Update app_g.py

app_g.py

@@ -6,19 +6,59 @@ import sys
 from huggingface_hub import snapshot_download
 import spaces
 
-# Setup paths
-PUSA_PATH = os.path.abspath("./PusaV1")
-if PUSA_PATH not in sys.path:
-    sys.path.insert(0, PUSA_PATH)
+import os
+import sys
+from huggingface_hub import snapshot_download
+
+# === Setup Paths ===
+PUSA_ROOT = os.path.abspath("./PusaV1")
+MODEL_ZOO_DIR = os.path.join(PUSA_ROOT, "modelzoo")
+WAN_SUBFOLDER = "Wan2.1-T2V-14B"
+WAN_MODEL_PATH = os.path.join(MODEL_ZOO_DIR, WAN_SUBFOLDER)
+LORA_PATH = os.path.join(MODEL_ZOO_DIR, "pusa_v1.pt")
 
-# Validate diffsynth presence
-DIFFSYNTH_PATH = os.path.join(PUSA_PATH, "diffsynth")
+# Add PUSA_ROOT to sys.path so Python can find local modules like diffsynth
+if PUSA_ROOT not in sys.path:
+    sys.path.insert(0, PUSA_ROOT)
+
+# Validate presence of diffsynth
+DIFFSYNTH_PATH = os.path.join(PUSA_ROOT, "diffsynth")
 if not os.path.exists(DIFFSYNTH_PATH):
     raise RuntimeError(
-        f"'diffsynth' package not found in {PUSA_PATH}. "
+        f"'diffsynth' package not found in {PUSA_ROOT}. "
         f"Ensure PusaV1 is correctly cloned and folder structure is intact."
     )
 
+# === Ensure model and weights are downloaded ===
+def ensure_model_downloaded():
+    if not os.path.exists(MODEL_ZOO_DIR):
+        print("Downloading RaphaelLiu/PusaV1 to ./PusaV1/modelzoo ...")
+        snapshot_download(
+            repo_id="RaphaelLiu/PusaV1",
+            local_dir=MODEL_ZOO_DIR,
+            repo_type="model",
+            local_dir_use_symlinks=False,
+        )
+        print("✅ PusaV1 base model downloaded.")
+
+    if not os.path.exists(WAN_MODEL_PATH):
+        print("Downloading Wan-AI/Wan2.1-T2V-14B to ./PusaV1/modelzoo/Wan2.1-T2V-14B ...")
+        snapshot_download(
+            repo_id="Wan-AI/Wan2.1-T2V-14B-Diffusers",  # Adjust if necessary
+            local_dir=WAN_MODEL_PATH,
+            repo_type="model",
+            local_dir_use_symlinks=False,
+        )
+        print("✅ Wan2.1-T2V-14B model downloaded.")
+
+    if not os.path.exists(LORA_PATH):
+        raise FileNotFoundError(
+            f"Expected LoRA weights 'pusa_v1.pt' not found at {LORA_PATH}. "
+            f"Please ensure the file exists or is downloaded manually."
+        )
+
+
+
 import gradio as gr
 import torch
 import os
@@ -33,38 +73,7 @@ import tempfile
 
 
 
-# Constants
-import os
-from huggingface_hub import snapshot_download
-
-# Constants
-MODEL_ZOO_DIR = "./model_zoo"
-PUSA_DIR = os.path.join(MODEL_ZOO_DIR, "PusaV1")
-WAN_SUBFOLDER = "Wan2.1-T2V-14B"
-WAN_MODEL_PATH = os.path.join(PUSA_DIR, WAN_SUBFOLDER)
-LORA_PATH = os.path.join(PUSA_DIR, "pusa_v1.pt")
-
-# Ensure model and weights are downloaded
-def ensure_model_downloaded():
-    if not os.path.exists(PUSA_DIR):
-        print("Downloading RaphaelLiu/PusaV1 to ./model_zoo/PusaV1 ...")
-        snapshot_download(
-            repo_id="RaphaelLiu/PusaV1",
-            local_dir=PUSA_DIR,
-            repo_type="model",
-            local_dir_use_symlinks=False,
-        )
-        print("✅ PusaV1 downloaded.")
 
-    if not os.path.exists(WAN_MODEL_PATH):
-        print("Downloading Wan-AI/Wan2.1-T2V-14B to ./model_zoo/PusaV1/Wan2.1-T2V-14B ...")
-        snapshot_download(
-            repo_id="Wan-AI/Wan2.1-T2V-14B-Diffusers",   #"Wan-AI/Wan2.1-T2V-14B",
-            local_dir=WAN_MODEL_PATH,  # Changed from WAN_DIR to WAN_MODEL_PATH
-            repo_type="model",
-            local_dir_use_symlinks=False,
-        )
-        print("✅ Wan2.1-T2V-14B downloaded.")
 
 
 class PusaVideoDemo:
@@ -74,7 +83,7 @@ class PusaVideoDemo:
         self.multi_frames_pipe = None
         self.v2v_pipe = None
         self.t2v_pipe = None
-        self.base_dir = "model_zoo/PusaV1/Wan2.1-T2V-14B"
+        self.base_dir = "PusaV1/model_zoo/PusaV1/Wan2.1-T2V-14B"
         self.output_dir = "outputs"
         os.makedirs(self.output_dir, exist_ok=True)
         
@@ -946,7 +955,7 @@ def create_demo():
                                 info="Controls generation quality and consistency"
                             )
                             steps_t2v = gr.Slider(
-                                minimum=10, maximum=50, value=10, step=5,
+                                minimum=1, maximum=50, value=10, step=5,
                                 label="Inference Steps"
                             )