2547 lines
97 KiB
Python
2547 lines
97 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 tracemalloc
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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 accelerate
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import PIL
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import transformers
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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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KarrasVePipeline,
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KarrasVeScheduler,
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LDMPipeline,
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LDMTextToImagePipeline,
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LMSDiscreteScheduler,
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OnnxStableDiffusionImg2ImgPipeline,
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OnnxStableDiffusionInpaintPipeline,
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OnnxStableDiffusionPipeline,
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PNDMPipeline,
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PNDMScheduler,
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ScoreSdeVePipeline,
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ScoreSdeVeScheduler,
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StableDiffusionImg2ImgPipeline,
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StableDiffusionInpaintPipeline,
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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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)
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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, load_image, slow, torch_device
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from diffusers.utils.testing_utils import get_tests_dir
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from packaging import version
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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 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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# 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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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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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)
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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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)
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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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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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@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_safety_checker(self):
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def check(images, *args, **kwargs):
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return images, [False] * len(images)
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return check
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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_ddim(self):
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unet = self.dummy_uncond_unet
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scheduler = DDIMScheduler()
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ddpm = DDIMPipeline(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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# Warmup pass when using mps (see #372)
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if torch_device == "mps":
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_ = ddpm(num_inference_steps=1)
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generator = torch.manual_seed(0)
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image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images
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generator = torch.manual_seed(0)
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image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type="numpy", return_dict=False)[0]
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image_slice = image[0, -3:, -3:, -1]
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image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
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assert image.shape == (1, 32, 32, 3)
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expected_slice = np.array(
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[1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]
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)
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tolerance = 1e-2 if torch_device != "mps" else 3e-2
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assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
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assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance
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def test_pndm_cifar10(self):
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unet = self.dummy_uncond_unet
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scheduler = PNDMScheduler()
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pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
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pndm.to(torch_device)
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pndm.set_progress_bar_config(disable=None)
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generator = torch.manual_seed(0)
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image = pndm(generator=generator, num_inference_steps=20, output_type="numpy").images
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generator = torch.manual_seed(0)
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image_from_tuple = pndm(generator=generator, num_inference_steps=20, output_type="numpy", return_dict=False)[0]
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image_slice = image[0, -3:, -3:, -1]
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image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
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assert image.shape == (1, 32, 32, 3)
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expected_slice = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
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def test_ldm_text2img(self):
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unet = self.dummy_cond_unet
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scheduler = DDIMScheduler()
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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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ldm = LDMTextToImagePipeline(vqvae=vae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
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ldm.to(torch_device)
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ldm.set_progress_bar_config(disable=None)
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prompt = "A painting of a squirrel eating a burger"
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# Warmup pass when using mps (see #372)
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if torch_device == "mps":
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generator = torch.manual_seed(0)
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_ = ldm(
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[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=1, output_type="numpy"
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).images
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generator = torch.manual_seed(0)
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image = ldm(
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[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="numpy"
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).images
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generator = torch.manual_seed(0)
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image_from_tuple = ldm(
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[prompt],
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generator=generator,
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guidance_scale=6.0,
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num_inference_steps=2,
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output_type="numpy",
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return_dict=False,
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)[0]
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image_slice = image[0, -3:, -3:, -1]
