looking for the same

I think the first step would be something like:

FluxTransformer2DModel.from_single_file("https://huggingface.co/lllyasviel/flux1-dev-bnb-nf4/blob/main/flux1-dev-bnb-nf4.safetensors")

Anyone has something that work?

please give example script with diffusers

Is there already someone out there who got this to work with Diffusers?

same question

Is there any update so far?

from diffusers import FluxPipeline
import torch

ckpt_id = "black-forest-labs/FLUX.1-schnell"
prompt = [
"an astronaut riding a horse",
# more prompts here
]
height, width = 1024, 1024

denoising

pipe = FluxPipeline.from_pretrained(
ckpt_id,
torch_dtype=torch.bfloat16,
)
pipe.vae.enable_tiling()
pipe.vae.enable_slicing()
pipe.enable_sequential_cpu_offload() # offloads modules to CPU on a submodule level (rather than model level)

image = pipe(
prompt,
num_inference_steps=1,
guidance_scale=0.0,
height=height,
width=width,
).images[0]
print('Max mem allocated (GB) while denoising:', torch.cuda.max_memory_allocated() / (1024 ** 3))

import matplotlib.pyplot as plt
plt.imshow(image)
plt.show()

it run in colab with black-forest-labs/FLUX.1-schnell
how How to modify it
with lllyasviel/flux1-dev-bnb-nf4