Fix image input type and tensor shape mismatch in inference pipeline #2
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…dimension mismatch - Fixed AttributeError caused by attempting to access '.shape' on a PIL.Image object. - Converted PIL image to tensor and added batch dimension using: 'image = transforms.ToTensor()(image).unsqueeze(0)' - Fixed RuntimeError from mismatched tensor sizes during torch.cat by ensuring input tensors have consistent dimensions.
Add time logger
Fix: AttributeError: module 'time' has no attribute 'tim'
Update from .debug to .info
Change logging.info -> print
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First of all, thank you for your amazing research. I'm very interested in your work, and while running the inference script, I encountered a couple of errors related to the input image's data type and dimensions. I investigated the issues and made some fixes, which I’m sharing through this pull request.
This pull request fixes two runtime errors encountered during inference in the TEMU-VTOFF project.
Error 1: AttributeError — 'Image' object has no attribute 'shape'
Cause: vton_image was a PIL.Image object, which does not have a .shape attribute.
Error 2: RuntimeError — Sizes of tensors must match
Cause: Mismatch in tensor dimensions when concatenating vton_model_input, mask, and masked_vton_latents using torch.cat.
Fix
Changes Made
Added image preprocessing line to convert PIL.Image to 4D tensor
Test
Verified inference runs without errors on Colab.
Output image was generated successfully with no runtime exceptions.