Text-to-Image
Diffusers
PyTorch
StableDiffusionPipeline
stable-diffusion
diffusion-models-class
dreambooth-hackathon
landscape
Instructions to use CCMat/fforiver-river with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CCMat/fforiver-river with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CCMat/fforiver-river", dtype=torch.bfloat16, device_map="cuda") prompt = "professional photo of fforiver river running alongside the Colosseum in Rome" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 0184e400c7ba2df55a61f61c0c795b5dadba60a57ae4c042710407a1da8a0a1d
- Size of remote file:
- 3.44 GB
- SHA256:
- 21fb9cf0dd3dec9ebc80bd1f5f68f8e39b4669f1a144eb801e0538f1f4e2b8db
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