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
File size: 988 Bytes
dc0392c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | import argparse
import sys
sys.path.append(".")
from base_classes import ImageToImageBenchmark, TurboImageToImageBenchmark # noqa: E402
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--ckpt",
type=str,
default="runwayml/stable-diffusion-v1-5",
choices=[
"runwayml/stable-diffusion-v1-5",
"stabilityai/stable-diffusion-2-1",
"stabilityai/stable-diffusion-xl-refiner-1.0",
"stabilityai/sdxl-turbo",
],
)
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 = ImageToImageBenchmark(args) if "turbo" not in args.ckpt else TurboImageToImageBenchmark(args)
benchmark_pipe.benchmark(args)
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