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image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.5074, 0.5026, 0.4998, 0.4056, 0.3523, 0.4649, 0.5289, 0.5299, 0.4897])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_ddim(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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unet = self.dummy_cond_unet
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scheduler = DDIMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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clip_sample=False,
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set_alpha_to_one=False,
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)
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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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# make sure here that pndm scheduler skips prk
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sd_pipe = StableDiffusionPipeline(
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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=self.dummy_safety_checker,
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feature_extractor=self.dummy_extractor,
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)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=device).manual_seed(0)
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output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
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image = output.images
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generator = torch.Generator(device=device).manual_seed(0)
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image_from_tuple = sd_pipe(
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[prompt],
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generator=generator,
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guidance_scale=6.0,
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num_inference_steps=2,
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output_type="np",
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return_dict=False,
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)[0]
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image_slice = image[0, -3:, -3:, -1]
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image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
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assert image.shape == (1, 128, 128, 3)
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expected_slice = np.array([0.5112, 0.4692, 0.4715, 0.5206, 0.4894, 0.5114, 0.5096, 0.4932, 0.4755])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_ddim_factor_8(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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unet = self.dummy_cond_unet
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scheduler = DDIMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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clip_sample=False,
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set_alpha_to_one=False,
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)
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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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# make sure here that pndm scheduler skips prk
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sd_pipe = StableDiffusionPipeline(
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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=self.dummy_safety_checker,
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feature_extractor=self.dummy_extractor,
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)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=device).manual_seed(0)
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output = sd_pipe(
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[prompt],
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generator=generator,
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guidance_scale=6.0,
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height=536,
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width=536,
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num_inference_steps=2,
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output_type="np",
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)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 134, 134, 3)
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expected_slice = np.array([0.7834, 0.5488, 0.5781, 0.46, 0.3609, 0.5369, 0.542, 0.4855, 0.5557])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_pndm(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
unet = self.dummy_cond_unet
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
|
|
|
|
image = output.images
|
|
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
image_from_tuple = sd_pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
expected_slice = np.array([0.4937, 0.4649, 0.4716, 0.5145, 0.4889, 0.513, 0.513, 0.4905, 0.4738])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_from_pretrained_error_message_uninstalled_packages(self):
|
|
# TODO(Patrick, Pedro) - need better test here for the future
|
|
pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-lms-pipe")
|
|
assert isinstance(pipe, StableDiffusionPipeline)
|
|
assert isinstance(pipe.scheduler, LMSDiscreteScheduler)
|
|
|
|
def test_stable_diffusion_no_safety_checker(self):
|
|
pipe = StableDiffusionPipeline.from_pretrained(
|
|
"hf-internal-testing/tiny-stable-diffusion-lms-pipe", safety_checker=None
|
|
)
|
|
assert isinstance(pipe, StableDiffusionPipeline)
|
|
assert isinstance(pipe.scheduler, LMSDiscreteScheduler)
|
|
assert pipe.safety_checker is None
|
|
|
|
image = pipe("example prompt", num_inference_steps=2).images[0]
|
|
assert image is not None
|
|
|
|
def test_stable_diffusion_k_lms(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
unet = self.dummy_cond_unet
|
|
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
|
|
|
|
image = output.images
|
|
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
image_from_tuple = sd_pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
expected_slice = np.array([0.5067, 0.4689, 0.4614, 0.5233, 0.4903, 0.5112, 0.524, 0.5069, 0.4785])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_attention_chunk(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
unet = self.dummy_cond_unet
|
|
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output_1 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
|
|
|
|
# make sure chunking the attention yields the same result
|
|
sd_pipe.enable_attention_slicing(slice_size=1)
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output_2 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
|
|
|
|
assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 1e-4
|
|
|
|
def test_stable_diffusion_negative_prompt(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
unet = self.dummy_cond_unet
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
negative_prompt = "french fries"
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output = sd_pipe(
|
|
prompt,
|
|
negative_prompt=negative_prompt,
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
)
|
|
|
|
image = output.images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
expected_slice = np.array([0.4851, 0.4617, 0.4765, 0.5127, 0.4845, 0.5153, 0.5141, 0.4886, 0.4719])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_score_sde_ve_pipeline(self):
|
|
unet = self.dummy_uncond_unet
|
|
scheduler = ScoreSdeVeScheduler()
|
|
|
|
sde_ve = ScoreSdeVePipeline(unet=unet, scheduler=scheduler)
|
|
sde_ve.to(torch_device)
|
|
sde_ve.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(0)
|
|
image = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator).images
|
|
|
|
generator = torch.manual_seed(0)
|
|
image_from_tuple = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator, return_dict=False)[
|
|
0
|
|
]
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 32, 32, 3)
|
|
expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_ldm_uncond(self):
|
|
unet = self.dummy_uncond_unet
|
|
scheduler = DDIMScheduler()
|
|
vae = self.dummy_vq_model
|
|
|
|
ldm = LDMPipeline(unet=unet, vqvae=vae, scheduler=scheduler)
|
|
ldm.to(torch_device)
|
|
ldm.set_progress_bar_config(disable=None)
|
|
|
|
# Warmup pass when using mps (see #372)
|
|
if torch_device == "mps":
|
|
generator = torch.manual_seed(0)
|
|
_ = ldm(generator=generator, num_inference_steps=1, output_type="numpy").images
|
|
|
|
generator = torch.manual_seed(0)
|
|
image = ldm(generator=generator, num_inference_steps=2, output_type="numpy").images
|
|
|
|
generator = torch.manual_seed(0)
|
|
image_from_tuple = ldm(generator=generator, num_inference_steps=2, output_type="numpy", return_dict=False)[0]
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 64, 64, 3)
|
|
expected_slice = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_karras_ve_pipeline(self):
|
|
