Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use NadaGh/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use NadaGh/working with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("NadaGh/working", dtype=torch.bfloat16, device_map="cuda") prompt = "tst chair" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| # Overview | |
| These examples show how to run [Diffuser](https://arxiv.org/abs/2205.09991) in Diffusers. | |
| There are two ways to use the script, `run_diffuser_locomotion.py`. | |
| The key option is a change of the variable `n_guide_steps`. | |
| When `n_guide_steps=0`, the trajectories are sampled from the diffusion model, but not fine-tuned to maximize reward in the environment. | |
| By default, `n_guide_steps=2` to match the original implementation. | |
| You will need some RL specific requirements to run the examples: | |
| ```sh | |
| pip install -f https://download.pytorch.org/whl/torch_stable.html \ | |
| free-mujoco-py \ | |
| einops \ | |
| gym==0.24.1 \ | |
| protobuf==3.20.1 \ | |
| git+https://github.com/rail-berkeley/d4rl.git \ | |
| mediapy \ | |
| Pillow==9.0.0 | |
| ``` | |