sygil-webui/frontend/frontend.py
2022-08-31 13:58:49 +01:00

357 lines
26 KiB
Python

import gradio as gr
from frontend.css_and_js import *
from frontend.css_and_js import css
import frontend.ui_functions as uifn
def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, txt2img_defaults={}, RealESRGAN=True, GFPGAN=True,
txt2img_toggles={}, txt2img_toggle_defaults='k_euler', show_embeddings=False, img2img_defaults={},
img2img_toggles={}, img2img_toggle_defaults={}, sample_img2img=None, img2img_mask_modes=None,
img2img_resize_modes=None, user_defaults={}, run_GFPGAN=lambda x: x, run_RealESRGAN=lambda x: x):
with gr.Blocks(css=css(opt), analytics_enabled=False, title="Stable Diffusion WebUI") as demo:
with gr.Tabs(elem_id='tabss') as tabs:
with gr.TabItem("Stable Diffusion Text-to-Image Unified", id='txt2img_tab'):
with gr.Row(elem_id="prompt_row"):
txt2img_prompt = gr.Textbox(label="Prompt",
elem_id='prompt_input',
placeholder="A corgi wearing a top hat as an oil painting.",
lines=1,
max_lines=1 if txt2img_defaults['submit_on_enter'] == 'Yes' else 25,
value=txt2img_defaults['prompt'],
show_label=False)
txt2img_btn = gr.Button("Generate", elem_id="generate", variant="primary")
with gr.Row(elem_id='body').style(equal_height=False):
with gr.Column():
txt2img_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width",
value=txt2img_defaults["width"])
txt2img_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height",
value=txt2img_defaults["height"])
txt2img_cfg = gr.Slider(minimum=-40.0, maximum=30.0, step=0.5,
label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)',
value=txt2img_defaults['cfg_scale'])
txt2img_seed = gr.Textbox(label="Seed (blank to randomize)", lines=1, max_lines=1,
value=txt2img_defaults["seed"])
txt2img_batch_count = gr.Slider(minimum=1, maximum=250, step=1,
label='Batch count (how many batches of images to generate)',
value=txt2img_defaults['n_iter'])
txt2img_batch_size = gr.Slider(minimum=1, maximum=8, step=1,
label='Batch size (how many images are in a batch; memory-hungry)',
value=txt2img_defaults['batch_size'])
txt2img_dimensions_info_text_box = gr.Textbox(label="Aspect ratio (4:3 = 1.333 | 16:9 = 1.777 | 21:9 = 2.333)")
with gr.Column():
output_txt2img_gallery = gr.Gallery(label="Images", elem_id="txt2img_gallery_output").style(grid=[4, 4])
with gr.Tabs():
with gr.TabItem("Generated image actions", id="text2img_actions_tab"):
gr.Markdown(
'Select an image from the gallery, then click one of the buttons below to perform an action.')
with gr.Row():
output_txt2img_copy_clipboard = gr.Button("Copy to clipboard").click(fn=None,
inputs=output_txt2img_gallery,
outputs=[],
_js=js_copy_to_clipboard('txt2img_gallery_output'))
output_txt2img_copy_to_input_btn = gr.Button("Push to img2img")
if RealESRGAN is not None:
output_txt2img_to_upscale_esrgan = gr.Button("Upscale w/ ESRGAN")
with gr.TabItem("Output Info", id="text2img_output_info_tab"):
output_txt2img_params = gr.Textbox(label="Generation parameters", interactive=False)
with gr.Row():
output_txt2img_copy_params = gr.Button("Copy full parameters").click(
inputs=output_txt2img_params, outputs=[],
_js='(x) => navigator.clipboard.writeText(x)', fn=None, show_progress=False)
output_txt2img_seed = gr.Number(label='Seed', interactive=False, visible=False)
output_txt2img_copy_seed = gr.Button("Copy only seed").click(
inputs=output_txt2img_seed, outputs=[],
_js='(x) => navigator.clipboard.writeText(x)', fn=None, show_progress=False)
output_txt2img_stats = gr.HTML(label='Stats')
with gr.Column():
txt2img_steps = gr.Slider(minimum=1, maximum=250, step=1, label="Sampling Steps",
value=txt2img_defaults['ddim_steps'])
txt2img_sampling = gr.Dropdown(label='Sampling method (k_lms is default k-diffusion sampler)',
choices=["DDIM", "PLMS", 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a',
'k_euler', 'k_heun', 'k_lms'],
value=txt2img_defaults['sampler_name'])
with gr.Tabs():
with gr.TabItem('Simple'):
txt2img_submit_on_enter = gr.Radio(['Yes', 'No'],
label="Submit on enter? (no means multiline)",
value=txt2img_defaults['submit_on_enter'],
interactive=True)
txt2img_submit_on_enter.change(
lambda x: gr.update(max_lines=1 if x == 'Yes' else 25), txt2img_submit_on_enter,
txt2img_prompt)
with gr.TabItem('Advanced'):
txt2img_toggles = gr.CheckboxGroup(label='', choices=txt2img_toggles,
value=txt2img_toggle_defaults, type="index")
txt2img_realesrgan_model_name = gr.Dropdown(label='RealESRGAN model',
choices=['RealESRGAN_x4plus',
'RealESRGAN_x4plus_anime_6B'],
value='RealESRGAN_x4plus',
visible=RealESRGAN is not None) # TODO: Feels like I shouldnt slot it in here.
