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Code Issues Projects Releases Wiki Activity

Img2img dev (#736)

Browse Source 
* #715 #699 #698 #663 #625 #617 #611 #604 (#716)

* Update README.md

* Add sampler name to metadata (#695)

Co-authored-by: EliEron <example@example.com>

* old-dev-merge

Co-authored-by: EliEron <subanimehd@gmail.com>
Co-authored-by: EliEron <example@example.com>

* img2img-fix (#717)

* Revert "img2img-fix (#717)"

This reverts commit 70d4b1ca2a.

* img2img fixes

* Revert "img2img fixes"

This reverts commit e66eddc621.

* Revert "Revert "img2img-fix (#717)""

This reverts commit bf08b617d4.

* img2img fixed

* feat: bring back Crop mode, formatting

Co-authored-by: EliEron <subanimehd@gmail.com>
Co-authored-by: EliEron <example@example.com>
Co-authored-by: Thomas Mello <work.mello@gmail.com>
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hlky 2022-09-07 20:19:00 +01:00 committed by GitHub
parent 2b1ac8daf7
commit 490bbbc103
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6 changed files with 522 additions and 216 deletions
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19
configs/webui/webui.yaml
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@ -4,15 +4,16 @@ txt2img:
prompt:
ddim_steps: 50
# Adding an int to toggles enables the corresponding feature.
# 0: Create prompt matrix (separate multiple prompts using |, and get all combinations of them)
# 1: Normalize Prompt Weights (ensure sum of weights add up to 1.0)
# 2: Save individual images
# 3: Save grid
# 4: Sort samples by prompt
# 5: Write sample info files
# 6: jpg samples
# 7: Fix faces using GFPGAN
# 8: Upscale images using Real-ESRGAN
# 0: Create prompt matrix (separate multiple prompts using |, and get all combinations of them)
# 1: Normalize Prompt Weights (ensure sum of weights add up to 1.0)
# 2: Save individual images
# 3: Save grid
# 4: Sort samples by prompt
# 5: Write sample info files
# 6: write sample info to log file
# 7: jpg samples
# 8: Fix faces using GFPGAN
# 9: Upscale images using RealESRGAN
toggles: [1, 2, 3, 4, 5]
sampler_name: k_lms
ddim_eta: 0.0 # legacy name, applies to all algorithms.

2
environment.yaml
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@ -38,4 +38,4 @@ dependencies:
- -e git+https://github.com/TencentARC/GFPGAN#egg=GFPGAN
- -e git+https://github.com/xinntao/Real-ESRGAN#egg=realesrgan
- -e git+https://github.com/hlky/k-diffusion-sd#egg=k_diffusion
- -e .
- -e .

