Instructions to use yitongl/sparse_quant_exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yitongl/sparse_quant_exp with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("yitongl/sparse_quant_exp", 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
| from fastvideo import VideoGenerator | |
| # from fastvideo.configs.sample import SamplingParam | |
| OUTPUT_PATH = "video_samples" | |
| def main(): | |
| # FastVideo will automatically use the optimal default arguments for the | |
| # model. | |
| # If a local path is provided, FastVideo will make a best effort | |
| # attempt to identify the optimal arguments. | |
| generator = VideoGenerator.from_pretrained( | |
| "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", | |
| # FastVideo will automatically handle distributed setup | |
| num_gpus=1, | |
| use_fsdp_inference=False, # set to True if GPU is out of memory | |
| dit_cpu_offload=False, | |
| vae_cpu_offload=False, | |
| text_encoder_cpu_offload=True, | |
| pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument" | |
| # image_encoder_cpu_offload=False, | |
| ) | |
| # sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers") | |
| # sampling_param.num_frames = 45 | |
| # sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg" | |
| # Generate videos with the same simple API, regardless of GPU count | |
| prompt = ( | |
| "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes " | |
| "wide with interest. The playful yet serene atmosphere is complemented by soft " | |
| "natural light filtering through the petals. Mid-shot, warm and cheerful tones." | |
| ) | |
| video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True) | |
| # video = generator.generate_video(prompt, sampling_param=sampling_param, output_path="wan_t2v_videos/") | |
| # Generate another video with a different prompt, without reloading the | |
| # model! | |
| prompt2 = ( | |
| "A majestic lion strides across the golden savanna, its powerful frame " | |
| "glistening under the warm afternoon sun. The tall grass ripples gently in " | |
| "the breeze, enhancing the lion's commanding presence. The tone is vibrant, " | |
| "embodying the raw energy of the wild. Low angle, steady tracking shot, " | |
| "cinematic.") | |
| video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True) | |
| if __name__ == "__main__": | |
| main() | |