Instructions to use worstcoder/SD3.5M-DiffusionNFT-MultiReward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use worstcoder/SD3.5M-DiffusionNFT-MultiReward with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("worstcoder/SD3.5M-DiffusionNFT-MultiReward", 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
- Local Apps
- Draw Things
- DiffusionBee
Improve model card: add metadata, project page, paper abstract, and update paper link
#1
by nielsr HF Staff - opened
This PR enhances the model card by:
- Adding
pipeline_tag: text-to-image, making the model discoverable on the Hub for text-to-image tasks. - Updating
library_nametodiffusers, which correctly reflects the primary library used to interact with this model (as indicated byadapter_config.json) and enables the automated "how to use" widget. - Including a link to the official project page: https://research.nvidia.com/labs/dir/DiffusionNFT.
- Updating the paper link to the Hugging Face Papers page: https://huggingface.co/papers/2509.16117.
- Incorporating the paper's abstract for a more comprehensive description of the model.
- Updating the model description to correctly refer to "Diffusion Negative-aware FineTuning (DiffusionNFT)".
worstcoder changed pull request status to merged