FLUX.1-Fill-dev-nf4 / README.md
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---
library_name: diffusers
license: other
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE.md
---
> [!NOTE]
> Contains the NF4 checkpoints (`transformer` and `text_encoder_2`) of [`black-forest-labs/FLUX.1-Fill-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Fill-dev). Please adhere to the original model licensing!
<details>
<summary>Code</summary>
```py
from diffusers import DiffusionPipeline, FluxFillPipeline, FluxTransformer2DModel
import torch
from transformers import T5EncoderModel
from diffusers.utils import load_image
import fire
def load_pipeline(four_bit=False):
orig_pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
if four_bit:
print("Using four bit.")
transformer = FluxTransformer2DModel.from_pretrained(
"sayakpaul/FLUX.1-Fill-dev-nf4", subfolder="transformer", torch_dtype=torch.bfloat16
)
text_encoder_2 = T5EncoderModel.from_pretrained(
"sayakpaul/FLUX.1-Fill-dev-nf4", subfolder="text_encoder_2", torch_dtype=torch.bfloat16
)
pipeline = FluxFillPipeline.from_pipe(
orig_pipeline, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=torch.bfloat16
)
else:
transformer = FluxTransformer2DModel.from_pretrained(
"black-forest-labs/FLUX.1-Fill-dev",
subfolder="transformer",
revision="refs/pr/4",
torch_dtype=torch.bfloat16,
)
pipeline = FluxFillPipeline.from_pipe(orig_pipeline, transformer=transformer, torch_dtype=torch.bfloat16)
pipeline.enable_model_cpu_offload()
return pipeline
def load_conditions():
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/cup.png")
mask = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/cup_mask.png")
return image, mask
def main(four_bit: bool = False):
pipe = load_pipeline(four_bit=four_bit)
ckpt_id = "sayakpaul/FLUX.1-Fill-dev-nf4"
image, mask = load_conditions()
image = pipe(
prompt="a white paper cup",
image=image,
mask_image=mask,
height=1024,
width=1024,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0),
).images[0]
filename = "output_" + ckpt_id.split("/")[-1].replace(".", "_")
filename += "_4bit" if four_bit else ""
image.save(f"{filename}.png")
if __name__ == "__main__":
fire.Fire(main)
```
</details>
## Outputs
<table>
<thead>
<tr>
<th>Original</th>
<th>NF4</th>
</tr>
</thead>
<tbody>
<tr>
<td>
<img src="./assets/output_FLUX_1-Fill-dev.png" alt="Original">
</td>
<td>
<img src="./assets/output_FLUX_1-Fill-dev_4bit.png" alt="NF4">
</td>
</tr>
</tbody>
</table>