mirror of
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92 lines
3.1 KiB
Python
92 lines
3.1 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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AutoencoderKL,
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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 TestAutoencoderKLSingleFile(SingleFileModelTesterMixin):
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model_class = AutoencoderKL
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ckpt_path = (
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"https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors"
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)
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repo_id = "stabilityai/sd-vae-ft-mse"
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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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generator = torch.Generator(torch_device)
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with torch.no_grad():
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sample_1 = model_1(image, generator=generator.manual_seed(0)).sample
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sample_2 = model_2(image, generator=generator.manual_seed(0)).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_arguments(self):
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model_default = self.model_class.from_single_file(self.ckpt_path, config=self.repo_id)
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assert model_default.config.scaling_factor == 0.18215
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assert model_default.config.sample_size == 256
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assert model_default.dtype == torch.float32
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scaling_factor = 2.0
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sample_size = 512
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torch_dtype = torch.float16
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model = self.model_class.from_single_file(
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self.ckpt_path,
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config=self.repo_id,
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sample_size=sample_size,
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scaling_factor=scaling_factor,
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torch_dtype=torch_dtype,
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)
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assert model.config.scaling_factor == scaling_factor
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assert model.config.sample_size == sample_size
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assert model.dtype == torch_dtype
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