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diffusers/tests/test_pipelines.py

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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 json
import os
import random
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
import numpy as np
import PIL
import safetensors.torch
import torch
from parameterized import parameterized
from PIL import Image
from requests.exceptions import HTTPError
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMPipeline,
DDIMScheduler,
DDPMPipeline,
DDPMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionImg2ImgPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionPipeline,
UNet2DConditionModel,
UNet2DModel,
logging,
)
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, is_flax_available, nightly, slow, torch_device
from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir, require_torch_gpu
torch.backends.cuda.matmul.allow_tf32 = False
class DownloadTests(unittest.TestCase):
def test_download_only_pytorch(self):
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
_ = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
)
all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots"))]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a flax file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack
assert not any(f.endswith(".msgpack") for f in files)
# We need to never convert this tiny model to safetensors for this test to pass
assert not any(f.endswith(".safetensors") for f in files)
def test_returned_cached_folder(self):
prompt = "hello"
pipe = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
_, local_path = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None, return_cached_folder=True
)
pipe_2 = StableDiffusionPipeline.from_pretrained(local_path)
pipe = pipe.to(torch_device)
pipe_2 = pipe_2.to(torch_device)
generator = torch.manual_seed(0)
out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
generator = torch.manual_seed(0)
out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
assert np.max(np.abs(out - out_2)) < 1e-3
def test_download_safetensors(self):
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
_ = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe-safetensors",
safety_checker=None,
cache_dir=tmpdirname,
)
all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots"))]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a pytorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack
assert not any(f.endswith(".bin") for f in files)
def test_download_no_safety_checker(self):
prompt = "hello"
pipe = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
pipe = pipe.to(torch_device)
generator = torch.manual_seed(0)
out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
pipe_2 = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
pipe_2 = pipe_2.to(torch_device)
generator = torch.manual_seed(0)
out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
assert np.max(np.abs(out - out_2)) < 1e-3
def test_load_no_safety_checker_explicit_locally(self):
prompt = "hello"
pipe = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
pipe = pipe.to(torch_device)
generator = torch.manual_seed(0)
out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None)
pipe_2 = pipe_2.to(torch_device)
generator = torch.manual_seed(0)
out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
assert np.max(np.abs(out - out_2)) < 1e-3
def test_load_no_safety_checker_default_locally(self):
prompt = "hello"
pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
pipe = pipe.to(torch_device)
generator = torch.manual_seed(0)
out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname)
pipe_2 = pipe_2.to(torch_device)
generator = torch.manual_seed(0)
out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
assert np.max(np.abs(out - out_2)) < 1e-3
def test_cached_files_are_used_when_no_internet(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
orig_pipe = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
orig_comps = {k: v for k, v in orig_pipe.components.items() if hasattr(v, "parameters")}
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.request", return_value=response_mock):
# Download this model to make sure it's in the cache.
pipe = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None, local_files_only=True
)
comps = {k: v for k, v in pipe.components.items() if hasattr(v, "parameters")}
for m1, m2 in zip(orig_comps.values(), comps.values()):
for p1, p2 in zip(m1.parameters(), m2.parameters()):
if p1.data.ne(p2.data).sum() > 0:
assert False, "Parameters not the same!"