209
frontend/css/custom.css Normal file
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@ -0,0 +1,209 @@
/* ----------------------------------------------
* Generated by Animista on 2022-9-3 12:0:51
* Licensed under FreeBSD License.
* See http://animista.net/license for more info.
* w: http://animista.net, t: @cssanimista
* ---------------------------------------------- */
/**
* ----------------------------------------
* animation fade-in
* ----------------------------------------
*/
@-webkit-keyframes fade-in {
0% {
opacity: 0;
}
100% {
opacity: 1;
}
}
@keyframes fade-in {
0% {
opacity: 0;
}
100% {
opacity: 1;
}
}
/* CSS HEX */
:root {
--eerie-black: #141414ff;
--jet: #373737ff;
--white: #ffffffff;
--rajah: #faa549ff;
--tart-orange: #9c85fb;
--light-steel-blue: #b7d0f1ff;
}
/* Gallery items (not working) */
.gallery-item.svelte-1g9btlg.svelte-1g9btlg{
border: none!important;
}
/* Loading background */
.dark .wrap.svelte-2fvq7v{
background-color: #373737ff;
}
/* generate button */
#generate, #img2img_mask_btn, #img2img_edit_btn{
transition: 0.3s;
color: #9c85fb!important;
border-color: #9c85fb!important;
}
#generate:hover, #img2img_mask_btn:hover, #img2img_edit_btn:hover{
color: #141414ff!important;
background-color: #9c85fb!important;
}
/* Generation paramters */
#highlight > div.textfield.bg-white.dark\:bg-transparent.rounded-sm.text-sm.box-border.max-w-full.break-word.leading-7.mt-7 > span:nth-child(2){
background: none!important;
color: white!important;
}
#highlight > div.textfield.bg-white.dark\:bg-transparent.rounded-sm.text-sm.box-border.max-w-full.break-word.leading-7.mt-7 > span:nth-child(4){
background: none!important;
color: white!important;
}
#highlight > div.textfield.bg-white.dark\:bg-transparent.rounded-sm.text-sm.box-border.max-w-full.break-word.leading-7.mt-7 > span:nth-child(6){
background: none!important;
color: white!important;
}
#highlight > div.textfield.bg-white.dark\:bg-transparent.rounded-sm.text-sm.box-border.max-w-full.break-word.leading-7.mt-7 > span:nth-child(8){
background: none!important;
color: white!important;
}
#highlight > div.textfield.bg-white.dark\:bg-transparent.rounded-sm.text-sm.box-border.max-w-full.break-word.leading-7.mt-7 > span:nth-child(10){
background: none!important;
color: white!important;
}
#highlight > div.textfield.bg-white.dark\:bg-transparent.rounded-sm.text-sm.box-border.max-w-full.break-word.leading-7.mt-7 > span:nth-child(12){
background: none!important;
color: white!important;
}
/* Mask background */
.dark .bg-gray-200{
background-color:rgba(55, 55, 55, 0.105)!important;
}
.cropper-wrap-box, .cropper-canvas{
background-color:rgba(55, 55, 55, 0.105)!important;
}
.cropper-bg {
background: none!important;
}
select {
background:#000;
color:#fff;
}
select * {
background:#373737ff;
color:#9c85fb;
}
/* General Background */
.gradio-container {background:#141414ff;}
/*General Text color on hover */
.dark .hover\:text-gray-700:hover{
color: #9d85fb8a!important;
}
/*General Text color */
.text-gray-400{
color:rgba(255, 255, 255, 0.504);
transition: 0.3s;
}
/* General container of everything */
.dark .dark\:bg-gray-950 {background-color: #141414ff!important;
-webkit-animation: fade-in 1s ease-in both;
animation: fade-in 1s ease-in both;
}
/* labels in frames of gallery */
.dark .dark\:bg-gray-900 {
background-color:#9d85fbdf!important;
border: none!important;}
/* Background for Gradio stuff along with colors for text */
.dark .gr-box {
background-color:rgba(55, 55, 55, 0.105)!important;
border: solid 0.5px!important;
border-color: rgba(55, 55, 55, 0.055)!important;
/* sampler selector color */
color: #9c85fb!important;}
/* Secondary Buttons color */
.dark .gr-button-secondary{
color: #9c85fb;
border-color: #9d85fb5c;
transition: 0.3s;}
.dark .gr-button-secondary:hover{
color: #141414ff!important;
background-color: #9c85fb!important;}
.dark .gr-button-primary{
color: #9c85fb;
border-color: #9d85fb5c;
transition: 0.3s;}
.dark .gr-button-primary:hover{
color: #141414ff!important;
background-color: #9c85fb!important;}
/* image lab process button */
div[id*="111"]{
width: 50% !important;
align-self: center !important;
}
/* Selected tabs color */
button, input, optgroup, select, textarea {color: #9c85fb;!important}
/* -or- text color wtf */
.text-gray-300{
color:rgba(255, 255, 255, 0.504);
}
/* Sliders colors */
button, input, optgroup, select, textarea{
accent-color: #9c85fb!important;
}
/* Text color for placeholder in prompt */
input.scroll-hide.block.gr-box.gr-input.w-full.gr-text-input::placeholder{
color: #9d85fb5c;
transition: 0.3s;
}
/* disabling borders for stuff */
.border-gray-200{
/* no border */
border: none;
}
.border-b-2{
/* no border */
border: none;
}
/* disabling backgrounds for labels and buttons */
button, select, textarea {
background: none!important;
}
/* radio selection border and background */
.dark .gr-input-label{
background: none!important;
border: none!important;
}
/* checkbox and radio buttons color when checked */
.dark .gr-check-radio:checked{
background-color: #9c85fb!important;
}
/* checkbox and radio buttons color when unchecked */
.dark .gr-check-radio{
background-color: #373737ff!important;
}

