528 lines
20 KiB
Python
528 lines
20 KiB
Python
# coding=utf-8
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# Copyright 2022 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 gc
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import os
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import random
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import tempfile
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import unittest
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import numpy as np
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import torch
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import PIL
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from diffusers import (
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AutoencoderKL,
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DDIMPipeline,
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DDIMScheduler,
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DDPMPipeline,
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DDPMScheduler,
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PNDMScheduler,
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StableDiffusionImg2ImgPipeline,
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StableDiffusionInpaintPipelineLegacy,
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StableDiffusionPipeline,
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UNet2DConditionModel,
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UNet2DModel,
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VQModel,
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logging,
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)
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from diffusers.pipeline_utils import DiffusionPipeline
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from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
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from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, slow, torch_device
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from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir
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from parameterized import parameterized
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from PIL import Image
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from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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torch.backends.cuda.matmul.allow_tf32 = False
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def test_progress_bar(capsys):
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model = UNet2DModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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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(num_train_timesteps=10)
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ddpm = DDPMPipeline(model, scheduler).to(torch_device)
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ddpm(output_type="numpy").images
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captured = capsys.readouterr()
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assert "10/10" in captured.err, "Progress bar has to be displayed"
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ddpm.set_progress_bar_config(disable=True)
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ddpm(output_type="numpy").images
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captured = capsys.readouterr()
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assert captured.err == "", "Progress bar should be disabled"
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class DownloadTests(unittest.TestCase):
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def test_download_only_pytorch(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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# pipeline has Flax weights
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_ = DiffusionPipeline.from_pretrained(
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"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
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)
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all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots"))]
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files = [item for sublist in all_root_files for item in sublist]
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# None of the downloaded files should be a flax file even if we have some here:
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# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack
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assert not any(f.endswith(".msgpack") for f in files)
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class CustomPipelineTests(unittest.TestCase):
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def test_load_custom_pipeline(self):
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pipeline = DiffusionPipeline.from_pretrained(
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"google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
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)
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pipeline = pipeline.to(torch_device)
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# NOTE that `"CustomPipeline"` is not a class that is defined in this library, but solely on the Hub
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# under https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L24
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assert pipeline.__class__.__name__ == "CustomPipeline"
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def test_run_custom_pipeline(self):
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pipeline = DiffusionPipeline.from_pretrained(
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"google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
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)
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pipeline = pipeline.to(torch_device)
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images, output_str = pipeline(num_inference_steps=2, output_type="np")
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assert images[0].shape == (1, 32, 32, 3)
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# compare output to https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L102
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assert output_str == "This is a test"
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def test_local_custom_pipeline(self):
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local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline")
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pipeline = DiffusionPipeline.from_pretrained(
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"google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path
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)
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pipeline = pipeline.to(torch_device)
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images, output_str = pipeline(num_inference_steps=2, output_type="np")
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assert pipeline.__class__.__name__ == "CustomLocalPipeline"
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assert images[0].shape == (1, 32, 32, 3)
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# compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102
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assert output_str == "This is a local test"
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@slow
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@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
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def test_load_pipeline_from_git(self):
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clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
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feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id, device_map="auto")
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clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16)
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pipeline = DiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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custom_pipeline="clip_guided_stable_diffusion",
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clip_model=clip_model,
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feature_extractor=feature_extractor,
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torch_dtype=torch.float16,
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revision="fp16",
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device_map="auto",
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)
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pipeline.enable_attention_slicing()
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pipeline = pipeline.to(torch_device)
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# NOTE that `"CLIPGuidedStableDiffusion"` is not a class that is defined in the pypi package of th e library, but solely on the community examples folder of GitHub under:
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# https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion.py
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assert pipeline.__class__.__name__ == "CLIPGuidedStableDiffusion"
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image = pipeline("a prompt", num_inference_steps=2, output_type="np").images[0]
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assert image.shape == (512, 512, 3)
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class PipelineFastTests(unittest.TestCase):
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@property
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def dummy_image(self):
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batch_size = 1
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num_channels = 3
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sizes = (32, 32)
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image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
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return image
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@property
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def dummy_uncond_unet(self):
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torch.manual_seed(0)
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model = UNet2DModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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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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return model
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@property
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def dummy_cond_unet(self):
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torch.manual_seed(0)
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model = UNet2DConditionModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=4,
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out_channels=4,
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down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
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up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
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cross_attention_dim=32,
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)
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return model
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@property
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def dummy_cond_unet_inpaint(self):
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torch.manual_seed(0)
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model = UNet2DConditionModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=9,
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out_channels=4,
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down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
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up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
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cross_attention_dim=32,
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)
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return model
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@property
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def dummy_vq_model(self):
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torch.manual_seed(0)
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model = VQModel(
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block_out_channels=[32, 64],
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
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latent_channels=3,
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)
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return model
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@property
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def dummy_vae(self):
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torch.manual_seed(0)
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model = AutoencoderKL(
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block_out_channels=[32, 64],
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
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latent_channels=4,
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)
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return model
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@property
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def dummy_text_encoder(self):
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torch.manual_seed(0)
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config = CLIPTextConfig(
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bos_token_id=0,
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eos_token_id=2,
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hidden_size=32,
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intermediate_size=37,
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layer_norm_eps=1e-05,
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num_attention_heads=4,
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num_hidden_layers=5,
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pad_token_id=1,
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vocab_size=1000,
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)
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return CLIPTextModel(config)
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@property
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def dummy_extractor(self):
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def extract(*args, **kwargs):
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class Out:
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def __init__(self):
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self.pixel_values = torch.ones([0])
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def to(self, device):
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self.pixel_values.to(device)
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return self
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return Out()
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return extract
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def test_components(self):
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"""Test that components property works correctly"""
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unet = self.dummy_cond_unet
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scheduler = PNDMScheduler(skip_prk_steps=True)
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
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init_image = Image.fromarray(np.uint8(image)).convert("RGB")
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mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
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# make sure here that pndm scheduler skips prk
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inpaint = StableDiffusionInpaintPipelineLegacy(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=None,
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feature_extractor=self.dummy_extractor,
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).to(torch_device)
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img2img = StableDiffusionImg2ImgPipeline(**inpaint.components).to(torch_device)
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text2img = StableDiffusionPipeline(**inpaint.components).to(torch_device)
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prompt = "A painting of a squirrel eating a burger"
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# Device type MPS is not supported for torch.Generator() api.
