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updated interface to use Blocks

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added mode toggle for img2img
added inpainting to readme
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AUTOMATIC 2022-08-30 21:51:30 +03:00
parent bb2faa5fb8
commit 7434b3ebcc
5 changed files with 261 additions and 70 deletions
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7
README.md
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@ -204,6 +204,10 @@ To use this feature, tick a checkbox in the img2img interface. Original
image will be upscaled to twice the original width and height, while width and height sliders
will specify the size of individual tiles. At the moment this method does not support batch size.
Rcommended parameters for upscaling:
- Sampling method: Euler a
- Denoising strength: 0.2, can go up to 0.4 if you feel adventureous
![](images/sd-upscale.jpg)
### User scripts
@ -261,4 +265,7 @@ compared to normal operation on my RTX 3090.
This is an independent implementation that does not require any modification to original Stable Diffusion code, and
with all code concenrated in one place rather than scattered around the program.
### Inpainting
In img2img tab, draw a mask over a part of image, and that part will be in-painted.
![](images/inpainting.png)

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61
style.css Normal file
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@ -0,0 +1,61 @@
button{
align-self: stretch !important;
}
#img2img_mode{
padding: 0 0 1em 0;
border: none !important;
}
#img2img_prompt, #txt2img_prompt{
padding: 0;
border: none !important;
}
#img2maskimg .h-60{
height: 30rem;
}
.overflow-hidden, .gr-panel{
overflow: visible !important;
}
fieldset span.text-gray-500{
position: absolute;
top: -0.425em;
background-color: rgb(249 250 251);
line-height: 0.7em;
padding: 0 0.5em;
}
.dark fieldset span.text-gray-500 { background-color: rgb(17 24 39); }
.gr-panel div.flex-col div.justify-between label, label.block span{
position: absolute;
top: -0.4em;
background-color: rgb(249 250 251);
line-height: 0.7em;
padding: 0 0.5em;
margin: 0;
}
.dark .gr-panel div.flex-col div.justify-between label{ background-color: rgb(17 24 39); }
.gr-panel div.flex-col div.justify-between label span{
margin: 0;
}
.gr-panel div.flex-col div.justify-between div{
position: absolute;
top: -0.1em;
right: 1em;
padding: 0 0.5em;
}
input[type="range"]{
margin: 0.5em 0 -0.3em 0;
}
#txt2img_sampling label{
padding-left: 0.6em;
padding-right: 0.6em;
}

263
webui.py
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@ -8,6 +8,7 @@ import torch
import torch.nn as nn
import numpy as np
import gradio as gr
import gradio.utils
from omegaconf import OmegaConf
from PIL import Image, ImageFont, ImageDraw, PngImagePlugin, ImageFilter, ImageOps
from torch import autocast
@ -24,18 +25,16 @@ from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
logging.set_verbosity_error()
except Exception:
pass
# fix gradio phoning home
gradio.utils.version_check = lambda: None
gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
mimetypes.init()
mimetypes.add_type('application/javascript', '.js')
script_path = os.path.dirname(os.path.realpath(__file__))
# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
opt_f = 8
@ -72,12 +71,12 @@ css_hide_progressbar = """
SamplerData = namedtuple('SamplerData', ['name', 'constructor'])
samplers = [
*[SamplerData(x[0], lambda funcname=x[1]: KDiffusionSampler(funcname)) for x in [
('Euler ancestral', 'sample_euler_ancestral'),
('Euler a', 'sample_euler_ancestral'),
('Euler', 'sample_euler'),
('LMS', 'sample_lms'),
('Heun', 'sample_heun'),
('DPM 2', 'sample_dpm_2'),
('DPM 2 Ancestral', 'sample_dpm_2_ancestral'),
('DPM2', 'sample_dpm_2'),
('DPM2 a', 'sample_dpm_2_ancestral'),
] if hasattr(k_diffusion.sampling, x[1])],
SamplerData('DDIM', lambda: VanillaStableDiffusionSampler(DDIMSampler)),
SamplerData('PLMS', lambda: VanillaStableDiffusionSampler(PLMSSampler)),
@ -1083,31 +1082,67 @@ class Flagging(gr.FlaggingCallback):
print("Logged:", filenames[0])
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
with gr.Row():
prompt = gr.Textbox(label="Prompt", elem_id="txt2img_prompt", show_label=False, placeholder="Prompt", lines=1)
