hot fix

tests/models/test_models_vae.py

@@ -20,7 +20,7 @@ import torch
 
 from diffusers import AutoencoderKL
 from diffusers.modeling_utils import ModelMixin
-from diffusers.utils import floats_tensor, require_torch_gpu, slow, torch_all_close, torch_device
+from diffusers.utils import floats_tensor, load_numpy, require_torch_gpu, slow, torch_all_close, torch_device
 from parameterized import parameterized
 
 from ..test_modeling_common import ModelTesterMixin
@@ -136,6 +136,9 @@ class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
 
 @slow
 class AutoencoderKLIntegrationTests(unittest.TestCase):
+    def get_file_format(self, seed, shape):
+        return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
+
     def tearDown(self):
         # clean up the VRAM after each test
         super().tearDown()
@@ -143,11 +146,8 @@ class AutoencoderKLIntegrationTests(unittest.TestCase):
         torch.cuda.empty_cache()
 
     def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):
-        batch_size, channels, height, width = shape
-        generator = torch.Generator(device=torch_device).manual_seed(seed)
         dtype = torch.float16 if fp16 else torch.float32
-        image = torch.randn(batch_size, channels, height, width, device=torch_device, generator=generator, dtype=dtype)
-
+        image = torch.from_numpy(load_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
         return image
 
     def get_sd_vae_model(self, model_id="CompVis/stable-diffusion-v1-4", fp16=False):