Instructions to use bytedance-research/OneReward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bytedance-research/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("bytedance-research/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
Upload folder using huggingface_hub
Browse files
flux.1-fill-dev-object-removal-lora/config.json
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{
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"peft_type": "LORA",
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"base_model_name_or_path": "black-forest-labs/FLUX.1-Fill-dev",
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"revision": null,
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"task_type": "UNDEFINED",
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"lora_alpha": 1,
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"r": null,
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"lora_dropout": 0.0,
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"bias": "none",
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"target_modules": [
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"transformer"
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]
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}
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flux.1-fill-dev-object-removal-lora/pytorch_lora_weights.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:63b3f284682ffe65eff22473179b48db107b0d1ff8f2b0a03c6dd2c8f21e8451
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size 264336088
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