Instructions to use elvis92/pets_rank_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use elvis92/pets_rank_2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("elvis92/pets_rank_2") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download diffusers/docs/source/en/training/ddpo.md from elvis92/pets_rank_2: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
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https://huggingface.co/elvis92/pets_rank_2/resolve/main/diffusers/docs/source/en/training/ddpo.md
- Command line
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hf download hf://elvis92/pets_rank_2/diffusers/docs/source/en/training/ddpo.md
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curl -L -o ddpo.md https://huggingface.co/elvis92/pets_rank_2/resolve/main/diffusers/docs/source/en/training/ddpo.md
1.21 kB
Reinforcement learning training with DDPO
You can fine-tune Stable Diffusion on a reward function via reinforcement learning with the 🤗 TRL library and 🤗 Diffusers. This is done with the Denoising Diffusion Policy Optimization (DDPO) algorithm introduced by Black et al. in Training Diffusion Models with Reinforcement Learning, which is implemented in 🤗 TRL with the [~trl.DDPOTrainer].
For more information, check out the [~trl.DDPOTrainer] API reference and the Finetune Stable Diffusion Models with DDPO via TRL blog post.