Instructions to use KamCastle/36framesV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use KamCastle/36framesV2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("KamCastle/36framesV2", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download vae/diffusion_pytorch_model.safetensors from KamCastle/36framesV2: direct link, hf CLI and curl.
- Browser
- Download file 335 MB
-
https://huggingface.co/KamCastle/36framesV2/resolve/main/vae/diffusion_pytorch_model.safetensors
- Command line
-
hf download hf://KamCastle/36framesV2/vae/diffusion_pytorch_model.safetensors
-
curl -L -o diffusion_pytorch_model.safetensors https://huggingface.co/KamCastle/36framesV2/resolve/main/vae/diffusion_pytorch_model.safetensors
335 MB
- Xet hash:
- fd57108e8345c6c4bfc09624b157b205d14fdd8e454d54a9615366010b664620
- Size of remote file:
- 335 MB
- SHA256:
- ff72232dce5ab2631364086a9b97fcb13b55df1104bcb49ff93384f24dc23848
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