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
Download diffusers/examples/controlnet/requirements.txt from NadaGh/working: direct link, hf CLI and curl.
- Browser
- Download file 78 Bytes
-
https://huggingface.co/NadaGh/working/resolve/main/diffusers/examples/controlnet/requirements.txt
- Command line
-
hf download hf://NadaGh/working/diffusers/examples/controlnet/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/NadaGh/working/resolve/main/diffusers/examples/controlnet/requirements.txt
78 Bytes
| accelerate>=0.16.0 | |
| torchvision | |
| transformers>=4.25.1 | |
| ftfy | |
| tensorboard | |
| datasets | |