keydown', modalKeyHandler, true)
+    modal.appendChild(modalPrev)
+
+    const modalNext = document.createElement('a')
+    modalNext.className = 'modalNext';
+    modalNext.innerHTML = '❯'
+    modalNext.tabIndex=0
+    modalNext.addEventListener('click',modalNextImage,true);
+    modalNext.addEventListener('keydown', modalKeyHandler, true)
+
+    modal.appendChild(modalNext)
+
+
+    gradioApp().getRootNode().appendChild(modal)
+    
+    document.body.appendChild(modalFragment);
+	
 });
 
 function selected_gallery_index(){
@@ -180,6 +314,15 @@ function submit(){
     for(var i=0;i<arguments.length;i++){
         res.push(arguments[i])
     }
+
+    // As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
+    // This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate.
+    // I don't know why gradio is seding outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
+    // If gradio at some point stops sending outputs, this may break something
+    if(Array.isArray(res[res.length - 3])){
+        res[res.length - 3] = null
+    }
+
     return res
 }
 

scripts/img2imgalt.py

@@ -59,7 +59,7 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
     return x / x.std()
 
 
-Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt"])
+Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt"])
 
 
 class Script(scripts.Script):
@@ -74,34 +74,45 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):
         original_prompt = gr.Textbox(label="Original prompt", lines=1)
+        original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1)
         cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
         st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
+        randomness = gr.Slider(label="randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
+        return [original_prompt, original_negative_prompt, cfg, st, randomness]
 
-        return [original_prompt, cfg, st]
-
-    def run(self, p, original_prompt, cfg, st):
+    def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness):
         p.batch_size = 1
         p.batch_count = 1
 
         def sample_extra(x, conditioning, unconditional_conditioning):
             lat = (p.init_latent.cpu().numpy() * 10).astype(int)
 
-            same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st and self.cache.original_prompt == original_prompt
+            same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st and self.cache.original_prompt == original_prompt and self.cache.original_negative_prompt == original_negative_prompt
             same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
 
             if same_everything:
-                noise = self.cache.noise
+                rec_noise = self.cache.noise
             else:
                 shared.state.job_count += 1
                 cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
-                uncond = p.sd_model.get_learned_conditioning(p.batch_size * [""])
-                noise = find_noise_for_image(p, cond, uncond, cfg, st)
-                self.cache = Cached(noise, cfg, st, lat, original_prompt)
+                uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt])
+                rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
+                self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt)
 
+            rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
+            
+            combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
+            
             sampler = samplers[p.sampler_index].constructor(p.sd_model)
 
-            samples_ddim = sampler.sample(p, noise, conditioning, unconditional_conditioning)
-            return samples_ddim
+            sigmas = sampler.model_wrap.get_sigmas(p.steps)
+            
+            noise_dt = combined_noise - ( p.init_latent / sigmas[0] )
+            
+            p.seed = p.seed + 1
+            
+            return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning)
+
 