txt2img_ddim_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA",
value=txt2img_defaults['ddim_eta'], visible=False)
txt2img_variant_amount = gr.Slider(minimum=0.0, maximum=1.0, label='Variation Amount',
value=txt2img_defaults['variant_amount'])
txt2img_variant_seed = gr.Textbox(label="Variant Seed (blank to randomize)", lines=1,
max_lines=1, value=txt2img_defaults["variant_seed"])
txt2img_embeddings = gr.File(label="Embeddings file for textual inversion",
visible=show_embeddings)
txt2img_btn.click(
txt2img,
[txt2img_prompt, txt2img_steps, txt2img_sampling, txt2img_toggles, txt2img_realesrgan_model_name,
txt2img_ddim_eta, txt2img_batch_count, txt2img_batch_size, txt2img_cfg, txt2img_seed,
txt2img_height, txt2img_width, txt2img_embeddings, txt2img_variant_amount, txt2img_variant_seed],
[output_txt2img_gallery, output_txt2img_seed, output_txt2img_params, output_txt2img_stats]
)
txt2img_prompt.submit(
txt2img,
[txt2img_prompt, txt2img_steps, txt2img_sampling, txt2img_toggles, txt2img_realesrgan_model_name,
txt2img_ddim_eta, txt2img_batch_count, txt2img_batch_size, txt2img_cfg, txt2img_seed,
txt2img_height, txt2img_width, txt2img_embeddings, txt2img_variant_amount, txt2img_variant_seed],
[output_txt2img_gallery, output_txt2img_seed, output_txt2img_params, output_txt2img_stats]
)
txt2img_width.change(fn=uifn.update_dimensions_info, inputs=[txt2img_width, txt2img_height], outputs=txt2img_dimensions_info_text_box)
txt2img_height.change(fn=uifn.update_dimensions_info, inputs=[txt2img_width, txt2img_height], outputs=txt2img_dimensions_info_text_box)
with gr.TabItem("Stable Diffusion Image-to-Image Unified", id="img2img_tab"):
with gr.Row(elem_id="prompt_row"):
img2img_prompt = gr.Textbox(label="Prompt",
elem_id='img2img_prompt_input',
placeholder="A fantasy landscape, trending on artstation.",
lines=1,
max_lines=1 if txt2img_defaults['submit_on_enter'] == 'Yes' else 25,
value=img2img_defaults['prompt'],
show_label=False).style()
img2img_btn_mask = gr.Button("Generate", variant="primary", visible=False,
elem_id="img2img_mask_btn")
img2img_btn_editor = gr.Button("Generate", variant="primary", elem_id="img2img_edit_btn")
with gr.Row().style(equal_height=False):
with gr.Column():
gr.Markdown('#### Img2Img Input')
img2img_image_editor = gr.Image(value=sample_img2img, source="upload", interactive=True,
type="pil", tool="select", elem_id="img2img_editor",
image_mode="RGBA")
img2img_image_mask = gr.Image(value=sample_img2img, source="upload", interactive=True,
type="pil", tool="sketch", visible=False,
elem_id="img2img_mask")
with gr.Tabs():
with gr.TabItem("Editor Options"):
with gr.Column():
img2img_image_editor_mode = gr.Radio(choices=["Mask", "Crop", "Uncrop"], label="Image Editor Mode",
value="Crop", elem_id='edit_mode_select')
img2img_mask = gr.Radio(choices=["Keep masked area", "Regenerate only masked area"],
label="Mask Mode", type="index",
value=img2img_mask_modes[img2img_defaults['mask_mode']], visible=False)
img2img_mask_blur_strength = gr.Slider(minimum=1, maximum=10, step=1,
label="How much blurry should the mask be? (to avoid hard edges)",
value=3, visible=False)
img2img_resize = gr.Radio(label="Resize mode",
choices=["Just resize"],
type="index",
value=img2img_resize_modes[img2img_defaults['resize_mode']])
img2img_painterro_btn = gr.Button("Advanced Editor")
with gr.TabItem("Hints"):
img2img_help = gr.Markdown(visible=False, value=uifn.help_text)
with gr.Column():
gr.Markdown('#### Img2Img Results')
output_img2img_gallery = gr.Gallery(label="Images", elem_id="img2img_gallery_output").style(grid=[4,4,4])
with gr.Tabs():
with gr.TabItem("Generated image actions", id="img2img_actions_tab"):
gr.Markdown("Select an image, then press one of the buttons below")
with gr.Row():
output_img2img_copy_to_clipboard_btn = gr.Button("Copy to clipboard")
output_img2img_copy_to_input_btn = gr.Button("Push to img2img input")
output_img2img_copy_to_mask_btn = gr.Button("Push to img2img input mask")
gr.Markdown("Warning: This will clear your current image and mask settings!")
