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:
- dae2b9c11da7fdd8e5538e85a207eb6867e15e7bb9145110161c85345cf7f83a
- Size of remote file:
- 1.22 GB
- SHA256:
- 16d28f2b37109f222cdc33620fdd262102ac32112be0352a7f77e9614b35a394
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