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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-29 19:05:05 +08:00
add execution timings to output
change the text output element to HTML
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parent
29f7e7ab89
commit
199123e98d
42
webui.py
42
webui.py
@ -12,6 +12,8 @@ from contextlib import contextmanager, nullcontext
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import mimetypes
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import mimetypes
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import random
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import random
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import math
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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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import k_diffusion as K
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from ldm.util import instantiate_from_config
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from ldm.util import instantiate_from_config
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@ -160,6 +162,11 @@ def save_image(image, path, basename, seed, prompt, extension, info=None, short_
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image.save(os.path.join(path, filename), quality=opt.jpeg_quality, pnginfo=pnginfo)
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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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def load_GFPGAN():
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model_name = 'GFPGANv1.3'
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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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model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
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@ -331,6 +338,20 @@ 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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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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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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"""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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@ -484,7 +505,7 @@ def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, p
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del sampler
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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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class Flagging(gr.FlaggingCallback):
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@ -529,7 +550,7 @@ class Flagging(gr.FlaggingCallback):
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txt2img_interface = gr.Interface(
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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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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.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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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
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@ -547,7 +568,7 @@ txt2img_interface = gr.Interface(
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outputs=[
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outputs=[
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gr.Gallery(label="Images"),
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gr.Gallery(label="Images"),
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gr.Number(label='Seed'),
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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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],
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title="Stable Diffusion Text-to-Image K",
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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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description="Generate images from text with Stable Diffusion (using K-LMS)",
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@ -650,14 +671,14 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
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del sampler
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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 = "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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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_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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inputs=[
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gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
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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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gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil"),
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@ -677,7 +698,7 @@ img2img_interface = gr.Interface(
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outputs=[
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outputs=[
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gr.Gallery(),
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gr.Gallery(),
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gr.Number(label='Seed'),
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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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],
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title="Stable Diffusion Image-to-Image",
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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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description="Generate images from images with Stable Diffusion",
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@ -698,7 +719,7 @@ def run_GFPGAN(image, strength):
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if strength < 1.0:
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if strength < 1.0:
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res = Image.blend(image, res, strength)
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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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if GFPGAN is not None:
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@ -710,6 +731,8 @@ if GFPGAN is not None:
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],
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],
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outputs=[
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outputs=[
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gr.Image(label="Result"),
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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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],
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title="GFPGAN",
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title="GFPGAN",
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description="Fix faces on images",
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description="Fix faces on images",
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@ -719,7 +742,10 @@ if GFPGAN is not None:
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demo = gr.TabbedInterface(
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demo = gr.TabbedInterface(
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interface_list=[x[0] for x in interfaces],
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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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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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)
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demo.launch()
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demo.launch()
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