`from_pipe` converts pipelines to float32 by default

[missionfloyd]

Describe the bug

Pipelines passed to from_pipe() are converted to float32 unless torch_dtype is specified, leading to higher memory usage and slower inference.

Reproduction

import torch
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline

pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
print(f"Before: {pipe.dtype} - {torch.cuda.memory_allocated() // 1048576} MB")

i2i = StableDiffusionImg2ImgPipeline.from_pipe(pipe)
print(f"After:  {pipe.dtype} - {torch.cuda.memory_allocated() // 1048576} MB")

Logs

Loading pipeline components...:   0%|                                                            | 0/7 [00:00<?, ?it/s]`torch_dtype` is deprecated! Use `dtype` instead!
Loading pipeline components...: 100%|████████████████████████████████████████████████████| 7/7 [00:08<00:00,  1.17s/it]
Before: torch.float16 - 2637 MB
After:  torch.float32 - 5258 MB

System Info

• 🤗 Diffusers version: 0.35.2
• Platform: Windows-10-10.0.19045-SP0
• Running on Google Colab?: No
• Python version: 3.10.11
• PyTorch version (GPU?): 2.9.1+cu126 (True)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Huggingface_hub version: 0.36.0
• Transformers version: 4.57.3
• Accelerate version: 1.12.0
• PEFT version: not installed
• Bitsandbytes version: not installed
• Safetensors version: 0.7.0
• xFormers version: not installed
• Accelerator: NVIDIA GeForce GTX 1080, 8192 MiB
• Using GPU in script?: Yes
• Using distributed or parallel set-up in script?: No

Who can help?

No response

[yiyixuxu]

hey! you need to pass torch_dtype in from_pipe

StableDiffusionImg2ImgPipeline.from_pipe(pipe, torch_dtype=torch.float16)

[missionfloyd]

Yes, but that shouldn't be necessary, nor do the docs mention it. It's meant to avoid allocating more memory, so shouldn't it keep the existing dtype?

[ghunkins]

Agreed that this a regression in terms of DX.

Had some code that broke because of this change back when it was pushed out. Originally, the same dtype would be maintained.

[yiyixuxu]

we try to keep API more or less consistent with from_pretrained(), instead of from a repo, you can create from an existing pipeline, but it accept other arguments from_pretrained() would accept, including dtype

we can update doc to include that info

[yiyixuxu]

if you look at from_pretrained, we don't maintain the original dtype of the checkpoint either