Instructions to use sparkling621/EditHF-Reward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sparkling621/EditHF-Reward with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sparkling621/EditHF-Reward", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Commit History
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Add model card for EditHF-Reward
Verified signatureHi! I'm Niels from the Hugging Face community team. This PR adds a model card for the **EditHF-Reward** model. This model is an advanced version of Qwen-Image-Edit, refined through reinforcement learning using human preference feedback as described in the paper [EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing](https://huggingface.co/papers/2603.14916). The PR includes: - Metadata for the `image-to-image` pipeline tag. - Metadata for the `diffusers` library. - Links to the official research paper and GitHub repository. - A description of the training process and the EditHF-1M dataset.8543475 -
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