432
frontend/frontend.py
View File

@ -4,12 +4,14 @@ from frontend.job_manager import JobManager
import frontend.ui_functions as uifn
import uuid
def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x,imgproc=lambda x: x, txt2img_defaults={}, RealESRGAN=True, GFPGAN=True,LDSR=True,
def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda x: x, txt2img_defaults={},
RealESRGAN=True, GFPGAN=True, LDSR=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, imgproc_defaults={},imgproc_mode_toggles={},user_defaults={}, run_GFPGAN=lambda x: x, run_RealESRGAN=lambda x: x,
img2img_resize_modes=None, imgproc_defaults={}, imgproc_mode_toggles={}, user_defaults={},
run_GFPGAN=lambda x: x, run_RealESRGAN=lambda x: x,
job_manager: JobManager = None) -> gr.Blocks:
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("Text-to-Image", id='txt2img_tab'):
@ -40,34 +42,37 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x,imgproc=lambda
txt2img_job_ui = job_manager.draw_gradio_ui() if job_manager else None
txt2img_dimensions_info_text_box = gr.Textbox(label="Aspect ratio (4:3 = 1.333 | 16:9 = 1.777 | 21:9 = 2.333)")
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():
with gr.Box():
output_txt2img_gallery = gr.Gallery(label="Images", elem_id="txt2img_gallery_output").style(grid=[4, 4])
gr.Markdown("Select an image from the gallery, then click one of the buttons below to perform an action.")
output_txt2img_gallery = gr.Gallery(label="Images", elem_id="txt2img_gallery_output").style(
grid=[4, 4])
gr.Markdown(
"Select an image from the gallery, then click one of the buttons below to perform an action.")
with gr.Row(elem_id='txt2img_actions_row'):
gr.Button("Copy to clipboard").click(fn=None,
inputs=output_txt2img_gallery,
outputs=[],
#_js=js_copy_to_clipboard( 'txt2img_gallery_output')
)
inputs=output_txt2img_gallery,
outputs=[],
# _js=js_copy_to_clipboard( 'txt2img_gallery_output')
)
output_txt2img_copy_to_input_btn = gr.Button("Push to img2img")
output_txt2img_to_imglab = gr.Button("Send to Lab",visible=True)
output_txt2img_to_imglab = gr.Button("Send to Lab", visible=True)
output_txt2img_params = gr.Highlightedtext(label="Generation parameters", interactive=False, elem_id='highlight')
output_txt2img_params = gr.Highlightedtext(label="Generation parameters", interactive=False,
elem_id='highlight')
with gr.Group():
with gr.Row(elem_id='txt2img_output_row'):
output_txt2img_copy_params = gr.Button("Copy full parameters").click(
inputs=[output_txt2img_params], outputs=[],
_js=js_copy_txt2img_output,
fn=None, show_progress=False)
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)',
@ -93,7 +98,7 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x,imgproc=lambda
choices=['RealESRGAN_x4plus',
'RealESRGAN_x4plus_anime_6B'],
value='RealESRGAN_x4plus',
visible=False)#RealESRGAN is not None # invisible until removed) # TODO: Feels like I shouldnt slot it in here.
visible=False) # RealESRGAN is not None # invisible until removed) # 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',
@ -133,7 +138,8 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x,imgproc=lambda
# 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)
live_prompt_params = [txt2img_prompt, txt2img_width, txt2img_height, txt2img_steps, txt2img_seed, txt2img_batch_count, txt2img_cfg]
live_prompt_params = [txt2img_prompt, txt2img_width, txt2img_height, txt2img_steps, txt2img_seed,
txt2img_batch_count, txt2img_cfg]
txt2img_prompt.change(
fn=None,
inputs=live_prompt_params,
@ -141,7 +147,6 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x,imgproc=lambda
_js=js_parse_prompt
)
with gr.TabItem("Image-to-Image Unified", id="img2img_tab"):
with gr.Row(elem_id="prompt_row"):
img2img_prompt = gr.Textbox(label="Prompt",
@ -158,39 +163,58 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x,imgproc=lambda
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")
img2img_image_mask = gr.Image(
value=sample_img2img,
source="upload",
interactive=True,
type="pil", tool="sketch",
elem_id="img2img_mask",
image_mode="RGBA"
)
img2img_image_editor = gr.Image(
value=sample_img2img,
source="upload",
interactive=True,
type="pil",
tool="select",
visible=False,
image_mode="RGBA",
elem_id="img2img_editor"
)
with gr.Tabs():
with gr.TabItem("Editor Options"):
with gr.Row():
img2img_image_editor_mode = gr.Radio(choices=["Mask", "Crop", "Uncrop"], label="Image Editor Mode",