unet = self.dummy_uncond_unet
|
|
scheduler = KarrasVeScheduler()
|
|
|
|
pipe = KarrasVePipeline(unet=unet, scheduler=scheduler)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(0)
|
|
image = pipe(num_inference_steps=2, generator=generator, output_type="numpy").images
|
|
|
|
generator = torch.manual_seed(0)
|
|
image_from_tuple = pipe(num_inference_steps=2, generator=generator, output_type="numpy", return_dict=False)[0]
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 32, 32, 3)
|
|
expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_img2img(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
unet = self.dummy_cond_unet
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
init_image = self.dummy_image.to(device)
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionImg2ImgPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output = sd_pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
)
|
|
|
|
image = output.images
|
|
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
image_from_tuple = sd_pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 32, 32, 3)
|
|
expected_slice = np.array([0.4492, 0.3865, 0.4222, 0.5854, 0.5139, 0.4379, 0.4193, 0.48, 0.4218])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_img2img_negative_prompt(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
unet = self.dummy_cond_unet
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
init_image = self.dummy_image.to(device)
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionImg2ImgPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
negative_prompt = "french fries"
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output = sd_pipe(
|
|
prompt,
|
|
negative_prompt=negative_prompt,
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
)
|
|
image = output.images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 32, 32, 3)
|
|
expected_slice = np.array([0.4065, 0.3783, 0.4050, 0.5266, 0.4781, 0.4252, 0.4203, 0.4692, 0.4365])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_img2img_multiple_init_images(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
unet = self.dummy_cond_unet
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
init_image = self.dummy_image.to(device).repeat(2, 1, 1, 1)
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionImg2ImgPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = 2 * ["A painting of a squirrel eating a burger"]
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output = sd_pipe(
|
|
prompt,
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
)
|
|
|
|
image = output.images
|
|
|
|
image_slice = image[-1, -3:, -3:, -1]
|
|
|
|
assert image.shape == (2, 32, 32, 3)
|
|
expected_slice = np.array([0.5144, 0.4447, 0.4735, 0.6676, 0.5526, 0.5454, 0.645, 0.5149, 0.4689])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_img2img_k_lms(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
unet = self.dummy_cond_unet
|
|
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
|
|
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
init_image = self.dummy_image.to(device)
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionImg2ImgPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output = sd_pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
)
|
|
image = output.images
|
|
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output = sd_pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
return_dict=False,
|
|
)
|
|
image_from_tuple = output[0]
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 32, 32, 3)
|
|
expected_slice = np.array([0.4367, 0.4986, 0.4372, 0.6706, 0.5665, 0.444, 0.5864, 0.6019, 0.5203])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_inpaint_legacy(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
unet = self.dummy_cond_unet
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
|
|
init_image = Image.fromarray(np.uint8(image)).convert("RGB")
|
|
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionInpaintPipelineLegacy(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output = sd_pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
mask_image=mask_image,
|
|
)
|
|
|
|
image = output.images
|
|
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
image_from_tuple = sd_pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
mask_image=mask_image,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 32, 32, 3)
|
|
expected_slice = np.array([0.4731, 0.5346, 0.4531, 0.6251, 0.5446, 0.4057, 0.5527, 0.5896, 0.5153])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_inpaint(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
unet = self.dummy_cond_unet_inpaint
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
|
|
init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128))
|
|
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionInpaintPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=None,
|
|
feature_extractor=None,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output = sd_pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
image=init_image,
|
|
mask_image=mask_image,
|
|
)
|
|
|
|
image = output.images
|
|
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
image_from_tuple = sd_pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
image=init_image,
|
|
mask_image=mask_image,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
expected_slice = np.array([0.5075, 0.4485, 0.4558, 0.5369, 0.5369, 0.5236, 0.5127, 0.4983, 0.4776])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_inpaint_legacy_negative_prompt(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
unet = self.dummy_cond_unet
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
|
|
init_image = Image.fromarray(np.uint8(image)).convert("RGB")
|
|
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionInpaintPipelineLegacy(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
negative_prompt = "french fries"
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output = sd_pipe(
|
|
prompt,
|
|
negative_prompt=negative_prompt,
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
mask_image=mask_image,
|
|
)
|
|
|
|
image = output.images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 32, 32, 3)
|
|
expected_slice = np.array([0.4765, 0.5339, 0.4541, 0.6240, 0.5439, 0.4055, 0.5503, 0.5891, 0.5150])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_num_images_per_prompt(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
unet = self.dummy_cond_unet
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
|
|
# test num_images_per_prompt=1 (default)
|
|
images = sd_pipe(prompt, num_inference_steps=2, output_type="np").images
|
|
|
|
assert images.shape == (1, 128, 128, 3)
|
|
|
|
# test num_images_per_prompt=1 (default) for batch of prompts
|
|
batch_size = 2
|
|
images = sd_pipe([prompt] * batch_size, num_inference_steps=2, output_type="np").images
|
|
|
|
assert images.shape == (batch_size, 128, 128, 3)
|
|
|
|
# test num_images_per_prompt for single prompt
|
|
num_images_per_prompt = 2
|
|
images = sd_pipe(
|
|
prompt, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt
|
|
).images
|
|
|
|
assert images.shape == (num_images_per_prompt, 128, 128, 3)
|
|
|
|
# test num_images_per_prompt for batch of prompts
|
|
batch_size = 2
|
|
images = sd_pipe(
|
|
[prompt] * batch_size, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt
|
|
).images
|
|
|
|
assert images.shape == (batch_size * num_images_per_prompt, 128, 128, 3)
|
|
|
|
def test_stable_diffusion_img2img_num_images_per_prompt(self):
|
|
device = "cpu"
|
|
unet = self.dummy_cond_unet
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
init_image = self.dummy_image.to(device)
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionImg2ImgPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
|
|
# test num_images_per_prompt=1 (default)
|
|
images = sd_pipe(
|
|
prompt,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
).images
|
|
|
|
assert images.shape == (1, 32, 32, 3)
|
|
|
|
# test num_images_per_prompt=1 (default) for batch of prompts
|
|
batch_size = 2
|
|
images = sd_pipe(
|
|
[prompt] * batch_size,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
).images
|
|
|
|
assert images.shape == (batch_size, 32, 32, 3)
|
|
|
|
# test num_images_per_prompt for single prompt
|
|
num_images_per_prompt = 2
|
|
images = sd_pipe(
|
|
prompt,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
).images
|
|
|
|
assert images.shape == (num_images_per_prompt, 32, 32, 3)
|
|
|
|
# test num_images_per_prompt for batch of prompts
|
|
batch_size = 2
|
|
images = sd_pipe(
|
|
[prompt] * batch_size,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
).images
|
|
|
|
assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3)
|
|
|
|
def test_stable_diffusion_inpaint_legacy_num_images_per_prompt(self):
|
|
device = "cpu"
|
|
unet = self.dummy_cond_unet
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
|
|
init_image = Image.fromarray(np.uint8(image)).convert("RGB")
|
|
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionInpaintPipelineLegacy(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
