add demo

examples/inference/basic/basic.py

@@ -1,6 +1,10 @@
 from fastvideo import VideoGenerator
 
-# from fastvideo.configs.sample import SamplingParam
+from fastvideo.configs.sample import SamplingParam
+
+import os
+
+os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
 
 OUTPUT_PATH = "video_samples"
 def main():
@@ -8,30 +12,41 @@ def main():
     # model.
     # If a local path is provided, FastVideo will make a best effort
     # attempt to identify the optimal arguments.
+    model_name = "FastVideo/FastWan2.1-T2V-14B-Diffusers"
     generator = VideoGenerator.from_pretrained(
-        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+        model_name,
         # 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,
+        text_encoder_cpu_offload=False,
         # Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
-        pin_cpu_memory=False,
+        pin_cpu_memory=True,
         # 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"
+    sampling_param = SamplingParam.from_pretrained(model_name)
+    # sampling_param.image_path = "test.jpg"
+    # sampling_param.num_inference_steps = 0
     # 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)
+    i2v_prompt = "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
+    i2v_prompt = "A little girl is packing a suitcase and the contents starts flying out of the suitcase everywhere."
+    prompt = i2v_prompt
+    # 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."
+    # )
+    results = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, sampling_param=sampling_param)
+    stage_names = results["stage_names"]
+    stage_execution_times = results["stage_execution_times"]
+    # print(logging_info)
+    print(stage_names)
+    print(stage_execution_times)
+
     # video = generator.generate_video(prompt, sampling_param=sampling_param, output_path="wan_t2v_videos/")
+    return
 
     # Generate another video with a different prompt, without reloading the
     # model!