Instructions to use na1taneja2821/diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use na1taneja2821/diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("na1taneja2821/diffusers") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Super-resolution
The Stable Diffusion upscaler diffusion model was created by the researchers and engineers from CompVis, Stability AI, and LAION. It is used to enhance the resolution of input images by a factor of 4.
Make sure to check out the Stable Diffusion Tips section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
If you're interested in using one of the official checkpoints for a task, explore the CompVis, Runway, and Stability AI Hub organizations!
StableDiffusionUpscalePipeline
[[autodoc]] StableDiffusionUpscalePipeline - all - call - enable_attention_slicing - disable_attention_slicing - enable_xformers_memory_efficient_attention - disable_xformers_memory_efficient_attention
StableDiffusionPipelineOutput
[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput