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AUTOMATIC 2022-09-06 19:33:51 +03:00
parent db6db585eb
commit fd66199769
7 changed files with 102 additions and 12 deletions
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5
modules/processing.py
View File

@ -176,6 +176,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
samples_ddim = p.sample(x=x, conditioning=c, unconditional_conditioning=uc)
if state.interrupted:
# if we are interruped, sample returns just noise
# use the image collected previously in sampler loop
samples_ddim = shared.state.current_latent
x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

9
modules/sd_samplers.py
View File

@ -42,6 +42,8 @@ def p_sample_ddim_hook(sampler_wrapper, x_dec, cond, ts, *args, **kwargs):
img_orig = sampler_wrapper.sampler.model.q_sample(sampler_wrapper.init_latent, ts)
x_dec = img_orig * sampler_wrapper.mask + sampler_wrapper.nmask * x_dec
state.current_latent = x_dec
return sampler_wrapper.orig_p_sample_ddim(x_dec, cond, ts, *args, **kwargs)
@ -141,6 +143,9 @@ class KDiffusionSampler:
self.func = getattr(k_diffusion.sampling, self.funcname)
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
def callback_state(self, d):
state.current_latent = d["denoised"]
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
sigmas = self.model_wrap.get_sigmas(p.steps)
@ -157,7 +162,7 @@ class KDiffusionSampler:
if hasattr(k_diffusion.sampling, 'trange'):
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
def sample(self, p, x, conditioning, unconditional_conditioning):
sigmas = self.model_wrap.get_sigmas(p.steps)
@ -166,6 +171,6 @@ class KDiffusionSampler:
if hasattr(k_diffusion.sampling, 'trange'):
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
return samples_ddim

4
modules/shared.py
View File

@ -39,6 +39,7 @@ gpu = torch.device("cuda")
device = gpu if torch.cuda.is_available() else cpu
batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
class State:
interrupted = False
job = ""
@ -46,6 +47,8 @@ class State:
job_count = 0
sampling_step = 0
sampling_steps = 0
current_latent = None
current_image = None
def interrupt(self):
self.interrupted = True
@ -99,6 +102,7 @@ class Options:
"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
"upscale_at_full_resolution_padding": OptionInfo(16, "Inpainting at full resolution: padding, in pixels, for the masked region.", gr.Slider, {"minimum": 0, "maximum": 128, "step": 4}),
"show_progressbar": OptionInfo(True, "Show progressbar"),
"show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
}
def __init__(self):