value="Crop", elem_id='edit_mode_select')
# disable Uncrop for now
# choices=["Mask", "Crop", "Uncrop"]
img2img_image_editor_mode = gr.Radio(choices=["Mask", "Crop"],
label="Image Editor Mode",
value="Mask", elem_id='edit_mode_select',
visible=True)
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)
label="Mask Mode", type="index",
value=img2img_mask_modes[img2img_defaults['mask_mode']],
visible=True)
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)
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']])
choices=["Just resize", "Crop and resize",
"Resize and fill"],
type="index",
value=img2img_resize_modes[
img2img_defaults['resize_mode']], visible=False)
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])
output_img2img_gallery = gr.Gallery(label="Images", elem_id="img2img_gallery_output").style(
grid=[4, 4, 4])
img2img_job_ui = job_manager.draw_gradio_ui() if job_manager else None
with gr.Tabs():
with gr.TabItem("Generated image actions", id="img2img_actions_tab"):
@ -206,7 +230,8 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x,imgproc=lambda
with gr.Row():
output_img2img_copy_params = gr.Button("Copy full parameters").click(
inputs=output_img2img_params, outputs=[],
_js='(x) => {navigator.clipboard.writeText(x.replace(": ",":"))}', fn=None, show_progress=False)
_js='(x) => {navigator.clipboard.writeText(x.replace(": ",":"))}', 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=[],
@ -229,7 +254,8 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x,imgproc=lambda
img2img_batch_count = gr.Slider(minimum=1, maximum=50, step=1,
label='Batch count (how many batches of images to generate)',
value=img2img_defaults['n_iter'])
img2img_dimensions_info_text_box = gr.Textbox(label="Aspect ratio (4:3 = 1.333 | 16:9 = 1.777 | 21:9 = 2.333)")
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'])
@ -256,16 +282,22 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x,imgproc=lambda
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_mode,
img2img_image_editor,
img2img_image_mask,
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
)
# img2img_image_editor_mode.change(
# 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,
@ -299,12 +331,13 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x,imgproc=lambda
)
img2img_func = img2img
img2img_inputs = [img2img_prompt, img2img_image_editor_mode, img2img_image_editor, img2img_image_mask, img2img_mask,
img2img_inputs = [img2img_prompt, img2img_image_editor_mode, img2img_mask,
img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles,
img2img_realesrgan_model_name, img2img_batch_count, img2img_cfg,
img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize,
img2img_embeddings]
img2img_outputs = [output_img2img_gallery, output_img2img_seed, output_img2img_params, output_img2img_stats]
img2img_image_editor, img2img_image_mask, img2img_embeddings]
img2img_outputs = [output_img2img_gallery, output_img2img_seed, output_img2img_params,
output_img2img_stats]
# If a JobManager was passed in then wrap the Generate functions
if img2img_job_ui:
@ -319,10 +352,16 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x,imgproc=lambda
img2img_inputs,
img2img_outputs
)
def img2img_submit_params():
# print([img2img_prompt, img2img_image_editor_mode, img2img_mask,
# img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles,
# img2img_realesrgan_model_name, img2img_batch_count, img2img_cfg,
# img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize,
# img2img_image_editor, img2img_image_mask, img2img_embeddings])
return (img2img_func,
img2img_inputs,
img2img_outputs)
img2img_inputs,
img2img_outputs)
img2img_btn_editor.click(*img2img_submit_params())
@ -337,26 +376,31 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x,imgproc=lambda
_js=call_JS("Painterro.init", toId="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)
with gr.TabItem("Image Lab", id='imgproc_tab'):
gr.Markdown("Post-process results")
with gr.Row():
with gr.Column():
with gr.Tabs():
with gr.TabItem('Single Image'):
imgproc_source = gr.Image(label="Source", source="upload", interactive=True, type="pil",elem_id="imglab_input")
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)
#gfpgan_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength",
with gr.TabItem("Image Lab", id='imgproc_tab'):
gr.Markdown("Post-process results")
with gr.Row():
with gr.Column():
