added progressbar
added an option to disable progressbar added interrupt support to DDIM/PLMS
This commit is contained in:
AUTOMATIC
parent
b6763fb884
commit
a243bc7859
11 changed files with 170 additions and 9 deletions
|
|
@ -55,7 +55,10 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
|
|||
initial_seed = None
|
||||
initial_info = None
|
||||
|
||||
state.job_count = n_iter
|
||||
|
||||
for i in range(n_iter):
|
||||
|
||||
p.n_iter = 1
|
||||
p.batch_size = 1
|
||||
p.do_not_save_grid = True
|
||||
|
|
@ -72,6 +75,8 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
|
|||
p.denoising_strength = max(p.denoising_strength * 0.95, 0.1)
|
||||
history.append(processed.images[0])
|
||||
|
||||
state.nextjob()
|
||||
|
||||
grid = images.image_grid(history, batch_size, rows=1)
|
||||
|
||||
images.save_image(grid, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename)
|
||||
|
|
@ -103,6 +108,8 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
|
|||
batch_count = math.ceil(len(work) / p.batch_size)
|
||||
print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} in a total of {batch_count} batches.")
|
||||
|
||||
state.job_count = batch_count
|
||||
|
||||
for i in range(batch_count):
|
||||
p.init_images = work[i*p.batch_size:(i+1)*p.batch_size]
|
||||
|
||||
|
|
@ -116,6 +123,8 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
|
|||
p.seed = processed.seed + 1
|
||||
work_results += processed.images
|
||||
|
||||
state.nextjob()
|
||||
|
||||
image_index = 0
|
||||
for y, h, row in grid.tiles:
|
||||
for tiledata in row:
|
||||
|
|
|
|||
|
|
@ -153,6 +153,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
with torch.no_grad(), precision_scope("cuda"), ema_scope():
|
||||
p.init()
|
||||
|
||||
state.job_count = p.n_iter
|
||||
|
||||
for n in range(p.n_iter):
|
||||
if state.interrupted:
|
||||
break
|
||||
|
|
@ -207,6 +209,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
|
||||
output_images.append(image)
|
||||
|
||||
state.nextjob()
|
||||
|
||||
unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple
|
||||
if not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
|
||||
return_grid = opts.return_grid
|
||||
|
|
|
|||
|
|
@ -1,10 +1,12 @@
|
|||
from collections import namedtuple
|
||||
|
||||
import ldm.models.diffusion.ddim
|
||||
import torch
|
||||
import tqdm
|
||||
|
||||
import k_diffusion.sampling
|
||||
from ldm.models.diffusion.ddim import DDIMSampler
|
||||
from ldm.models.diffusion.plms import PLMSSampler
|
||||
import ldm.models.diffusion.ddim
|
||||
import ldm.models.diffusion.plms
|
||||
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
import modules.shared as shared
|
||||
|
|
@ -29,8 +31,8 @@ samplers_data_k_diffusion = [
|
|||
|
||||
samplers = [
|
||||
*samplers_data_k_diffusion,
|
||||
SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(DDIMSampler, model), []),
|
||||
SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(PLMSSampler, model), []),
|
||||
SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
|
||||
SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
|
||||
]
|
||||
samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
|
||||
|
||||
|
|
@ -43,6 +45,23 @@ def p_sample_ddim_hook(sampler_wrapper, x_dec, cond, ts, *args, **kwargs):
|
|||
return sampler_wrapper.orig_p_sample_ddim(x_dec, cond, ts, *args, **kwargs)
|
||||
|
||||
|
||||
def extended_tdqm(sequence, *args, desc=None, **kwargs):
|
||||
state.sampling_steps = len(sequence)
|
||||
state.sampling_step = 0
|
||||
|
||||
for x in tqdm.tqdm(sequence, *args, desc=state.job, **kwargs):
|
||||
if state.interrupted:
|
||||
break
|
||||
|
||||
yield x
|
||||
|
||||
state.sampling_step += 1
|
||||
|
||||
|
||||
ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
|
||||
ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
|
||||
|
||||
|
||||
