huggingface / huggingface/diffusers
FLUX.1-Kontext-dev batch inference throughput issue
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Description
Describe the bug
I am trying to use FLUX.1-Kontext-dev in batch inference, but the inference time for batch is linear to batch size, e.g., time of batch size 2 ~= 2* time of batch size 1. This makes me confused, did I get something wrong or this is the issue with this pipeline?
Reproduction
import torch
from diffusers import FluxKontextPipeline
from diffusers.utils import load_image
# Load the pipeline
pipe = FluxKontextPipeline.from_pretrained(
"black-forest-labs/FLUX.1-Kontext-dev",
torch_dtype=torch.bfloat16
)
pipe.to("cuda")
input_images = [
load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"),
load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
]
prompts = [
"Make it look like a watercolor painting",
"Add dramatic lighting and shadows",
]
with torch.inference_mode():
images = pipe(
image=input_images,
prompt=prompts,
guidance_scale=2.5,
num_inference_steps=28,
generator=[torch.Generator("cuda").manual_seed(42),torch.Generator("cuda").manual_seed(123)]
).images
for i, img in enumerate(images):
img.save(f"output_i2i_batch_{i}.png")
Logs
System Info
- 🤗 Diffusers version: 0.36.0.dev0
- Platform: Linux-6.8.0-63-generic-x86_64-with-glibc2.39
- Running on Google Colab?: No
- Python version: 3.10.18
- PyTorch version (GPU?): 2.8.0+cu128 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.35.3
- Transformers version: 4.57.0
- Accelerate version: 1.10.1
- PEFT version: not installed
- Bitsandbytes version: not installed
- Safetensors version: 0.6.2
- xFormers version: not installed
- Accelerator: NVIDIA H100 NVL, 95830 MiB
NVIDIA H100 NVL, 95830 MiB
NVIDIA H100 NVL, 95830 MiB
NVIDIA H100 NVL, 95830 MiB
NVIDIA H100 NVL, 95830 MiB
NVIDIA H100 NVL, 95830 MiB
NVIDIA H100 NVL, 95830 MiB
NVIDIA H100 NVL, 95830 MiB
Who can help?
@yiyixuxu @DN6
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the reproduced FluxKontextPipeline call in the issue and measure batch sizes 1 and 2 using the supplied environment and inputs. Trace the inference path to determine whether linear timing is expected or indicates a batching problem; done means the behavior is explained and the issue is corrected or documented.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100