update

Author: DN6

tests/single_file/test_model_autoencoder_dc_single_file.py

@@ -0,0 +1,126 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import gc
+import unittest
+
+import torch
+
+from diffusers import (
+    AutoencoderDC,
+)
+from diffusers.utils.testing_utils import (
+    backend_empty_cache,
+    enable_full_determinism,
+    load_hf_numpy,
+    numpy_cosine_similarity_distance,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+
+
+enable_full_determinism()
+
+
+@slow
+@require_torch_accelerator
+class AutoencoderDCSingleFileTests(unittest.TestCase):
+    model_class = AutoencoderDC
+    ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0/blob/main/model.safetensors"
+    repo_id = "mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers"
+    main_input_name = "sample"
+    base_precision = 1e-2
+
+    def setUp(self):
+        super().setUp()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def get_file_format(self, seed, shape):
+        return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
+
+    def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):
+        dtype = torch.float16 if fp16 else torch.float32
+        image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
+        return image
+
+    def test_single_file_inference_same_as_pretrained(self):
+        model_1 = self.model_class.from_pretrained(self.repo_id).to(torch_device)
+        model_2 = self.model_class.from_single_file(self.ckpt_path, config=self.repo_id).to(torch_device)
+
+        image = self.get_sd_image(33)
+
+        with torch.no_grad():
+            sample_1 = model_1(image).sample
+            sample_2 = model_2(image).sample
+
+        assert sample_1.shape == sample_2.shape
+
+        output_slice_1 = sample_1.flatten().float().cpu()
+        output_slice_2 = sample_2.flatten().float().cpu()
+
+        assert numpy_cosine_similarity_distance(output_slice_1, output_slice_2) < 1e-4
+
+    def test_single_file_components(self):
+        model = self.model_class.from_pretrained(self.repo_id)
+        model_single_file = self.model_class.from_single_file(self.ckpt_path)
+
+        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
+        for param_name, param_value in model_single_file.config.items():
+            if param_name in PARAMS_TO_IGNORE:
+                continue
+            assert (
+                model.config[param_name] == param_value
+            ), f"{param_name} differs between pretrained loading and single file loading"
+
+    def test_single_file_in_type_variant_components(self):
+        # `in` variant checkpoints require passing in a `config` parameter
+        # in order to set the scaling factor correctly.
+        # `in` and `mix` variants have the same keys and we cannot automatically infer a scaling factor.
+        # We default to using teh `mix` config
+        repo_id = "mit-han-lab/dc-ae-f128c512-in-1.0-diffusers"
+        ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f128c512-in-1.0/blob/main/model.safetensors"
+
+        model = self.model_class.from_pretrained(repo_id)
+        model_single_file = self.model_class.from_single_file(ckpt_path, config=repo_id)
+
+        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
+        for param_name, param_value in model_single_file.config.items():
+            if param_name in PARAMS_TO_IGNORE:
+                continue
+            assert (
+                model.config[param_name] == param_value
+            ), f"{param_name} differs between pretrained loading and single file loading"
+
+    def test_single_file_mix_type_variant_components(self):
+        repo_id = "mit-han-lab/dc-ae-f128c512-mix-1.0-diffusers"
+        ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f128c512-mix-1.0/blob/main/model.safetensors"
+
+        model = self.model_class.from_pretrained(repo_id)
+        model_single_file = self.model_class.from_single_file(ckpt_path, config=repo_id)
+
+        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
+        for param_name, param_value in model_single_file.config.items():
+            if param_name in PARAMS_TO_IGNORE:
+                continue
+            assert (
+                model.config[param_name] == param_value
+            ), f"{param_name} differs between pretrained loading and single file loading"