         p.sample = sample_extra
 

scripts/outpainting_mk_2.py

@@ -0,0 +1,290 @@
+import math
+
+import numpy as np
+import skimage
+
+import modules.scripts as scripts
+import gradio as gr
+from PIL import Image, ImageDraw
+
+from modules import images, processing, devices
+from modules.processing import Processed, process_images
+from modules.shared import opts, cmd_opts, state
+
+
+def expand(x, dir, amount, power=0.75):
+    is_left = dir == 3
+    is_right = dir == 1
+    is_up = dir == 0
+    is_down = dir == 2
+
+    if is_left or is_right:
+        noise = np.zeros((x.shape[0], amount, 3), dtype=float)
+        indexes = np.random.random((x.shape[0], amount)) ** power * (1 - np.arange(amount) / amount)
+        if is_right:
+            indexes = 1 - indexes
+        indexes = (indexes * (x.shape[1] - 1)).astype(int)
+
+        for row in range(x.shape[0]):
+            if is_left:
+                noise[row] = x[row][indexes[row]]
+            else:
+                noise[row] = np.flip(x[row][indexes[row]], axis=0)
+
+        x = np.concatenate([noise, x] if is_left else [x, noise], axis=1)
+        return x
+
+    if is_up or is_down:
+        noise = np.zeros((amount, x.shape[1], 3), dtype=float)
+        indexes = np.random.random((x.shape[1], amount)) ** power * (1 - np.arange(amount) / amount)
+        if is_down:
+            indexes = 1 - indexes
+        indexes = (indexes * x.shape[0] - 1).astype(int)
+
+        for row in range(x.shape[1]):
+            if is_up:
+                noise[:, row] = x[:, row][indexes[row]]
+            else:
+                noise[:, row] = np.flip(x[:, row][indexes[row]], axis=0)
+
+        x = np.concatenate([noise, x] if is_up else [x, noise], axis=0)
+        return x
+
+
+def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05):
+    # helper fft routines that keep ortho normalization and auto-shift before and after fft
+    def _fft2(data):
+        if data.ndim > 2:  # has channels
+            out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
+            for c in range(data.shape[2]):
+                c_data = data[:, :, c]
+                out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho")
+                out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c])
+        else:  # one channel
+            out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
+            out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho")
+            out_fft[:, :] = np.fft.ifftshift(out_fft[:, :])
+
+        return out_fft
+
+    def _ifft2(data):
+        if data.ndim > 2:  # has channels
+            out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
+            for c in range(data.shape[2]):
+                c_data = data[:, :, c]
+                out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho")
+                out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c])
+        else:  # one channel
+            out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
+            out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho")
+            out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :])
+
+        return out_ifft
+
+    def _get_gaussian_window(width, height, std=3.14, mode=0):
+        window_scale_x = float(width / min(width, height))
+        window_scale_y = float(height / min(width, height))
+
+        window = np.zeros((width, height))
+        x = (np.arange(width) / width * 2. - 1.) * window_scale_x
+        for y in range(height):
+            fy = (y / height * 2. - 1.) * window_scale_y
+            if mode == 0:
+                window[:, y] = np.exp(-(x ** 2 + fy ** 2) * std)
+            else:
+                window[:, y] = (1 / ((x ** 2 + 1.) * (fy ** 2 + 1.))) ** (std / 3.14)  # hey wait a minute that's not gaussian
+
+        return window
+
+    def _get_masked_window_rgb(np_mask_grey, hardness=1.):
+        np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3))
+        if hardness != 1.:
+            hardened = np_mask_grey[:] ** hardness
+        else:
+            hardened = np_mask_grey[:]
+        for c in range(3):
+            np_mask_rgb[:, :, c] = hardened[:]
+        return np_mask_rgb
+
+    width = _np_src_image.shape[0]
+    height = _np_src_image.shape[1]
+    num_channels = _np_src_image.shape[2]
+
+    np_src_image = _np_src_image[:] * (1. - np_mask_rgb)
+    np_mask_grey = (np.sum(np_mask_rgb, axis=2) / 3.)
+    img_mask = np_mask_grey > 1e-6
+    ref_mask = np_mask_grey < 1e-3
+
+    windowed_image = _np_src_image * (1. - _get_masked_window_rgb(np_mask_grey))
+    windowed_image /= np.max(windowed_image)
+    windowed_image += np.average(_np_src_image) * np_mask_rgb  # / (1.-np.average(np_mask_rgb))  # rather than leave the masked area black, we get better results from fft by filling the average unmasked color
+
+    src_fft = _fft2(windowed_image)  # get feature statistics from masked src img
+    src_dist = np.absolute(src_fft)
+    src_phase = src_fft / src_dist
+
+    noise_window = _get_gaussian_window(width, height, mode=1)  # start with simple gaussian noise
+    noise_rgb = np.random.random_sample((width, height, num_channels))
+    noise_grey = (np.sum(noise_rgb, axis=2) / 3.)
+    noise_rgb *= color_variation  # the colorfulness of the starting noise is blended to greyscale with a parameter
+    for c in range(num_channels):
+        noise_rgb[:, :, c] += (1. - color_variation) * noise_grey
+
+    noise_fft = _fft2(noise_rgb)
+    for c in range(num_channels):
+        noise_fft[:, :, c] *= noise_window
+    noise_rgb = np.real(_ifft2(noise_fft))
+    shaped_noise_fft = _fft2(noise_rgb)
+    shaped_noise_fft[:, :, :] = np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist ** noise_q) * src_phase  # perform the actual shaping
+
+    brightness_variation = 0.  # color_variation # todo: temporarily tieing brightness variation to color variation for now
+    contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2.