class VanillaStableDiffusionSampler:
|
||||
def __init__(self, constructor, sd_model):
|
||||
self.sampler = constructor(sd_model)
|
||||
|
|
@ -102,13 +121,18 @@ class CFGDenoiser(torch.nn.Module):
|
|||
return denoised
|
||||
|
||||
|
||||
def extended_trange(*args, **kwargs):
|
||||
for x in tqdm.trange(*args, desc=state.job, **kwargs):
|
||||
def extended_trange(count, *args, **kwargs):
|
||||
state.sampling_steps = count
|
||||
state.sampling_step = 0
|
||||
|
||||
for x in tqdm.trange(count, *args, desc=state.job, **kwargs):
|
||||
if state.interrupted:
|
||||
break
|
||||
|
||||
yield x
|
||||
|
||||
state.sampling_step += 1
|
||||
|
||||
|
||||
class KDiffusionSampler:
|
||||
def __init__(self, funcname, sd_model):
|
||||
|
|
|
|||
|
|
@ -42,10 +42,18 @@ batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram o
|
|||
class State:
|
||||
interrupted = False
|
||||
job = ""
|
||||
job_no = 0
|
||||
job_count = 0
|
||||
sampling_step = 0
|
||||
sampling_steps = 0
|
||||
|
||||
def interrupt(self):
|
||||
self.interrupted = True
|
||||
|
||||
def nextjob(self):
|
||||
self.job_no += 1
|
||||
self.sampling_step = 0
|
||||
|
||||
state = State()
|
||||
|
||||
artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
|
||||
|
|
@ -89,6 +97,7 @@ class Options:
|
|||
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscaling. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
|
||||
"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"),
|
||||
}
|
||||
|
||||
def __init__(self):
|
||||
|
|
|
|||
|
|
@ -48,7 +48,6 @@ css_hide_progressbar = """
|
|||
.meta-text { display:none!important; }
|
||||
"""
|
||||
|
||||
|
||||
def plaintext_to_html(text):
|
||||
text = "".join([f"<p>{html.escape(x)}</p>\n" for x in text.split('\n')])
|
||||
return text
|
||||
|
|
@ -134,6 +133,24 @@ def wrap_gradio_call(func):
|
|||
return f
|
||||
|
||||
|
||||
def check_progress_call():
|
||||
if not opts.show_progressbar:
|
||||
return ""
|
||||
|
||||
if shared.state.job_count == 0:
|
||||
return ""
|
||||
|
||||
progress = shared.state.job_no / shared.state.job_count
|
||||
if shared.state.sampling_steps > 0:
|
||||
progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
|
||||
|
||||
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>"""
|
||||
|
||||
return f"<span style='display: none'>{time.time()}</span><p>{progressbar}</p>"
|
||||
|
||||
|
||||
def roll_artist(prompt):
|
||||
allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
|
||||
artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
|
||||
|
|
@ -154,8 +171,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
with gr.Row():
|
||||
prompt = gr.Textbox(label="Prompt", elem_id="txt2img_prompt", show_label=False, placeholder="Prompt", lines=1)
|
||||
negative_prompt = gr.Textbox(label="Negative prompt", elem_id="txt2img_negative_prompt", show_label=False, placeholder="Negative prompt", lines=1, visible=False)
|
||||
roll = gr.Button('Roll', elem_id="txt2img_roll", visible=len(shared.artist_db.artists)>0)
|
||||
roll = gr.Button('Roll', elem_id="txt2img_roll", visible=len(shared.artist_db.artists) > 0)
|
||||
submit = gr.Button('Generate', elem_id="txt2img_generate", variant='primary')
|
||||
check_progress = gr.Button('Check progress', elem_id="check_progress", visible=False)
|
||||
|
||||
with gr.Row().style(equal_height=False):
|
||||
with gr.Column(variant='panel'):
|
||||
|
|
@ -185,6 +203,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
with gr.Group():
|
||||
txt2img_gallery = gr.Gallery(label='Output', elem_id='txt2img_gallery')
|
||||
|
||||
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
save = gr.Button('Save')
|
||||
|
|
@ -193,12 +212,16 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
send_to_extras = gr.Button('Send to extras')
|
||||
interrupt = gr.Button('Interrupt')
|
||||
|
||||
progressbar = gr.HTML(elem_id="progressbar")