with gr.TabItem("Output info", id="img2img_output_info_tab"):
output_img2img_params = gr.Textbox(label="Generation parameters")
with gr.Row():
output_img2img_copy_params = gr.Button("Copy full parameters").click(
inputs=output_img2img_params, outputs=[],
_js='(x) => navigator.clipboard.writeText(x)', fn=None, show_progress=False)
output_img2img_seed = gr.Number(label='Seed', interactive=False, visible=False)
output_img2img_copy_seed = gr.Button("Copy only seed").click(
inputs=output_img2img_seed, outputs=[],
_js='(x) => navigator.clipboard.writeText(x)', fn=None, show_progress=False)
output_img2img_stats = gr.HTML(label='Stats')
gr.Markdown('# img2img settings')
with gr.Row():
with gr.Column():
img2img_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width",
value=img2img_defaults["width"])
img2img_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height",
value=img2img_defaults["height"])
img2img_cfg = gr.Slider(minimum=-40.0, maximum=30.0, step=0.5,
label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)',
value=img2img_defaults['cfg_scale'])
img2img_seed = gr.Textbox(label="Seed (blank to randomize)", lines=1, max_lines=1,
value=img2img_defaults["seed"])
img2img_batch_count = gr.Slider(minimum=1, maximum=250, step=1,
label='Batch count (how many batches of images to generate)',
value=img2img_defaults['n_iter'])
img2img_batch_size = gr.Slider(minimum=1, maximum=8, step=1,
label='Batch size (how many images are in a batch; memory-hungry)',
value=img2img_defaults['batch_size'])
img2img_dimensions_info_text_box = gr.Textbox(label="Aspect ratio (4:3 = 1.333 | 16:9 = 1.777 | 21:9 = 2.333)")
with gr.Column():
img2img_steps = gr.Slider(minimum=1, maximum=250, step=1, label="Sampling Steps",
value=img2img_defaults['ddim_steps'])
img2img_sampling = gr.Dropdown(label='Sampling method (k_lms is default k-diffusion sampler)',
choices=["DDIM", 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler',
'k_heun', 'k_lms'],
value=img2img_defaults['sampler_name'])
img2img_denoising = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength',
value=img2img_defaults['denoising_strength'])
img2img_toggles = gr.CheckboxGroup(label='', choices=img2img_toggles,
value=img2img_toggle_defaults, type="index")
img2img_realesrgan_model_name = gr.Dropdown(label='RealESRGAN model',
choices=['RealESRGAN_x4plus',
'RealESRGAN_x4plus_anime_6B'],
value='RealESRGAN_x4plus',
visible=RealESRGAN is not None) # TODO: Feels like I shouldnt slot it in here.
