fix for live progress breaking lowvram and medvram optimizations

README.md

@@ -33,6 +33,9 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Running custom code from UI
 - Mouseover hints fo most UI elements
 - Possible to change defaults/mix/max/step values for UI elements via text config
+- Random artist button
+- Tiling support: UI checkbox to create images that can be tiled like textures
+- Progress bar and live image generation preview
 
 ## Installing and running
 

modules/sd_samplers.py

 from collections import namedtuple
-
-import ldm.models.diffusion.ddim
+import numpy as np
 import torch
 import tqdm
+from PIL import Image
 
 import k_diffusion.sampling
 import ldm.models.diffusion.ddim
@@ -37,12 +37,28 @@ samplers = [
 samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
 
 
+def sample_to_image(samples):
+    x_sample = shared.sd_model.decode_first_stage(samples[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)
+    return Image.fromarray(x_sample)
+
+
+def store_latent(decoded):
+    state.current_latent = decoded
+
+    if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
+        if not shared.parallel_processing_allowed:
+            shared.state.current_image = sample_to_image(decoded)
+
+
 def p_sample_ddim_hook(sampler_wrapper, x_dec, cond, ts, *args, **kwargs):
     if sampler_wrapper.mask is not None:
         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
+    store_latent(x_dec)
 
     return sampler_wrapper.orig_p_sample_ddim(x_dec, cond, ts, *args, **kwargs)
 
@@ -144,7 +160,7 @@ class KDiffusionSampler:
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
 
     def callback_state(self, d):
-        state.current_latent = d["denoised"]
+        store_latent(d["denoised"])
 
     def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
         t_enc = int(min(p.denoising_strength, 0.999) * p.steps)

modules/shared.py

@@ -38,7 +38,7 @@ cpu = torch.device("cpu")
 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)
-
+parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 
 class State:
     interrupted = False
@@ -49,7 +49,8 @@ class State:
     sampling_steps = 0
     current_latent = None
     current_image = None
-    current_progress_index = 0
+    current_image_sampling_step = 0
+
 
     def interrupt(self):
         self.interrupted = True
@@ -57,6 +58,7 @@ class State:
     def nextjob(self):
         self.job_no += 1
         self.sampling_step = 0
+        self.current_image_sampling_step = 0
 
 
 state = State()
@@ -103,7 +105,7 @@ 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 progress pudates. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
+        "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
     }
 
     def __init__(self):

modules/ui.py

@@ -160,13 +160,11 @@ def check_progress_call():
     preview_visibility = gr_show(False)
 
     if opts.show_progress_every_n_steps > 0:
-        if shared.state.current_progress_index % 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)
+        if shared.parallel_processing_allowed:
 
+            if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None:
+                shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)
+                shared.state.current_image_sampling_step = shared.state.sampling_step
 
         image = shared.state.current_image
 
@@ -175,8 +173,6 @@ def check_progress_call():
         else:
             preview_visibility = gr_show(True)
 
-    shared.state.current_progress_index += 1
-
     return f"<span style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image
 
 

webui.py

@@ -127,7 +127,7 @@ def wrap_gradio_gpu_call(func):
         shared.state.job_no = 0
         shared.state.current_latent = None
         shared.state.current_image = None
-        shared.state.current_progress_index = 0
+        shared.state.current_image_sampling_step = 0
 
         with queue_lock:
             res = func(*args, **kwargs)