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schedulers fix zero-sigma final-step

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2026-07-06 13:21:19 +02:00
parent fc2231b6cf
commit 870cca30fa
8 changed files with 101 additions and 29 deletions
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modules/processing_vae.py
+32 -13
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@@ -249,24 +249,28 @@ def vae_postprocess(tensor, model, output_type='np'):
if tensor.ndim == 4 and tensor.shape[1] == 3:
tensor = tensor.unsqueeze(2)
try:
images = model.video_processor.postprocess_video(tensor, output_type='pil')
except Exception as e:
log.warning(f'VAE postprocess: type=video tensor={tensor.shape}:{tensor.device}:{tensor.dtype} error={e}')
with np.errstate(all='raise'):
images = model.video_processor.postprocess_video(tensor, output_type='pil')
except (Exception, FloatingPointError) as e:
amin, amax = tensor.min().item(), tensor.max().item()
log.warning(f'VAE postprocess: type=video tensor={tensor.shape}:{tensor.device}:{tensor.dtype} min={amin} max={amax} error="{e}"')
images = tensor
if debug:
errors.display(e, 'VAE postprocess video')
errors.display(e, 'VAE postprocess: type=video')
if isinstance(images, list) and len(images) > 0 and isinstance(images[0], list):
images = [frame for batch in images for frame in batch]
elif hasattr(model, 'image_processor'):
if tensor.ndim == 5 and tensor.shape[1] == 3: # Qwen Image
tensor = tensor[:, :, 0]
try:
images = model.image_processor.postprocess(tensor, output_type=output_type)
except Exception as e:
log.warning(f'VAE postprocess: type=image tensor={tensor.shape}:{tensor.device}:{tensor.dtype} error={e}')
with np.errstate(all='raise'):
images = model.image_processor.postprocess(tensor, output_type=output_type)
except (Exception, FloatingPointError) as e:
amin, amax = tensor.min().item(), tensor.max().item()
log.warning(f'VAE postprocess: type=image tensor={tensor.shape}:{tensor.device}:{tensor.dtype} min={amin} max={amax} error="{e}"')
images = tensor
if debug:
errors.display(e, 'VAE postprocess image')
errors.display(e, 'VAE postprocess: type=image')
elif hasattr(model, "vqgan"):
images = tensor.permute(0, 2, 3, 1).cpu().float().numpy()
if output_type == "pil":
@@ -277,12 +281,27 @@ def vae_postprocess(tensor, model, output_type='np'):
if tensor.ndim == 5 and tensor.shape[1] == 3: # Qwen Image
tensor = tensor[:, :, 0]
images = model.image_processor.postprocess(tensor, output_type=output_type)
if torch.is_tensor(images): # failed to postprocess, do naive conversion
images = images.permute(0, 2, 3, 1).cpu().float().numpy()
if images.min() < 0 or images.max() > 1:
images = (images - images.min()) / (images.max() - images.min()) # naive normalization
if output_type == "pil":
images = model.numpy_to_pil(images)
try:
if torch.isnan(images).any().item():
log.error(f'VAE postprocess: type=fallback tensor={images.shape}:{images.device}:{images.dtype} error="image contains invalid NaN values"')
images.nan_to_num_(nan=0.0)
while images.ndim > 4:
images = images.squeeze(0)
if images.shape[0] == 3:
images = images.permute(1, 2, 3, 0).cpu().float().numpy()
else:
images = images.permute(0, 2, 3, 1).cpu().float().numpy()
if images.min() < 0 or images.max() > 1:
images = (images - images.min()) / (images.max() - images.min()) # naive normalization
if output_type == "pil":
images = model.numpy_to_pil(images)
except (Exception, FloatingPointError) as e:
amin, amax = images.min().item(), images.max().item()
log.warning(f'VAE postprocess: type=fallback tensor={images.shape}:{images.device}:{images.dtype} min={amin} max={amax} error="{e}"')
if debug:
errors.display(e, 'VAE postprocess unknown')
else:
images = tensor if isinstance(tensor, list) or isinstance(tensor, np.ndarray) else [tensor]
except Exception as e: