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/research_projects/diffusion_orpo/README.md from NadaGh/working: direct link, hf CLI and curl.
- Browser
- Download file 244 Bytes
-
https://huggingface.co/NadaGh/working/resolve/main/diffusers/examples/research_projects/diffusion_orpo/README.md
- Command line
-
hf download hf://NadaGh/working/diffusers/examples/research_projects/diffusion_orpo/README.md
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curl -L -o README.md https://huggingface.co/NadaGh/working/resolve/main/diffusers/examples/research_projects/diffusion_orpo/README.md
244 Bytes
This project has a new home now: https://mapo-t2i.github.io/. We formally studied the use of ORPO in the context of diffusion models and open-sourced our codebase, models, and datasets. We released our paper too!