# coding=utf-8
# Copyright 2023 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 tempfile
import time
import unittest
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
logging,
)
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import CaptureLogger, require_torch_gpu
from ...models.test_models_unet_2d_condition import create_lora_layers
from ...pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class StableDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = 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,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = 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,
)
torch.manual_seed(0)
text_encoder_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,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5643, 0.6017, 0.4799, 0.5267, 0.5584, 0.4641, 0.5159, 0.4963, 0.4791])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_lora(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
# forward 1
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
# set lora layers
lora_attn_procs = create_lora_layers(sd_pipe.unet)
sd_pipe.unet.set_attn_processor(lora_attn_procs)
sd_pipe = sd_pipe.to(torch_device)
# forward 2
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs, cross_attention_kwargs={"scale": 0.0})
image = output.images
image_slice_1 = image[0, -3:, -3:, -1]
# forward 3
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs, cross_attention_kwargs={"scale": 0.5})
image = output.images
image_slice_2 = image[0, -3:, -3:, -1]
assert np.abs(image_slice - image_slice_1).max() < 1e-2
assert np.abs(image_slice - image_slice_2).max() > 1e-2
def test_stable_diffusion_prompt_embeds(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
text_inputs = sd_pipe.tokenizer(
prompt,
padding="max_length",
max_length=sd_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
prompt_embeds = sd_pipe.text_encoder(text_inputs)[0]
inputs["prompt_embeds"] = prompt_embeds
# forward
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_stable_diffusion_negative_prompt_embeds(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
embeds = []
for p in [prompt, negative_prompt]:
text_inputs = sd_pipe.tokenizer(
p,
padding="max_length",
max_length=sd_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
embeds.append(sd_pipe.text_encoder(text_inputs)[0])
inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
# forward
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_stable_diffusion_ddim_factor_8(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs, height=136, width=136)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 136, 136, 3)
expected_slice = np.array([0.5524, 0.5626, 0.6069, 0.4727, 0.386, 0.3995, 0.4613, 0.4328, 0.4269])
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
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe.scheduler = PNDMScheduler(skip_prk_steps=True)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5094, 0.5674, 0.4667, 0.5125, 0.5696, 0.4674, 0.5277, 0.4964, 0.4945])
assert np.abs(image_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
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array(
[
0.47082293033599854,
0.5371589064598083,
0.4562119245529175,
0.5220914483070374,
0.5733777284622192,
0.4795039892196655,
0.5465868711471558,
0.5074326395988464,
0.5042197108268738,
]
)
assert np.abs(image_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
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array(
[
0.4707113206386566,
0.5372191071510315,
0.4563021957874298,
0.5220003724098206,
0.5734264850616455,
0.4794946610927582,
0.5463782548904419,
0.5074145197868347,
0.504422664642334,
]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_k_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array(
[
0.47082313895225525,
0.5371587872505188,
0.4562119245529175,
0.5220913887023926,
0.5733776688575745,
0.47950395941734314,
0.546586811542511,
0.5074326992034912,
0.5042197108268738,
]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_vae_slicing(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
image_count = 4
inputs = self.get_dummy_inputs(device)
inputs["prompt"] = [inputs["prompt"]] * image_count
output_1 = sd_pipe(**inputs)
# make sure sliced vae decode yields the same result
sd_pipe.enable_vae_slicing()
inputs = self.get_dummy_inputs(device)
inputs["prompt"] = [inputs["prompt"]] * image_count
output_2 = sd_pipe(**inputs)
# there is a small discrepancy at image borders vs. full batch decode
assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 3e-3
def test_stable_diffusion_vae_tiling(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
# make sure here that pndm scheduler skips prk
components["safety_checker"] = None
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
# Test that tiled decode at 512x512 yields the same result as the non-tiled decode
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 tiled vae decode yields the same result
sd_pipe.enable_vae_tiling()
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() < 5e-1
# test that tiled decode works with various shapes
shapes = [(1, 4, 73, 97), (1, 4, 97, 73), (1, 4, 49, 65), (1, 4, 65, 49)]
for shape in shapes:
zeros = torch.zeros(shape).to(device)
sd_pipe.vae.decode(zeros)
def test_stable_diffusion_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "french fries"
