VaeImageProcessor.denormalize() raises AttributeError for NumPy inputs #15011

Describe the bug

VaeImageProcessor.denormalize() is documented to accept both NumPy arrays (np.ndarray) and PyTorch tensors (torch.Tensor). However, passing a NumPy array raises an AttributeError because the implementation uses the PyTorch-specific .clamp() method.

Root cause

In src/diffusers/image_processor.py, line 234:

return (images * 0.5 + 0.5).clamp(0, 1)

NumPy arrays do not implement .clamp(), despite being included in the function's type annotations and documentation.

Expected behavior

The function should denormalize NumPy inputs to [0, 1], preserving the input type and existing PyTorch behavior.

Proposed direction

Use input-type-appropriate clipping, such as np.clip() for NumPy and .clamp() for PyTorch, or clarify the supported types if NumPy support is not intended.

I'd be happy to contribute a fix after discussing the preferred approach with maintainers.

Reproduction

import numpy as np
import torch
from diffusers.image_processor import VaeImageProcessor

values = [-2.0, -1.0, 0.0, 1.0, 2.0]

numpy_input = np.array(values, dtype=np.float32)
torch_input = torch.tensor(values, dtype=torch.float32)

# PyTorch succeeds
print("PyTorch:", VaeImageProcessor.denormalize(torch_input))

# NumPy raises AttributeError
print("NumPy:", VaeImageProcessor.denormalize(numpy_input))

The issue is reproducible on CPU without pretrained models or external downloads.

Logs

File "E:\diffusers\src\diffusers\image_processor.py", line 234, in denormalize
    return (images * 0.5 + 0.5).clamp(0, 1)
AttributeError: 'numpy.ndarray' object has no attribute 'clamp'. Did you mean: 'clip'?


Additional validation:


PyTorch actual: [0.0, 0.0, 0.5, 1.0, 1.0]
NumPy expected: [0.0, 0.0, 0.5, 1.0, 1.0]
Outputs match: True

System Info

• 🤗 Diffusers version: 0.41.0.dev0
• Platform: Windows-10-10.0.22631-SP0
• Running on Google Colab?: No
• Python version: 3.11.16
• PyTorch version (GPU?): 2.14.1+cpu (False)
• NumPy version: 2.4.6
• Huggingface_hub version: 1.33.0
• Transformers version: 5.18.0
• Accelerate version: 1.15.0
• PEFT version: 0.21.2
• Safetensors version: 0.8.0

Source revision: 899c9f3fd0e64f3206781c00c71c28249eff9ca5

Reproduction environment: Windows, CPU only. No pretrained models or external downloads required.

Who can help?

@DN6 @sayakpaul

I'd appreciate your feedback on the expected NumPy support and the preferred approach to resolving this issue. I'm happy to contribute a fix once the implementation approach is agreed upon.