def test_download_from_variant_folder(self):
for safe_avail in [False, True]:
import diffusers
diffusers.utils.import_utils._safetensors_available = safe_avail
other_format = ".bin" if safe_avail else ".safetensors"
with tempfile.TemporaryDirectory() as tmpdirname:
StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname
)
all_root_files = [
t[-1] for t in os.walk(os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots"))
]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a variant file even if we have some here:
# https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet
assert len(files) == 15, f"We should only download 15 files, not {len(files)}"
assert not any(f.endswith(other_format) for f in files)
# no variants
assert not any(len(f.split(".")) == 3 for f in files)
diffusers.utils.import_utils._safetensors_available = True
def test_download_variant_all(self):
for safe_avail in [False, True]:
import diffusers
diffusers.utils.import_utils._safetensors_available = safe_avail
other_format = ".bin" if safe_avail else ".safetensors"
this_format = ".safetensors" if safe_avail else ".bin"
variant = "fp16"
with tempfile.TemporaryDirectory() as tmpdirname:
StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname, variant=variant
)
all_root_files = [
t[-1] for t in os.walk(os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots"))
]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a non-variant file even if we have some here:
# https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet
assert len(files) == 15, f"We should only download 15 files, not {len(files)}"
# unet, vae, text_encoder, safety_checker
assert len([f for f in files if f.endswith(f"{variant}{this_format}")]) == 4
# all checkpoints should have variant ending
assert not any(f.endswith(this_format) and not f.endswith(f"{variant}{this_format}") for f in files)
assert not any(f.endswith(other_format) for f in files)
diffusers.utils.import_utils._safetensors_available = True
def test_download_variant_partly(self):
for safe_avail in [False, True]:
import diffusers
diffusers.utils.import_utils._safetensors_available = safe_avail
other_format = ".bin" if safe_avail else ".safetensors"
this_format = ".safetensors" if safe_avail else ".bin"
variant = "no_ema"
with tempfile.TemporaryDirectory() as tmpdirname:
StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname, variant=variant
)
snapshots = os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots")
all_root_files = [t[-1] for t in os.walk(snapshots)]
files = [item for sublist in all_root_files for item in sublist]
unet_files = os.listdir(os.path.join(snapshots, os.listdir(snapshots)[0], "unet"))
# Some of the downloaded files should be a non-variant file, check:
# https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet
assert len(files) == 15, f"We should only download 15 files, not {len(files)}"
# only unet has "no_ema" variant
assert f"diffusion_pytorch_model.{variant}{this_format}" in unet_files
assert len([f for f in files if f.endswith(f"{variant}{this_format}")]) == 1
# vae, safety_checker and text_encoder should have no variant
assert sum(f.endswith(this_format) and not f.endswith(f"{variant}{this_format}") for f in files) == 3
assert not any(f.endswith(other_format) for f in files)
diffusers.utils.import_utils._safetensors_available = True
def test_download_broken_variant(self):
for safe_avail in [False, True]:
import diffusers
diffusers.utils.import_utils._safetensors_available = safe_avail
# text encoder is missing no variant and "no_ema" variant weights, so the following can't work
for variant in [None, "no_ema"]:
with self.assertRaises(OSError) as error_context:
with tempfile.TemporaryDirectory() as tmpdirname:
StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/stable-diffusion-broken-variants",
cache_dir=tmpdirname,
variant=variant,
)
assert "Error no file name" in str(error_context.exception)
# text encoder has fp16 variants so we can load it
with tempfile.TemporaryDirectory() as tmpdirname:
pipe = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/stable-diffusion-broken-variants", cache_dir=tmpdirname, variant="fp16"
)
assert pipe is not None
snapshots = os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots")
all_root_files = [t[-1] for t in os.walk(snapshots)]
files = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a non-variant file even if we have some here:
# https://huggingface.co/hf-internal-testing/stable-diffusion-broken-variants/tree/main/unet
assert len(files) == 15, f"We should only download 15 files, not {len(files)}"
# only unet has "no_ema" variant
diffusers.utils.import_utils._safetensors_available = True
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"
)
pipeline = pipeline.to(torch_device)
# 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_load_custom_github(self):
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="main"
)
# make sure that on "main" pipeline gives only ones because of: https://github.com/huggingface/diffusers/pull/1690
with torch.no_grad():
output = pipeline()
assert output.numel() == output.sum()
# hack since Python doesn't like overwriting modules: https://stackoverflow.com/questions/3105801/unload-a-module-in-python