img2img_embeddings = gr.File(label="Embeddings file for textual inversion",
visible=show_embeddings)
img2img_image_editor_mode.change(
uifn.change_image_editor_mode,
[img2img_image_editor_mode, img2img_image_editor, img2img_resize, img2img_width, img2img_height],
[img2img_image_editor, img2img_image_mask, img2img_btn_editor, img2img_btn_mask,
img2img_painterro_btn, img2img_mask, img2img_mask_blur_strength]
)
img2img_image_editor.edit(
uifn.update_image_mask,
[img2img_image_editor, img2img_resize, img2img_width, img2img_height],
img2img_image_mask
)
output_txt2img_copy_to_input_btn.click(
uifn.copy_img_to_input,
[output_txt2img_gallery],
[img2img_image_editor, img2img_image_mask, tabs],
_js=js_move_image('txt2img_gallery_output', 'img2img_editor')
)
output_img2img_copy_to_input_btn.click(
uifn.copy_img_to_edit,
[output_img2img_gallery],
[img2img_image_editor, tabs, img2img_image_editor_mode],
_js=js_move_image('img2img_gallery_output', 'img2img_editor')
)
output_img2img_copy_to_mask_btn.click(
uifn.copy_img_to_mask,
[output_img2img_gallery],
[img2img_image_mask, tabs, img2img_image_editor_mode],
_js=js_move_image('img2img_gallery_output', 'img2img_editor')
)
output_img2img_copy_to_clipboard_btn.click(fn=None, inputs=output_img2img_gallery, outputs=[],
_js=js_copy_to_clipboard('img2img_gallery_output'))
img2img_btn_mask.click(
img2img,
[img2img_prompt, img2img_image_editor_mode, img2img_image_mask, img2img_mask,
img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles,
img2img_realesrgan_model_name, img2img_batch_count, img2img_batch_size, img2img_cfg,
img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize,
img2img_embeddings],
[output_img2img_gallery, output_img2img_seed, output_img2img_params, output_img2img_stats]
)
def img2img_submit_params():
return (img2img,
[img2img_prompt, img2img_image_editor_mode, img2img_image_editor, img2img_mask,
img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles,
img2img_realesrgan_model_name, img2img_batch_count, img2img_batch_size, img2img_cfg,
img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize,
img2img_embeddings],
[output_img2img_gallery, output_img2img_seed, output_img2img_params, output_img2img_stats])
img2img_btn_editor.click(*img2img_submit_params())
# GENERATE ON ENTER
img2img_prompt.submit(None, None, None,
_js=js_img2img_submit("prompt_row"))
img2img_painterro_btn.click(None, [img2img_image_editor], [img2img_image_editor, img2img_image_mask], _js=js_painterro_launch('img2img_editor'))
img2img_width.change(fn=uifn.update_dimensions_info, inputs=[img2img_width, img2img_height], outputs=img2img_dimensions_info_text_box)
img2img_height.change(fn=uifn.update_dimensions_info, inputs=[img2img_width, img2img_height], outputs=img2img_dimensions_info_text_box)
if GFPGAN is not None:
gfpgan_defaults = {
'strength': 100,
}
if 'gfpgan' in user_defaults:
gfpgan_defaults.update(user_defaults['gfpgan'])
with gr.TabItem("GFPGAN", id='cfpgan_tab'):
gr.Markdown("Fix faces on images")
with gr.Row():
with gr.Column():
gfpgan_source = gr.Image(label="Source", source="upload", interactive=True, type="pil")
gfpgan_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength",
value=gfpgan_defaults['strength'])
gfpgan_btn = gr.Button("Generate", variant="primary")
with gr.Column():
gfpgan_output = gr.Image(label="Output")
gfpgan_btn.click(
run_GFPGAN,
[gfpgan_source, gfpgan_strength],
[gfpgan_output]
)
if RealESRGAN is not None:
with gr.TabItem("RealESRGAN", id='realesrgan_tab'):
gr.Markdown("Upscale images")
with gr.Row():
with gr.Column():
realesrgan_source = gr.Image(label="Source", source="upload", interactive=True, type="pil")
realesrgan_model_name = gr.Dropdown(label='RealESRGAN model', choices=['RealESRGAN_x4plus',
'RealESRGAN_x4plus_anime_6B'],
value='RealESRGAN_x4plus')
realesrgan_btn = gr.Button("Generate")
with gr.Column():
realesrgan_output = gr.Image(label="Output")
realesrgan_btn.click(
run_RealESRGAN,
[realesrgan_source, realesrgan_model_name],
[realesrgan_output]
)
output_txt2img_to_upscale_esrgan.click(
uifn.copy_img_to_upscale_esrgan,
output_txt2img_gallery,
[realesrgan_source, tabs],
_js=js_move_image('txt2img_gallery_output', 'img2img_editor'))
gr.HTML("""
<div id="90" style="max-width: 100%; font-size: 14px; text-align: center;" class="output-markdown gr-prose border-solid border border-gray-200 rounded gr-panel">
<p>For help and advanced usage guides, visit the <a href="https://github.com/hlky/stable-diffusion-webui/wiki" target="_blank">Project Wiki</a></p>
<p>Stable Diffusion WebUI is an open-source project. You can find the latest stable builds on the <a href="https://github.com/hlky/stable-diffusion" target="_blank">main repository</a>.
If you would like to contribute to development or test bleeding edge builds, you can visit the <a href="https://github.com/hlky/stable-diffusion-webui" target="_blank">developement repository</a>.</p>
</div>
""")
# Hack: Detect the load event on the frontend
# Won't be needed in the next version of gradio
# See the relevant PR: https://github.com/gradio-app/gradio/pull/2108
load_detector = gr.Number(value=0, label="Load Detector", visible=False)
load_detector.change(None, None, None, _js=js(opt))
demo.load(lambda x: 42, inputs=load_detector, outputs=load_detector)
return demo