updated interface to use Blocks
added mode toggle for img2img added inpainting to readme
This commit is contained in:
AUTOMATIC
parent
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commit
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5 changed files with 261 additions and 70 deletions
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@ -204,6 +204,10 @@ To use this feature, tick a checkbox in the img2img interface. Original
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image will be upscaled to twice the original width and height, while width and height sliders
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will specify the size of individual tiles. At the moment this method does not support batch size.
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Rcommended parameters for upscaling:
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- Sampling method: Euler a
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- Denoising strength: 0.2, can go up to 0.4 if you feel adventureous
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### User scripts
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@ -261,4 +265,7 @@ compared to normal operation on my RTX 3090.
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This is an independent implementation that does not require any modification to original Stable Diffusion code, and
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with all code concenrated in one place rather than scattered around the program.
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### Inpainting
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In img2img tab, draw a mask over a part of image, and that part will be in-painted.
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BIN
images/inpainting.png
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images/inpainting.png
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After Width: | Height: | Size: 1.3 MiB |
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screenshot.png
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screenshot.png
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Before Width: | Height: | Size: 865 KiB After Width: | Height: | Size: 513 KiB |
61
style.css
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61
style.css
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@ -0,0 +1,61 @@
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button{
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align-self: stretch !important;
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}
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#img2img_mode{
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padding: 0 0 1em 0;
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border: none !important;
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}
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#img2img_prompt, #txt2img_prompt{
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padding: 0;
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border: none !important;
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}
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#img2maskimg .h-60{
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height: 30rem;
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}
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.overflow-hidden, .gr-panel{
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overflow: visible !important;
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}
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fieldset span.text-gray-500{
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position: absolute;
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top: -0.425em;
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background-color: rgb(249 250 251);
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line-height: 0.7em;
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padding: 0 0.5em;
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}
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.dark fieldset span.text-gray-500 { background-color: rgb(17 24 39); }
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.gr-panel div.flex-col div.justify-between label, label.block span{
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position: absolute;
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top: -0.4em;
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background-color: rgb(249 250 251);
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line-height: 0.7em;
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padding: 0 0.5em;
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margin: 0;
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}
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.dark .gr-panel div.flex-col div.justify-between label{ background-color: rgb(17 24 39); }
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.gr-panel div.flex-col div.justify-between label span{
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margin: 0;
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}
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.gr-panel div.flex-col div.justify-between div{
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position: absolute;
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top: -0.1em;
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right: 1em;
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padding: 0 0.5em;
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}
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input[type="range"]{
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margin: 0.5em 0 -0.3em 0;
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}
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#txt2img_sampling label{
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padding-left: 0.6em;
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padding-right: 0.6em;
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}
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263
webui.py
263
webui.py
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@ -8,6 +8,7 @@ import torch
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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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import gradio.utils
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from omegaconf import OmegaConf
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from PIL import Image, ImageFont, ImageDraw, PngImagePlugin, ImageFilter, ImageOps
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from torch import autocast
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@ -24,18 +25,16 @@ from ldm.util import instantiate_from_config
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.models.diffusion.plms import PLMSSampler
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try:
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# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
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from transformers import logging
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logging.set_verbosity_error()
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except Exception:
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pass
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# fix gradio phoning home
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gradio.utils.version_check = lambda: None
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gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
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# 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
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mimetypes.init()
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mimetypes.add_type('application/javascript', '.js')
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script_path = os.path.dirname(os.path.realpath(__file__))
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# some of those options should not be changed at all because they would break the model, so I removed them from options.
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opt_C = 4
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opt_f = 8
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@ -72,12 +71,12 @@ css_hide_progressbar = """
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SamplerData = namedtuple('SamplerData', ['name', 'constructor'])
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samplers = [
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*[SamplerData(x[0], lambda funcname=x[1]: KDiffusionSampler(funcname)) for x in [
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('Euler ancestral', 'sample_euler_ancestral'),
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('Euler a', 'sample_euler_ancestral'),
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('Euler', 'sample_euler'),
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('LMS', 'sample_lms'),
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('Heun', 'sample_heun'),
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('DPM 2', 'sample_dpm_2'),
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('DPM 2 Ancestral', 'sample_dpm_2_ancestral'),
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('DPM2', 'sample_dpm_2'),
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('DPM2 a', 'sample_dpm_2_ancestral'),
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] if hasattr(k_diffusion.sampling, x[1])],
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SamplerData('DDIM', lambda: VanillaStableDiffusionSampler(DDIMSampler)),
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SamplerData('PLMS', lambda: VanillaStableDiffusionSampler(PLMSSampler)),
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@ -1083,31 +1082,67 @@ class Flagging(gr.FlaggingCallback):
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print("Logged:", filenames[0])
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with gr.Blocks(analytics_enabled=False) as txt2img_interface:
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with gr.Row():
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prompt = gr.Textbox(label="Prompt", elem_id="txt2img_prompt", show_label=False, placeholder="Prompt", lines=1)
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submit = gr.Button('Generate', variant='primary')
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with gr.Row().style(equal_height=False):
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with gr.Column(variant='panel'):
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steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
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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")
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with gr.Row():
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use_GFPGAN = gr.Checkbox(label='GFPGAN', value=False, visible=have_gfpgan)
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prompt_matrix = gr.Checkbox(label='Prompt matrix', value=False)
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with gr.Row():
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batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
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batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
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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)
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with gr.Group():
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height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
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width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
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seed = gr.Number(label='Seed', value=-1)
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code = gr.Textbox(label="Python script", visible=cmd_opts.allow_code, lines=1)
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with gr.Column(variant='panel'):
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with gr.Group():
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gallery = gr.Gallery(label='Output')
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output_seed = gr.Number(label='Seed', visible=False)
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html_info = gr.HTML()
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txt2img_args = dict(
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fn=wrap_gradio_call(txt2img),
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inputs=[
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prompt,
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steps,
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sampler_index,
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use_GFPGAN,
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prompt_matrix,
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batch_count,
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batch_size,
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cfg_scale,
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seed,
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height,
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width,
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code