with gr.Tabs():
with gr.TabItem('Single Image'):
imgproc_source = gr.Image(label="Source", source="upload", interactive=True, type="pil",
elem_id="imglab_input")
# gfpgan_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength",
# value=gfpgan_defaults['strength'])
#select folder with images to process
with gr.TabItem('Batch Process'):
imgproc_folder = gr.File(label="Batch Process", file_count="multiple",source="upload", interactive=True, type="file")
imgproc_pngnfo = gr.Textbox(label="PNG Metadata", placeholder="PngNfo", visible=False, max_lines=5)
with gr.Row():
imgproc_btn = gr.Button("Process", variant="primary")
gr.HTML("""
# select folder with images to process
with gr.TabItem('Batch Process'):
imgproc_folder = gr.File(label="Batch Process", file_count="multiple", source="upload",
interactive=True, type="file")
imgproc_pngnfo = gr.Textbox(label="PNG Metadata", placeholder="PngNfo", visible=False,
max_lines=5)
with gr.Row():
imgproc_btn = gr.Button("Process", variant="primary")
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><b>Upscale Modes Guide</b></p>
<p></p>
@ -370,142 +414,170 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x,imgproc=lambda
<p>A 8X upscaler with high VRAM usage, uses GoBig to add details and then uses a Latent Diffusion model to upscale the image, this will result in less artifacting/sharpeninng, use the settings to feed GoBig settings that will contribute to the result, this mode is considerbly slower</p>
</div>
""")
with gr.Column():
with gr.Tabs():
with gr.TabItem('Output'):
imgproc_output = gr.Gallery(label="Output", elem_id="imgproc_gallery_output")
with gr.Row(elem_id="proc_options_row"):
with gr.Box():
with gr.Column():
gr.Markdown("<b>Processor Selection</b>")
imgproc_toggles = gr.CheckboxGroup(label = '',choices=imgproc_mode_toggles, type="index")
#.change toggles to show options
#imgproc_toggles.change()
with gr.Box(visible=False) as gfpgan_group:
with gr.Column():
with gr.Tabs():
with gr.TabItem('Output'):
imgproc_output = gr.Gallery(label="Output", elem_id="imgproc_gallery_output")
with gr.Row(elem_id="proc_options_row"):
with gr.Box():
with gr.Column():
gr.Markdown("<b>Processor Selection</b>")
imgproc_toggles = gr.CheckboxGroup(label='', choices=imgproc_mode_toggles,
type="index")
# .change toggles to show options
# imgproc_toggles.change()
with gr.Box(visible=False) as gfpgan_group:
gfpgan_defaults = {
'strength': 100,
}
gfpgan_defaults = {
'strength': 100,
}
if 'gfpgan' in user_defaults:
gfpgan_defaults.update(user_defaults['gfpgan'])
if GFPGAN is None:
gr.HTML("""
if 'gfpgan' in user_defaults:
gfpgan_defaults.update(user_defaults['gfpgan'])
if GFPGAN is None:
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><b> Please download GFPGAN to activate face fixing features</b>, instructions are available at the <a href='https://github.com/hlky/stable-diffusion-webui'>Github</a></p>
</div>
""")
#gr.Markdown("")
#gr.Markdown("<b> Please download GFPGAN to activate face fixing features</b>, instructions are available at the <a href='https://github.com/hlky/stable-diffusion-webui'>Github</a>")
with gr.Column():
gr.Markdown("<b>GFPGAN Settings</b>")
imgproc_gfpgan_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength",
value=gfpgan_defaults['strength'],visible=GFPGAN is not None)
with gr.Box(visible=False) as upscale_group:
# gr.Markdown("")
# gr.Markdown("<b> Please download GFPGAN to activate face fixing features</b>, instructions are available at the <a href='https://github.com/hlky/stable-diffusion-webui'>Github</a>")
with gr.Column():
gr.Markdown("<b>GFPGAN Settings</b>")
imgproc_gfpgan_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.001,
label="Effect strength",
value=gfpgan_defaults['strength'],
visible=GFPGAN is not None)
with gr.Box(visible=False) as upscale_group:
if LDSR:
upscaleModes = ['RealESRGAN','GoBig','Latent Diffusion SR','GoLatent ']
else:
gr.HTML("""
if LDSR:
upscaleModes = ['RealESRGAN', 'GoBig', 'Latent Diffusion SR', 'GoLatent ']
else:
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><b> Please download LDSR to activate more upscale features</b>, instructions are available at the <a href='https://github.com/hlky/stable-diffusion-webui'>Github</a></p>
</div>
""")
upscaleModes = ['RealESRGAN','GoBig']
upscaleModes = ['RealESRGAN', 'GoBig']
with gr.Column():
gr.Markdown("<b>Upscaler Selection</b>")
