Diffusers

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Diffusers Installation Quickstart Command line interface

Inference

Using diffusion pipelines

Adapters and personalization

Optimize and scale

Basic performance

Inference

Community methods

Quantization

Hardware-specific acceleration

Serving and scaling

Modular Diffusers

Train and fine-tune

Overview Create a dataset for training Adapt a model to a new task Train a diffusion model

Model recipes

Training methods

Textual Inversion DreamBooth LoRA Custom Diffusion Latent Consistency Distillation Reinforcement learning training with DDPO

NeMo Automodel

Resources

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API

You are viewing main version, which requires installation from source. If you'd like regular pip install, checkout the latest stable version (v0.40.0).

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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 DDPOTrainer.

For more information, check out the DDPOTrainer API reference and the Finetune Stable Diffusion Models with DDPO via TRL blog post.

Update on GitHub

←Latent Consistency Distillation NeMo Automodel→