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gradio_app.py

@@ -38,58 +38,90 @@ download_models()
 # -----------------------------
 # Step 2: Inference Logic
 # -----------------------------
+
 def run_epic_inference(video_path, caption, motion_type):
     temp_input_path = "/app/temp_input.mp4"
-    output_dir = f"/app/output_{motion_type}"
-    
-    video_output_path = f"{output_dir}/masked_videos/temp_input.mp4"
+    output_dir = f"/app/output_anchor"
+    video_output_path = f"{output_dir}/videos/output.mp4"
     traj_name = motion_type
     traj_txt = f"/app/inference/v2v_data/test/trajs/{traj_name}.txt"
-
     # Save uploaded video
     if video_path:
         os.system(f"cp '{video_path}' {temp_input_path}")
-    
+
     command = [
-    "python",
-    "/app/inference/v2v_data/inference.py",
-    "--video_path",
-    temp_input_path,
-    "--stride",
-    "1",
-    "--out_dir",
-    output_dir,
-    "--radius_scale",
-    "1",
-    "--camera",
-    "traj",
-    "--mask",
+    "python", "/app/inference/v2v_data/inference.py",
+    "--video_path", temp_input_path,
+    "--stride", "1",
+    "--out_dir", output_dir,
+    "--radius_scale", "1",
+    "--camera", "traj",
+    "--mask", 
     "--target_pose", "0", "30", "-0.6", "0", "0",
-    "--traj_txt",
-    traj_txt,
-    "--save_name",
-    f"temp_input",
-    "--mode",
-    "gradual",
-    "--out_dir",
-    output_dir,
+    "--traj_txt", traj_txt,
+    "--save_name", "output",
+    "--mode", "gradual",
     ]
 
     # Run inference command
     try:
+    
         result = subprocess.run(command, capture_output=True, text=True, check=True)
+        print("Getting Anchor Videos run successfully.")
         logs = result.stdout
     except subprocess.CalledProcessError as e:
         logs = f"❌ Inference failed:\n{e.stderr}"
         return logs, None
 
     # Locate the output video
-    output_video = Path(output_dir) / f"amalfi-coast_traj_{traj_name}.mp4"
     if video_output_path:
         return logs, str(video_output_path)
     else:
         return f"Inference succeeded but no output video found in {output_dir}", None
 
+def inference(video_path, caption, motion_type):
+
+    MODEL_PATH="/app/pretrained/CogVideoX-5b-I2V"
+
+    ckpt_steps=500
+    ckpt_dir="/app/out/EPiC_pretrained"
+    ckpt_file=f"checkpoint-{ckpt_steps}.pt"
+    ckpt_path=f"{ckpt_dir}/{ckpt_file}"
+
+    video_root_dir= f"/app/output_anchor"
+    out_dir=f"/app/output"
+
+    command = [
+        "python", "/app/inference/cli_demo_camera_i2v_pcd.py",
+        "--video_root_dir", video_root_dir,
+        "--base_model_path", MODEL_PATH,
+        "--controlnet_model_path", ckpt_path,
+        "--output_path", out_dir,
+        "--start_camera_idx", "0",
+        "--end_camera_idx", "8",
+        "--controlnet_weights", "1.0",
+        "--controlnet_guidance_start", "0.0",
+        "--controlnet_guidance_end", "0.4",
+        "--controlnet_input_channels", "3",
+        "--controlnet_transformer_num_attn_heads", "4",
+        "--controlnet_transformer_attention_head_dim", "64",
+        "--controlnet_transformer_out_proj_dim_factor", "64",
+        "--controlnet_transformer_out_proj_dim_zero_init",
+        "--vae_channels", "16",
+        "--num_frames", "49",
+        "--controlnet_transformer_num_layers", "8",
+        "--infer_with_mask",
+        "--pool_style", "max",
+        "--seed", "43"
+    ]
+
+    # Run the command
+    result = subprocess.run(command, capture_output=True, text=True)
+    if result.returncode == 0:
+        print("Inference completed successfully.")
+    else:
+        print(f"Error occurred during inference: {result.stderr}")
+    
 
 # -----------------------------
 # Step 3: Create Gradio UI