imgproc_upscale_toggles = gr.Radio(label='', choices=upscaleModes, type="index",
visible=RealESRGAN is not None, value='RealESRGAN')
with gr.Box(visible=False) as upscalerSettings_group:
with gr.Box(visible=True) as realesrgan_group:
with gr.Column():
gr.Markdown("<b>Upscaler Selection</b>")
imgproc_upscale_toggles = gr.Radio(label = '',choices=upscaleModes, type="index",visible=RealESRGAN is not None,value='RealESRGAN')
with gr.Box(visible=False) as upscalerSettings_group:
with gr.Box(visible=True) as realesrgan_group:
gr.Markdown("<b>RealESRGAN Settings</b>")
imgproc_realesrgan_model_name = gr.Dropdown(label='RealESRGAN model',
interactive=RealESRGAN is not None,
choices=['RealESRGAN_x4plus',
'RealESRGAN_x4plus_anime_6B',
'RealESRGAN_x2plus',
'RealESRGAN_x2plus_anime_6B'],
value='RealESRGAN_x4plus',
visible=RealESRGAN is not None) # TODO: Feels like I shouldnt slot it in here.
with gr.Box(visible=False) as ldsr_group:
with gr.Row(elem_id="ldsr_settings_row"):
with gr.Column():
gr.Markdown("<b>RealESRGAN Settings</b>")
imgproc_realesrgan_model_name = gr.Dropdown(label='RealESRGAN model', interactive=RealESRGAN is not None,
choices= ['RealESRGAN_x4plus',
'RealESRGAN_x4plus_anime_6B','RealESRGAN_x2plus',
'RealESRGAN_x2plus_anime_6B'],
value='RealESRGAN_x4plus',
visible=RealESRGAN is not None) # TODO: Feels like I shouldnt slot it in here.
with gr.Box(visible=False) as ldsr_group:
with gr.Row(elem_id="ldsr_settings_row"):
with gr.Column():
gr.Markdown("<b>Latent Diffusion Super Sampling Settings</b>")
imgproc_ldsr_steps = gr.Slider(minimum=0, maximum=500, step=10, label="LDSR Sampling Steps",
value=100,visible=LDSR is not None)
imgproc_ldsr_pre_downSample = gr.Dropdown(label='LDSR Pre Downsample mode (Lower resolution before processing for speed)',
choices=["None", '1/2', '1/4'],value="None",visible=LDSR is not None)
imgproc_ldsr_post_downSample = gr.Dropdown(label='LDSR Post Downsample mode (aka SuperSampling)',
choices=["None", "Original Size", '1/2', '1/4'],value="None",visible=LDSR is not None)
with gr.Box(visible=False) as gobig_group:
with gr.Row(elem_id="proc_prompt_row"):
with gr.Column():
gr.Markdown("<b>GoBig Settings</b>")
imgproc_prompt = gr.Textbox(label="",
elem_id='prompt_input',
placeholder="A corgi wearing a top hat as an oil painting.",
lines=1,
max_lines=1,
value=imgproc_defaults['prompt'],
show_label=True,
visible=RealESRGAN is not None)
imgproc_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=imgproc_defaults['sampler_name'],visible=RealESRGAN is not None)
imgproc_steps = gr.Slider(minimum=1, maximum=250, step=1, label="Sampling Steps",
value=imgproc_defaults['ddim_steps'],visible=RealESRGAN is not None)
imgproc_cfg = gr.Slider(minimum=1.0, maximum=30.0, step=0.5,
label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)',
value=imgproc_defaults['cfg_scale'],visible=RealESRGAN is not None)
imgproc_denoising = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength',
value=imgproc_defaults['denoising_strength'],visible=RealESRGAN is not None)
imgproc_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height",
value=imgproc_defaults["height"],visible=False) # not currently implemented
imgproc_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width",
value=imgproc_defaults["width"],visible=False) # not currently implemented
imgproc_seed = gr.Textbox(label="Seed (blank to randomize)", lines=1, max_lines=1,
value=imgproc_defaults["seed"],visible=RealESRGAN is not None)
imgproc_btn.click(
imgproc,
[imgproc_source, imgproc_folder,imgproc_prompt,imgproc_toggles,
imgproc_upscale_toggles,imgproc_realesrgan_model_name,imgproc_sampling, imgproc_steps, imgproc_height,
imgproc_width, imgproc_cfg, imgproc_denoising, imgproc_seed,imgproc_gfpgan_strength,imgproc_ldsr_steps,imgproc_ldsr_pre_downSample,imgproc_ldsr_post_downSample],
[imgproc_output])
gr.Markdown("<b>Latent Diffusion Super Sampling Settings</b>")
imgproc_ldsr_steps = gr.Slider(minimum=0, maximum=500, step=10,
label="LDSR Sampling Steps",
value=100, visible=LDSR is not None)
imgproc_ldsr_pre_downSample = gr.Dropdown(
label='LDSR Pre Downsample mode (Lower resolution before processing for speed)',
choices=["None", '1/2', '1/4'], value="None", visible=LDSR is not None)
imgproc_ldsr_post_downSample = gr.Dropdown(
label='LDSR Post Downsample mode (aka SuperSampling)',
choices=["None", "Original Size", '1/2', '1/4'], value="None",
visible=LDSR is not None)