|
|
# test num_images_per_prompt=1 (default)
|
|
images = sd_pipe(
|
|
prompt,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
mask_image=mask_image,
|
|
).images
|
|
|
|
assert images.shape == (1, 32, 32, 3)
|
|
|
|
# test num_images_per_prompt=1 (default) for batch of prompts
|
|
batch_size = 2
|
|
images = sd_pipe(
|
|
[prompt] * batch_size,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
mask_image=mask_image,
|
|
).images
|
|
|
|
assert images.shape == (batch_size, 32, 32, 3)
|
|
|
|
# test num_images_per_prompt for single prompt
|
|
num_images_per_prompt = 2
|
|
images = sd_pipe(
|
|
prompt,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
mask_image=mask_image,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
).images
|
|
|
|
assert images.shape == (num_images_per_prompt, 32, 32, 3)
|
|
|
|
# test num_images_per_prompt for batch of prompts
|
|
batch_size = 2
|
|
images = sd_pipe(
|
|
[prompt] * batch_size,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
mask_image=mask_image,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
).images
|
|
|
|
assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3)
|
|
|
|
@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
|
|
def test_stable_diffusion_fp16(self):
|
|
"""Test that stable diffusion works with fp16"""
|
|
unet = self.dummy_cond_unet
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
# put models in fp16
|
|
unet = unet.half()
|
|
vae = vae.half()
|
|
bert = bert.half()
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
image = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="np").images
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
|
|
@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
|
|
def test_stable_diffusion_img2img_fp16(self):
|
|
"""Test that stable diffusion img2img works with fp16"""
|
|
unet = self.dummy_cond_unet
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
init_image = self.dummy_image.to(torch_device)
|
|
|
|
# put models in fp16
|
|
unet = unet.half()
|
|
vae = vae.half()
|
|
bert = bert.half()
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionImg2ImgPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=self.dummy_safety_checker,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
image = sd_pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
init_image=init_image,
|
|
).images
|
|
|
|
assert image.shape == (1, 32, 32, 3)
|
|
|
|
@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
|
|
def test_stable_diffusion_inpaint_fp16(self):
|
|
"""Test that stable diffusion inpaint_legacy works with fp16"""
|
|
unet = self.dummy_cond_unet_inpaint
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
|
|
init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128))
|
|
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
|
|
|
|
# put models in fp16
|
|
unet = unet.half()
|
|
vae = vae.half()
|
|
bert = bert.half()
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionInpaintPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=None,
|
|
feature_extractor=None,
|
|
)
|
|
sd_pipe = sd_pipe.to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
image = sd_pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
image=init_image,
|
|
mask_image=mask_image,
|
|
).images
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
|
|
|
|
class PipelineTesterMixin(unittest.TestCase):
|
|
def tearDown(self):
|
|
# clean up the VRAM after each test
|
|
super().tearDown()
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
def test_smart_download(self):
|
|
model_id = "hf-internal-testing/unet-pipeline-dummy"
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
_ = DiffusionPipeline.from_pretrained(model_id, cache_dir=tmpdirname, force_download=True)
|
|
local_repo_name = "--".join(["models"] + model_id.split("/"))
|
|
snapshot_dir = os.path.join(tmpdirname, local_repo_name, "snapshots")
|
|
snapshot_dir = os.path.join(snapshot_dir, os.listdir(snapshot_dir)[0])
|
|
|
|
# inspect all downloaded files to make sure that everything is included
|
|
assert os.path.isfile(os.path.join(snapshot_dir, DiffusionPipeline.config_name))
|
|
assert os.path.isfile(os.path.join(snapshot_dir, CONFIG_NAME))
|
|
assert os.path.isfile(os.path.join(snapshot_dir, SCHEDULER_CONFIG_NAME))
|
|
assert os.path.isfile(os.path.join(snapshot_dir, WEIGHTS_NAME))
|
|
assert os.path.isfile(os.path.join(snapshot_dir, "scheduler", SCHEDULER_CONFIG_NAME))
|
|
assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
|
|
assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
|
|
# let's make sure the super large numpy file:
|
|
# https://huggingface.co/hf-internal-testing/unet-pipeline-dummy/blob/main/big_array.npy
|
|
# is not downloaded, but all the expected ones
|
|
assert not os.path.isfile(os.path.join(snapshot_dir, "big_array.npy"))
|
|
|
|
@property
|
|
def dummy_safety_checker(self):
|
|
def check(images, *args, **kwargs):
|
|
return images, [False] * len(images)
|
|
|
|
return check
|
|
|
|
def test_from_pretrained_save_pretrained(self):
|
|
# 1. Load models
|
|
model = UNet2DModel(
|
|
block_out_channels=(32, 64),
|
|
layers_per_block=2,
|
|
sample_size=32,
|
|
in_channels=3,
|
|
out_channels=3,
|
|
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
|
|
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
|
|
)
|
|
schedular = DDPMScheduler(num_train_timesteps=10)
|
|
|
|
ddpm = DDPMPipeline(model, schedular)
|
|
ddpm.to(torch_device)
|
|
ddpm.set_progress_bar_config(disable=None)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
ddpm.save_pretrained(tmpdirname)
|
|
new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)
|
|
new_ddpm.to(torch_device)
|
|
|
|
generator = torch.manual_seed(0)
|
|
image = ddpm(generator=generator, output_type="numpy").images
|
|
|
|
generator = generator.manual_seed(0)
|
|
new_image = new_ddpm(generator=generator, output_type="numpy").images
|
|
|
|
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
|
|
|
|
@slow
|
|
def test_from_pretrained_hub(self):
|
|
model_path = "google/ddpm-cifar10-32"
|
|
|
|
scheduler = DDPMScheduler(num_train_timesteps=10)
|
|
|
|
ddpm = DDPMPipeline.from_pretrained(model_path, scheduler=scheduler)
|
|
ddpm.to(torch_device)
|
|
ddpm.set_progress_bar_config(disable=None)
|
|
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
|
|
ddpm_from_hub.to(torch_device)
|
|
ddpm_from_hub.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(0)
|
|
image = ddpm(generator=generator, output_type="numpy").images
|
|
|
|
generator = generator.manual_seed(0)
|
|
new_image = ddpm_from_hub(generator=generator, output_type="numpy").images
|
|
|
|
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
|
|
|
|
@slow
|
|
def test_from_pretrained_hub_pass_model(self):
|
|
model_path = "google/ddpm-cifar10-32"
|
|
|
|
scheduler = DDPMScheduler(num_train_timesteps=10)
|
|
|
|
# pass unet into DiffusionPipeline
|
|
unet = UNet2DModel.from_pretrained(model_path)
|
|
ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet, scheduler=scheduler)
|
|
ddpm_from_hub_custom_model.to(torch_device)
|
|
ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)
|
|
|
|
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
|
|
ddpm_from_hub.to(torch_device)
|
|
ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(0)
|
|
image = ddpm_from_hub_custom_model(generator=generator, output_type="numpy").images
|
|
|
|
generator = generator.manual_seed(0)
|
|
new_image = ddpm_from_hub(generator=generator, output_type="numpy").images
|
|
|
|
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
|
|
|
|
@slow
|
|
def test_output_format(self):
|
|
model_path = "google/ddpm-cifar10-32"
|
|
|
|
pipe = DDIMPipeline.from_pretrained(model_path)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(0)
|
|
images = pipe(generator=generator, output_type="numpy").images
|
|
assert images.shape == (1, 32, 32, 3)
|
|
assert isinstance(images, np.ndarray)
|
|
|
|
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)
|
|
|
|
@slow
|
|
def test_ddpm_cifar10(self):
|
|
model_id = "google/ddpm-cifar10-32"
|
|
|
|
unet = UNet2DModel.from_pretrained(model_id)
|
|
scheduler = DDPMScheduler.from_config(model_id)
|
|
|
|
ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
|
|
ddpm.to(torch_device)
|
|
ddpm.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(0)
|
|
image = ddpm(generator=generator, output_type="numpy").images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 32, 32, 3)
|
|
expected_slice = np.array([0.41995, 0.35885, 0.19385, 0.38475, 0.3382, 0.2647, 0.41545, 0.3582, 0.33845])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
@slow
|
|
def test_ddim_lsun(self):
|
|
model_id = "google/ddpm-ema-bedroom-256"
|
|
|
|
unet = UNet2DModel.from_pretrained(model_id)
|
|
scheduler = DDIMScheduler.from_config(model_id)
|
|
|
|
ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
|
|
ddpm.to(torch_device)
|
|
ddpm.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(0)
|
|
