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100 lines
4.0 KiB
Python
100 lines
4.0 KiB
Python
# coding=utf-8
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# Copyright 2025 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from diffusers import (
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AutoencoderDC,
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)
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from ..testing_utils import (
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enable_full_determinism,
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load_hf_numpy,
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numpy_cosine_similarity_distance,
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torch_device,
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)
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from .single_file_testing_utils import SingleFileModelTesterMixin
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enable_full_determinism()
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class TestAutoencoderDCSingleFile(SingleFileModelTesterMixin):
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model_class = AutoencoderDC
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ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0/blob/main/model.safetensors"
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repo_id = "mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers"
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main_input_name = "sample"
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base_precision = 1e-2
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def get_file_format(self, seed, shape):
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return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
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def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):
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dtype = torch.float16 if fp16 else torch.float32
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image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
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return image
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def test_single_file_inference_same_as_pretrained(self):
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model_1 = self.model_class.from_pretrained(self.repo_id).to(torch_device)
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model_2 = self.model_class.from_single_file(self.ckpt_path, config=self.repo_id).to(torch_device)
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image = self.get_sd_image(33)
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with torch.no_grad():
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sample_1 = model_1(image).sample
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sample_2 = model_2(image).sample
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assert sample_1.shape == sample_2.shape
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output_slice_1 = sample_1.flatten().float().cpu()
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output_slice_2 = sample_2.flatten().float().cpu()
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assert numpy_cosine_similarity_distance(output_slice_1, output_slice_2) < 1e-4
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def test_single_file_in_type_variant_components(self):
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# `in` variant checkpoints require passing in a `config` parameter
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# in order to set the scaling factor correctly.
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# `in` and `mix` variants have the same keys and we cannot automatically infer a scaling factor.
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# We default to using the `mix` config
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repo_id = "mit-han-lab/dc-ae-f128c512-in-1.0-diffusers"
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ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f128c512-in-1.0/blob/main/model.safetensors"
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model = self.model_class.from_pretrained(repo_id)
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model_single_file = self.model_class.from_single_file(ckpt_path, config=repo_id)
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PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
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for param_name, param_value in model_single_file.config.items():
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if param_name in PARAMS_TO_IGNORE:
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continue
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assert model.config[param_name] == param_value, (
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f"{param_name} differs between pretrained loading and single file loading"
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)
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def test_single_file_mix_type_variant_components(self):
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repo_id = "mit-han-lab/dc-ae-f128c512-mix-1.0-diffusers"
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ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f128c512-mix-1.0/blob/main/model.safetensors"
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model = self.model_class.from_pretrained(repo_id)
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model_single_file = self.model_class.from_single_file(ckpt_path, config=repo_id)
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PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
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for param_name, param_value in model_single_file.config.items():
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if param_name in PARAMS_TO_IGNORE:
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continue
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assert model.config[param_name] == param_value, (
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f"{param_name} differs between pretrained loading and single file loading"
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)
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