with gr.Box(visible=False) as gobig_group:
with gr.Row(elem_id="proc_prompt_row"):
with gr.Column():
gr.Markdown("<b>GoBig Settings</b>")
imgproc_prompt = gr.Textbox(label="",
elem_id='prompt_input',
placeholder="A corgi wearing a top hat as an oil painting.",
lines=1,
max_lines=1,
value=imgproc_defaults['prompt'],
show_label=True,
visible=RealESRGAN is not None)
imgproc_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=imgproc_defaults['sampler_name'], visible=RealESRGAN is not None)
imgproc_steps = gr.Slider(minimum=1, maximum=250, step=1,
label="Sampling Steps",
value=imgproc_defaults['ddim_steps'],
visible=RealESRGAN is not None)
imgproc_cfg = gr.Slider(minimum=1.0, maximum=30.0, step=0.5,
label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)',
value=imgproc_defaults['cfg_scale'],
visible=RealESRGAN is not None)
imgproc_denoising = gr.Slider(minimum=0.0, maximum=1.0, step=0.01,
label='Denoising Strength',
value=imgproc_defaults['denoising_strength'],
visible=RealESRGAN is not None)
imgproc_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height",
value=imgproc_defaults["height"],
visible=False) # not currently implemented
imgproc_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width",
value=imgproc_defaults["width"],
visible=False) # not currently implemented
imgproc_seed = gr.Textbox(label="Seed (blank to randomize)", lines=1,
max_lines=1,
value=imgproc_defaults["seed"],
visible=RealESRGAN is not None)
imgproc_btn.click(
imgproc,
[imgproc_source, imgproc_folder, imgproc_prompt, imgproc_toggles,
imgproc_upscale_toggles, imgproc_realesrgan_model_name, imgproc_sampling,
imgproc_steps, imgproc_height,
imgproc_width, imgproc_cfg, imgproc_denoising, imgproc_seed,
imgproc_gfpgan_strength, imgproc_ldsr_steps, imgproc_ldsr_pre_downSample,
imgproc_ldsr_post_downSample],
[imgproc_output])
imgproc_source.change(
uifn.get_png_nfo,
[imgproc_source],
[imgproc_pngnfo] )
imgproc_source.change(
uifn.get_png_nfo,
[imgproc_source],
[imgproc_pngnfo])
output_txt2img_to_imglab.click(
fn=uifn.copy_img_params_to_lab,
inputs = [output_txt2img_params],
outputs = [imgproc_prompt,imgproc_seed,imgproc_steps,imgproc_cfg,imgproc_sampling],
)
output_txt2img_to_imglab.click(
fn=uifn.copy_img_to_lab,
inputs = [output_txt2img_gallery],
outputs = [imgproc_source, tabs],
_js=call_JS("moveImageFromGallery",
fromId="txt2img_gallery_output",
toId="imglab_input")
)
if RealESRGAN is None:
with gr.Row():
with gr.Column():
#seperator
gr.HTML("""
output_txt2img_to_imglab.click(
fn=uifn.copy_img_params_to_lab,
inputs=[output_txt2img_params],
outputs=[imgproc_prompt, imgproc_seed, imgproc_steps, imgproc_cfg,
imgproc_sampling],
)
output_txt2img_to_imglab.click(
fn=uifn.copy_img_to_lab,
inputs=[output_txt2img_gallery],
outputs=[imgproc_source, tabs],
_js=call_JS("moveImageFromGallery",
fromId="txt2img_gallery_output",
toId="imglab_input")
)
if RealESRGAN is None:
with gr.Row():
with gr.Column():
# seperator
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><b> Please download RealESRGAN to activate upscale features</b>, instructions are available at the <a href='https://github.com/hlky/stable-diffusion-webui'>Github</a></p>
</div>
""")
imgproc_toggles.change(fn=uifn.toggle_options_gfpgan, inputs=[imgproc_toggles], outputs=[gfpgan_group])
imgproc_toggles.change(fn=uifn.toggle_options_upscalers, inputs=[imgproc_toggles], outputs=[upscale_group])
imgproc_toggles.change(fn=uifn.toggle_options_upscalers, inputs=[imgproc_toggles], outputs=[upscalerSettings_group])
imgproc_upscale_toggles.change(fn=uifn.toggle_options_realesrgan, inputs=[imgproc_upscale_toggles], outputs=[realesrgan_group])
imgproc_upscale_toggles.change(fn=uifn.toggle_options_ldsr, inputs=[imgproc_upscale_toggles], outputs=[ldsr_group])
imgproc_upscale_toggles.change(fn=uifn.toggle_options_gobig, inputs=[imgproc_upscale_toggles], outputs=[gobig_group])
imgproc_toggles.change(fn=uifn.toggle_options_upscalers, inputs=[imgproc_toggles],
outputs=[upscalerSettings_group])
imgproc_upscale_toggles.change(fn=uifn.toggle_options_realesrgan, inputs=[imgproc_upscale_toggles],
outputs=[realesrgan_group])
imgproc_upscale_toggles.change(fn=uifn.toggle_options_ldsr, inputs=[imgproc_upscale_toggles],
outputs=[ldsr_group])
imgproc_upscale_toggles.change(fn=uifn.toggle_options_gobig, inputs=[imgproc_upscale_toggles],
outputs=[gobig_group])
"""
if GFPGAN is not None:

