Fix: use resample-aware bilinear+antialias interpolation for tensor/numpy resize in VaeImageProcessor

[GitGlimpse895]

What does this PR do?

VaeImageProcessor exposes a resample config parameter (defaulting to "lanczos")
and correctly applies it when resizing PIL images via PIL_INTERPOLATION. However,
the two torch.nn.functional.interpolate calls handling torch.Tensor and
np.ndarray inputs passed no mode argument — causing PyTorch to silently default
to "nearest" neighbor interpolation, regardless of the configured resample filter.
No antialias=True was set either, causing aliasing artifacts on downsampling.

This fix:

• Adds a TORCH_INTERPOLATION dict in pil_utils.py mapping the same resample-string
  keys as PIL_INTERPOLATION to their torch.nn.functional.interpolate equivalents
  (with antialias eligibility). "lanczos" maps to bilinear+antialias, the closest
  high-quality torch substitute.
• Updates both tensor branches of VaeImageProcessor.resize() to use the mapped mode
  and antialias flag, making tensor/numpy resize quality consistent with the PIL path.

This silently affected every pipeline that passes torch.Tensor inputs to
VaeImageProcessor (ControlNet conditioning, IP-Adapter, img2img, etc.).

Fixes # (issue)

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Who can review?

@yiyixuxu @sayakpaul @DN6

[ParamChordiya]

Code Review: Fix resample-aware interpolation in VaeImageProcessor

Summary

This fixes a legitimate quality bug: VaeImageProcessor.resize() correctly applies the configured resample filter for PIL inputs, but the torch.Tensor and np.ndarray branches called F.interpolate with no mode argument, defaulting to "nearest" neighbor — producing blocky/aliased results. The TORCH_INTERPOLATION mapping dict and (mode, antialias) tuple pattern is clean.

Issues

1. "lanczos" maps to "bilinear" — should it be "bicubic"? — "bicubic" is a closer approximation to Lanczos in frequency response and sharpness. Since "lanczos" is the default resample value, this mapping affects the vast majority of users. Worth discussing.

2. No new tests — Existing tests only check output shapes, not interpolation quality or mode. Needs at minimum:

   • A test verifying interpolation is not "nearest" (e.g. checkerboard pattern resize)
   • A test that each key in TORCH_INTERPOLATION works without error
   • A test comparing tensor vs PIL resize output similarity (PSNR/MSE threshold)

3. Import path inconsistency — PIL_INTERPOLATION is exported via utils/__init__.py, but TORCH_INTERPOLATION is imported directly from utils.pil_utils. Should be consistent.

4. Silent behavior change — This changes outputs for all existing pipelines passing tensor/numpy inputs. Deserves a changelog entry flagging it as a fix that changes output values.

5. No KeyError guard on mapping lookup — Invalid resample strings will produce an unhelpful KeyError. A ValueError with supported options listed would be better.

6. Duplicate code — The TORCH_INTERPOLATION lookup is identical in both the tensor and numpy branches. Could factor it out above with isinstance(image, (torch.Tensor, np.ndarray)).

7. resize_and_crop_tensor not updated — This static method also calls F.interpolate with hardcoded mode="bilinear" and no antialias. Worth a follow-up.

Verdict

Request changes — The core fix is correct, but needs tests, the lanczos mapping deserves discussion, and the import path should be consistent.

[HuggingFaceDocBuilderDev]

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[yiyixuxu]

thanks for the PR!
i left one comment

[yiyixuxu]

ohh, so if this option is not supported in torch, let's not map it to anything
just send a warning that says this resample mode is not supported for tensor/ndarray so it will be ignored (the default nearest is used intead). this way we don't change the default behavior for resize
what do you think?

[GitGlimpse895]

Thanks @yiyixuxu — updated! Revised approach:

• TORCH_INTERPOLATION now only maps modes torch natively supports
  (bilinear, bicubic, nearest).
• For unsupported modes like "lanczos", the code now emits a
  logger.warning and falls back to "nearest", preserving existing
  default behavior with no silent output change.
• Also factored the duplicate lookup out of both tensor and numpy
  branches into a single shared isinstance(image, (torch.Tensor, np.ndarray))
  branch, eliminating the code duplication @ParamChordiya flagged.