image = ddpm(generator=generator, output_type="numpy").images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 256, 256, 3)
|
|
expected_slice = np.array([0.00605, 0.0201, 0.0344, 0.00235, 0.00185, 0.00025, 0.00215, 0.0, 0.00685])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
@slow
|
|
def test_ddim_cifar10(self):
|
|
model_id = "google/ddpm-cifar10-32"
|
|
|
|
unet = UNet2DModel.from_pretrained(model_id)
|
|
scheduler = DDIMScheduler()
|
|
|
|
ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
|
|
ddim.to(torch_device)
|
|
ddim.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(0)
|
|
image = ddim(generator=generator, eta=0.0, output_type="numpy").images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 32, 32, 3)
|
|
expected_slice = np.array([0.17235, 0.16175, 0.16005, 0.16255, 0.1497, 0.1513, 0.15045, 0.1442, 0.1453])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
@slow
|
|
def test_pndm_cifar10(self):
|
|
model_id = "google/ddpm-cifar10-32"
|
|
|
|
unet = UNet2DModel.from_pretrained(model_id)
|
|
scheduler = PNDMScheduler()
|
|
|
|
pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
|
|
pndm.to(torch_device)
|
|
pndm.set_progress_bar_config(disable=None)
|
|
generator = torch.manual_seed(0)
|
|
image = pndm(generator=generator, output_type="numpy").images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 32, 32, 3)
|
|
expected_slice = np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
@slow
|
|
def test_ldm_text2img(self):
|
|
ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
|
|
ldm.to(torch_device)
|
|
ldm.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.manual_seed(0)
|
|
image = ldm(
|
|
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy"
|
|
).images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 256, 256, 3)
|
|
expected_slice = np.array([0.9256, 0.9340, 0.8933, 0.9361, 0.9113, 0.8727, 0.9122, 0.8745, 0.8099])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
@slow
|
|
def test_ldm_text2img_fast(self):
|
|
ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
|
|
ldm.to(torch_device)
|
|
ldm.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.manual_seed(0)
|
|
image = ldm(prompt, generator=generator, num_inference_steps=1, output_type="numpy").images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 256, 256, 3)
|
|
expected_slice = np.array([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion(self):
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1")
|
|
sd_pipe = sd_pipe.to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
with torch.autocast("cuda"):
|
|
output = sd_pipe(
|
|
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np"
|
|
)
|
|
|
|
image = output.images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.8887, 0.915, 0.91, 0.894, 0.909, 0.912, 0.919, 0.925, 0.883])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_fast_ddim(self):
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1")
|
|
sd_pipe = sd_pipe.to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
scheduler = DDIMScheduler(
|
|
beta_start=0.00085,
|
|
beta_end=0.012,
|
|
beta_schedule="scaled_linear",
|
|
clip_sample=False,
|
|
set_alpha_to_one=False,
|
|
)
|
|
sd_pipe.scheduler = scheduler
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
|
|
with torch.autocast("cuda"):
|
|
output = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="numpy")
|
|
image = output.images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.9326, 0.923, 0.951, 0.9365, 0.9214, 0.951, 0.9365, 0.9414, 0.918])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
@slow
|
|
def test_score_sde_ve_pipeline(self):
|
|
model_id = "google/ncsnpp-church-256"
|
|
model = UNet2DModel.from_pretrained(model_id)
|
|
|
|
scheduler = ScoreSdeVeScheduler.from_config(model_id)
|
|
|
|
sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
|
|
sde_ve.to(torch_device)
|
|
sde_ve.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(0)
|
|
image = sde_ve(num_inference_steps=10, output_type="numpy", generator=generator).images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 256, 256, 3)
|
|
|
|
expected_slice = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
@slow
|
|
def test_ldm_uncond(self):
|
|
ldm = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
|
|
ldm.to(torch_device)
|
|
ldm.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(0)
|
|
image = ldm(generator=generator, num_inference_steps=5, output_type="numpy").images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 256, 256, 3)
|
|
expected_slice = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
@slow
|
|
def test_ddpm_ddim_equality(self):
|
|
model_id = "google/ddpm-cifar10-32"
|
|
|
|
unet = UNet2DModel.from_pretrained(model_id)
|
|
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(0)
|
|
ddpm_image = ddpm(generator=generator, output_type="numpy").images
|
|
|
|
generator = torch.manual_seed(0)
|
|
ddim_image = ddim(generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy").images
|
|
|
|
# the values aren't exactly equal, but the images look the same visually
|
|
assert np.abs(ddpm_image - ddim_image).max() < 1e-1
|
|
|
|
@unittest.skip("(Anton) The test is failing for large batch sizes, needs investigation")
|
|
def test_ddpm_ddim_equality_batched(self):
|
|
model_id = "google/ddpm-cifar10-32"
|
|
|
|
unet = UNet2DModel.from_pretrained(model_id)
|
|
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(0)
|
|
ddpm_images = ddpm(batch_size=4, generator=generator, output_type="numpy").images
|
|
|
|
generator = torch.manual_seed(0)
|
|
ddim_images = ddim(
|
|
batch_size=4, generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy"
|
|
).images
|
|
|
|
# the values aren't exactly equal, but the images look the same visually
|
|
assert np.abs(ddpm_images - ddim_images).max() < 1e-1
|
|
|
|
@slow
|
|
def test_karras_ve_pipeline(self):
|
|
model_id = "google/ncsnpp-celebahq-256"
|
|
model = UNet2DModel.from_pretrained(model_id)
|
|
scheduler = KarrasVeScheduler()
|
|
|
|
pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(0)
|
|
image = pipe(num_inference_steps=20, generator=generator, output_type="numpy").images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
assert image.shape == (1, 256, 256, 3)
|
|
expected_slice = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_lms_stable_diffusion_pipeline(self):
|
|
model_id = "CompVis/stable-diffusion-v1-1"
|
|
pipe = StableDiffusionPipeline.from_pretrained(model_id).to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
scheduler = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler")
|
|
pipe.scheduler = scheduler
|
|
|
|
prompt = "a photograph of an astronaut riding a horse"
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
image = pipe(
|
|
[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
|
|
).images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.9077, 0.9254, 0.9181, 0.9227, 0.9213, 0.9367, 0.9399, 0.9406, 0.9024])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_memory_chunking(self):
|
|
torch.cuda.reset_peak_memory_stats()
|
|
model_id = "CompVis/stable-diffusion-v1-4"
|
|
pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16).to(
|
|
torch_device
|
|
)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "a photograph of an astronaut riding a horse"
|
|
|
|
# make attention efficient
|
|
pipe.enable_attention_slicing()
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
with torch.autocast(torch_device):
|
|
output_chunked = pipe(
|
|
[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
|
|
)
|
|
image_chunked = output_chunked.images
|
|
|
|
mem_bytes = torch.cuda.max_memory_allocated()
|
|
torch.cuda.reset_peak_memory_stats()
|
|
# make sure that less than 3.75 GB is allocated
|
|
assert mem_bytes < 3.75 * 10**9
|
|
|
|
# disable chunking
|
|
pipe.disable_attention_slicing()
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
with torch.autocast(torch_device):
|
|
output = pipe(
|
|
[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
|
|
)
|
|
image = output.images
|
|
|
|
# make sure that more than 3.75 GB is allocated
|
|
mem_bytes = torch.cuda.max_memory_allocated()
|
|
assert mem_bytes > 3.75 * 10**9
|
|
assert np.abs(image_chunked.flatten() - image.flatten()).max() < 1e-3
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_text2img_pipeline_fp16(self):
|
|
torch.cuda.reset_peak_memory_stats()
|
|
model_id = "CompVis/stable-diffusion-v1-4"
|
|
pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16).to(
|
|
torch_device
|
|
)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "a photograph of an astronaut riding a horse"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
output_chunked = pipe(
|
|
[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
|
|
)
|
|
image_chunked = output_chunked.images
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
with torch.autocast(torch_device):
|
|
output = pipe(
|
|
[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
|
|
)
|
|
image = output.images
|
|
|
|
# Make sure results are close enough
|
|
diff = np.abs(image_chunked.flatten() - image.flatten())
