Merge branch 'master' of https://github.com/AUTOMATIC1111/stable-diffusion-webui
This commit is contained in:
dogewanwan
commit
781f054a20
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@ -146,3 +146,12 @@ to get otherwise.
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Example: (cherrypicked result; original picture by anon)
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### Png info
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Adds information about generation parameters to PNG as a text chunk. You
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can view this information later using any software that supports viewing
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PNG chunk info, for example: https://www.nayuki.io/page/png-file-chunk-inspector
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This can be disabled using the `--disable-pnginfo` command line option.
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Binary file not shown.
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After Width: | Height: | Size: 113 KiB |
104
webui.py
104
webui.py
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@ -4,7 +4,7 @@ import torch.nn as nn
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import numpy as np
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import gradio as gr
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from omegaconf import OmegaConf
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from PIL import Image, ImageFont, ImageDraw
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from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
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from itertools import islice
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from einops import rearrange, repeat
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from torch import autocast
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@ -12,6 +12,8 @@ from contextlib import contextmanager, nullcontext
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import mimetypes
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import random
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import math
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import html
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import time
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import k_diffusion as K
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from ldm.util import instantiate_from_config
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@ -49,8 +51,13 @@ parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=(
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parser.add_argument("--no-verify-input", action='store_true', help="do not verify input to check if it's too long")
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parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
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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 accleration in browser)")
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parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
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parser.add_argument("--grid-format", type=str, default='png', help="file format for saved grids; can be png or jpg")
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parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
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parser.add_argument("--save-format", type=str, default='png', help="file format for saved indiviual samples; can be png or jpg")
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parser.add_argument("--grid-format", type=str, default='png', help="file format for saved grids; can be png or jpg")
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parser.add_argument("--grid-extended-filename", action='store_true', help="save grid images to filenames with extended info: seed, prompt")
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parser.add_argument("--jpeg-quality", type=int, default=80, help="quality for saved jpeg images")
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parser.add_argument("--disable-pnginfo", action='store_true', help="disable saving text information about generation parameters as chunks to png files")
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parser.add_argument("--inversion", action='store_true', help="switch to stable inversion version; allows for uploading embeddings; this option should be used only with textual inversion repo")
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opt = parser.parse_args()
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@ -130,6 +137,37 @@ def create_random_tensors(shape, seeds):
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return x
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def torch_gc():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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def sanitize_filename_part(text):
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return text.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]
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def save_image(image, path, basename, seed, prompt, extension, info=None, short_filename=False):
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prompt = sanitize_filename_part(prompt)
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if short_filename:
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filename = f"{basename}.{extension}"
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else:
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filename = f"{basename}-{seed}-{prompt[:128]}.{extension}"
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if extension == 'png' and not opt.disable_pnginfo:
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pnginfo = PngImagePlugin.PngInfo()
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pnginfo.add_text("parameters", info)
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else:
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pnginfo = None
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image.save(os.path.join(path, filename), quality=opt.jpeg_quality, pnginfo=pnginfo)
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def plaintext_to_html(text):
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text = "".join([f"<p>{html.escape(x)}</p>\n" for x in text.split('\n')])
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return text
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def load_GFPGAN():
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model_name = 'GFPGANv1.3'
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model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
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@ -301,11 +339,25 @@ def check_prompt_length(prompt, comments):
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comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
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def wrap_gradio_call(func):
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def f(*p1, **p2):
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t = time.perf_counter()
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res = list(func(*p1, **p2))
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elapsed = time.perf_counter() - t
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# last item is always HTML
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res[-1] = res[-1] + f"<p class='performance'>Time taken: {elapsed:.2f}s</p>"
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return tuple(res)
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return f
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def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, do_not_save_grid=False):
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"""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"""
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assert prompt is not None
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torch.cuda.empty_cache()
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torch_gc()
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if seed == -1:
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seed = random.randrange(4294967294)
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@ -351,6 +403,11 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
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all_prompts = batch_size * n_iter * [prompt]
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all_seeds = [seed + x for x in range(len(all_prompts))]
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info = f"""
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{prompt}
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Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