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],
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outputs=[
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gallery,
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output_seed,
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html_info
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]
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)
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prompt.submit(**txt2img_args)
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submit.click(**txt2img_args)
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txt2img_interface = gr.Interface(
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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=20),
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gr.Radio(label='Sampling method', choices=[x.name for x in samplers], value=samplers[0].name, type="index"),
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gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=have_gfpgan),
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gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
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gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1),
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gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
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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),
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gr.Number(label='Seed', value=-1),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
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gr.Textbox(label="Python script", visible=cmd_opts.allow_code, lines=1)
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],
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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.HTML(),
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],
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title="Stable Diffusion Text-to-Image",
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flagging_callback=Flagging()
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)
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def fill(image, mask):
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image_mod = Image.new('RGBA', (image.width, image.height))
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@ -1223,10 +1258,15 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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return samples_ddim
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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):
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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):
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outpath = opts.outdir or "outputs/img2img-samples"
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if init_img_with_mask is not None:
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is_classic = mode == 0
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is_inpaint = mode == 1
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is_loopback = mode == 2
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is_upscale = mode == 3
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if is_inpaint:
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image = init_img_with_mask['image']
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mask = init_img_with_mask['mask']
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else:
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@ -1242,7 +1282,7 @@ def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_
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sampler_index=sampler_index,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=ddim_steps,
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steps=steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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@ -1257,7 +1297,7 @@ def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_
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extra_generation_params={"Denoising Strength": denoising_strength}
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)
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if loopback:
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if is_loopback:
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output_images, info = None, None
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history = []
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initial_seed = None
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@ -1286,7 +1326,7 @@ def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_
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processed = Processed(history, initial_seed, initial_info)
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elif sd_upscale:
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elif is_upscale:
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initial_seed = None
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initial_info = None
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@ -1345,36 +1385,103 @@ def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_
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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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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(label="Image for img2img", source="upload", interactive=True, type="pil"),
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gr.Image(label="Image for inpainting with mask", source="upload", interactive=True, type="pil", tool="sketch"),
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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20),
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gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index"),
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gr.Slider(label='Inpainting: mask blur', minimum=0, maximum=64, step=1, value=4),
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gr.Radio(label='Inpainting: masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index"),
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gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=have_gfpgan),
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gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
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gr.Checkbox(label='Loopback (use images from previous batch when creating next batch)', value=False),
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gr.Checkbox(label='Stable Diffusion upscale', value=False),
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gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1),
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gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
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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),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),
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gr.Number(label='Seed', value=-1),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
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gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
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],
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outputs=[
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gr.Gallery(),
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gr.Number(label='Seed'),
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gr.HTML(),
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],
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allow_flagging="never",
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)
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with gr.Blocks(analytics_enabled=False) as img2img_interface:
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with gr.Row():
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prompt = gr.Textbox(label="Prompt", elem_id="img2img_prompt", show_label=False, placeholder="Prompt", lines=1)
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submit = gr.Button('Generate', variant='primary')
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with gr.Row().style(equal_height=False):
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with gr.Column(variant='panel'):
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with gr.Group():
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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)
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init_img = gr.Image(label="Image for img2img", source="upload", interactive=True, type="pil")
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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)
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resize_mode = gr.Radio(label="Resize mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
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steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
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sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
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mask_blur = gr.Slider(label='Inpainting: mask blur', minimum=0, maximum=64, step=1, value=4, visible=False)
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inpainting_fill = gr.Radio(label='Inpainting: masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
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with gr.Row():
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use_GFPGAN = gr.Checkbox(label='GFPGAN', value=False, visible=have_gfpgan)
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prompt_matrix = gr.Checkbox(label='Prompt matrix', value=False)
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with gr.Row():
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batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
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batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
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with gr.Group():
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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)
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denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75)
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with gr.Group():
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height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
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width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
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seed = gr.Number(label='Seed', value=-1)
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with gr.Column(variant='panel'):
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with gr.Group():
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gallery = gr.Gallery(label='Output')
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output_seed = gr.Number(label='Seed', visible=False)
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html_info = gr.HTML()
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def apply_mode(mode):
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is_classic = mode == 0
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is_inpaint = mode == 1
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is_loopback = mode == 2
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is_upscale = mode == 3
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return {
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init_img: gr.update(visible=not is_inpaint),
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init_img_with_mask: gr.update(visible=is_inpaint),
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mask_blur: gr.update(visible=is_inpaint),
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inpainting_fill: gr.update(visible=is_inpaint),
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prompt_matrix: gr.update(visible=is_classic),
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batch_count: gr.update(visible=not is_upscale),
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batch_size: gr.update(visible=not is_loopback),
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}
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switch_mode.change(
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apply_mode,
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inputs=[switch_mode],
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outputs=[init_img, init_img_with_mask, mask_blur, inpainting_fill, prompt_matrix, batch_count, batch_size]
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)
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img2img_args = dict(
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fn=wrap_gradio_call(img2img),
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inputs=[
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prompt,
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init_img,
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init_img_with_mask,
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||||
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)
|
||||
|
|
|
|||
Loading…
Reference in a new issue