|
|
# They ARE different since ops are not run always at the same precision
|
|
# however, they should be extremely close.
|
|
assert diff.mean() < 2e-2
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_text2img_pipeline(self):
|
|
expected_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/text2img/astronaut_riding_a_horse.png"
|
|
)
|
|
expected_image = np.array(expected_image, dtype=np.float32) / 255.0
|
|
|
|
model_id = "CompVis/stable-diffusion-v1-4"
|
|
pipe = StableDiffusionPipeline.from_pretrained(
|
|
model_id,
|
|
safety_checker=self.dummy_safety_checker,
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
prompt = "astronaut riding a horse"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
output = pipe(prompt=prompt, strength=0.75, guidance_scale=7.5, generator=generator, output_type="np")
|
|
image = output.images[0]
|
|
|
|
assert image.shape == (512, 512, 3)
|
|
assert np.abs(expected_image - image).max() < 1e-2
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_img2img_pipeline(self):
|
|
init_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/img2img/sketch-mountains-input.jpg"
|
|
)
|
|
expected_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/img2img/fantasy_landscape.png"
|
|
)
|
|
init_image = init_image.resize((768, 512))
|
|
expected_image = np.array(expected_image, dtype=np.float32) / 255.0
|
|
|
|
model_id = "CompVis/stable-diffusion-v1-4"
|
|
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
|
|
model_id,
|
|
safety_checker=self.dummy_safety_checker,
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
prompt = "A fantasy landscape, trending on artstation"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
output = pipe(
|
|
prompt=prompt,
|
|
init_image=init_image,
|
|
strength=0.75,
|
|
guidance_scale=7.5,
|
|
generator=generator,
|
|
output_type="np",
|
|
)
|
|
image = output.images[0]
|
|
|
|
assert image.shape == (512, 768, 3)
|
|
# img2img is flaky across GPUs even in fp32, so using MAE here
|
|
assert np.abs(expected_image - image).mean() < 1e-2
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_img2img_pipeline_k_lms(self):
|
|
init_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/img2img/sketch-mountains-input.jpg"
|
|
)
|
|
expected_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/img2img/fantasy_landscape_k_lms.png"
|
|
)
|
|
init_image = init_image.resize((768, 512))
|
|
expected_image = np.array(expected_image, dtype=np.float32) / 255.0
|
|
|
|
lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
|
|
|
|
model_id = "CompVis/stable-diffusion-v1-4"
|
|
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
|
|
model_id,
|
|
scheduler=lms,
|
|
safety_checker=self.dummy_safety_checker,
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
prompt = "A fantasy landscape, trending on artstation"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
output = pipe(
|
|
prompt=prompt,
|
|
init_image=init_image,
|
|
strength=0.75,
|
|
guidance_scale=7.5,
|
|
generator=generator,
|
|
output_type="np",
|
|
)
|
|
image = output.images[0]
|
|
|
|
assert image.shape == (512, 768, 3)
|
|
# img2img is flaky across GPUs even in fp32, so using MAE here
|
|
assert np.abs(expected_image - image).mean() < 1e-2
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_inpaint_pipeline(self):
|
|
init_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo.png"
|
|
)
|
|
mask_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
|
|
)
|
|
expected_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/yellow_cat_sitting_on_a_park_bench.png"
|
|
)
|
|
expected_image = np.array(expected_image, dtype=np.float32) / 255.0
|
|
|
|
model_id = "fusing/sd-inpaint-temp"
|
|
pipe = StableDiffusionInpaintPipeline.from_pretrained(
|
|
model_id,
|
|
safety_checker=self.dummy_safety_checker,
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
output = pipe(
|
|
prompt=prompt,
|
|
image=init_image,
|
|
mask_image=mask_image,
|
|
generator=generator,
|
|
output_type="np",
|
|
)
|
|
image = output.images[0]
|
|
|
|
assert image.shape == (512, 512, 3)
|
|
assert np.abs(expected_image - image).max() < 1e-2
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_inpaint_pipeline_fp16(self):
|
|
init_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo.png"
|
|
)
|
|
mask_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
|
|
)
|
|
expected_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/yellow_cat_sitting_on_a_park_bench.png"
|
|
)
|
|
expected_image = np.array(expected_image, dtype=np.float32) / 255.0
|
|
|
|
model_id = "fusing/sd-inpaint-temp"
|
|
pipe = StableDiffusionInpaintPipeline.from_pretrained(
|
|
model_id,
|
|
revision="fp16",
|
|
torch_dtype=torch.float16,
|
|
safety_checker=None,
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
output = pipe(
|
|
prompt=prompt,
|
|
image=init_image,
|
|
mask_image=mask_image,
|
|
generator=generator,
|
|
output_type="np",
|
|
)
|
|
image = output.images[0]
|
|
|
|
assert image.shape == (512, 512, 3)
|
|
assert np.abs(expected_image - image).max() < 1e-2
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_inpaint_legacy_pipeline(self):
|
|
init_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo.png"
|
|
)
|
|
mask_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
|
|
)
|
|
expected_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/red_cat_sitting_on_a_park_bench.png"
|
|
)
|
|
expected_image = np.array(expected_image, dtype=np.float32) / 255.0
|
|
|
|
model_id = "CompVis/stable-diffusion-v1-4"
|
|
pipe = StableDiffusionInpaintPipeline.from_pretrained(
|
|
model_id,
|
|
safety_checker=self.dummy_safety_checker,
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
prompt = "A red cat sitting on a park bench"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
output = pipe(
|
|
prompt=prompt,
|
|
init_image=init_image,
|
|
mask_image=mask_image,
|
|
strength=0.75,
|
|
guidance_scale=7.5,
|
|
