Instructions to use d-wang26/wd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use d-wang26/wd with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("d-wang26/wd", 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
Download diffusers/benchmarks/benchmark_t2i_lcm_lora.py from d-wang26/wd: direct link, hf CLI and curl.
- Browser
- Download file 695 Bytes
-
https://huggingface.co/d-wang26/wd/resolve/main/diffusers/benchmarks/benchmark_t2i_lcm_lora.py
- Command line
-
hf download hf://d-wang26/wd/diffusers/benchmarks/benchmark_t2i_lcm_lora.py
-
curl -L -o benchmark_t2i_lcm_lora.py https://huggingface.co/d-wang26/wd/resolve/main/diffusers/benchmarks/benchmark_t2i_lcm_lora.py
695 Bytes
| import argparse | |
| import sys | |
| sys.path.append(".") | |
| from base_classes import LCMLoRATextToImageBenchmark # noqa: E402 | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| "--ckpt", | |
| type=str, | |
| default="stabilityai/stable-diffusion-xl-base-1.0", | |
| ) | |
| parser.add_argument("--batch_size", type=int, default=1) | |
| parser.add_argument("--num_inference_steps", type=int, default=4) | |
| parser.add_argument("--model_cpu_offload", action="store_true") | |
| parser.add_argument("--run_compile", action="store_true") | |
| args = parser.parse_args() | |
| benchmark_pipe = LCMLoRATextToImageBenchmark(args) | |
| benchmark_pipe.benchmark(args) | |