This is a diffusers version of
nyanko7/flux-dev-de-distill
that generates exactly the same results as the original. But please note that there is a certain degree of quality degradation in comparison with
black-forest-labs/FLUX.1-dev
in our test.
We are training its fine-tuning models to check its effectiveness, feel free to let us know if you have any interesting findings.
Usage
import torch
from diffusers import FluxTransformer2DModel
from pipeline_flux_de_distill import FluxPipeline
model_path = "black-forest-labs/FLUX.1-dev"
transformer = FluxTransformer2DModel.from_pretrained(
"InstantX/flux-dev-de-distill-diffusers",
torch_dtype=torch.bfloat16
)
pipeline = FluxPipeline.from_pretrained(model_path, transformer=transformer, torch_dtype=torch.bfloat16).to("cuda")
prompt = "a tiny astronaut hatching from an egg on the moon"
negative_prompt = "bad photo"
image = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=3.5,
num_inference_steps=24,
).images[0]
image.save("de-distill.jpg")
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flux-dev-de-distill-diffusers is an open source model from GitHub that offers a free installation service, and any user can find flux-dev-de-distill-diffusers on GitHub to install. At the same time, huggingface.co provides the effect of flux-dev-de-distill-diffusers install, users can directly use flux-dev-de-distill-diffusers installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
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