save_pretrained(safe_serialization=False) leaves the old safetensors checkpoint behind, and from_pretrained loads it instead of the new weights

Inizia in src/diffusers/models/modeling_utils.py alle righe 804-820 e segui la pulizia eseguita da save_pretrained quando si passa da safetensors a .bin. Riproduci il problema con lo script DiffusionPipeline fornito, quindi aggiungi o esegui il test di regressione menzionato nell’issue. Il lavoro è completato quando il vecchio checkpoint safetensors e l’indice non rimangono più per la stessa variante e from_pretrained carica i pesi appena salvati.

Describe the bug

If a directory already has a safetensors checkpoint and I save the model again with safe_serialization=False, the old safetensors files are not removed. from_pretrained checks for safetensors first, so the next load silently returns the old weights. There's no error or warning.

The cleanup in save_pretrained (modeling_utils.py#L804-L820) only deletes files matching the shard pattern (...-00001-of-00002). An unsharded diffusion_pytorch_model.safetensors and the diffusion_pytorch_model.safetensors.index.json never match, so they survive a .bin save.

What happens after saving safetensors first, then .bin (default from_pretrained):

Saving an unsharded checkpoint twice in the same format works, and so does bin -> safetensors. (Going from sharded to unsharded in the same format has its own stale-index problem, which is #14719.)

This is related to #14719 but not the same problem. That issue is about deleting another variant's shards and a stale index when going sharded -> unsharded in the same format. Here it's the other format's checkpoint being left behind.

In the pipeline repro below only the diffusers components are affected: transformers components like text_encoder/ are written as model.safetensors even with safe_serialization=False, so they never end up with two formats.

Reproduction

import tempfile, glob, os, torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
with torch.no_grad():
    pipe.unet.conv_in.weight.zero_()

with tempfile.TemporaryDirectory() as p:
    pipe.save_pretrained(p)                               # unet/diffusion_pytorch_model.safetensors

    with torch.no_grad():
        pipe.unet.conv_in.weight.fill_(7.0)
    pipe.save_pretrained(p, safe_serialization=False)     # unet/diffusion_pytorch_model.bin

    print(sorted(os.path.basename(f) for f in glob.glob(p + "/unet/*")))
    reloaded = DiffusionPipeline.from_pretrained(p, safety_checker=None)
    print(reloaded.unet.conv_in.weight[0, 0, 0, 0].item())

Output:

['config.json', 'diffusion_pytorch_model.bin', 'diffusion_pytorch_model.safetensors']
0.0

Expected 7.0.

Logs

No error or warning is printed.

System Info

• 🤗 Diffusers version: 0.41.0.dev0 (main @ 83107dc)
• Platform: Linux-7.0.0-31-generic-x86_64-with-glibc2.43
• Running on Google Colab?: No
• Python version: 3.13.3
• PyTorch version (GPU?): 2.14.0+cu130 (False)
• Huggingface_hub version: 1.31.0
• Transformers version: 5.17.0
• Accelerate version: 1.15.0
• Safetensors version: 0.8.0
• Using GPU in script?: No
• Using distributed or parallel set-up in script?: No

Who can help?

@sayakpaul @DN6

I have a small fix ready (removes the other format's weights file and index for the same variant when saving, plus a regression test). Would a PR be welcome? One thing to decide: it changes behaviour for anyone who saves both formats into one folder on purpose. I couldn't find that pattern anywhere in the repo, but it's your call.