diff --git a/examples/inference/optimizations/sageattn_inference_example_custom_weights_5090_16.py b/examples/inference/optimizations/attn_qat_inference_example.py similarity index 97% rename from examples/inference/optimizations/sageattn_inference_example_custom_weights_5090_16.py rename to examples/inference/optimizations/attn_qat_inference_example.py index 05fd40202..b612959fd 100644 --- a/examples/inference/optimizations/sageattn_inference_example_custom_weights_5090_16.py +++ b/examples/inference/optimizations/attn_qat_inference_example.py @@ -23,7 +23,7 @@ def main(): text_encoder_cpu_offload=True, pin_cpu_memory=False, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument" # image_encoder_cpu_offload=False, - # Load custom weights from checkpoint-5000 + # Load custom weights from checkpoint init_weights_from_safetensors="safetensors_path" ) diff --git a/examples/inference/sageattn_inference_example.py b/examples/inference/sageattn_inference_example.py deleted file mode 100644 index dceb6a3c2..000000000 --- a/examples/inference/sageattn_inference_example.py +++ /dev/null @@ -1,49 +0,0 @@ -from fastvideo import VideoGenerator -import os -# from fastvideo.configs.sample import SamplingParam - -OUTPUT_PATH = "video_samples" -os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SAGE_ATTN_THREE" - -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=True, - dit_cpu_offload=False, - vae_cpu_offload=False, - text_encoder_cpu_offload=True, - pin_cpu_memory=False, # 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() diff --git a/examples/inference/sageattn_inference_example_custom_weights.py b/examples/inference/sageattn_inference_example_custom_weights.py deleted file mode 100644 index 5457a7325..000000000 --- a/examples/inference/sageattn_inference_example_custom_weights.py +++ /dev/null @@ -1,54 +0,0 @@ -from fastvideo import VideoGenerator -import os -from pathlib import Path -# from fastvideo.configs.sample import SamplingParam - -OUTPUT_PATH = "video_samples" -os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SAGE_ATTN_THREE" - -CHECKPOINT_PATH = Path(__file__).parent.parent.parent / "checkpoints" / "wan_t2v_finetune_qat" / "checkpoint-5000" / "transformer" - -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=True, - dit_cpu_offload=False, - vae_cpu_offload=False, - text_encoder_cpu_offload=True, - pin_cpu_memory=False, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument" - # image_encoder_cpu_offload=False, - # Load custom weights from checkpoint-5000 - init_weights_from_safetensors=str(CHECKPOINT_PATH), - ) - - # 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()