diff options
| -rw-r--r-- | modules/processing.py | 5 | ||||
| -rw-r--r-- | modules/sd_samplers.py | 9 | ||||
| -rw-r--r-- | modules/shared.py | 4 | ||||
| -rw-r--r-- | modules/ui.py | 59 | ||||
| -rw-r--r-- | script.js | 17 | ||||
| -rw-r--r-- | style.css | 15 | ||||
| -rw-r--r-- | webui.py | 5 |
7 files changed, 102 insertions, 12 deletions
diff --git a/modules/processing.py b/modules/processing.py index e8923a7a..e615ffdc 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -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)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 896e8b3f..ff7e686e 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -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
diff --git a/modules/shared.py b/modules/shared.py index d57aba37..e9c88e31 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -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):
diff --git a/modules/ui.py b/modules/ui.py index 1df74070..8e7a3ee4 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -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>"""
+
+ 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)
- return f"<span style='display: none'>{time.time()}</span><p>{progressbar}</p>"
+ 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'):
@@ -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 }) @@ -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;
}
+
@@ -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)
|