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML()
|
||||
generation_info = gr.Textbox(visible=False)
|
||||
|
||||
|
||||
txt2img_args = dict(
|
||||
fn=txt2img,
|
||||
_js="submit",
|
||||
inputs=[
|
||||
prompt,
|
||||
negative_prompt,
|
||||
|
|
@ -223,6 +246,13 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
prompt.submit(**txt2img_args)
|
||||
submit.click(**txt2img_args)
|
||||
|
||||
check_progress.click(
|
||||
fn=check_progress_call,
|
||||
inputs=[],
|
||||
outputs=[progressbar],
|
||||
)
|
||||
|
||||
|
||||
interrupt.click(
|
||||
fn=lambda: shared.state.interrupt(),
|
||||
inputs=[],
|
||||
|
|
@ -252,10 +282,12 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
]
|
||||
)
|
||||
|
||||
|
||||
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', elem_id="img2img_generate", variant='primary')
|
||||
check_progress = gr.Button('Check progress', elem_id="check_progress", visible=False)
|
||||
|
||||
with gr.Row().style(equal_height=False):
|
||||
|
||||
|
|
@ -310,6 +342,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
save = gr.Button('Save')
|
||||
img2img_send_to_extras = gr.Button('Send to extras')
|
||||
|
||||
progressbar = gr.HTML(elem_id="progressbar")
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML()
|
||||
generation_info = gr.Textbox(visible=False)
|
||||
|
|
@ -352,6 +386,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
|
||||
img2img_args = dict(
|
||||
fn=img2img,
|
||||
_js="submit",
|
||||
inputs=[
|
||||
prompt,
|
||||
init_img,
|
||||
|
|
@ -386,6 +421,12 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
prompt.submit(**img2img_args)
|
||||
submit.click(**img2img_args)
|
||||
|
||||
check_progress.click(
|
||||
fn=check_progress_call,
|
||||
inputs=[],
|
||||
outputs=[progressbar],
|
||||
)
|
||||
|
||||
interrupt.click(
|
||||
fn=lambda: shared.state.interrupt(),
|
||||
inputs=[],
|
||||
|
|
|
|||
33
script.js
33
script.js
|
|
@ -51,6 +51,8 @@ function gradioApp(){
|
|||
return document.getElementsByTagName('gradio-app')[0].shadowRoot;
|
||||
}
|
||||
|
||||
global_progressbar = null
|
||||
|
||||
function addTitles(root){
|
||||
root.querySelectorAll('span, button, select').forEach(function(span){
|
||||
tooltip = titles[span.textContent];
|
||||
|
|
@ -71,6 +73,17 @@ function addTitles(root){
|
|||
select.title = titles[select.value] || "";
|
||||
}
|
||||
})
|
||||
|
||||
progressbar = root.getElementById('progressbar')
|
||||
if(progressbar!= null && progressbar != global_progressbar){
|
||||
global_progressbar = progressbar
|
||||
|
||||
var mutationObserver = new MutationObserver(function(m){
|
||||
window.setTimeout(requestProgress, 500)
|
||||
});
|
||||
mutationObserver.observe( progressbar, { childList:true, subtree:true })
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
document.addEventListener("DOMContentLoaded", function() {
|
||||
|
|
@ -78,7 +91,6 @@ document.addEventListener("DOMContentLoaded", function() {
|
|||
addTitles(gradioApp());
|
||||
});
|
||||
mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
|
||||
|
||||
});
|
||||
|
||||
function selected_gallery_index(){
|
||||
|
|
@ -105,3 +117,22 @@ function extract_image_from_gallery(gallery){
|
|||
|
||||
return gallery[index];
|
||||
}
|
||||
|
||||
|
||||
function requestProgress(){
|
||||
btn = gradioApp().getElementById("check_progress");
|
||||
if(btn==null) return;
|
||||
|
||||
btn.click();
|
||||
}
|
||||
|
||||
function submit(){
|
||||
window.setTimeout(requestProgress, 500)
|
||||
|
||||
res = []
|
||||
for(var i=0;i<arguments.length;i++){
|
||||
res.push(arguments[i])
|
||||
}
|
||||
console.log(res)
|
||||
return res
|
||||
}
|
||||
|
|
@ -78,6 +78,8 @@ class Script(scripts.Script):
|
|||
batch_count = len(work)
|
||||
print(f"Poor man's outpainting will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)}.")
