[Tests] no random latents anymore

src/diffusers/utils/__init__.py

@@ -43,6 +43,7 @@
     from .testing_utils import (
         floats_tensor,
         load_image,
+        load_numpy,
         parse_flag_from_env,
         require_torch_gpu,
         slow,

src/diffusers/utils/testing_utils.py

@@ -4,11 +4,14 @@
 import random
 import re
 import unittest
+import urllib.parse
 from distutils.util import strtobool
-from io import StringIO
+from io import BytesIO, StringIO
 from pathlib import Path
 from typing import Union
 
+import numpy as np
+
 import PIL.Image
 import PIL.ImageOps
 import requests
@@ -165,6 +168,19 @@ def load_image(image: Union[str, PIL.Image.Image]) -> PIL.Image.Image:
     return image
 
 
+def load_numpy(path) -> np.ndarray:
+    if not path.startswith("http://") or path.startswith("https://"):
+        path = os.path.join(
+            "https://huggingface.co/datasets/fusing/diffusers-testing/resolve/main", urllib.parse.quote(path)
+        )
+
+    response = requests.get(path)
+    response.raise_for_status()
+    array = np.load(BytesIO(response.content))
+
+    return array
+
+
 # --- pytest conf functions --- #
 
 # to avoid multiple invocation from tests/conftest.py and examples/conftest.py - make sure it's called only once

tests/models/test_models_unet_2d.py

@@ -21,7 +21,7 @@
 import torch
 
 from diffusers import UNet2DConditionModel, UNet2DModel
-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
@@ -411,18 +411,18 @@ def test_forward_with_norm_groups(self):
 
 @slow
 class UNet2DConditionModelIntegrationTests(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()
         gc.collect()
         torch.cuda.empty_cache()
 
     def get_latents(self, seed=0, shape=(4, 4, 64, 64), 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_unet_model(self, fp16=False, model_id="CompVis/stable-diffusion-v1-4"):
@@ -437,9 +437,9 @@ def get_unet_model(self, fp16=False, model_id="CompVis/stable-diffusion-v1-4"):
         return model
 
     def get_encoder_hidden_states(self, seed=0, shape=(4, 77, 768), fp16=False):
-        generator = torch.Generator(device=torch_device).manual_seed(seed)
         dtype = torch.float16 if fp16 else torch.float32
-        return torch.randn(shape, device=torch_device, generator=generator, dtype=dtype)
+        hidden_states = torch.from_numpy(load_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
+        return hidden_states
 
     @parameterized.expand(
         [

tests/models/test_models_vae.py

@@ -20,7 +20,7 @@
 
 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
@@ -136,18 +136,18 @@ def test_output_pretrained(self):
 
 @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()
         gc.collect()
         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)
+    def get_sd_image(self, seed=0, shape=(4, 4, 64, 64), fp16=False):
         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):