22

app.py

@@ -61,14 +61,15 @@ pipe = QwenImagePipeline.from_pretrained(
 
 @spaces.GPU
 def generate_lora(
-    images,
+    input_images,
     progress=gr.Progress(track_tqdm=True),
 ):
 
     ulid = str(ULID()).lower()[:12]
     print(f"ulid: {ulid}")
 
-    if not images:
+    input_images = list(input_images)
+    if not input_images:
         print("images is empty.")
     # Load images 
     # images = [
@@ -82,7 +83,7 @@ def generate_lora(
 
     # Model inference
     with torch.no_grad():
-        embs = QwenImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=images)
+        embs = QwenImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=input_images)
         lora = QwenImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"]
 
     lora_name = f"{ulid}.safetensors"