|
||||
|
||||
state.job_count = batch_count
|
||||
|
||||
for i in range(batch_count):
|
||||
p.init_images = [work[i]]
|
||||
p.image_mask = work_mask[i]
|
||||
|
|
@ -93,6 +95,8 @@ class Script(scripts.Script):
|
|||
p.seed = processed.seed + 1
|
||||
work_results += processed.images
|
||||
|
||||
state.nextjob()
|
||||
|
||||
image_index = 0
|
||||
for y, h, row in grid.tiles:
|
||||
for tiledata in row:
|
||||
|
|
|
|||
|
|
@ -20,6 +20,8 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
|
|||
|
||||
first_pocessed = None
|
||||
|
||||
state.job_count = len(xs) * len(ys)
|
||||
|
||||
for iy, y in enumerate(ys):
|
||||
for ix, x in enumerate(xs):
|
||||
state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
|
||||
|
|
@ -29,6 +31,7 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
|
|||
first_pocessed = processed
|
||||
|
||||
res.append(processed.images[0])
|
||||
state.nextjob()
|
||||
|
||||
grid = images.image_grid(res, rows=len(ys))
|
||||
grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
|
||||
|
|
|
|||
|
|
@ -67,6 +67,8 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
|
|||
|
||||
first_pocessed = None
|
||||
|
||||
state.job_count = len(xs) * len(ys)
|
||||
|
||||
for iy, y in enumerate(ys):
|
||||
for ix, x in enumerate(xs):
|
||||
state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
|
||||
|
|
@ -77,6 +79,8 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
|
|||
|
||||
res.append(processed.images[0])
|
||||
|
||||
state.nextjob()
|
||||
|
||||
grid = images.image_grid(res, rows=len(ys))
|
||||
grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
|
||||
|
||||
|
|
|
|||
25
style.css
25
style.css
|
|
@ -71,3 +71,28 @@ input[type="range"]{
|
|||
padding-left: 0.6em;
|
||||
padding-right: 0.6em;
|
||||
}
|
||||
|
||||
|
||||
|
||||
.progressDiv{
|
||||
width: 100%;
|
||||
height: 30px;
|
||||
background: #b4c0cc;
|
||||
border-radius: 8px;
|
||||
}
|
||||
|
||||
.dark .progressDiv{
|
||||
background: #424c5b;
|
||||
}
|
||||
|
||||
.progressDiv .progress{
|
||||
width: 0%;
|
||||
height: 30px;
|
||||
background: #0060df;
|
||||
color: white;
|
||||
font-weight: bold;
|
||||
line-height: 30px;
|
||||
padding: 0 8px 0 0;
|
||||
text-align: right;
|
||||
border-radius: 8px;
|
||||
}
|
||||
|
|
|
|||
7
webui.py
7
webui.py
|
|
@ -53,6 +53,7 @@ def load_model_from_config(config, ckpt, verbose=False):
|
|||
|
||||
cached_images = {}
|
||||
|
||||
|
||||
def run_extras(image, gfpgan_strength, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
|
||||
processing.torch_gc()
|
||||
|
||||
|
|
@ -121,10 +122,16 @@ queue_lock = threading.Lock()
|
|||
|
||||
def wrap_gradio_gpu_call(func):
|
||||
def f(*args, **kwargs):
|
||||
shared.state.sampling_step = 0
|
||||
shared.state.job_count = 1
|
||||
shared.state.job_no = 0
|
||||
|
||||
|
||||
with queue_lock:
|
||||
res = func(*args, **kwargs)
|
||||
|
||||
shared.state.job = ""
|
||||
shared.state.job_count = 0
|
||||
|
||||
return res
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue