Update app.py

app.py

@@ -1,5 +1,6 @@
 import spaces
 import torch
+import os  # ← مضاف
 from diffusers.pipelines.wan.pipeline_wan_i2v import WanImageToVideoPipeline
 from diffusers.models.transformers.transformer_wan import WanTransformer3DModel
 from diffusers.utils.export_utils import export_to_video
@@ -12,13 +13,12 @@ import gc
 
 from torchao.quantization import quantize_
 from torchao.quantization import Float8DynamicActivationFloat8WeightConfig, Int8WeightOnlyConfig
-
 import aoti
 
 # =========================================================
 # MODEL CONFIGURATION
 # =========================================================
-MODEL_ID = "dream2589632147/Dream-wan2-2-faster-Pro"  # ← المسار الجديد للنموذج
+MODEL_ID = "dream2589632147/Dream-wan2-2-faster-Pro"
 
 MAX_DIM = 832
 MIN_DIM = 480
@@ -26,32 +26,40 @@ SQUARE_DIM = 640
 MULTIPLE_OF = 16
 
 MAX_SEED = np.iinfo(np.int32).max
-
 FIXED_FPS = 16
 MIN_FRAMES_MODEL = 8
-MAX_FRAMES_MODEL = 80
-
+MAX_FRAMES_MODEL = 720
 MIN_DURATION = round(MIN_FRAMES_MODEL / FIXED_FPS, 1)
 MAX_DURATION = round(MAX_FRAMES_MODEL / FIXED_FPS, 1)
 
+# =========================================================
+# LOAD TRANSFORMERS (باستخدام HF_TOKEN)
+# =========================================================
+transformer = WanTransformer3DModel.from_pretrained(
+    MODEL_ID,
+    subfolder="transformer",
+    torch_dtype=torch.bfloat16,
+    device_map="cuda",
+    token=os.environ.get("HF_TOKEN")  # ← يستخدم التوكن للوصول إلى النموذج
+)
+
+transformer_2 = WanTransformer3DModel.from_pretrained(
+    MODEL_ID,
+    subfolder="transformer_2",
+    torch_dtype=torch.bfloat16,
+    device_map="cuda",
+    token=os.environ.get("HF_TOKEN")
+)
+
 # =========================================================
 # LOAD PIPELINE
 # =========================================================
 pipe = WanImageToVideoPipeline.from_pretrained(
     MODEL_ID,
-    transformer=WanTransformer3DModel.from_pretrained(
-        MODEL_ID,
-        subfolder="transformer",
-        torch_dtype=torch.bfloat16,
-        device_map="cuda",
-    ),
-    transformer_2=WanTransformer3DModel.from_pretrained(
-        MODEL_ID,
-        subfolder="transformer_2",
-        torch_dtype=torch.bfloat16,
-        device_map="cuda",
-    ),
+    transformer=transformer,
+    transformer_2=transformer_2,
     torch_dtype=torch.bfloat16,
+    token=os.environ.get("HF_TOKEN")
 ).to("cuda")
 
 # =========================================================
@@ -105,7 +113,6 @@ def resize_image(image: Image.Image) -> Image.Image:
     aspect_ratio = width / height
     MAX_ASPECT_RATIO = MAX_DIM / MIN_DIM
     MIN_ASPECT_RATIO = MIN_DIM / MAX_DIM
-
     image_to_resize = image
 
     if aspect_ratio > MAX_ASPECT_RATIO:
@@ -126,14 +133,12 @@ def resize_image(image: Image.Image) -> Image.Image:
 
     final_w = round(target_w / MULTIPLE_OF) * MULTIPLE_OF
     final_h = round(target_h / MULTIPLE_OF) * MULTIPLE_OF
-
     final_w = max(MIN_DIM, min(MAX_DIM, final_w))
     final_h = max(MIN_DIM, min(MAX_DIM, final_h))
-
     return image_to_resize.resize((final_w, final_h), Image.LANCZOS)
 
 # =========================================================
-# UTILITY FUNCTIONS
+# UTILITIES
 # =========================================================
 def get_num_frames(duration_seconds: float):
     return 1 + int(np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL))
@@ -152,7 +157,7 @@ def get_duration(
     return 10 + int(steps) * step_duration
 
 # =========================================================
-# MAIN GENERATION FUNCTION
+# MAIN FUNCTION
 # =========================================================
 @spaces.GPU(duration=get_duration)
 def generate_video(