Instructions to use kvablack/ddpo-alignment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kvablack/ddpo-alignment with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kvablack/ddpo-alignment", dtype=torch.bfloat16, device_map="cuda") prompt = "a horse playing chess" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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This model was finetuned from [Stable Diffusion v1-5](https:/runwayml/stable-diffusion-v1-5) using [DDPO](https://arxiv.org/abs/2305.13301) and a reward function that uses [LLaVA](https://llava-vl.github.io/) to measure prompt-image alignment. See [the project website](https://rl-diffusion.github.io/) for more details.
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The model was finetuned for
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Activities:
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- washing dishes
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This model was finetuned from [Stable Diffusion v1-5](https:/runwayml/stable-diffusion-v1-5) using [DDPO](https://arxiv.org/abs/2305.13301) and a reward function that uses [LLaVA](https://llava-vl.github.io/) to measure prompt-image alignment. See [the project website](https://rl-diffusion.github.io/) for more details.
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The model was finetuned for 200 iterations with a batch size of 256 samples per iteration. During finetuning, we used prompts of the form: "_a(n) \<animal\> \<activity\>_". We selected the animal and activity from the following lists, so try those for the best results. However, we also observed limited generalization to other prompts.
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Activities:
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- washing dishes
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