fix: seed to gnerator

inference.py

@@ -40,20 +40,7 @@ class DiffusionInference:
         }
         
         # Handle seed parameter
-        if seed is not None:
-            try:
-                # Convert to integer and add to params
-                generator = torch.Generator(device="cuda").manual_seed(seed)
-            except (ValueError, TypeError):
-                # Use random seed if conversion fails
-                random_seed = random.randint(0, 3999999999)  # Max 32-bit integer
-                generator = torch.Generator(device="cuda").manual_seed(random_seed)
-                print(f"Warning: Invalid seed value: {seed}, using random seed {random_seed} instead")
-        else:
-            # Generate random seed when none is provided
-            random_seed = random.randint(0, 3999999999)  # Max 32-bit integer
-            generator = torch.Generator(device="cuda").manual_seed(random_seed)
-            print(f"Using random seed: {random_seed}")
+
         
         # Add negative prompt if provided
         if negative_prompt is not None:
@@ -66,7 +53,7 @@ class DiffusionInference:
         
         try:
             # Call the API with all parameters as kwargs
-            image = self.run_text_to_image_pipeline(model, generator, **params)
+            image = self.run_text_to_image_pipeline(model, seed, **params)
             return image
         except Exception as e:
             print(f"Error generating image: {e}")
@@ -163,7 +150,21 @@ class DiffusionInference:
                     print(f"Warning: Could not delete temporary file {temp_file}: {e}")
 
     @spaces.GPU
-    def run_text_to_image_pipeline(self, model_name, generator, **kwargs):
+    def run_text_to_image_pipeline(self, model_name, seed, **kwargs):
+        if seed is not None:
+            try:
+                # Convert to integer and add to params
+                generator = torch.Generator(device="cuda").manual_seed(seed)
+            except (ValueError, TypeError):
+                # Use random seed if conversion fails
+                random_seed = random.randint(0, 3999999999)  # Max 32-bit integer
+                generator = torch.Generator(device="cuda").manual_seed(random_seed)
+                print(f"Warning: Invalid seed value: {seed}, using random seed {random_seed} instead")
+        else:
+            # Generate random seed when none is provided
+            random_seed = random.randint(0, 3999999999)  # Max 32-bit integer
+            generator = torch.Generator(device="cuda").manual_seed(random_seed)
+            print(f"Using random seed: {random_seed}")
         pipeline = AutoPipelineForText2Image.from_pretrained(model_name, generator=generator, torch_dtype=torch.float16).to("cuda")
         image = pipeline(**kwargs).images[0]
         return image