Live preview + progress bar

diffusion/gaussian_diffusion.py

@@ -426,7 +426,8 @@ class GaussianDiffusion:
         cond_fn=None,
         model_kwargs=None,
         device=None,
-        progress=False,
+        pbar=None,
+        previewer=None,
     ):
         """
         Generate samples from the model.
@@ -456,7 +457,8 @@ class GaussianDiffusion:
             cond_fn=cond_fn,
             model_kwargs=model_kwargs,
             device=device,
-            progress=progress,
+            pbar=pbar,
+            previewer=previewer,
         ):
             final = sample
         return final["sample"]
@@ -471,7 +473,8 @@ class GaussianDiffusion:
         cond_fn=None,
         model_kwargs=None,
         device=None,
-        progress=False,
+        pbar=None,
+        previewer=None,
     ):
         """
         Generate samples from the model and yield intermediate samples from
@@ -489,12 +492,6 @@ class GaussianDiffusion:
             img = th.randn(*shape, device=device)
         indices = list(range(self.num_timesteps))[::-1]
 
-        if progress:
-            # Lazy import so that we don't depend on tqdm.
-            from tqdm.auto import tqdm
-
-            indices = tqdm(indices)
-
         for i in indices:
             t = th.tensor([i] * shape[0], device=device)
             with th.no_grad():
@@ -509,6 +506,11 @@ class GaussianDiffusion:
                 )
                 yield out
                 img = out["sample"]
+            if pbar:
+                preview_bytes = None
+                if previewer:
+                    preview_bytes = previewer.decode_latent_to_preview_image("JPEG", img)
+                pbar.update_absolute(indices.index(i)+1, self.num_timesteps, preview_bytes)
 
     def ddim_sample(
         self,

nodes.py

@@ -6,6 +6,7 @@ import comfy.model_management
 import comfy.model_patcher
 import comfy.utils
 import comfy.latent_formats
+import latent_preview
 
 from .models import DiT_models
 from .diffusion import create_diffusion
@@ -121,6 +122,8 @@ class DiTSampler:
 		# pre
 		comfy.model_management.load_model_gpu(model)
 		real_model = model.model
+		pbar = comfy.utils.ProgressBar(steps)
+		previewer = latent_preview.get_previewer(device, model.model.latent_format)
 
 		# Create sampling noise:
 		z = torch.randn(batch_size, 4, real_model.latent_size, real_model.latent_size, device=device)
@@ -134,7 +137,7 @@ class DiTSampler:
 
 		# Sample images:
 		samples = diffusion.p_sample_loop(
-			model.model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device
+			model.model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, pbar=pbar, previewer=previewer, device=device
 		)
 		samples, _ = samples.chunk(2, dim=0)  # Remove null class samples
 		samples = real_model.latent_format.process_out(samples.to(torch.float32))