+
+    # scikit-image is used for histogram matching, very convenient!
+    shaped_noise = np.real(_ifft2(shaped_noise_fft))
+    shaped_noise -= np.min(shaped_noise)
+    shaped_noise /= np.max(shaped_noise)
+    shaped_noise[img_mask, :] = skimage.exposure.match_histograms(shaped_noise[img_mask, :] ** 1., contrast_adjusted_np_src[ref_mask, :], channel_axis=1)
+    shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb
+
+    matched_noise = shaped_noise[:]
+
+    return np.clip(matched_noise, 0., 1.)
+
+
+
+class Script(scripts.Script):
+    def title(self):
+        return "Outpainting mk2"
+
+    def show(self, is_img2img):
+        return is_img2img
+
+    def ui(self, is_img2img):
+        if not is_img2img:
+            return None
+
+        info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8</p>")
+
+        pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128)
+        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, visible=False)
+        direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'])
+        noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0)
+        color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05)
+
+        return [info, pixels, mask_blur, direction, noise_q, color_variation]
+
+    def run(self, p, _, pixels, mask_blur, direction, noise_q, color_variation):
+        initial_seed_and_info = [None, None]
+
+        process_width = p.width
+        process_height = p.height
+
+        p.mask_blur = mask_blur*4
+        p.inpaint_full_res = False
+        p.inpainting_fill = 1
+        p.do_not_save_samples = True
+        p.do_not_save_grid = True
+
+        left = pixels if "left" in direction else 0
+        right = pixels if "right" in direction else 0
+        up = pixels if "up" in direction else 0
+        down = pixels if "down" in direction else 0
+
+        init_img = p.init_images[0]
+        target_w = math.ceil((init_img.width + left + right) / 64) * 64
+        target_h = math.ceil((init_img.height + up + down) / 64) * 64
+
+        if left > 0:
+            left = left * (target_w - init_img.width) // (left + right)
+        if right > 0:
+            right = target_w - init_img.width - left
+
+        if up > 0:
+            up = up * (target_h - init_img.height) // (up + down)
+
+        if down > 0:
+            down = target_h - init_img.height - up
+
+        init_image = p.init_images[0]
+
+        state.job_count = (1 if left > 0 else 0) + (1 if right > 0 else 0)+ (1 if up > 0 else 0)+ (1 if down > 0 else 0)
+
+        def expand(init, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
+            is_horiz = is_left or is_right
+            is_vert = is_top or is_bottom
+            pixels_horiz = expand_pixels if is_horiz else 0
+            pixels_vert = expand_pixels if is_vert else 0
+
+            img = Image.new("RGB", (init.width + pixels_horiz, init.height + pixels_vert))
+            img.paste(init, (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
+            mask = Image.new("RGB", (init.width + pixels_horiz, init.height + pixels_vert), "white")
+            draw = ImageDraw.Draw(mask)
+            draw.rectangle((
+                expand_pixels + mask_blur if is_left else 0,
+                expand_pixels + mask_blur if is_top else 0,
+                mask.width - expand_pixels - mask_blur if is_right else mask.width,
+                mask.height - expand_pixels - mask_blur if is_bottom else mask.height,
+            ), fill="black")
+
+            np_image = (np.asarray(img) / 255.0).astype(np.float64)
+            np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
+            noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
+            out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")
+
+            target_width = min(process_width, init.width + pixels_horiz) if is_horiz else img.width
+            target_height = min(process_height, init.height + pixels_vert) if is_vert else img.height
+
+            crop_region = (
+                0 if is_left else out.width - target_width,
+                0 if is_top else out.height - target_height,
+                target_width if is_left else out.width,
+                target_height if is_top else out.height,
+            )
+
+            image_to_process = out.crop(crop_region)
+            mask = mask.crop(crop_region)
+
+            p.width = target_width if is_horiz else img.width
+            p.height = target_height if is_vert else img.height
+            p.init_images = [image_to_process]
+            p.image_mask = mask
+
+            latent_mask = Image.new("RGB", (p.width, p.height), "white")
+            draw = ImageDraw.Draw(latent_mask)
+            draw.rectangle((
+                expand_pixels + mask_blur * 2 if is_left else 0,
+                expand_pixels + mask_blur * 2 if is_top else 0,
+                mask.width - expand_pixels - mask_blur * 2 if is_right else mask.width,
+                mask.height - expand_pixels - mask_blur * 2 if is_bottom else mask.height,
+            ), fill="black")
+            p.latent_mask = latent_mask
+
+            proc = process_images(p)
+            proc_img = proc.images[0]
+
+            if initial_seed_and_info[0] is None:
+                initial_seed_and_info[0] = proc.seed
+                initial_seed_and_info[1] = proc.info
+
+            out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height))
+            return out
+
+        img = init_image
+
+        if left > 0:
+            img = expand(img, left, is_left=True)
+        if right > 0:
+            img = expand(img, right, is_right=True)
+        if up > 0:
+            img = expand(img, up, is_top=True)
+        if down > 0:
+            img = expand(img, down, is_bottom=True)
+
+        res = Processed(p, [img], initial_seed_and_info[0], initial_seed_and_info[1])
+
+        if opts.samples_save:
+            images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
+
+        return res
+

scripts/prompts_from_file.py

@@ -13,28 +13,42 @@ from modules.shared import opts, cmd_opts, state
 
 class Script(scripts.Script):
     def title(self):
-        return "Prompts from file"
+        return "Prompts from file or textbox"
 
     def ui(self, is_img2img):
+        # This checkbox would look nicer as two tabs, but there are two problems:
+        # 1) There is a bug in Gradio 3.3 that prevents visibility from working on Tabs
+        # 2) Even with Gradio 3.3.1, returning a control (like Tabs) that can't be used as input
+        #    causes a AttributeError: 'Tabs' object has no attribute 'preprocess' assert,
+        #    due to the way Script assumes all controls returned can be used as inputs.
+        # Therefore, there's no good way to use grouping components right now,
+        # so we will use a checkbox! :)
+        checkbox_txt = gr.Checkbox(label="Show Textbox", value=False)
         file = gr.File(label="File with inputs", type='bytes')
+        prompt_txt = gr.TextArea(label="Prompts")
+        checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt])
+        return [checkbox_txt, file, prompt_txt]
 
-        return [file]
-
-    def run(self, p, data: bytes):
-        lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
+    def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
+        if (checkbox_txt):
+            lines = [x.strip() for x in prompt_txt.splitlines()]
+        else:
+            lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
         lines = [x for x in lines if len(x) > 0]
 
-        batch_count = math.ceil(len(lines) / p.batch_size)
-        print(f"Will process {len(lines) * p.n_iter} images in {batch_count * p.n_iter} batches.")
+        img_count = len(lines) * p.n_iter
+        batch_count = math.ceil(img_count / p.batch_size)
+        loop_count = math.ceil(batch_count / p.n_iter)
+        print(f"Will process {img_count} images in {batch_count} batches.")
 
         p.do_not_save_grid = True
 
         state.job_count = batch_count
 
         images = []
-        for batch_no in range(batch_count):
-            state.job = f"{batch_no + 1} out of {batch_count * p.n_iter}"
-            p.prompt = lines[batch_no*p.batch_size:(batch_no+1)*p.batch_size] * p.n_iter
+        for loop_no in range(loop_count):
+            state.job = f"{loop_no + 1} out of {loop_count}"
+            p.prompt = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter
             proc = process_images(p)
             images += proc.images
 

scripts/xy_grid.py

@@ -10,7 +10,9 @@ import gradio as gr
 from modules import images
 from modules.processing import process_images, Processed
 from modules.shared import opts, cmd_opts, state
+import modules.shared as shared
 import modules.sd_samplers
+import modules.sd_models
 import re
 
