Discrete diffusion pipelines fail with a CPU generator when running on GPU/MPS #15285

Describe the bug

The reproducibility guide recommends a CPU torch.Generator. The image pipelines support this because randn_tensor samples on the CPU and moves the result to the device. The discrete diffusion code instead passes the generator straight to torch.randint / torch.multinomial / torch.rand on the pipeline's device, and PyTorch rejects a CPU generator there.

• DiffusionGemmaPipeline fails on every call, even with greedy decoding, because the initial canvas is drawn with torch.randint(..., device=device, generator=generator).
• LLaDA2Pipeline fails whenever temperature > 0.
• The same pattern is in BlockRefinementScheduler, DiscreteDDIMScheduler and EntropyBoundScheduler (_sample_from_logits, the random-token renoising in step, and add_noise).

I tested on MPS. I don't have a CUDA machine, but PyTorch does the same device check on CUDA (see #6501), so CUDA should fail the same way.

Proposed fix: draw on generator.device and move the result to the pipeline's device, the same approach as #13228 for Helios. I'd like to open a PR for this with tests. #14741 keeps the same calls.

Reproduction

This uses the tiny checkpoint from the DiffusionGemma tests. The full model (google/diffusiongemma-26B-A4B-it) doesn't fit on my machine.

import torch
from transformers import AutoProcessor, DiffusionGemmaForBlockDiffusion

from diffusers import BlockRefinementScheduler, DiffusionGemmaPipeline


model_id = "trl-internal-testing/tiny-DiffusionGemmaForBlockDiffusion"
pipe = DiffusionGemmaPipeline(
    model=DiffusionGemmaForBlockDiffusion.from_pretrained(model_id, dtype=torch.float32),
    scheduler=BlockRefinementScheduler(),
    processor=AutoProcessor.from_pretrained(model_id),
).to("mps")

generator = torch.Generator("cpu").manual_seed(0)
out = pipe(prompt="Name a color.", gen_length=32, num_inference_steps=4, generator=generator)
print(out.texts)

It runs with torch.Generator("mps"), or with both on CPU.

Context

No response

Logs

File "src/diffusers/pipelines/diffusion_gemma/pipeline_diffusion_gemma.py", line 347, in __call__
    canvas = torch.randint(
RuntimeError: Expected a 'mps' device type for generator but found 'cpu'

System Info

• 🤗 Diffusers version: 0.42.0.dev0 (main @ f598e6e)
• Platform: macOS-26.6.2-arm64-arm-64bit
• Python version: 3.11.14
• PyTorch version (GPU?): 2.15.0 (False)
• Transformers version: 5.22.0
• Accelerator: Apple M1 Pro
• Using GPU in script?: Yes (MPS)
• Using distributed or parallel set-up in script?: No

Who can help?

@yiyixuxu @dg845