submit = gr.Button('Generate', variant='primary')
with gr.Row().style(equal_height=False):
with gr.Column(variant='panel'):
steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers_for_img2img[0].name, type="index")
with gr.Row():
use_GFPGAN = gr.Checkbox(label='GFPGAN', value=False, visible=have_gfpgan)
prompt_matrix = gr.Checkbox(label='Prompt matrix', value=False)
with gr.Row():
batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0)
with gr.Group():
height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
seed = gr.Number(label='Seed', value=-1)
code = gr.Textbox(label="Python script", visible=cmd_opts.allow_code, lines=1)
with gr.Column(variant='panel'):
with gr.Group():
gallery = gr.Gallery(label='Output')
output_seed = gr.Number(label='Seed', visible=False)
html_info = gr.HTML()
txt2img_args = dict(
fn=wrap_gradio_call(txt2img),
inputs=[
prompt,
steps,
sampler_index,
use_GFPGAN,
prompt_matrix,
batch_count,
batch_size,
cfg_scale,
seed,
height,
width,
code
],
outputs=[
gallery,
output_seed,
html_info
]
)
prompt.submit(**txt2img_args)
submit.click(**txt2img_args)
txt2img_interface = gr.Interface(
wrap_gradio_call(txt2img),
inputs=[
gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20),
gr.Radio(label='Sampling method', choices=[x.name for x in samplers], value=samplers[0].name, type="index"),
gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=have_gfpgan),
gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1),
gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.5),
gr.Number(label='Seed', value=-1),
gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
gr.Textbox(label="Python script", visible=cmd_opts.allow_code, lines=1)
],
outputs=[
gr.Gallery(label="Images"),
gr.Number(label='Seed'),
gr.HTML(),
],
title="Stable Diffusion Text-to-Image",
flagging_callback=Flagging()
)
def fill(image, mask):
image_mod = Image.new('RGBA', (image.width, image.height))
@ -1223,10 +1258,15 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
return samples_ddim
def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, sd_upscale: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int):
def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, prompt_matrix, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int):
outpath = opts.outdir or "outputs/img2img-samples"
if init_img_with_mask is not None:
is_classic = mode == 0
is_inpaint = mode == 1
is_loopback = mode == 2
is_upscale = mode == 3
if is_inpaint:
image = init_img_with_mask['image']
mask = init_img_with_mask['mask']
else:
@ -1242,7 +1282,7 @@ def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_
sampler_index=sampler_index,
batch_size=batch_size,
n_iter=n_iter,
steps=ddim_steps,
steps=steps,
cfg_scale=cfg_scale,
width=width,
height=height,
@ -1257,7 +1297,7 @@ def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_
extra_generation_params={"Denoising Strength": denoising_strength}
)
if loopback:
if is_loopback:
output_images, info = None, None
history = []
initial_seed = None
@ -1286,7 +1326,7 @@ def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_
processed = Processed(history, initial_seed, initial_info)
elif sd_upscale:
elif is_upscale:
initial_seed = None
initial_info = None
@ -1345,36 +1385,103 @@ def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_
sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
img2img_interface = gr.Interface(
wrap_gradio_call(img2img),
inputs=[
gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
gr.Image(label="Image for img2img", source="upload", interactive=True, type="pil"),
gr.Image(label="Image for inpainting with mask", source="upload", interactive=True, type="pil", tool="sketch"),
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20),
gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index"),
gr.Slider(label='Inpainting: mask blur', minimum=0, maximum=64, step=1, value=4),
gr.Radio(label='Inpainting: masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index"),
gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=have_gfpgan),
gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
gr.Checkbox(label='Loopback (use images from previous batch when creating next batch)', value=False),
gr.Checkbox(label='Stable Diffusion upscale', value=False),
gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1),
gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),
gr.Number(label='Seed', value=-1),
gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
],
outputs=[
gr.Gallery(),
gr.Number(label='Seed'),
gr.HTML(),
],
allow_flagging="never",
)
with gr.Blocks(analytics_enabled=False) as img2img_interface:
with gr.Row():
prompt = gr.Textbox(label="Prompt", elem_id="img2img_prompt", show_label=False, placeholder="Prompt", lines=1)
submit = gr.Button('Generate', variant='primary')
with gr.Row().style(equal_height=False):
with gr.Column(variant='panel'):
with gr.Group():
switch_mode = gr.Radio(label='Mode', elem_id="img2img_mode", choices=['Redraw whole image', 'Inpaint a part of image', 'Loopback', 'SD upscale'], value='Redraw whole image', type="index", show_label=False)
init_img = gr.Image(label="Image for img2img", source="upload", interactive=True, type="pil")
init_img_with_mask = gr.Image(label="Image for inpainting with mask", elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", visible=False)
resize_mode = gr.Radio(label="Resize mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
mask_blur = gr.Slider(label='Inpainting: mask blur', minimum=0, maximum=64, step=1, value=4, visible=False)
inpainting_fill = gr.Radio(label='Inpainting: masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
with gr.Row():
use_GFPGAN = gr.Checkbox(label='GFPGAN', value=False, visible=have_gfpgan)
prompt_matrix = gr.Checkbox(label='Prompt matrix', value=False)
with gr.Row():
batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
with gr.Group():
cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0)
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75)
with gr.Group():
height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
seed = gr.Number(label='Seed', value=-1)
with gr.Column(variant='panel'):
with gr.Group():
gallery = gr.Gallery(label='Output')
output_seed = gr.Number(label='Seed', visible=False)
html_info = gr.HTML()
def apply_mode(mode):
is_classic = mode == 0
is_inpaint = mode == 1
is_loopback = mode == 2
is_upscale = mode == 3
return {
init_img: gr.update(visible=not is_inpaint),
init_img_with_mask: gr.update(visible=is_inpaint),
mask_blur: gr.update(visible=is_inpaint),
inpainting_fill: gr.update(visible=is_inpaint),
prompt_matrix: gr.update(visible=is_classic),
batch_count: gr.update(visible=not is_upscale),
batch_size: gr.update(visible=not is_loopback),
}
switch_mode.change(
apply_mode,
inputs=[switch_mode],
outputs=[init_img, init_img_with_mask, mask_blur, inpainting_fill, prompt_matrix, batch_count, batch_size]
)
img2img_args = dict(
fn=wrap_gradio_call(img2img),
inputs=[
prompt,
init_img,
init_img_with_mask,
steps,
sampler_index,
mask_blur,
inpainting_fill,
use_GFPGAN,
prompt_matrix,
switch_mode,
batch_count,
batch_size,
cfg_scale,
denoising_strength,
seed,
height,
width,
resize_mode
],
outputs=[
gallery,
output_seed,
html_info
]
)
prompt.submit(**img2img_args)
submit.click(**img2img_args)
def upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index):
@ -1434,6 +1541,7 @@ extras_interface = gr.Interface(
gr.HTML(),
],
allow_flagging="never",
analytics_enabled=False,
)
@ -1463,6 +1571,7 @@ pnginfo_interface = gr.Interface(
gr.HTML(),
],
allow_flagging="never",
analytics_enabled=False,
)
@ -1514,6 +1623,7 @@ settings_interface = gr.Interface(
title=None,
description=None,
allow_flagging="never",
analytics_enabled=False,
)
interfaces = [
@ -1524,6 +1634,15 @@ interfaces = [
(settings_interface, "Settings"),
]
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
logging.set_verbosity_error()
except Exception:
pass
sd_config = OmegaConf.load(cmd_opts.config)
sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
sd_model = (sd_model if cmd_opts.no_half else sd_model.half())
@ -1537,13 +1656,17 @@ else:
model_hijack = StableDiffusionModelHijack()
model_hijack.hijack(sd_model)
with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
css = file.read()
demo = gr.TabbedInterface(
interface_list=[x[0] for x in interfaces],
tab_names=[x[1] for x in interfaces],
css=("" if cmd_opts.no_progressbar_hiding else css_hide_progressbar) + """
.output-html p {margin: 0 0.5em;}
.performance { font-size: 0.85em; color: #444; }
"""
""" + css,
analytics_enabled=False,
)
demo.queue(concurrency_count=1)