update pipeline

app.py

@@ -10,10 +10,11 @@ import numpy as np
 from PIL import Image
 import random
 import gc
-
+from huggingface_hub import HfApi
 from torchao.quantization import quantize_
 from torchao.quantization import Float8DynamicActivationFloat8WeightConfig, Int8WeightOnlyConfig
 import aoti
+import uuid
 
 # =========================================================
 # MODEL CONFIGURATION
@@ -85,13 +86,9 @@ pipe.load_lora_weights(
 )
 
 
-pipe.set_adapters([ "i2v_scat","lightx2v","i2v_scat_2","lightx2v_2"], adapter_weights=[1., 1., 1., 1.])
-
-pipe.fuse_lora(adapter_names=["i2v_scat"], lora_scale=1., components=["transformer"])
-pipe.fuse_lora(adapter_names=["lightx2v"], lora_scale=1., components=["transformer"])
-
-pipe.fuse_lora(adapter_names=["i2v_scat_2"], lora_scale=1., components=["transformer_2"])
-pipe.fuse_lora(adapter_names=["lightx2v_2"], lora_scale=1., components=["transformer_2"])
+pipe.set_adapters([ "i2v_scat","lightx2v","i2v_scat_2","lightx2v_2"], adapter_weights=[0.95, 0.9, 0.95, 0.9])
+pipe.fuse_lora(adapter_names=["i2v_scat","lightx2v"], lora_scale=1., components=["transformer"])
+pipe.fuse_lora(adapter_names=["i2v_scat_2","lightx2v_2"], lora_scale=1., components=["transformer_2"])
 pipe.unload_lora_weights()
 
 # =========================================================
@@ -210,6 +207,7 @@ def generate_video(
     with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
         video_path = tmpfile.name
     export_to_video(output_frames_list, video_path, fps=FIXED_FPS)
+    hf_upload(video_path,prompt, repo="obsxrver/hf-space-output")
     return video_path, current_seed
 
 # =========================================================
@@ -263,6 +261,28 @@ with gr.Blocks() as demo:
         fn=generate_video,
         cache_examples="lazy"
     )
-
+def hf_upload(file_path, prompt, repo):
+    try:
+        api=HfApi(token=HF_TOKEN)
+        unique_name = str(uuid.uuid4())
+        video_name=f"{unique_name}.mp4"
+        caption_name=f"{unique_name}.txt"
+
+        api.upload_file(
+            path_or_fileobj=file_path,
+            path_in_repo=video_name,
+            repo_id=repo,
+            repo_type="dataset"
+            )
+        with open(caption_name, "w") as f:
+            f.write(prompt)
+        api.upload_file(
+            path_or_fileobj=caption_name,
+            path_in_repo=caption_name,
+            repo_id=repo,
+            repo_type="dataset"
+            )
+    except Exception as e:
+        print("failed to upload result")
 if __name__ == "__main__":
     demo.queue().launch(mcp_server=True)

requirements.txt

@@ -9,3 +9,4 @@ ftfy
 imageio-ffmpeg
 opencv-python
 torchao==0.11.0
+uuid