generator

Author: sayakpaul

tests/models/test_lora_layers.py

@@ -1046,7 +1046,7 @@ def test_sdxl_1_0_lora_fusion(self):
         self.assertTrue(np.allclose(images, expected, atol=1e-4))
 
     def test_sdxl_1_0_lora_unfusion(self):
-        generator = torch.manual_seed(0)
+        generator = torch.Generator().manual_seed(0)
 
         pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
         lora_model_id = "hf-internal-testing/sdxl-1.0-lora"
@@ -1061,19 +1061,19 @@ def test_sdxl_1_0_lora_unfusion(self):
         images_with_fusion = images[0, -3:, -3:, -1].flatten()
 
         pipe.unet.unfuse_lora()
-        generator = torch.manual_seed(0)
+        generator = torch.Generator().manual_seed(0)
         images = pipe(
             "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
         ).images
         images_without_fusion = images[0, -3:, -3:, -1].flatten()
 
-        self.assertFalse(np.allclose(images_with_fusion, images_without_fusion))
+        self.assertFalse(np.allclose(images_with_fusion, images_without_fusion, atol=1e-3))
 
     def test_sdxl_1_0_lora_unfusion_effectivity(self):
         pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
         pipe.enable_model_cpu_offload()
 
-        generator = torch.manual_seed(0)
+        generator = torch.Generator().manual_seed(0)
         images = pipe(
             "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
         ).images
@@ -1084,13 +1084,13 @@ def test_sdxl_1_0_lora_unfusion_effectivity(self):
         pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
         pipe.unet.fuse_lora()
 
-        generator = torch.manual_seed(0)
+        generator = torch.Generator().manual_seed(0)
         _ = pipe(
             "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
         ).images
 
         pipe.unet.unfuse_lora()
-        generator = torch.manual_seed(0)
+        generator = torch.Generator().manual_seed(0)
         images = pipe(
             "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
         ).images
@@ -1123,6 +1123,7 @@ def test_sdxl_1_0_lora_fusion_efficiency(self):
         pipe.enable_model_cpu_offload()
 
         start_time = time.time()
+        generator = torch.Generator().manual_seed(0)
         for _ in range(3):
             pipe(
                 "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2