Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
diffusers-training
lora
Instructions to use ahmed-3m/DM_cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ahmed-3m/DM_cifar10 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("ahmed-3m/DM_cifar10") 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
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.