Instructions to use ccc8/cc8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ccc8/cc8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dataautogpt3/OpenDalleV1.1", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ccc8/cc8") prompt = "masterpiece forest" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 9c9a7b7f63d6d16b063ba39ec8ecdb7bcfbc8d6420a6e601631f4a834487eab8
- Size of remote file:
- 116 MB
- SHA256:
- f6e775bd3d14db8e9406c1c8efdfbe78c771a808141989b7e4d2ca829a849e90
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