mirror of
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129 lines
4.9 KiB
Python
129 lines
4.9 KiB
Python
# coding=utf-8
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# Copyright 2026 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 numpy as np
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import torch
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from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
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from ...testing_utils import (
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assert_tensors_close,
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enable_full_determinism,
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require_torch_accelerator,
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slow,
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torch_device,
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)
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from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
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from ..testing_utils import BasePipelineTesterConfig, MemoryTesterMixin, PipelineTesterMixin
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enable_full_determinism()
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class DDPMPipelineTesterConfig(BasePipelineTesterConfig):
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pipeline_class = DDPMPipeline
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required_input_params_in_call_signature = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
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batch_input_params = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
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# DDPM is unconditional and samples its own noise: there is no prompt to repeat
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# (`num_images_per_prompt`) and no user-suppliable `latents`.
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optional_input_params = frozenset(["num_inference_steps", "generator", "output_type", "return_dict"])
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output_shape = (3, 8, 8)
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def get_dummy_components(self):
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torch.manual_seed(0)
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unet = UNet2DModel(
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block_out_channels=(4, 8),
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layers_per_block=1,
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norm_num_groups=4,
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sample_size=8,
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in_channels=3,
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out_channels=3,
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down_block_types=("DownBlock2D", "AttnDownBlock2D"),
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up_block_types=("AttnUpBlock2D", "UpBlock2D"),
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)
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scheduler = DDPMScheduler()
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return {"unet": unet, "scheduler": scheduler}
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def get_dummy_inputs(self):
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return {
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"batch_size": 1,
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"generator": self.get_generator(0),
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"num_inference_steps": 2,
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# Request torch outputs so tests compare torch tensors directly (see `BasePipelineTesterConfig`).
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"output_type": "pt",
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}
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class TestDDPMPipeline(DDPMPipelineTesterConfig, PipelineTesterMixin):
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def test_inference(self):
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# Run on CPU: the expected slice below is CPU-specific.
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pipe = self.get_pipeline()
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image = pipe(**self.get_dummy_inputs()).images
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generated_image = image[0]
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assert generated_image.shape == self.output_shape
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# fmt: off
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expected_slice = torch.tensor([0.0, 0.9996672, 0.00329116, 1.0, 0.9995991, 1.0, 0.0060907, 0.00115037, 0.0])
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# fmt: on
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# `"pt"` images are `(channels, height, width)`, so the trailing-channel corner slice of the old
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# `"np"` layout is the last channel's bottom-right 3x3 block here.
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generated_slice = generated_image[-1, -3:, -3:].flatten()
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assert_tensors_close(generated_slice, expected_slice, atol=1e-2)
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def test_inference_predict_sample(self):
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# `prediction_type="sample"` makes the UNet output the denoised sample rather than the noise, so the
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# scheduler consumes it differently and the pipeline must produce a different image than the default
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# `epsilon` parameterization.
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pipe = self.get_pipeline().to(torch_device)
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output_epsilon = self.run_pipe(pipe)
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components = self.get_dummy_components()
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components["scheduler"] = DDPMScheduler(prediction_type="sample")
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pipe_sample = self.get_pipeline(**components).to(torch_device)
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output_sample = self.run_pipe(pipe_sample)
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assert output_sample.shape == output_epsilon.shape
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assert not torch.isnan(output_sample).any()
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assert not torch.allclose(output_sample, output_epsilon, atol=1e-3)
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class TestDDPMPipelineMemory(DDPMPipelineTesterConfig, MemoryTesterMixin):
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"""Memory optimization tests (CPU offload, group offload, layerwise casting) for the DDPM pipeline."""
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@slow
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@require_torch_accelerator
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class TestDDPMPipelineIntegration:
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def test_inference_cifar10(self):
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model_id = "google/ddpm-cifar10-32"
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unet = UNet2DModel.from_pretrained(model_id)
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scheduler = DDPMScheduler.from_pretrained(model_id)
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ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
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ddpm.to(torch_device)
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ddpm.set_progress_bar_config(disable=None)
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generator = torch.manual_seed(0)
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image = ddpm(generator=generator, output_type="np").images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 32, 32, 3)
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expected_slice = np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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