generator=generator,
|
|
output_type="np",
|
|
)
|
|
image = output.images[0]
|
|
|
|
assert image.shape == (512, 512, 3)
|
|
assert np.abs(expected_image - image).max() < 1e-2
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_inpaint_pipeline_pndm(self):
|
|
init_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo.png"
|
|
)
|
|
mask_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
|
|
)
|
|
expected_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/yellow_cat_sitting_on_a_park_bench_pndm.png"
|
|
)
|
|
expected_image = np.array(expected_image, dtype=np.float32) / 255.0
|
|
|
|
pndm = PNDMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True)
|
|
model_id = "fusing/sd-inpaint-temp"
|
|
pipe = StableDiffusionInpaintPipeline.from_pretrained(
|
|
model_id, safety_checker=self.dummy_safety_checker, scheduler=pndm
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
output = pipe(
|
|
prompt=prompt,
|
|
image=init_image,
|
|
mask_image=mask_image,
|
|
generator=generator,
|
|
output_type="np",
|
|
)
|
|
image = output.images[0]
|
|
|
|
assert image.shape == (512, 512, 3)
|
|
assert np.abs(expected_image - image).max() < 1e-2
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_inpaint_legacy_pipeline_k_lms(self):
|
|
# TODO(Anton, Patrick) - I think we can remove this test soon
|
|
init_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo.png"
|
|
)
|
|
mask_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
|
|
)
|
|
expected_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/red_cat_sitting_on_a_park_bench_k_lms.png"
|
|
)
|
|
expected_image = np.array(expected_image, dtype=np.float32) / 255.0
|
|
|
|
lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
|
|
|
|
model_id = "CompVis/stable-diffusion-v1-4"
|
|
pipe = StableDiffusionInpaintPipeline.from_pretrained(
|
|
model_id,
|
|
scheduler=lms,
|
|
safety_checker=self.dummy_safety_checker,
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
prompt = "A red cat sitting on a park bench"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
output = pipe(
|
|
prompt=prompt,
|
|
init_image=init_image,
|
|
mask_image=mask_image,
|
|
strength=0.75,
|
|
guidance_scale=7.5,
|
|
generator=generator,
|
|
output_type="np",
|
|
)
|
|
image = output.images[0]
|
|
|
|
assert image.shape == (512, 512, 3)
|
|
assert np.abs(expected_image - image).max() < 1e-2
|
|
|
|
@slow
|
|
def test_stable_diffusion_onnx(self):
|
|
sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
|
|
)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
np.random.seed(0)
|
|
output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=5, output_type="np")
|
|
image = output.images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.3602, 0.3688, 0.3652, 0.3895, 0.3782, 0.3747, 0.3927, 0.4241, 0.4327])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
|
|
|
@slow
|
|
def test_stable_diffusion_img2img_onnx(self):
|
|
init_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/img2img/sketch-mountains-input.jpg"
|
|
)
|
|
init_image = init_image.resize((768, 512))
|
|
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
|
|
)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A fantasy landscape, trending on artstation"
|
|
|
|
np.random.seed(0)
|
|
output = pipe(
|
|
prompt=prompt,
|
|
init_image=init_image,
|
|
strength=0.75,
|
|
guidance_scale=7.5,
|
|
num_inference_steps=8,
|
|
output_type="np",
|
|
)
|
|
images = output.images
|
|
image_slice = images[0, 255:258, 383:386, -1]
|
|
|
|
assert images.shape == (1, 512, 768, 3)
|
|
expected_slice = np.array([0.4830, 0.5242, 0.5603, 0.5016, 0.5131, 0.5111, 0.4928, 0.5025, 0.5055])
|
|
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
|
|
|
|
@slow
|
|
def test_stable_diffusion_inpaint_onnx(self):
|
|
init_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo.png"
|
|
)
|
|
mask_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
|
|
)
|
|
|
|
pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
|
|
)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A red cat sitting on a park bench"
|
|
|
|
np.random.seed(0)
|
|
output = pipe(
|
|
prompt=prompt,
|
|
init_image=init_image,
|
|
mask_image=mask_image,
|
|
strength=0.75,
|
|
guidance_scale=7.5,
|
|
num_inference_steps=8,
|
|
output_type="np",
|
|
)
|
|
images = output.images
|
|
image_slice = images[0, 255:258, 255:258, -1]
|
|
|
|
assert images.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.3524, 0.3289, 0.3464, 0.3872, 0.4129, 0.3566, 0.3709, 0.4128, 0.3734])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_text2img_intermediate_state(self):
|
|
number_of_steps = 0
|
|
|
|
def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
|
|
test_callback_fn.has_been_called = True
|
|
nonlocal number_of_steps
|
|
number_of_steps += 1
|
|
if step == 0:
|
|
latents = latents.detach().cpu().numpy()
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array(
|
|
[1.8285, 1.2857, -0.1024, 1.2406, -2.3068, 1.0747, -0.0818, -0.6520, -2.9506]
|
|
)
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
|
|
elif step == 50:
|
|
latents = latents.detach().cpu().numpy()
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array(
|
|
[1.1078, 1.5803, 0.2773, -0.0589, -1.7928, -0.3665, -0.4695, -1.0727, -1.1601]
|
|
)
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
test_callback_fn.has_been_called = False
|
|
|
|
pipe = StableDiffusionPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
|
|
)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
prompt = "Andromeda galaxy in a bottle"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
with torch.autocast(torch_device):
|
|
pipe(
|
|
prompt=prompt,
|
|
num_inference_steps=50,
|
|
guidance_scale=7.5,
|
|
generator=generator,
|
|
callback=test_callback_fn,
|
|
callback_steps=1,
|
|
)
|
|
assert test_callback_fn.has_been_called
|
|
assert number_of_steps == 51
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_img2img_intermediate_state(self):
