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# 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 random
import tempfile
import time
import unittest
import numpy as np
import torch
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
UNet2DModel,
VQModel,
logging,
)
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import CaptureLogger, require_torch_gpu
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class StableDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@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_stable_diffusion_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
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=None,
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([0.5112, 0.4692, 0.4715, 0.5206, 0.4894, 0.5114, 0.5096, 0.4932, 0.4755])
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_ddim_factor_8(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
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=None,
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,
height=536,
width=536,
num_inference_steps=2,
output_type="np",
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 134, 134, 3)
expected_slice = np.array([0.7834, 0.5488, 0.5781, 0.46, 0.3609, 0.5369, 0.542, 0.4855, 0.5557])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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=None,
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_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
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe = StableDiffusionPipeline.from_pretrained(tmpdirname)
# sanity check that the pipeline still works
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=None,
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_k_euler_ancestral(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = EulerAncestralDiscreteScheduler(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=None,
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_k_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = EulerDiscreteScheduler(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=None,
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=None,
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=None,
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_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=None,
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)
@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=None,
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)
def test_stable_diffusion_long_prompt(self):
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=None,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
do_classifier_free_guidance = True
negative_prompt = None
num_images_per_prompt = 1
logger = logging.get_logger("diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion")
prompt = 25 * "@"
with CaptureLogger(logger) as cap_logger_3:
text_embeddings_3 = sd_pipe._encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
prompt = 100 * "@"
with CaptureLogger(logger) as cap_logger:
text_embeddings = sd_pipe._encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
negative_prompt = "Hello"
with CaptureLogger(logger) as cap_logger_2:
text_embeddings_2 = sd_pipe._encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
assert text_embeddings_3.shape == text_embeddings_2.shape == text_embeddings.shape
assert text_embeddings.shape[1] == 77
assert cap_logger.out == cap_logger_2.out
# 100 - 77 + 1 (BOS token) + 1 (EOS token) = 25
assert cap_logger.out.count("@") == 25
assert cap_logger_3.out == ""
@slow
@require_torch_gpu
class StableDiffusionPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
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
def test_stable_diffusion_fast_ddim(self):
scheduler = DDIMScheduler.from_pretrained("CompVis/stable-diffusion-v1-1", subfolder="scheduler")
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1", scheduler=scheduler)
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, 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
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_pretrained(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
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)
pipe.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
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)
pipe = pipe.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
def test_stable_diffusion_text2img_pipeline_default(self):
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text2img/astronaut_riding_a_horse.npy"
)
model_id = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionPipeline.from_pretrained(model_id, safety_checker=None)
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() < 5e-3
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
def test_stable_diffusion_low_cpu_mem_usage(self):
pipeline_id = "CompVis/stable-diffusion-v1-4"
start_time = time.time()
pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(
pipeline_id, revision="fp16", torch_dtype=torch.float16
)
pipeline_low_cpu_mem_usage.to(torch_device)
low_cpu_mem_usage_time = time.time() - start_time
start_time = time.time()
_ = StableDiffusionPipeline.from_pretrained(
pipeline_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True, low_cpu_mem_usage=False
)
normal_load_time = time.time() - start_time
assert 2 * low_cpu_mem_usage_time < normal_load_time
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipeline_id = "CompVis/stable-diffusion-v1-4"
prompt = "Andromeda galaxy in a bottle"
pipeline = StableDiffusionPipeline.from_pretrained(pipeline_id, revision="fp16", torch_dtype=torch.float16)
pipeline = pipeline.to(torch_device)
pipeline.enable_attention_slicing(1)
pipeline.enable_sequential_cpu_offload()
generator = torch.Generator(device=torch_device).manual_seed(0)
_ = pipeline(prompt, generator=generator, num_inference_steps=5)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.8 GB is allocated
assert mem_bytes < 2.8 * 10**9
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