Same seed produces different results

[thisislance98]

Describe the bug

Basically is you set the scheduler to EulerAncestralDiscreteScheduler and the custom pipeline to lpw_stable_diffusion you will get different images when you generate

Reproduction

Here is a colab to reproduce the results
https://colab.research.google.com/drive/1ypHAf2TBWnvxkLJf08cSMko3r48WYNYn?usp=sharing

Logs

No response

System Info

I'm using colab with standard gpu

[averad]

@thisislance98 looking at your code example it appears your not passing the seed information as latents to the pipe. Here is some information that should get you generating images using set seeds.

Stable Diffusion with Repeatable Seeds (excerpt below)

Latents Generation
In order to reuse the seeds we need to generate the latents ourselves. Otherwise, the pipeline will do it internally and we won't have a way to replicate them.

Latents are the initial random Gaussian noise that gets transformed to actual images during the diffusion process.

To generate them, we'll use a different random seed for each latent, and we'll save them so we can reuse them later.

generator = torch.Generator(device=device)

latents = None
seeds = []
for _ in range(num_images):
    # Get a new random seed, store it and use it as the generator state
    seed = generator.seed()
    seeds.append(seed)
    generator = generator.manual_seed(seed)
    
    image_latents = torch.randn(
        (1, pipe.unet.in_channels, height // 8, width // 8),
        generator = generator,
        device = device
    )
    latents = image_latents if latents is None else torch.cat((latents, image_latents))
    
# latents should have shape (4, 4, 64, 64) in this case
latents.shape

[Marcophono2]

I can confirm that I have the same problem if using torch.bfloat16. The results are similar but different is details. I.E. the same lady in the same position but sometimes smiling, sometimes not. Or one time looking to the left, another time to the right. If using torch.float16 same seeds = same results. Also I am using the Euler(A) scheduler.

[averad]

@Marcophono2 can you share the code you are using to generate the image?

[camenduru]

maybe second pipe missing generator=generator

generator = torch.Generator(device="cuda").manual_seed(87)
display(pipe.text2img(prompt, negative_prompt=neg_prompt, width=512,height=512,max_embeddings_multiples=3, generator=generator).images[0])

generator = torch.Generator(device="cuda").manual_seed(87)
display(pipe.text2img(prompt, negative_prompt=neg_prompt, width=512,height=512,max_embeddings_multiples=3).images[0])

[thisislance98]

@averad I tried just the same latents in to text2img and am still getting different results

https://colab.research.google.com/drive/1ypHAf2TBWnvxkLJf08cSMko3r48WYNYn?usp=sharing

feel free to update the colab if you can get it to work

[camenduru]

@thisislance98 try generator = torch.cuda.manual_seed_all(seed) or generator = torch.cuda.manual_seed(seed)

for _ in range(2):
    # Get a new random seed, store it and use it as the generator state
    seed = 23 #generator.seed()
    seeds.append(seed)
    generator = torch.cuda.manual_seed_all(seed)
    
    image_latents = torch.randn(
        (1, pipe.unet.in_channels, height // 8, width // 8),
        generator = generator,
        device = device
    )
    # latents = image_latents if latents is None else torch.cat((latents, image_latents))
    # image_latents = image_latents.half()

    display(pipe.text2img(prompt, negative_prompt=neg_prompt,latents=image_latents, width=512,height=512,max_embeddings_multiples=3, num_inference_steps=steps).images[0])

[Marcophono2]

Sure, @averad .
I found out that it's the combination of EulerA + xformer which outputs different images under same conditions, including seed of course. The differences are only visible in details. The hairs are a bit different or else. Have a look to the four attached images. Initially I thought that two of that images are identical. But indeed they aren't.

from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler

euler = EulerAncestralDiscreteScheduler.from_config("runwayml/stable-diffusion-v1-5", subfolder="scheduler")

pipe1 = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",scheduler=euler, revision="fp16",torch_dtype=torch.float16).to("cuda:1")

	pipe1.enable_xformers_memory_efficient_attention() 
	generator = torch.Generator(device='cuda').manual_seed(int(666))
	with torch.inference_mode():
		
		image = pipe1("a beautiful woman", num_inference_steps=int(20), width=int(512), height=int(512), generator = generator, guidance_scale=float(7)).images[0]

[Marcophono2]

I made some more tests and could take notice that the differences decrease with increasing number of steps. That is surprising. I would have awaited a multiplication of the differences with every step. The very noisy image after step 1 is identical. But starting from step 2 there are differences. But after 250 steps there is only a very, very small difference in general noising left. But only if you switch between two different images at the same position.

[patrickvonplaten]

That's very interesting! @Marcophono2, are you running the code always on the same device, same hardware, etc...?

[Marcophono2]

Yes, @patrickvonplaten.

[adhikjoshi]

That's very interesting! @Marcophono2, are you running the code always on the same device, same hardware, etc...?

lpw_stable_diffusion needs lots of updates

[antoche]

I have just noticed that I am getting non-deterministic results too with EulerAncestral and enforcing all RNG to happen on the CPU (see #1514). The interesting thing is, results deterministic if I call disable_xformers_memory_efficient_attention(). Looks like the memory-efficient attention has a strong effect on determinism.

[patrickvonplaten]

BTW, this PR should help with this here a bit: #1718

[amrakm]

I can confirm that xformer is the culprit here.

I tried passing a list of generators using the new method described in PR #1718 but it didn't work
generator = [torch.Generator(device="cuda").manual_seed(i) for i in range(n_images)]

Only solution was to disable xformers. Note that this will affect the performance

pipe.disable_xformers_memory_efficient_attention()

[takuma104]

Hi,
According to the xFomers team, the default cutlass backend for mem_eff_attention() is not guaranteed to be deterministic. In contrast, Flash Attention would have deterministic behavior, according to them. I have written a patch to apply to Diffusers and it is almost reproducible over multiple generations. I'm thinking of submitting a pull request once I have refined the code a bit more.

https://gist.github.com/takuma104/9d25bb87ae3b52e41e0132aa737c0b03

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