Update app_t2v.py

app_t2v.py

@@ -29,13 +29,26 @@ print("Loading Stable Diffusion XL model...")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Initialize SDXL pipeline
+# sdxl_pipe = StableDiffusionXLPipeline.from_pretrained(
+#     "votepurchase/pornmasterPro_noobV3VAE",
+#     torch_dtype=torch.float16,
+#     variant="fp16",
+#     use_safetensors=True
+# )
+
+
+
 sdxl_pipe = StableDiffusionXLPipeline.from_pretrained(
-    "votepurchase/pornmasterPro_noobV3VAE",
+    "stablediffusionapi/omnigenxl-nsfw-sfw",
     torch_dtype=torch.float16,
     variant="fp16",
     use_safetensors=True
 )
 
+
+
+
+
 sdxl_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sdxl_pipe.scheduler.config)
 sdxl_pipe.to(device)
 
@@ -90,12 +103,12 @@ wan_pipeline = wan.WanTI2V(
 ###LORA ####
 
 
-LORA_REPO_ID = "Kijai/WanVideo_comfy"
-LORA_FILENAME = "Wan22-Lightning/Wan2.2-Lightning_T2V-A14B-4steps-lora_LOW_fp16.safetensors"
-causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME)
-wan_pipeline.load_lora_weights(causvid_path, adapter_name="causvid_lora")
-wan_pipeline.set_adapters(["causvid_lora"], adapter_weights=[0.95])
-wan_pipeline.fuse_lora()
+# LORA_REPO_ID = "Kijai/WanVideo_comfy"
+# LORA_FILENAME = "Wan22-Lightning/Wan2.2-Lightning_T2V-A14B-4steps-lora_LOW_fp16.safetensors"
+# causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME)
+# wan_pipeline.load_lora_weights(causvid_path, adapter_name="causvid_lora")
+# wan_pipeline.set_adapters(["causvid_lora"], adapter_weights=[0.95])
+# wan_pipeline.fuse_lora()