11
frontend/ui_functions.py
View File

@ -6,14 +6,17 @@ import base64
import re
def change_image_editor_mode(choice, cropped_image, resize_mode, width, height):
def change_image_editor_mode(choice, cropped_image, masked_image, resize_mode, width, height):
if choice == "Mask":
return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)]
return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
update_image_result = update_image_mask(cropped_image, resize_mode, width, height)
return [gr.update(visible=False), update_image_result, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)]
update_image_result = update_image_mask(masked_image["image"] if masked_image is not None else None, resize_mode, width, height)
return [update_image_result, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
def update_image_mask(cropped_image, resize_mode, width, height):
resized_cropped_image = resize_image(resize_mode, cropped_image, width, height) if cropped_image else None
return gr.update(value=resized_cropped_image)
return gr.update(value=resized_cropped_image, visible=True)
def toggle_options_gfpgan(selection):
if 0 in selection:

65
scripts/webui.py
View File

@ -1,5 +1,7 @@
import argparse, os, sys, glob, re
import cv2
from frontend.frontend import draw_gradio_ui
from frontend.job_manager import JobManager, JobInfo
from frontend.ui_functions import resize_image
@ -1223,9 +1225,14 @@ class Flagging(gr.FlaggingCallback):
print("Logged:", filenames[0])
def img2img(prompt: str, image_editor_mode: str, init_info: any, init_info_mask: any, mask_mode: str, mask_blur_strength: int, ddim_steps: int, sampler_name: str,
def img2img(prompt: str, image_editor_mode: str, mask_mode: str, mask_blur_strength: int, ddim_steps: int, sampler_name: str,
toggles: List[int], realesrgan_model_name: str, n_iter: int, cfg_scale: float, denoising_strength: float,
seed: int, height: int, width: int, resize_mode: int, fp = None, job_info: JobInfo = None):
seed: int, height: int, width: int, resize_mode: int, init_info: any = None, init_info_mask: any = None, fp = None, job_info: JobInfo = None):
# print([prompt, image_editor_mode, init_info, init_info_mask, mask_mode,
# mask_blur_strength, ddim_steps, sampler_name, toggles,
# realesrgan_model_name, n_iter, cfg_scale,
# denoising_strength, seed, height, width, resize_mode,
# fp])
outpath = opt.outdir_img2img or opt.outdir or "outputs/img2img-samples"
err = False
seed = seed_to_int(seed)
@ -1274,13 +1281,15 @@ def img2img(prompt: str, image_editor_mode: str, init_info: any, init_info_mask:
init_img = init_info_mask["image"]
init_img = init_img.convert("RGB")
init_img = resize_image(resize_mode, init_img, width, height)
init_img = init_img.convert("RGB")
init_mask = init_info_mask["mask"]
init_mask = resize_image(resize_mode, init_mask, width, height)
keep_mask = mask_mode == 0
init_mask = init_mask.convert("RGB")
init_mask = resize_image(resize_mode, init_mask, width, height)
init_mask = init_mask.convert("RGB")
keep_mask = mask_mode == 0
init_mask = init_mask if keep_mask else ImageOps.invert(init_mask)
else:
init_img = init_info.convert("RGB")
init_img = init_info
init_mask = None
keep_mask = False
@ -1290,14 +1299,14 @@ def img2img(prompt: str, image_editor_mode: str, init_info: any, init_info_mask:
def init():
image = init_img.convert("RGB")
image = resize_image(resize_mode, image, width, height)
#image = image.convert("RGB") #todo: mask mode -> ValueError: could not convert string to float:
#image = image.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
mask_channel = None
if image_editor_mode == "Uncrop":
alpha = init_img.convert("RGB")
alpha = init_img.convert("RGBA")
alpha = resize_image(resize_mode, alpha, width // 8, height // 8)
mask_channel = alpha.split()[-1]
mask_channel = mask_channel.filter(ImageFilter.GaussianBlur(4))
@ -1305,7 +1314,7 @@ def img2img(prompt: str, image_editor_mode: str, init_info: any, init_info_mask:
mask_channel[mask_channel >= 255] = 255
mask_channel[mask_channel < 255] = 0
mask_channel = Image.fromarray(mask_channel).filter(ImageFilter.GaussianBlur(2))
elif init_mask is not None:
elif image_editor_mode == "Mask":
alpha = init_mask.convert("RGBA")
alpha = resize_image(resize_mode, alpha, width // 8, height // 8)
mask_channel = alpha.split()[1]
@ -1324,7 +1333,7 @@ def img2img(prompt: str, image_editor_mode: str, init_info: any, init_info_mask:
init_image = init_image.to(device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
init_latent = (model if not opt.optimized else modelFS).get_first_stage_encoding((model if not opt.optimized else modelFS).encode_first_stage(init_image)) # move to latent space
if opt.optimized:
mem = torch.cuda.memory_allocated()/1e6
modelFS.to("cpu")
@ -1382,7 +1391,17 @@ def img2img(prompt: str, image_editor_mode: str, init_info: any, init_info_mask:
history = []
initial_seed = None
do_color_correction = False
try:
from skimage import exposure
do_color_correction = True
except:
print("Install scikit-image to perform color correction on loopback")
for i in range(n_iter):
if do_color_correction and i == 0:
correction_target = cv2.cvtColor(np.asarray(init_img.copy()), cv2.COLOR_RGB2LAB)
output_images, seed, info, stats = process_images(
outpath=outpath,
func_init=init,
@ -1424,6 +1443,17 @@ def img2img(prompt: str, image_editor_mode: str, init_info: any, init_info_mask:
initial_seed = seed
init_img = output_images[0]
if do_color_correction and correction_target is not None:
init_img = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
cv2.cvtColor(
np.asarray(init_img),
cv2.COLOR_RGB2LAB
),
correction_target,
channel_axis=2
), cv2.COLOR_LAB2RGB).astype("uint8"))
if not random_seed_loopback:
seed = seed + 1
else:
@ -2012,9 +2042,9 @@ imgproc_mode_toggles = [
'Upscale'
]
sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
#sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
#sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
sample_img2img = None
# make sure these indicies line up at the top of img2img()
img2img_toggles = [
'Create prompt matrix (separate multiple prompts using |, and get all combinations of them)',
@ -2078,22 +2108,13 @@ def update_image_mask(cropped_image, resize_mode, width, height):
resized_cropped_image = resize_image(resize_mode, cropped_image, width, height) if cropped_image else None
return gr.update(value=resized_cropped_image)
def copy_img_to_input(img):
try:
image_data = re.sub('^data:image/.+;base64,', '', img)
processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
tab_update = gr.update(selected='img2img_tab')
img_update = gr.update(value=processed_image)
return {img2img_image_mask: processed_image, img2img_image_editor: img_update, tabs: tab_update}
except IndexError:
return [None, None]
def copy_img_to_upscale_esrgan(img):
update = gr.update(selected='realesrgan_tab')
image_data = re.sub('^data:image/.+;base64,', '', img)
processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
return {realesrgan_source: processed_image, tabs: update}
return {'realesrgan_source': processed_image, 'tabs': update}
help_text = """