|
|
number_of_steps = 0
|
|
|
|
def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
|
|
test_callback_fn.has_been_called = True
|
|
nonlocal number_of_steps
|
|
number_of_steps += 1
|
|
if step == 0:
|
|
latents = latents.detach().cpu().numpy()
|
|
assert latents.shape == (1, 4, 64, 96)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array([0.9052, -0.0184, 0.4810, 0.2898, 0.5851, 1.4920, 0.5362, 1.9838, 0.0530])
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
|
|
elif step == 37:
|
|
latents = latents.detach().cpu().numpy()
|
|
assert latents.shape == (1, 4, 64, 96)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array([0.7071, 0.7831, 0.8300, 1.8140, 1.7840, 1.9402, 1.3651, 1.6590, 1.2828])
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
test_callback_fn.has_been_called = False
|
|
|
|
init_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/img2img/sketch-mountains-input.jpg"
|
|
)
|
|
init_image = init_image.resize((768, 512))
|
|
|
|
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
prompt = "A fantasy landscape, trending on artstation"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
with torch.autocast(torch_device):
|
|
pipe(
|
|
prompt=prompt,
|
|
init_image=init_image,
|
|
strength=0.75,
|
|
num_inference_steps=50,
|
|
guidance_scale=7.5,
|
|
generator=generator,
|
|
callback=test_callback_fn,
|
|
callback_steps=1,
|
|
)
|
|
assert test_callback_fn.has_been_called
|
|
assert number_of_steps == 38
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_inpaint_legacy_intermediate_state(self):
|
|
number_of_steps = 0
|
|
|
|
def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
|
|
test_callback_fn.has_been_called = True
|
|
nonlocal number_of_steps
|
|
number_of_steps += 1
|
|
if step == 0:
|
|
latents = latents.detach().cpu().numpy()
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array(
|
|
[-0.5472, 1.1218, -0.5505, -0.9390, -1.0794, 0.4063, 0.5158, 0.6429, -1.5246]
|
|
)
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
|
|
elif step == 37:
|
|
latents = latents.detach().cpu().numpy()
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array([0.4781, 1.1572, 0.6258, 0.2291, 0.2554, -0.1443, 0.7085, -0.1598, -0.5659])
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
|
|
|
|
test_callback_fn.has_been_called = False
|
|
|
|
init_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo.png"
|
|
)
|
|
mask_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
|
|
)
|
|
|
|
pipe = StableDiffusionInpaintPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
prompt = "A red cat sitting on a park bench"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
with torch.autocast(torch_device):
|
|
pipe(
|
|
prompt=prompt,
|
|
init_image=init_image,
|
|
mask_image=mask_image,
|
|
strength=0.75,
|
|
num_inference_steps=50,
|
|
guidance_scale=7.5,
|
|
generator=generator,
|
|
callback=test_callback_fn,
|
|
callback_steps=1,
|
|
)
|
|
assert test_callback_fn.has_been_called
|
|
assert number_of_steps == 38
|
|
|
|
@slow
|
|
def test_stable_diffusion_onnx_intermediate_state(self):
|
|
number_of_steps = 0
|
|
|
|
def test_callback_fn(step: int, timestep: int, latents: np.ndarray) -> None:
|
|
test_callback_fn.has_been_called = True
|
|
nonlocal number_of_steps
|
|
number_of_steps += 1
|
|
if step == 0:
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array(
|
|
[-0.5950, -0.3039, -1.1672, 0.1594, -1.1572, 0.6719, -1.9712, -0.0403, 0.9592]
|
|
)
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
|
|
elif step == 5:
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array(
|
|
[-0.4776, -0.0119, -0.8519, -0.0275, -0.9764, 0.9820, -0.3843, 0.3788, 1.2264]
|
|
)
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
|
|
|
|
test_callback_fn.has_been_called = False
|
|
|
|
pipe = OnnxStableDiffusionPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
|
|
)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "Andromeda galaxy in a bottle"
|
|
|
|
np.random.seed(0)
|
|
pipe(prompt=prompt, num_inference_steps=5, guidance_scale=7.5, callback=test_callback_fn, callback_steps=1)
|
|
assert test_callback_fn.has_been_called
|
|
assert number_of_steps == 6
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
|
def test_stable_diffusion_accelerate_load_works(self):
|
|
if version.parse(version.parse(transformers.__version__).base_version) < version.parse("4.23"):
|
|
return
|
|
|
|
if version.parse(version.parse(accelerate.__version__).base_version) < version.parse("0.14"):
|
|
return
|
|
|
|
model_id = "CompVis/stable-diffusion-v1-4"
|
|
_ = StableDiffusionPipeline.from_pretrained(
|
|
model_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True, device_map="auto"
|
|
).to(torch_device)
|
|
|
|
@slow
|
|
@unittest.skipIf(torch_device == "cpu", "This test is supposed to run on GPU")
|
|
def test_stable_diffusion_accelerate_load_reduces_memory_footprint(self):
|
|
if version.parse(version.parse(transformers.__version__).base_version) < version.parse("4.23"):
|
|
return
|
|
|
|
if version.parse(version.parse(accelerate.__version__).base_version) < version.parse("0.14"):
|
|
return
|
|
|
|
pipeline_id = "CompVis/stable-diffusion-v1-4"
|
|
|
|
torch.cuda.empty_cache()
|
|
gc.collect()
|
|
|
|
tracemalloc.start()
|
|
pipeline_normal_load = StableDiffusionPipeline.from_pretrained(
|
|
pipeline_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True
|
|
)
|
|
pipeline_normal_load.to(torch_device)
|
|
_, peak_normal = tracemalloc.get_traced_memory()
|
|
tracemalloc.stop()
|
|
|
|
del pipeline_normal_load
|
|
torch.cuda.empty_cache()
|
|
gc.collect()
|
|
|
|
tracemalloc.start()
|
|
_ = StableDiffusionPipeline.from_pretrained(
|
|
pipeline_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True, device_map="auto"
|
|
)
|
|
_, peak_accelerate = tracemalloc.get_traced_memory()
|
|
|
|
tracemalloc.stop()
|
|
|
|
assert peak_accelerate < peak_normal
|