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", 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
Download checkpoint-1000/pytorch_model_1.bin from RadwaH/CustomDiffusionAgnes2: direct link, hf CLI and curl.
- Browser
- Download file 1.36 GB
-
https://huggingface.co/RadwaH/CustomDiffusionAgnes2/resolve/main/checkpoint-1000/pytorch_model_1.bin
- Command line
-
hf download hf://RadwaH/CustomDiffusionAgnes2/checkpoint-1000/pytorch_model_1.bin
-
curl -L -o pytorch_model_1.bin https://huggingface.co/RadwaH/CustomDiffusionAgnes2/resolve/main/checkpoint-1000/pytorch_model_1.bin
1.36 GB
- Xet hash:
- b7d4b283eb19860f4dadd3651fa1598175a10066aa0e8653e0a4976cc63c4b06
- Size of remote file:
- 1.36 GB
- SHA256:
- 4e4856c9f06a409b3b0dcb682a9e845d64c64f813e3107ffe60e27887588dae3
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