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", torch_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
| import argparse | |
| import sys | |
| sys.path.append(".") | |
| from base_classes import ControlNetBenchmark, ControlNetSDXLBenchmark # noqa: E402 | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| "--ckpt", | |
| type=str, | |
| default="lllyasviel/sd-controlnet-canny", | |
| choices=["lllyasviel/sd-controlnet-canny", "diffusers/controlnet-canny-sdxl-1.0"], | |
| ) | |
| parser.add_argument("--batch_size", type=int, default=1) | |
| parser.add_argument("--num_inference_steps", type=int, default=50) | |
| parser.add_argument("--model_cpu_offload", action="store_true") | |
| parser.add_argument("--run_compile", action="store_true") | |
| args = parser.parse_args() | |
| benchmark_pipe = ( | |
| ControlNetBenchmark(args) if args.ckpt == "lllyasviel/sd-controlnet-canny" else ControlNetSDXLBenchmark(args) | |
| ) | |
| benchmark_pipe.benchmark(args) | |