 
@@ -41,6 +43,15 @@ def apply_sampler(p, x, xs):
     p.sampler_index = sampler_index
 
 
+def apply_checkpoint(p, x, xs):
+    applicable = [info for info in modules.sd_models.checkpoints_list.values() if x in info.title]
+    assert len(applicable) > 0, f'Checkpoint {x} for found'
+
+    info = applicable[0]
+
+    modules.sd_models.reload_model_weights(shared.sd_model, info)
+
+
 def format_value_add_label(p, opt, x):
     if type(x) == float:
         x = round(x, 8)
@@ -74,15 +85,16 @@ axis_options = [
     AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label),
     AxisOption("Prompt S/R", str, apply_prompt, format_value),
     AxisOption("Sampler", str, apply_sampler, format_value),
+    AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
     AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), #  as it is now all AxisOptionImg2Img items must go after AxisOption ones
 ]
 
 
-def draw_xy_grid(p, xs, ys, x_label, y_label, cell, draw_legend):
+def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
     res = []
 
-    ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
-    hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
+    ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
+    hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
 
     first_pocessed = None
 
@@ -206,8 +218,8 @@ class Script(scripts.Script):
             p,
             xs=xs,
             ys=ys,
-            x_label=lambda x: x_opt.format_value(p, x_opt, x),
-            y_label=lambda y: y_opt.format_value(p, y_opt, y),
+            x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
+            y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
             cell=cell,
             draw_legend=draw_legend
         )
@@ -215,4 +227,7 @@ class Script(scripts.Script):
         if opts.grid_save:
             images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed, grid=True, p=p)
 
+        # restore checkpoint in case it was changed by axes
+        modules.sd_models.reload_model_weights(shared.sd_model)
+
         return processed

style.css

@@ -1,17 +1,48 @@
 .output-html p {margin: 0 0.5em;}
-.performance { font-size: 0.85em; color: #444; }
+
+.performance {
+    font-size: 0.85em;
+    color: #444;
+    display: flex;
+    justify-content: space-between;
+    white-space: nowrap;
+}
+
+.performance .time {
+    margin-right: 0;
+}
+
+.performance .vram {
+    margin-left: 0;
+    text-align: right;
+}
 
 #generate{
     min-height: 4.5em;
 }
 
-#txt2img_gallery, #img2img_gallery{
-    min-height: 768px;
+@media screen and (min-width: 2500px) {
+    #txt2img_gallery, #img2img_gallery {
+        min-height: 768px;
+    }
 }
+
 #txt2img_gallery img, #img2img_gallery img{
     object-fit: scale-down;
 }
 
+.justify-center.overflow-x-scroll {
+    justify-content: left;
+}
+
+.justify-center.overflow-x-scroll button:first-of-type {
+    margin-left: auto;
+}
+
+.justify-center.overflow-x-scroll button:last-of-type {
+    margin-right: auto;
+}
+
 #subseed_show{
     min-width: 6em;
     max-width: 6em;
@@ -151,6 +182,12 @@ input[type="range"]{
 #txt2img_negative_prompt, #img2img_negative_prompt{
 }
 
+#progressbar{
+  position: absolute;
+  z-index: 1000;
+  right: 0;
+}
+
 .progressDiv{
   width: 100%;
   height: 30px;
@@ -174,3 +211,66 @@ input[type="range"]{
   border-radius: 8px;
 }
 