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if torch_device == "mps":
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generator = torch.manual_seed(0)
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else:
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generator = torch.Generator(device=torch_device).manual_seed(0)
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image_inpaint = inpaint(
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[prompt],
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generator=generator,
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num_inference_steps=2,
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output_type="np",
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init_image=init_image,
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mask_image=mask_image,
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).images
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image_img2img = img2img(
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[prompt],
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generator=generator,
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num_inference_steps=2,
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output_type="np",
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init_image=init_image,
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).images
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image_text2img = text2img(
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[prompt],
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generator=generator,
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num_inference_steps=2,
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output_type="np",
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).images
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assert image_inpaint.shape == (1, 32, 32, 3)
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assert image_img2img.shape == (1, 32, 32, 3)
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assert image_text2img.shape == (1, 128, 128, 3)
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@slow
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class PipelineSlowTests(unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def test_smart_download(self):
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model_id = "hf-internal-testing/unet-pipeline-dummy"
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with tempfile.TemporaryDirectory() as tmpdirname:
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_ = DiffusionPipeline.from_pretrained(
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model_id, cache_dir=tmpdirname, force_download=True, device_map="auto"
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)
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local_repo_name = "--".join(["models"] + model_id.split("/"))
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snapshot_dir = os.path.join(tmpdirname, local_repo_name, "snapshots")
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snapshot_dir = os.path.join(snapshot_dir, os.listdir(snapshot_dir)[0])
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# inspect all downloaded files to make sure that everything is included
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assert os.path.isfile(os.path.join(snapshot_dir, DiffusionPipeline.config_name))
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assert os.path.isfile(os.path.join(snapshot_dir, CONFIG_NAME))
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assert os.path.isfile(os.path.join(snapshot_dir, SCHEDULER_CONFIG_NAME))
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assert os.path.isfile(os.path.join(snapshot_dir, WEIGHTS_NAME))
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assert os.path.isfile(os.path.join(snapshot_dir, "scheduler", SCHEDULER_CONFIG_NAME))
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assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
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assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
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# let's make sure the super large numpy file:
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# https://huggingface.co/hf-internal-testing/unet-pipeline-dummy/blob/main/big_array.npy
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# is not downloaded, but all the expected ones
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assert not os.path.isfile(os.path.join(snapshot_dir, "big_array.npy"))
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def test_warning_unused_kwargs(self):
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model_id = "hf-internal-testing/unet-pipeline-dummy"
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logger = logging.get_logger("diffusers.pipeline_utils")
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with tempfile.TemporaryDirectory() as tmpdirname:
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with CaptureLogger(logger) as cap_logger:
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DiffusionPipeline.from_pretrained(
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model_id, not_used=True, cache_dir=tmpdirname, force_download=True, device_map="auto"
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)
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assert cap_logger.out == "Keyword arguments {'not_used': True} not recognized.\n"
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def test_from_pretrained_save_pretrained(self):
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# 1. Load models
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model = UNet2DModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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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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schedular = DDPMScheduler(num_train_timesteps=10)
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ddpm = DDPMPipeline(model, schedular)
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ddpm.to(torch_device)
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ddpm.set_progress_bar_config(disable=None)
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with tempfile.TemporaryDirectory() as tmpdirname:
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ddpm.save_pretrained(tmpdirname)
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new_ddpm = DDPMPipeline.from_pretrained(tmpdirname, device_map="auto")
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new_ddpm.to(torch_device)
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generator = torch.manual_seed(0)
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image = ddpm(generator=generator, output_type="numpy").images