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""".strip() + "".join(["\n\n" + x for x in comments])
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precision_scope = autocast if opt.precision == "autocast" else nullcontext
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output_images = []
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with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
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@ -385,9 +442,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
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x_sample = restored_img
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image = Image.fromarray(x_sample)
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filename = f"{base_count:05}-{seeds[i]}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"
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image.save(os.path.join(sample_path, filename))
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save_image(image, sample_path, f"{base_count:05}", seeds[i], prompts[i], opt.save_format, info=info)
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output_images.append(image)
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base_count += 1
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@ -406,17 +461,10 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
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output_images.insert(0, grid)
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grid.save(os.path.join(outpath, f'grid-{grid_count:04}.{opt.grid_format}'))
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save_image(grid, outpath, f"grid-{grid_count:04}", seed, prompt, opt.grid_format, info=info, short_filename=not opt.grid_extended_filename)
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grid_count += 1
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info = f"""
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{prompt}
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Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
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""".strip()
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for comment in comments:
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info += "\n\n" + comment
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torch_gc()
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return output_images, seed, info
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@ -465,7 +513,7 @@ def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, p
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del sampler
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return output_images, seed, info
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return output_images, seed, plaintext_to_html(info)
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class Flagging(gr.FlaggingCallback):
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@ -510,7 +558,7 @@ class Flagging(gr.FlaggingCallback):
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txt2img_interface = gr.Interface(
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txt2img,
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wrap_gradio_call(txt2img),
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inputs=[
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gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
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@ -529,7 +577,7 @@ txt2img_interface = gr.Interface(
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outputs=[
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gr.Gallery(label="Images"),
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gr.Number(label='Seed'),
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gr.Textbox(label="Copy-paste generation parameters"),
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gr.HTML(),
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],
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title="Stable Diffusion Text-to-Image K",
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description="Generate images from text with Stable Diffusion (using K-LMS)",
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@ -608,7 +656,8 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
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grid_count = len(os.listdir(outpath)) - 1
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grid = image_grid(history, batch_size, force_n_rows=1)
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grid.save(os.path.join(outpath, f'grid-{grid_count:04}.{opt.grid_format}'))
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save_image(grid, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opt.grid_format, info=info, short_filename=not opt.grid_extended_filename)
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output_images = history
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seed = initial_seed
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@ -633,14 +682,14 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
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del sampler
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return output_images, seed, info
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return output_images, seed, plaintext_to_html(info)
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sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
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sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
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img2img_interface = gr.Interface(
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img2img,
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wrap_gradio_call(img2img),
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inputs=[
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gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
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gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil"),
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@ -661,7 +710,7 @@ img2img_interface = gr.Interface(
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outputs=[
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gr.Gallery(),
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gr.Number(label='Seed'),
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gr.Textbox(label="Copy-paste generation parameters"),
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gr.HTML(),
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],
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title="Stable Diffusion Image-to-Image",
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description="Generate images from images with Stable Diffusion",
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@ -682,7 +731,7 @@ def run_GFPGAN(image, strength):
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if strength < 1.0:
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res = Image.blend(image, res, strength)
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return res
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return res, 0, ''
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if GFPGAN is not None:
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@ -694,6 +743,8 @@ if GFPGAN is not None:
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],
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outputs=[
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gr.Image(label="Result"),
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gr.Number(label='Seed', visible=False),
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gr.HTML(),
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],
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title="GFPGAN",
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description="Fix faces on images",
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@ -704,7 +755,10 @@ if GFPGAN is not None:
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demo = gr.TabbedInterface(
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interface_list=[x[0] for x in interfaces],
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tab_names=[x[1] for x in interfaces],
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css=("" if opt.no_progressbar_hiding else css_hide_progressbar)
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css=("" if opt.no_progressbar_hiding else css_hide_progressbar) + """
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.output-html p {margin: 0 0.5em;}
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.performance { font-size: 0.85em; color: #444; }
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"""
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)
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demo.launch()
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