# Could in the future work with hashes instead.
del sys.modules["diffusers_modules.git.one_step_unet"]
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="0.10.2"
)
with torch.no_grad():
output = pipeline()
assert output.numel() != output.sum()
assert pipeline.__class__.__name__ == "UnetSchedulerOneForwardPipeline"
def test_run_custom_pipeline(self):
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
)
pipeline = pipeline.to(torch_device)
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_repo(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
)
pipeline = pipeline.to(torch_device)
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"
def test_local_custom_pipeline_file(self):
local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline")
local_custom_pipeline_path = os.path.join(local_custom_pipeline_path, "what_ever.py")
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path
)
pipeline = pipeline.to(torch_device)
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
@require_torch_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,
)
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):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
import diffusers
diffusers.utils.import_utils._safetensors_available = True
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
def dummy_uncond_unet(self, sample_size=32):
torch.manual_seed(0)
model = UNet2DModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=sample_size,
in_channels=3,
out_channels=3,
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
)
return model
def dummy_cond_unet(self, sample_size=32):
torch.manual_seed(0)
model = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=sample_size,
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_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
@parameterized.expand(
[
[DDIMScheduler, DDIMPipeline, 32],
[DDPMScheduler, DDPMPipeline, 32],
[DDIMScheduler, DDIMPipeline, (32, 64)],
[DDPMScheduler, DDPMPipeline, (64, 32)],
]
)
def test_uncond_unet_components(self, scheduler_fn=DDPMScheduler, pipeline_fn=DDPMPipeline, sample_size=32):
unet = self.dummy_uncond_unet(sample_size)
scheduler = scheduler_fn()
pipeline = pipeline_fn(unet, scheduler).to(torch_device)
generator = torch.manual_seed(0)
out_image = pipeline(
generator=generator,
num_inference_steps=2,
output_type="np",
).images
sample_size = (sample_size, sample_size) if isinstance(sample_size, int) else sample_size
assert out_image.shape == (1, *sample_size, 3)
def test_stable_diffusion_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((32, 32))
# 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"
generator = torch.manual_seed(0)
image_inpaint = inpaint(
[prompt],
generator=generator,
num_inference_steps=2,
output_type="np",
image=init_image,
mask_image=mask_image,
).images
image_img2img = img2img(
[prompt],
generator=generator,
num_inference_steps=2,
output_type="np",
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, 64, 64, 3)
def test_set_scheduler(self):
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")
sd = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, DDIMScheduler)
sd.scheduler = DDPMScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, DDPMScheduler)
sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, PNDMScheduler)
sd.scheduler = LMSDiscreteScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, LMSDiscreteScheduler)
sd.scheduler = EulerDiscreteScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, EulerDiscreteScheduler)
sd.scheduler = EulerAncestralDiscreteScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, EulerAncestralDiscreteScheduler)
sd.scheduler = DPMSolverMultistepScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, DPMSolverMultistepScheduler)
def test_set_scheduler_consistency(self):
unet = self.dummy_cond_unet()
pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
ddim = DDIMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
sd = StableDiffusionPipeline(
unet=unet,
scheduler=pndm,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
pndm_config = sd.scheduler.config
sd.scheduler = DDPMScheduler.from_config(pndm_config)
sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config)
pndm_config_2 = sd.scheduler.config
pndm_config_2 = {k: v for k, v in pndm_config_2.items() if k in pndm_config}
assert dict(pndm_config) == dict(pndm_config_2)
sd = StableDiffusionPipeline(
unet=unet,
scheduler=ddim,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)
ddim_config = sd.scheduler.config
sd.scheduler = LMSDiscreteScheduler.from_config(ddim_config)
sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config)
ddim_config_2 = sd.scheduler.config
ddim_config_2 = {k: v for k, v in ddim_config_2.items() if k in ddim_config}
assert dict(ddim_config) == dict(ddim_config_2)
def test_save_safe_serialization(self):
pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
with tempfile.TemporaryDirectory() as tmpdirname:
pipeline.save_pretrained(tmpdirname, safe_serialization=True)
# Validate that the VAE safetensor exists and are of the correct format
vae_path = os.path.join(tmpdirname, "vae", "diffusion_pytorch_model.safetensors")
assert os.path.exists(vae_path), f"Could not find {vae_path}"
_ = safetensors.torch.load_file(vae_path)
# Validate that the UNet safetensor exists and are of the correct format
unet_path = os.path.join(tmpdirname, "unet", "diffusion_pytorch_model.safetensors")
assert os.path.exists(unet_path), f"Could not find {unet_path}"
_ = safetensors.torch.load_file(unet_path)
# Validate that the text encoder safetensor exists and are of the correct format
text_encoder_path = os.path.join(tmpdirname, "text_encoder", "model.safetensors")
assert os.path.exists(text_encoder_path), f"Could not find {text_encoder_path}"
_ = safetensors.torch.load_file(text_encoder_path)
pipeline = StableDiffusionPipeline.from_pretrained(tmpdirname)
assert pipeline.unet is not None
assert pipeline.vae is not None
assert pipeline.text_encoder is not None
assert pipeline.scheduler is not None
assert pipeline.feature_extractor is not None
def test_no_pytorch_download_when_doing_safetensors(self):
# by default we don't download
with tempfile.TemporaryDirectory() as tmpdirname:
_ = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/diffusers-stable-diffusion-tiny-all", cache_dir=tmpdirname
)
path = os.path.join(
tmpdirname,
"models--hf-internal-testing--diffusers-stable-diffusion-tiny-all",
"snapshots",
"07838d72e12f9bcec1375b0482b80c1d399be843",
"unet",
)
# safetensors exists
assert os.path.exists(os.path.join(path, "diffusion_pytorch_model.safetensors"))
# pytorch does not
assert not os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin"))
def test_no_safetensors_download_when_doing_pytorch(self):
# mock diffusers safetensors not available
import diffusers
diffusers.utils.import_utils._safetensors_available = False
with tempfile.TemporaryDirectory() as tmpdirname:
_ = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/diffusers-stable-diffusion-tiny-all", cache_dir=tmpdirname
)
path = os.path.join(
tmpdirname,
"models--hf-internal-testing--diffusers-stable-diffusion-tiny-all",
"snapshots",
"07838d72e12f9bcec1375b0482b80c1d399be843",
"unet",
)
# safetensors does not exists
assert not os.path.exists(os.path.join(path, "diffusion_pytorch_model.safetensors"))
# pytorch does
assert os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin"))
diffusers.utils.import_utils._safetensors_available = True
def test_optional_components(self):
unet = self.dummy_cond_unet()
pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
orig_sd = StableDiffusionPipeline(
unet=unet,
scheduler=pndm,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=unet,
feature_extractor=self.dummy_extractor,
)
sd = orig_sd
assert sd.config.requires_safety_checker is True
with tempfile.TemporaryDirectory() as tmpdirname:
sd.save_pretrained(tmpdirname)
# Test that passing None works
sd = StableDiffusionPipeline.from_pretrained(
tmpdirname, feature_extractor=None, safety_checker=None, requires_safety_checker=False
)
assert sd.config.requires_safety_checker is False
assert sd.config.safety_checker == (None, None)
assert sd.config.feature_extractor == (None, None)
with tempfile.TemporaryDirectory() as tmpdirname:
sd.save_pretrained(tmpdirname)
# Test that loading previous None works
sd = StableDiffusionPipeline.from_pretrained(tmpdirname)
assert sd.config.requires_safety_checker is False
assert sd.config.safety_checker == (None, None)
assert sd.config.feature_extractor == (None, None)
orig_sd.save_pretrained(tmpdirname)
# Test that loading without any directory works
shutil.rmtree(os.path.join(tmpdirname, "safety_checker"))
with open(os.path.join(tmpdirname, sd.config_name)) as f:
config = json.load(f)
config["safety_checker"] = [None, None]
with open(os.path.join(tmpdirname, sd.config_name), "w") as f:
json.dump(config, f)
sd = StableDiffusionPipeline.from_pretrained(tmpdirname, requires_safety_checker=False)
sd.save_pretrained(tmpdirname)
sd = StableDiffusionPipeline.from_pretrained(tmpdirname)
assert sd.config.requires_safety_checker is False
assert sd.config.safety_checker == (None, None)
assert sd.config.feature_extractor == (None, None)
# Test that loading from deleted model index works
with open(os.path.join(tmpdirname, sd.config_name)) as f:
config = json.load(f)
del config["safety_checker"]
del config["feature_extractor"]
with open(os.path.join(tmpdirname, sd.config_name), "w") as f:
json.dump(config, f)
sd = StableDiffusionPipeline.from_pretrained(tmpdirname)
assert sd.config.requires_safety_checker is False
assert sd.config.safety_checker == (None, None)
assert sd.config.feature_extractor == (None, None)
