Update app.py

app.py

@@ -19,33 +19,42 @@ os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:512'
 os.environ['HF_HUB_CACHE'] = '/tmp/hub'  # Use temp directory to avoid filling persistent storage
 
 
-MODEL_ID = "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers"
-
-
-#LORA_REPO_ID = "Kijai/WanVideo_comfy"
-#LORA_FILENAME = "Wan21_CausVid_14B_T2V_lora_rank32.safetensors"
-
-
+from diffusers import UniPCMultistepScheduler
+from transformers import CLIPTextModel, CLIPTokenizer
+from wan_diffusers import WanTextToVideoPipeline  # Use correct import for Wan's T2V pipeline
+from safetensors.torch import load_file
+from huggingface_hub import hf_hub_download
+import torch
 
-LORA_REPO_ID = "vrgamedevgirl84/Wan14BT2VFusioniX"
+# --- Base model setup (Wan T2V) ---
+MODEL_ID = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
 LORA_FILENAME = "FusionX_LoRa/Wan2.1_T2V_14B_FusionX_LoRA.safetensors"
 
+print("🚀 Loading Wan2.1 T2V base pipeline...")
 
-#LORA_REPO_ID = "romanfratric234/WAN_2-1_Furry_nsfw_14b_img2vid_and_nsfw_motion_in_general"
-#LORA_FILENAME = "furry_nsfw_1.1_e22.safetensors"
-
-image_encoder = CLIPVisionModel.from_pretrained(MODEL_ID, subfolder="image_encoder", torch_dtype=torch.float32)
-vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float32)
-pipe = WanImageToVideoPipeline.from_pretrained(
-    MODEL_ID, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16
+pipe = WanTextToVideoPipeline.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
 )
+
+# Optional: replace scheduler for more stable generation
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=8.0)
 pipe.to("cuda")
 
-causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME)
-pipe.load_lora_weights(causvid_path, adapter_name="causvid_lora")
-pipe.set_adapters(["causvid_lora"], adapter_weights=[0.95])
-pipe.fuse_lora()
+# --- Load FusionX-style LoRA ---
+print("🔧 Loading FusionX LoRA...")
+try:
+    lora_path = hf_hub_download(repo_id="FusionX_LoRa", filename=LORA_FILENAME)
+    pipe.load_lora_weights(lora_path, adapter_name="fusionx_lora")
+    pipe.set_adapters(["fusionx_lora"], adapter_weights=[1.0])
+    pipe.fuse_lora()
+    print("✅ FusionX LoRA applied (strength: 1.0)")
+except Exception as e:
+    print(f"⚠️ Failed to load FusionX LoRA: {e}")
+
+# --- Ready to generate ---
+print("✨ T2V model ready for text-to-video generation!")
+
 
 MOD_VALUE = 32
 DEFAULT_H_SLIDER_VALUE = 512