indices should be either on cpu or on the same device as the indexed tensor (cpu)

Describe the bug

image_to_image.py line 92 throws the error above.

  `  init_latents = self.scheduler.add_noise(init_latents, noise, timesteps)`

I’ve tried adding .to(self.device) to the 3 parameters. Device should be ‘cuda’ though.

Reproduction

device = "cuda"

pipei2i = StableDiffusionImg2ImgPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    revision="fp16", 
    torch_dtype=torch.float16,
    use_auth_token=True
).to(device)

response = requests.get('https://pbs.twimg.com/media/Fa1_7_vWYAEwfX-.png')

init_image = Image.open(io.BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((512, 512))
init_image = preprocess(init_image)

outputs = pipei2i(prompt=prompt, init_image=init_image, strength=0.75, num_inference_steps=75,guidance_scale=0.75)

Logs

No response

System Info

diffusers==0.2.4
nvidia/cuda:11.3.0-cudnn8-devel-ubuntu20.04

[andydhancock]

Solved: Maybe I should listen to the error message sometime… it should all be cpu init_latents = self.scheduler.add_noise(init_latents, noise, timesteps) becomes init_latents = self.scheduler.add_noise(init_latents, noise, timesteps.cpu()) and all is well.

Is .to(device) not supported for timesteps then?

I think it was supported but ‘device’ was cuda, and it’s trying to do something with numpy which uses cpu

[andydhancock]

Solved: Maybe I should listen to the error message sometime… it should all be cpu init_latents = self.scheduler.add_noise(init_latents, noise, timesteps) becomes init_latents = self.scheduler.add_noise(init_latents, noise, timesteps.cpu()) and all is well.