[mps] training / inferencing deepfloyd must be done in float32

### Describe the bug

_**Just to keep track of this issue, because I'm not sure if I've done something wrong or if it's due to the current issues in Pytorch's MPS support.**_

I'm having bad results running inference on Apple MPS with DeepFloyd stage I / II in Diffusers:

# Stage I (400M)

## CPU (float32)


## MPS (float32)


# Stage II (450M)

## CPU (float32)


## MPS (float32)


### Reproduction

```py
from diffusers import DiffusionPipeline
import torch

prompts = {
'jester': 'a stunning portrait of a jester at the twisted carnival'
}

deepfloyd_lora_path = "ptx0/deepcinema"
deepfloyd_base_model_path = "DeepFloyd/IF-I-M-v1.0"
deepfloyd_stage_two_path = "DeepFloyd/IF-II-M-v1.0"

width = 96
height = 64
torch_device = "cuda" if torch.cuda.is_available() else "cpu" if torch.backends.mps.is_available() else "xpu" if torch.xpu.is_available() else "cpu"

pipe = DiffusionPipeline.from_pretrained(deepfloyd_base_model_path, watermarker=None, safety_checker=None, local_files_only=True).to(device=torch_device, dtype=torch.float32)
lora_pipe = DiffusionPipeline.from_pretrained(deepfloyd_base_model_path, **pipe.components, local_files_only=True).to(device=torch_device, dtype=torch.float32)
lora_pipe.load_lora_weights(deepfloyd_lora_path, weight_name="pytorch_lora_weights.safetensors")
lora_pipe.scheduler = pipe.scheduler.__class__.from_config(pipe.scheduler.config, variance_type="fixed_small")

from diffusers.pipelines import IFSuperResolutionPipeline
stage2_pipe = IFSuperResolutionPipeline.from_pretrained(deepfloyd_stage_two_path, watermarker=None, safety_checker=None, local_files_only=True).to(device=torch_device, dtype=torch.float32)

import os
for shortname, prompt in prompts.items():
output_dir = f"outputs/{shortname}"
if os.path.exists(output_dir):
continue
os.makedirs(output_dir, exist_ok=True)
torch.manual_seed(42)
image_base = pipe(prompt=prompt, width=width, height=height, guidance_scale=5.5, num_inference_steps=30).images[0]
image_base.save(os.path.join(output_dir, "base.png"))

torch.manual_seed(42)
image_lora = lora_pipe(prompt=prompt, width=width, height=height, guidance_scale=5.5, num_inference_steps=30).images[0]
image_lora.save(os.path.join(output_dir, "base_lora.png"))

torch.manual_seed(84)
image_base_2 = stage2_pipe(prompt=prompt, image=image_base, guidance_scale=5.5, num_inference_steps=30, width=width * 4, height = height * 4).images[0]
image_base_2.save(os.path.join(output_dir, "base_stage2.png"))
torch.manual_seed(84)
image_lora_2 = stage2_pipe(prompt=prompt, image=image_lora, guidance_scale=5.5, num_inference_steps=30, width=width * 4, height = height * 4).images[0]
image_lora_2.save(os.path.join(output_dir, "lora_stage2.png"))
```

I modified the value for if mps is available between 'mps' and 'cpu' manually for this test

### Logs

_No response_

### System Info

- `diffusers` version: 0.27.2
- Platform: macOS-14.4.1-arm64-arm-64bit
- Python version: 3.10.14
- PyTorch version (GPU?): 2.4.0.dev20240421 (False)
- Huggingface_hub version: 0.22.2
- Transformers version: 4.40.0.dev0
- Accelerate version: 0.26.1

### Who can help?

_No response_

Start with the provided DiffusionPipeline and IFSuperResolutionPipeline reproduction, comparing the CPU and MPS outputs under float32 on the listed versions. Determine whether the discrepancy is in diffusers or PyTorch MPS; done requires a confirmed cause and a clear fix or documented upstream limitation.

Rédigé par le modèle d'indexation à partir du texte de l'issue.