Instructions to use yichengup/flux.1-fill-dev-OneReward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yichengup/flux.1-fill-dev-OneReward with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("yichengup/flux.1-fill-dev-OneReward", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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For more details and examples see original model repo: [**OneReward**](https://huggingface.co/bytedance-research/OneReward)
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## Sample Usage
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The following code snippet illustrates how to use the model with the `diffusers` library. Note that this requires the custom `FluxFillCFGPipeline` defined in the [official source code](https://github.com/bytedance/OneReward/blob/main/src/pipeline_flux_fill_with_cfg.py).
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For more details and examples see original model repo: [**OneReward**](https://huggingface.co/bytedance-research/OneReward)
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Partial redrawing workflow: [**OneReward redrawing**](https://www.runninghub.ai/post/1965826858283634690/?inviteCode=rh-v1091)
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Image expansion workflow: [**OneReward Image expansion**](https://www.runninghub.ai/post/1966338160613601281/?inviteCode=rh-v1091)
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## Sample Usage
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The following code snippet illustrates how to use the model with the `diffusers` library. Note that this requires the custom `FluxFillCFGPipeline` defined in the [official source code](https://github.com/bytedance/OneReward/blob/main/src/pipeline_flux_fill_with_cfg.py).
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