Instructions to use RadwaH/CustomDiffusionAgnes2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RadwaH/CustomDiffusionAgnes2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("RadwaH/CustomDiffusionAgnes2", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a <new1> girl" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| license: creativeml-openrail-m | |
| base_model: stabilityai/stable-diffusion-2 | |
| instance_prompt: photo of a <new1> girl | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - custom-diffusion | |
| inference: true | |
| # Custom Diffusion - RadwaH/CustomDiffusionAgnes2 | |
| These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2. The weights were trained on photo of a <new1> girl using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. | |
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| For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion). | |