+#lightboxModal{
+  display: none;
+  position: fixed;
+  z-index: 900;
+  padding-top: 100px;
+  left: 0;
+  top: 0;
+  width: 100%;
+  height: 100%;
+  overflow: auto;
+  background-color: rgba(20, 20, 20, 0.95);
+}
+
+.modalClose {
+  color: white;
+  position: absolute;
+  top: 10px;
+  right: 25px;
+  font-size: 35px;
+  font-weight: bold;
+}
+
+.modalClose:hover,
+.modalClose:focus {
+  color: #999;
+  text-decoration: none;
+  cursor: pointer;
+}
+
+#modalImage {
+    display: block;
+    margin-left: auto;
+    margin-right: auto;
+    margin-top: auto;
+    width: auto;
+}
+
+.modalPrev,
+.modalNext {
+  cursor: pointer;
+  position: absolute;
+  top: 50%;
+  width: auto;
+  padding: 16px;
+  margin-top: -50px;
+  color: white;
+  font-weight: bold;
+  font-size: 20px;
+  transition: 0.6s ease;
+  border-radius: 0 3px 3px 0;
+  user-select: none;
+  -webkit-user-select: none;
+}
+
+.modalNext {
+  right: 0;
+  border-radius: 3px 0 0 3px;
+}
+
+.modalPrev:hover,
+.modalNext:hover {
+  background-color: rgba(0, 0, 0, 0.8);
+}

webui.py

@@ -3,13 +3,8 @@ import threading
 
 from modules.paths import script_path
 
-import torch
-from omegaconf import OmegaConf
-
 import signal
 
-from ldm.util import instantiate_from_config
-
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.ui
@@ -24,6 +19,7 @@ import modules.extras
 import modules.lowvram
 import modules.txt2img
 import modules.img2img
+import modules.sd_models
 
 
 modules.codeformer_model.setup_codeformer()
@@ -33,31 +29,19 @@ shared.face_restorers.append(modules.face_restoration.FaceRestoration())
 esrgan.load_models(cmd_opts.esrgan_models_path)
 realesrgan.setup_realesrgan()
 
-
-def load_model_from_config(config, ckpt, verbose=False):
-    print(f"Loading model [{shared.sd_model_hash}] from {ckpt}")
-    pl_sd = torch.load(ckpt, map_location="cpu")
-    if "global_step" in pl_sd:
-        print(f"Global Step: {pl_sd['global_step']}")
-    sd = pl_sd["state_dict"]
-
-    model = instantiate_from_config(config.model)
-    m, u = model.load_state_dict(sd, strict=False)
-    if len(m) > 0 and verbose:
-        print("missing keys:")
-        print(m)
-    if len(u) > 0 and verbose:
-        print("unexpected keys:")
-        print(u)
-    if cmd_opts.opt_channelslast:
-        model = model.to(memory_format=torch.channels_last)
-    model.eval()
-    return model
-
-
 queue_lock = threading.Lock()
 
 
+def wrap_queued_call(func):
+    def f(*args, **kwargs):
+        with queue_lock:
+            res = func(*args, **kwargs)
+
+        return res
+
+    return f
+
+
 def wrap_gradio_gpu_call(func):
     def f(*args, **kwargs):
         shared.state.sampling_step = 0
@@ -80,33 +64,8 @@ def wrap_gradio_gpu_call(func):
 
 modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
 
-try:
-    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
-
-    from transformers import logging
-
-    logging.set_verbosity_error()
-except Exception:
-    pass
-
-with open(cmd_opts.ckpt, "rb") as file:
-    import hashlib
-    m = hashlib.sha256()
-
-    file.seek(0x100000)
-    m.update(file.read(0x10000))
-    shared.sd_model_hash = m.hexdigest()[0:8]
-
-sd_config = OmegaConf.load(cmd_opts.config)
-shared.sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
-shared.sd_model = (shared.sd_model if cmd_opts.no_half else shared.sd_model.half())
-
-if cmd_opts.lowvram or cmd_opts.medvram:
-    modules.lowvram.setup_for_low_vram(shared.sd_model, cmd_opts.medvram)
-else:
-    shared.sd_model = shared.sd_model.to(shared.device)
-
-modules.sd_hijack.model_hijack.hijack(shared.sd_model)
+shared.sd_model = modules.sd_models.load_model()
+shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
 
 
 def webui():