Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use KCS97/can with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use KCS97/can with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("KCS97/can", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of sks can" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- f800afd1eb87f237666578a1ec89a71cd8c784d61e7c9051f7bf794a14e43901
- Size of remote file:
- 6.88 GB
- SHA256:
- af2d5bccb5720f2cc4e30183945c3b9c9a8a49a22762ae75b06353d1f7ae88fb
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