Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use ShadeEngine/SD_kirby_dreambooth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ShadeEngine/SD_kirby_dreambooth with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ShadeEngine/SD_kirby_dreambooth", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of kirby" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Video Processor
The [VideoProcessor] provides a unified API for video pipelines to prepare inputs for VAE encoding and post-processing outputs once they're decoded. The class inherits [VaeImageProcessor] so it includes transformations such as resizing, normalization, and conversion between PIL Image, PyTorch, and NumPy arrays.
VideoProcessor
[[autodoc]] video_processor.VideoProcessor.preprocess_video
[[autodoc]] video_processor.VideoProcessor.postprocess_video