Instructions to use sayakpaul/test-kerascv_sd_diffusers_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sayakpaul/test-kerascv_sd_diffusers_pipeline with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sayakpaul/test-kerascv_sd_diffusers_pipeline", 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
- Draw Things
- DiffusionBee
KerasCV Stable Diffusion in Diffusers π§¨π€
The pipeline contained in this repository was created using this Space. The purpose is to convert the KerasCV Stable Diffusion weights in a way that is compatible with Diffusers. This allows users to fine-tune using KerasCV and use the fine-tuned weights in Diffusers taking advantage of its nifty features (like schedulers, fast attention, etc.).
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Model tree for sayakpaul/test-kerascv_sd_diffusers_pipeline
Base model
CompVis/stable-diffusion-v1-4