59
modules/ui.py
View File

@ -9,6 +9,8 @@ import sys
import time
import traceback
import numpy as np
import torch
from PIL import Image
import gradio as gr
@ -119,6 +121,9 @@ def wrap_gradio_call(func):
print("Arguments:", args, kwargs, file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
shared.state.job = ""
shared.state.job_count = 0
res = [None, '', f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
elapsed = time.perf_counter() - t
@ -134,11 +139,9 @@ def wrap_gradio_call(func):
def check_progress_call():
if not opts.show_progressbar:
return ""
if shared.state.job_count == 0:
return ""
return "", gr_show(False), gr_show(False)
progress = 0
@ -149,9 +152,29 @@ def check_progress_call():
progress = min(progress, 1)
progressbar = f"""<div class='progressDiv'><div class='progress' style="width:{progress * 100}%">{str(int(progress*100))+"%" if progress > 0.01 else ""}</div></div>"""
progressbar = ""
if opts.show_progressbar:
progressbar = f"""<div class='progressDiv'><div class='progress' style="width:{progress * 100}%">{str(int(progress*100))+"%" if progress > 0.01 else ""}</div></div>"""
return f"<span style='display: none'>{time.time()}</span><p>{progressbar}</p>"
image = gr_show(False)
preview_visibility = gr_show(False)
if opts.show_progress_every_n_steps > 0:
if (shared.state.sampling_step-1) % opts.show_progress_every_n_steps == 0 and shared.state.current_latent is not None:
x_sample = shared.sd_model.decode_first_stage(shared.state.current_latent[0:1].type(shared.sd_model.dtype))[0]
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
shared.state.current_image = Image.fromarray(x_sample)
image = shared.state.current_image
if image is None or progress >= 1:
image = gr.update(value=None)
else:
preview_visibility = gr_show(True)
return f"<span style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image
def roll_artist(prompt):
@ -204,6 +227,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
with gr.Column(variant='panel'):
with gr.Group():
txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
txt2img_gallery = gr.Gallery(label='Output', elem_id='txt2img_gallery')
@ -251,8 +275,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
check_progress.click(
fn=check_progress_call,
show_progress=False,
inputs=[],
outputs=[progressbar],
outputs=[progressbar, txt2img_preview, txt2img_preview],
)
@ -337,13 +362,16 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
with gr.Column(variant='panel'):
with gr.Group():
img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
img2img_gallery = gr.Gallery(label='Output', elem_id='img2img_gallery')
with gr.Group():
with gr.Row():
interrupt = gr.Button('Interrupt')
save = gr.Button('Save')
img2img_send_to_img2img = gr.Button('Send to img2img')
img2img_send_to_inpaint = gr.Button('Send to inpaint')
img2img_send_to_extras = gr.Button('Send to extras')
interrupt = gr.Button('Interrupt')
progressbar = gr.HTML(elem_id="progressbar")
@ -426,8 +454,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
check_progress.click(
fn=check_progress_call,
show_progress=False,
inputs=[],
outputs=[progressbar],
outputs=[progressbar, img2img_preview, img2img_preview],
)
interrupt.click(
@ -463,6 +492,20 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
outputs=[init_img_with_mask],
)
img2img_send_to_img2img.click(
fn=lambda x: image_from_url_text(x),
_js="extract_image_from_gallery",
inputs=[img2img_gallery],
outputs=[init_img],
)
img2img_send_to_inpaint.click(
fn=lambda x: image_from_url_text(x),
_js="extract_image_from_gallery",
inputs=[img2img_gallery],
outputs=[init_img_with_mask],
)
with gr.Blocks(analytics_enabled=False) as extras_interface:
with gr.Row().style(equal_height=False):
with gr.Column(variant='panel'):

17
script.js
View File

@ -79,6 +79,23 @@ function addTitles(root){
global_progressbar = progressbar
var mutationObserver = new MutationObserver(function(m){
txt2img_preview = gradioApp().getElementById('txt2img_preview')
txt2img_gallery = gradioApp().getElementById('txt2img_gallery')
img2img_preview = gradioApp().getElementById('img2img_preview')
img2img_gallery = gradioApp().getElementById('img2img_gallery')
if(txt2img_preview != null && txt2img_gallery != null){
txt2img_preview.style.width = txt2img_gallery.clientWidth + "px"
txt2img_preview.style.height = txt2img_gallery.clientHeight + "px"
}
if(img2img_preview != null && img2img_gallery != null){
img2img_preview.style.width = img2img_gallery.clientWidth + "px"
img2img_preview.style.height = img2img_gallery.clientHeight + "px"
}
window.setTimeout(requestProgress, 500)
});
mutationObserver.observe( progressbar, { childList:true, subtree:true })

15
style.css
View File

@ -31,6 +31,20 @@ button{
max-width: 10em;
}
#txt2img_preview, #img2img_preview{
position: absolute;
width: 320px;
left: 0;
right: 0;
margin-left: auto;
margin-right: auto;
z-index: 100;
}
#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0{
display: none;
}
fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block span{
position: absolute;
top: -0.6em;
@ -96,3 +110,4 @@ input[type="range"]{
text-align: right;
border-radius: 8px;
}

5
webui.py
View File

@ -125,7 +125,8 @@ def wrap_gradio_gpu_call(func):
shared.state.sampling_step = 0
shared.state.job_count = -1
shared.state.job_no = 0
shared.state.current_latent = None
shared.state.current_image = None
with queue_lock:
res = func(*args, **kwargs)
@ -163,7 +164,7 @@ modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
if __name__ == "__main__":
# make the program just exit at ctrl+c without waiting for anything
def sigint_handler(sig, frame):
print(f'Interrupted with singal {sig} in {frame}')
print(f'Interrupted with signal {sig} in {frame}')
os._exit(0)