with tempfile.TemporaryDirectory() as tmpdirname:
sd.save_pretrained(tmpdirname)
# Test that partially loading works
sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor)
assert sd.config.requires_safety_checker is False
assert sd.config.safety_checker == (None, None)
assert sd.config.feature_extractor != (None, None)
# Test that partially loading works
sd = StableDiffusionPipeline.from_pretrained(
tmpdirname,
feature_extractor=self.dummy_extractor,
safety_checker=unet,
requires_safety_checker=[True, True],
)
assert sd.config.requires_safety_checker == [True, True]
assert sd.config.safety_checker != (None, None)
assert sd.config.feature_extractor != (None, None)
with tempfile.TemporaryDirectory() as tmpdirname:
sd.save_pretrained(tmpdirname)
sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor)
assert sd.config.requires_safety_checker == [True, True]
assert sd.config.safety_checker != (None, None)
assert sd.config.feature_extractor != (None, None)
@slow
@require_torch_gpu
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.pipelines")
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} are not expected by DDPMPipeline and will be ignored.\n"
)
def test_from_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.Generator(device=torch_device).manual_seed(0)
image = ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images
generator = torch.Generator(device=torch_device).manual_seed(0)
new_image = new_ddpm(generator=generator, num_inference_steps=5, 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 = ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
ddpm_from_hub = ddpm_from_hub.to(torch_device)
ddpm_from_hub.set_progress_bar_config(disable=None)
generator = torch.Generator(device=torch_device).manual_seed(0)
image = ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images
generator = torch.Generator(device=torch_device).manual_seed(0)
new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, 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 = 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 = ddpm_from_hub.to(torch_device)
ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)
generator = torch.Generator(device=torch_device).manual_seed(0)
image = ddpm_from_hub_custom_model(generator=generator, num_inference_steps=5, output_type="numpy").images
generator = torch.Generator(device=torch_device).manual_seed(0)
new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, 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"
scheduler = DDIMScheduler.from_pretrained(model_path)
pipe = DDIMPipeline.from_pretrained(model_path, scheduler=scheduler)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
images = pipe(output_type="numpy").images
assert images.shape == (1, 32, 32, 3)
assert isinstance(images, np.ndarray)
images = pipe(output_type="pil", num_inference_steps=4).images
assert isinstance(images, list)
assert len(images) == 1
assert isinstance(images[0], PIL.Image.Image)
# use PIL by default
images = pipe(num_inference_steps=4).images
assert isinstance(images, list)
assert isinstance(images[0], PIL.Image.Image)
def test_from_flax_from_pt(self):
pipe_pt = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
)
pipe_pt.to(torch_device)
if not is_flax_available():
raise ImportError("Make sure flax is installed.")
from diffusers import FlaxStableDiffusionPipeline
with tempfile.TemporaryDirectory() as tmpdirname:
pipe_pt.save_pretrained(tmpdirname)
pipe_flax, params = FlaxStableDiffusionPipeline.from_pretrained(
tmpdirname, safety_checker=None, from_pt=True
)
with tempfile.TemporaryDirectory() as tmpdirname:
pipe_flax.save_pretrained(tmpdirname, params=params)
pipe_pt_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None, from_flax=True)
pipe_pt_2.to(torch_device)
prompt = "Hello"
generator = torch.manual_seed(0)
image_0 = pipe_pt(
[prompt],
generator=generator,
num_inference_steps=2,
output_type="np",
).images[0]
generator = torch.manual_seed(0)
image_1 = pipe_pt_2(
[prompt],
generator=generator,
num_inference_steps=2,
output_type="np",
).images[0]
assert np.abs(image_0 - image_1).sum() < 1e-5, "Models don't give the same forward pass"
@nightly
@require_torch_gpu
class PipelineNightlyTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_ddpm_ddim_equality_batched(self):
seed = 0
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.Generator(device=torch_device).manual_seed(seed)
ddpm_images = ddpm(batch_size=2, generator=generator, output_type="numpy").images
generator = torch.Generator(device=torch_device).manual_seed(seed)
ddim_images = ddim(
batch_size=2,
generator=generator,
num_inference_steps=1000,
eta=1.0,
output_type="numpy",
use_clipped_model_output=True, # Need this to make DDIM match DDPM
).images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(ddpm_images - ddim_images).max() < 1e-1
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