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# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import os
import random
import tempfile
import unittest
import numpy as np
import torch
import PIL
from diffusers import (
AutoencoderKL,
DDIMPipeline,
DDIMScheduler,
DDPMPipeline,
DDPMScheduler,
PNDMScheduler,
StableDiffusionImg2ImgPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionPipeline,
UNet2DConditionModel,
UNet2DModel,
VQModel,
logging,
)
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir
from PIL import Image
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
torch.backends.cuda.matmul.allow_tf32 = False
def test_progress_bar(capsys):
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"),
)
scheduler = DDPMScheduler(num_train_timesteps=10)
ddpm = DDPMPipeline(model, scheduler).to(torch_device)
ddpm(output_type="numpy").images
captured = capsys.readouterr()
assert "10/10" in captured.err, "Progress bar has to be displayed"
ddpm.set_progress_bar_config(disable=True)
ddpm(output_type="numpy").images
captured = capsys.readouterr()
assert captured.err == "", "Progress bar should be disabled"
class CustomPipelineTests(unittest.TestCase):
def test_load_custom_pipeline(self):
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
)
# NOTE that `"CustomPipeline"` is not a class that is defined in this library, but solely on the Hub
# under https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L24
assert pipeline.__class__.__name__ == "CustomPipeline"
def test_run_custom_pipeline(self):
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
)
images, output_str = pipeline(num_inference_steps=2, output_type="np")
assert images[0].shape == (1, 32, 32, 3)
# compare output to https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L102
assert output_str == "This is a test"
def test_local_custom_pipeline(self):
local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline")
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path
)
images, output_str = pipeline(num_inference_steps=2, output_type="np")
assert pipeline.__class__.__name__ == "CustomLocalPipeline"
assert images[0].shape == (1, 32, 32, 3)
# compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102
assert output_str == "This is a local test"
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
def test_load_pipeline_from_git(self):
clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id)
clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16)
pipeline = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
custom_pipeline="clip_guided_stable_diffusion",
clip_model=clip_model,
feature_extractor=feature_extractor,
torch_dtype=torch.float16,
revision="fp16",
)
pipeline.enable_attention_slicing()
pipeline = pipeline.to(torch_device)
# 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:
# https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion.py
assert pipeline.__class__.__name__ == "CLIPGuidedStableDiffusion"
image = pipeline("a prompt", num_inference_steps=2, output_type="np").images[0]
assert image.shape == (512, 512, 3)
class PipelineFastTests(unittest.TestCase):
@property
def dummy_image(self):
batch_size = 1
num_channels = 3
sizes = (32, 32)
image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
return image
@property
def dummy_uncond_unet(self):
torch.manual_seed(0)
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"),
)
return model
@property
def dummy_cond_unet(self):
torch.manual_seed(0)
model = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
return model
@property
def dummy_cond_unet_inpaint(self):
torch.manual_seed(0)
model = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=9,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
return model
@property
def dummy_vq_model(self):
torch.manual_seed(0)
model = VQModel(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=3,
)
return model
@property
def dummy_vae(self):
torch.manual_seed(0)
model = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
return model
@property
def dummy_text_encoder(self):
torch.manual_seed(0)
config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
return CLIPTextModel(config)
@property
def dummy_extractor(self):
def extract(*args, **kwargs):
class Out:
def __init__(self):
self.pixel_values = torch.ones([0])
def to(self, device):
self.pixel_values.to(device)
return self
return Out()
return extract
def test_components(self):
"""Test that components property works correctly"""
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
inpaint = StableDiffusionInpaintPipelineLegacy(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
).to(torch_device)
img2img = StableDiffusionImg2ImgPipeline(**inpaint.components).to(torch_device)
text2img = StableDiffusionPipeline(**inpaint.components).to(torch_device)
prompt = "A painting of a squirrel eating a burger"
# Device type MPS is not supported for torch.Generator() api.
if torch_device == "mps":
generator = torch.manual_seed(0)
else:
generator = torch.Generator(device=torch_device).manual_seed(0)
image_inpaint = inpaint(
[prompt],
generator=generator,
num_inference_steps=2,
output_type="np",
init_image=init_image,
mask_image=mask_image,
).images
image_img2img = img2img(
[prompt],
generator=generator,
num_inference_steps=2,
output_type="np",
init_image=init_image,
).images
image_text2img = text2img(
[prompt],
generator=generator,
num_inference_steps=2,
output_type="np",
).images
assert image_inpaint.shape == (1, 32, 32, 3)
assert image_img2img.shape == (1, 32, 32, 3)
assert image_text2img.shape == (1, 128, 128, 3)
@slow
class PipelineSlowTests(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"))
def test_warning_unused_kwargs(self):
model_id = "hf-internal-testing/unet-pipeline-dummy"
logger = logging.get_logger("diffusers.pipeline_utils")
with tempfile.TemporaryDirectory() as tmpdirname:
with CaptureLogger(logger) as cap_logger:
DiffusionPipeline.from_pretrained(model_id, not_used=True, cache_dir=tmpdirname, force_download=True)
assert cap_logger.out == "Keyword arguments {'not_used': True} not recognized.\n"
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"
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"
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"
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)
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
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