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
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Download diffusers/examples/reinforcement_learning/README.md from NadaGh/working: direct link, hf CLI and curl.
- Browser
- Download file 825 Bytes
-
https://huggingface.co/NadaGh/working/resolve/main/diffusers/examples/reinforcement_learning/README.md
- Command line
-
hf download hf://NadaGh/working/diffusers/examples/reinforcement_learning/README.md
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curl -L -o README.md https://huggingface.co/NadaGh/working/resolve/main/diffusers/examples/reinforcement_learning/README.md
825 Bytes
Overview
These examples show how to run Diffuser 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:
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