Batched Text to image generation really slow with pipeline

[nighting0le01]

Describe the bug

batched diffusers pipeline inference is really slow

Reproduction

 class prompt_data(Dataset):
        def __init__(self,prompt_file):
            self.txt_prompts = []
            with open(prompt_file,'r') as file:self.txt_prompts = file.readlines()
        def __len__(self):
            return len(self.txt_prompts)
        def __getitem__(self, idx):
            return self.txt_prompts[idx].rstrip()

    prompt_dataset = prompt_data(args.prompt_file)
    dataloader = DataLoader(prompt_dataset, batch_size=args.batch_size, shuffle=False,num_workers=6,)

    args.negative_prompt = [args.negative_prompt]* args.batch_size
    images_list = []
    for i_batch, prompts_batched in tqdm(enumerate(dataloader)):
        torch.cuda.empty_cache()
        with torch.no_grad():
            image = pipeline(
                prompts_batched,
                num_inference_steps=args.num_inference_steps,
                guidance_scale=args.guidance_scale,
                num_images_per_prompt=1,
                generator=generator,
                negative_prompt=args.negative_prompt,
                height=args.height,
                width=args.width,
                **args.pipe_add_kwargs,
            )
            print("----------batch generated----------")
            images = image[0]
            images_list.extend(images)
            # saving images ....

Logs

No response

System Info

diffusers 0.27, torch Version: 2.0.1

Who can help?

No response

[sayakpaul]

The reproduction code is not really in a place where we can take a look at it and provider you with feedback. Please format it properly and let us know.

And also, provide more information on your system, etc.

[nighting0le01]

hi @sayakpaul apologies for the formating, can you PTAL now? prompts_batched is a list of len 32

[sayakpaul]

The reproduction code should be more minimal. Could we eliminate the class structure and have a more simplistic reproduction code?

[asomoza]

FWIW if you do inference just a single image and it works ok and this happens when you do batch inference, probably you're hitting the limit of your VRAM.

Also it's kind of a given that batches will take more time, so probably what you will need to give us also is:

• Amount of VRAM you have
• What model are you using
• How big is the batch you're using.
• Specify what is "really slow" with empirical data.

Right now we don't know any of this and we can't really help you, you're providing a code with a lot of unknowns.

[nighting0le01]

hi @asomoza , i checked it never hits the vram limit, i'm using simple sd1.5, it just gets stuck

[nighting0le01]

@asomoza @sayakpaul the reason of providing the dataset and dataloader is to show how the data is read from a text file and batched before feeding into a SD1.5 pipeline

[sayakpaul]

IMO that can probably be just replicated with something much simpler:

prompt = ["a dog"]
batched_prompts = [prompt] * batch_size

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