diff --git a/webui.py b/webui.py index 87718fc3..6998464c 100644 --- a/webui.py +++ b/webui.py @@ -9,7 +9,7 @@ import torch.nn as nn import numpy as np import gradio as gr from omegaconf import OmegaConf -from PIL import Image, ImageFont, ImageDraw, PngImagePlugin +from PIL import Image, ImageFont, ImageDraw, PngImagePlugin, ImageFilter, ImageOps from torch import autocast import mimetypes import random @@ -158,6 +158,7 @@ class Options: "samples_save": OptionInfo(True, "Save indiviual samples"), "samples_format": OptionInfo('png', 'File format for indiviual samples'), "grid_save": OptionInfo(True, "Save image grids"), + "return_grid": OptionInfo(True, "Show grid in results for web"), "grid_format": OptionInfo('png', 'File format for grids'), "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"), "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"), @@ -957,6 +958,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple if (p.prompt_matrix or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count: + return_grid = opts.return_grid + if p.prompt_matrix: grid = image_grid(output_images, p.batch_size, rows=1 << ((len(prompt_matrix_parts)-1)//2)) @@ -967,10 +970,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed: print("Error creating prompt_matrix text:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) - output_images.insert(0, grid) + return_grid = True else: grid = image_grid(output_images, p.batch_size) + if return_grid: + output_images.insert(0, grid) + save_image(grid, p.outpath, f"grid-{grid_count:04}", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename) grid_count += 1 @@ -1042,7 +1048,7 @@ class Flagging(gr.FlaggingCallback): os.makedirs("log/images", exist_ok=True) # those must match the "txt2img" function - prompt, ddim_steps, sampler_name, use_gfpgan, prompt_matrix, ddim_eta, n_iter, n_samples, cfg_scale, request_seed, height, width, code, images, seed, comment = flag_data + prompt, steps, sampler_index, use_gfpgan, prompt_matrix, n_iter, batch_size, cfg_scale, seed, height, width, code, images, seed, comment = flag_data filenames = [] @@ -1067,7 +1073,7 @@ class Flagging(gr.FlaggingCallback): filenames.append(filename) - writer.writerow([prompt, seed, width, height, cfg_scale, ddim_steps, filenames[0]]) + writer.writerow([prompt, seed, width, height, cfg_scale, steps, filenames[0]]) print("Logged:", filenames[0]) @@ -1097,27 +1103,64 @@ txt2img_interface = gr.Interface( flagging_callback=Flagging() ) +def fill(image, mask): + image_mod = Image.new('RGBA', (image.width, image.height)) + + image_masked = Image.new('RGBa', (image.width, image.height)) + image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert('L'))) + + image_masked = image_masked.convert('RGBa') + + for radius, repeats in [(64, 1), (16, 2), (4, 4), (2, 2), (0, 1)]: + blurred = image_masked.filter(ImageFilter.GaussianBlur(radius)).convert('RGBA') + for _ in range(repeats): + image_mod.alpha_composite(blurred) + + return image_mod.convert("RGB") + class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None - def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, **kwargs): + def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, **kwargs): super().__init__(**kwargs) self.init_images = init_images self.resize_mode: int = resize_mode self.denoising_strength: float = denoising_strength self.init_latent = None + self.original_mask = mask + self.mask_blur = mask_blur + self.mask = None + self.nmask = None def init(self): self.sampler = samplers_for_img2img[self.sampler_index].constructor() + if self.original_mask is not None: + if self.mask_blur > 0: + self.original_mask = self.original_mask.filter(ImageFilter.GaussianBlur(self.mask_blur)).convert('L') + + latmask = self.original_mask.convert('RGB').resize((64, 64)) + latmask = np.moveaxis(np.array(latmask, dtype=np.float), 2, 0) / 255 + latmask = latmask[0] + latmask = np.tile(latmask[None], (4, 1, 1)) + + self.mask = torch.asarray(1.0 - latmask).to(device).type(sd_model.dtype) + self.nmask = torch.asarray(latmask).to(device).type(sd_model.dtype) + + imgs = [] for img in self.init_images: image = img.convert("RGB") image = resize_image(self.resize_mode, image, self.width, self.height) + + if self.original_mask is not None + image = fill(image, self.original_mask) + image = np.array(image).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) + imgs.append(image) if len(imgs) == 1: @@ -1139,16 +1182,33 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sigmas = self.sampler.model_wrap.get_sigmas(self.steps) noise = x * sigmas[self.steps - t_enc - 1] - xi = self.init_latent + noise sigma_sched = sigmas[self.steps - t_enc - 1:] - samples_ddim = self.sampler.func(self.sampler.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': self.cfg_scale}, disable=False) + + #if self.mask is not None: + # xi = xi * self.mask + noise * self.nmask + + def mask_cb(v): + v["denoised"][:] = v["denoised"][:] * self.nmask + self.init_latent * self.mask + + samples_ddim = self.sampler.func(self.sampler.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': self.cfg_scale}, disable=False, callback=mask_cb if self.mask is not None else None) + + if self.mask is not None: + samples_ddim = samples_ddim * self.nmask + self.init_latent * self.mask + return samples_ddim -def img2img(prompt: str, init_img, ddim_steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, sd_upscale: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int): +def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, sd_upscale: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int): outpath = opts.outdir or "outputs/img2img-samples" + if init_img_with_mask is not None: + image = init_img_with_mask['image'] + mask = init_img_with_mask['mask'] + else: + image = init_img + mask = None + assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]' p = StableDiffusionProcessingImg2Img( @@ -1164,7 +1224,8 @@ def img2img(prompt: str, init_img, ddim_steps: int, sampler_index: int, use_GFPG height=height, prompt_matrix=prompt_matrix, use_GFPGAN=use_GFPGAN, - init_images=[init_img], + init_images=[image], + mask=mask, resize_mode=resize_mode, denoising_strength=denoising_strength, extra_generation_params={"Denoising Strength": denoising_strength} @@ -1262,7 +1323,8 @@ img2img_interface = gr.Interface( wrap_gradio_call(img2img), inputs=[ gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1), - gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil"), + gr.Image(label="Image for img2img", source="upload", interactive=True, type="pil"), + gr.Image(label="Image for inpainting with mask", source="upload", interactive=True, type="pil", tool="sketch"), gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20), gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index"), gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=have_gfpgan),