fix: seed to gnerator

inference.py

@@ -43,16 +43,16 @@ class DiffusionInference:
         if seed is not None:
             try:
                 # Convert to integer and add to params
-                params["seed"] = int(seed)
+                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
-                params["seed"] = random_seed
+                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
-            params["seed"] = random_seed
+            generator = torch.Generator(device="cuda").manual_seed(random_seed)
             print(f"Using random seed: {random_seed}")
         
         # Add negative prompt if provided
@@ -66,7 +66,7 @@ class DiffusionInference:
         
         try:
             # Call the API with all parameters as kwargs
-            image = self.run_text_to_image_pipeline(model, **params)
+            image = self.run_text_to_image_pipeline(model, generator, **params)
             return image
         except Exception as e:
             print(f"Error generating image: {e}")
@@ -164,6 +164,6 @@ class DiffusionInference:
 
     @spaces.GPU
     def run_text_to_image_pipeline(self, model_name, **kwargs):
-        pipeline = AutoPipelineForText2Image.from_pretrained(model_name, torch_dtype=torch.float16).to("cuda")
+        pipeline = AutoPipelineForText2Image.from_pretrained(model_name, generator=generator, torch_dtype=torch.float16).to("cuda")
         image = pipeline(**kwargs).images[0]
         return image