adding files

download/download.py

@@ -17,7 +17,7 @@ def download_model():
         local_dir_use_symlinks=False,
     )
     snapshot_download(
-        repo_id="THUDM/CogVideoX-5b-I2V",
+        repo_id="THUDM/CogVideoX1.5-5B-SAT",
         local_dir="/app/pretrained/CogVideoX-5b-I2V",
         local_dir_use_symlinks=False,
     )

download/download_models.sh

@@ -1,16 +1,3 @@
-
 mkdir -p /app/pretrained/RAFT
-echo "=== Current Directory ==="
-pwd
-
-echo -e "\n=== Path: ./ ==="
-realpath ./
-
-echo -e "\n=== Path: ../ ==="
-realpath ../
-
-echo -e "\n=== Path: ../../ ==="
-realpath ../../
-
 gdown 1MqDajR89k-xLV0HIrmJ0k-n8ZpG6_suM -O /app/pretrained/RAFT/raft-things.pth
 python /app/download/download.py

gradio_app.py

@@ -115,7 +115,7 @@ def inference(
     seed, height, width, downscale_coef, vae_channels,
     controlnet_input_channels, controlnet_transformer_num_layers
 ):
-    MODEL_PATH = "/app/pretrained/CogVideoX-5b-I2V"
+    model_path = "/app/pretrained/CogVideoX-5b-I2V"
     ckpt_path = "/app/out/EPiC_pretrained/checkpoint-500.pt"
     video_root_dir = "/app/output_anchor"
     out_dir = "/app/output"
@@ -123,7 +123,7 @@ def inference(
     command = [
         "python", "/app/inference/cli_demo_camera_i2v_pcd.py",
         "--video_root_dir", video_root_dir,
-        "--base_model_path", MODEL_PATH,
+        "--base_model_path", model_path,
         "--controlnet_model_path", ckpt_path,
         "--output_path", out_dir,
         "--controlnet_weights", str(controlnet_weights),

inference/cli_demo_camera_i2v_pcd.py

@@ -252,6 +252,7 @@ def generate_video(
         controlnet=controlnet,
         scheduler=scheduler,
     ).to('cuda')
+    
     # If you're using with lora, add this code
     if lora_path:
         pipe.load_lora_weights(lora_path, weight_name="pytorch_lora_weights.safetensors", adapter_name="test_1")

inference/v2v_data/inference.py

@@ -109,7 +109,7 @@ def get_parser():
         default='DDIM_Origin',
         help='Choose the sampler',
     )
-    # parser.add_argument('--transformer_path', type=str, default='checkpoints/TrajectoryCrafter/crosstransformer', help='Path to the pretrained transformer model')
+    # parser.add_argument('--transformer_path', type=str,kdefault='checkpoints/TrajectoryCrafter/crosstransformer', help='Path to the pretrained transformer model')
     parser.add_argument(
         '--transformer_path',
         type=str,