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generator = generator.manual_seed(0)
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new_image = new_ddpm(generator=generator, output_type="numpy").images
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assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
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def test_from_pretrained_hub(self):
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model_path = "google/ddpm-cifar10-32"
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scheduler = DDPMScheduler(num_train_timesteps=10)
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ddpm = DDPMPipeline.from_pretrained(model_path, scheduler=scheduler, device_map="auto")
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ddpm = ddpm.to(torch_device)
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ddpm.set_progress_bar_config(disable=None)
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ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler, device_map="auto")
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ddpm_from_hub = ddpm_from_hub.to(torch_device)
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ddpm_from_hub.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="numpy").images
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generator = generator.manual_seed(0)
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new_image = ddpm_from_hub(generator=generator, output_type="numpy").images
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assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
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def test_from_pretrained_hub_pass_model(self):
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model_path = "google/ddpm-cifar10-32"
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scheduler = DDPMScheduler(num_train_timesteps=10)
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# pass unet into DiffusionPipeline
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unet = UNet2DModel.from_pretrained(model_path, device_map="auto")
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ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(
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model_path, unet=unet, scheduler=scheduler, device_map="auto"
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)
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ddpm_from_hub_custom_model = ddpm_from_hub_custom_model.to(torch_device)
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ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)
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ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler, device_map="auto")
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ddpm_from_hub = ddpm_from_hub.to(torch_device)
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ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)
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generator = torch.manual_seed(0)
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image = ddpm_from_hub_custom_model(generator=generator, output_type="numpy").images
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generator = generator.manual_seed(0)
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new_image = ddpm_from_hub(generator=generator, output_type="numpy").images
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assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
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def test_output_format(self):
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model_path = "google/ddpm-cifar10-32"
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pipe = DDIMPipeline.from_pretrained(model_path, device_map="auto")
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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generator = torch.manual_seed(0)
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images = pipe(generator=generator, output_type="numpy").images
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assert images.shape == (1, 32, 32, 3)
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assert isinstance(images, np.ndarray)
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images = pipe(generator=generator, output_type="pil").images
|
|
assert isinstance(images, list)
|
|
assert len(images) == 1
|
|
assert isinstance(images[0], PIL.Image.Image)
|
|
|
|
# use PIL by default
|
|
images = pipe(generator=generator).images
|
|
assert isinstance(images, list)
|
|
assert isinstance(images[0], PIL.Image.Image)
|
|
|
|
# Make sure the test passes for different values of random seed
|
|
@parameterized.expand([(0,), (4,)])
|
|
def test_ddpm_ddim_equality(self, seed):
|
|
model_id = "google/ddpm-cifar10-32"
|
|
|
|
unet = UNet2DModel.from_pretrained(model_id, device_map="auto")
|
|
ddpm_scheduler = DDPMScheduler()
|
|
ddim_scheduler = DDIMScheduler()
|
|
|
|
ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
|
|
ddpm.to(torch_device)
|
|
ddpm.set_progress_bar_config(disable=None)
|
|
ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
|
|
ddim.to(torch_device)
|
|
ddim.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(seed)
|
|
ddpm_image = ddpm(generator=generator, output_type="numpy").images
|
|
|
|
generator = torch.manual_seed(seed)
|
|
ddim_image = ddim(
|
|
generator=generator,
|
|
num_inference_steps=1000,
|
|
eta=1.0,
|
|
output_type="numpy",
|
|
use_clipped_model_output=True, # Need this to make DDIM match DDPM
|
|
).images
|
|
|
|
# the values aren't exactly equal, but the images look the same visually
|
|
assert np.abs(ddpm_image - ddim_image).max() < 1e-1
|
|
|
|
# Make sure the test passes for different values of random seed
|
|
@parameterized.expand([(0,), (4,)])
|
|
def test_ddpm_ddim_equality_batched(self, seed):
|
|
model_id = "google/ddpm-cifar10-32"
|
|
|
|
unet = UNet2DModel.from_pretrained(model_id, device_map="auto")
|
|
ddpm_scheduler = DDPMScheduler()
|
|
ddim_scheduler = DDIMScheduler()
|
|
|
|
ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
|
|
ddpm.to(torch_device)
|
|
ddpm.set_progress_bar_config(disable=None)
|
|
|
|
ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
|
|
ddim.to(torch_device)
|
|
ddim.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(seed)
|
|
ddpm_images = ddpm(batch_size=4, generator=generator, output_type="numpy").images
|
|
|
|
generator = torch.manual_seed(seed)
|
|
ddim_images = ddim(
|
|
batch_size=4,
|
|
generator=generator,
|
|
num_inference_steps=1000,
|
|
eta=1.0,
|
|
output_type="numpy",
|
|
use_clipped_model_output=True, # Need this to make DDIM match DDPM
|
|
).images
|
|
|
|
# the values aren't exactly equal, but the images look the same visually
|
|
assert np.abs(ddpm_images - ddim_images).max() < 1e-1
|