output = sd_pipe(**inputs, negative_prompt=negative_prompt)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array(
[
0.5108221173286438,
0.5688379406929016,
0.4685141146183014,
0.5098261833190918,
0.5657756328582764,
0.4631010890007019,
0.5226285457611084,
0.49129390716552734,
0.4899061322212219,
]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_long_prompt(self):
components = self.get_dummy_components()
components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)
sd_pipe = StableDiffusionPipeline(**components)
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 == ""
def test_stable_diffusion_height_width_opt(self):
components = self.get_dummy_components()
components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "hey"
output = sd_pipe(prompt, num_inference_steps=1, output_type="np")
image_shape = output.images[0].shape[:2]
assert image_shape == (64, 64)
output = sd_pipe(prompt, num_inference_steps=1, height=96, width=96, output_type="np")
image_shape = output.images[0].shape[:2]
assert image_shape == (96, 96)
config = dict(sd_pipe.unet.config)
config["sample_size"] = 96
sd_pipe.unet = UNet2DConditionModel.from_config(config).to(torch_device)
output = sd_pipe(prompt, num_inference_steps=1, output_type="np")
image_shape = output.images[0].shape[:2]
assert image_shape == (192, 192)
@slow
@require_torch_gpu
class StableDiffusionPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_1_1_pndm(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)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.43625, 0.43554, 0.36670, 0.40660, 0.39703, 0.38658, 0.43936, 0.43557, 0.40592])
assert np.abs(image_slice - expected_slice).max() < 1e-4
def test_stable_diffusion_1_4_pndm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.57400, 0.47841, 0.31625, 0.63583, 0.58306, 0.55056, 0.50825, 0.56306, 0.55748])
assert np.abs(image_slice - expected_slice).max() < 1e-4
def test_stable_diffusion_ddim(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.38019, 0.28647, 0.27321, 0.40377, 0.38290, 0.35446, 0.39218, 0.38165, 0.42239])
assert np.abs(image_slice - expected_slice).max() < 1e-4
def test_stable_diffusion_lms(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.10542, 0.09620, 0.07332, 0.09015, 0.09382, 0.07597, 0.08496, 0.07806, 0.06455])
assert np.abs(image_slice - expected_slice).max() < 1e-4
def test_stable_diffusion_dpm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.03503, 0.03494, 0.01087, 0.03128, 0.02552, 0.00803, 0.00742, 0.00372, 0.00000])
assert np.abs(image_slice - expected_slice).max() < 1e-4
def test_stable_diffusion_attention_slicing(self):
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
# enable attention slicing
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
image_sliced = pipe(**inputs).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 slicing
pipe.disable_attention_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
image = pipe(**inputs).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_sliced - image).max() < 1e-3
def test_stable_diffusion_vae_slicing(self):
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
# enable vae slicing
pipe.enable_vae_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
inputs["prompt"] = [inputs["prompt"]] * 4
inputs["latents"] = torch.cat([inputs["latents"]] * 4)
image_sliced = pipe(**inputs).images
mem_bytes = torch.cuda.max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
# make sure that less than 4 GB is allocated
assert mem_bytes < 4e9
# disable vae slicing
pipe.disable_vae_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
inputs["prompt"] = [inputs["prompt"]] * 4
inputs["latents"] = torch.cat([inputs["latents"]] * 4)
image = pipe(**inputs).images
# make sure that more than 4 GB is allocated
mem_bytes = torch.cuda.max_memory_allocated()
assert mem_bytes > 4e9
# There is a small discrepancy at the image borders vs. a fully batched version.
assert np.abs(image_sliced - image).max() < 1e-2
def test_stable_diffusion_vae_tiling(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.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
pipe.unet = pipe.unet.to(memory_format=torch.channels_last)
pipe.vae = pipe.vae.to(memory_format=torch.channels_last)
prompt = "a photograph of an astronaut riding a horse"
# enable vae tiling
pipe.enable_vae_tiling()
pipe.enable_model_cpu_offload()
generator = torch.Generator(device="cpu").manual_seed(0)
output_chunked = pipe(
[prompt],
width=1024,
height=1024,
generator=generator,
guidance_scale=7.5,
num_inference_steps=2,
output_type="numpy",
)
image_chunked = output_chunked.images
mem_bytes = torch.cuda.max_memory_allocated()
# disable vae tiling
pipe.disable_vae_tiling()
generator = torch.Generator(device="cpu").manual_seed(0)
output = pipe(
[prompt],
width=1024,
height=1024,
generator=generator,
guidance_scale=7.5,
num_inference_steps=2,
output_type="numpy",
)
image = output.images
assert mem_bytes < 1e10
assert np.abs(image_chunked.flatten() - image.flatten()).max() < 1e-2
def test_stable_diffusion_fp16_vs_autocast(self):
# this test makes sure that the original model with autocast
# and the new model with fp16 yield the same result
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device, dtype=torch.float16)
image_fp16 = pipe(**inputs).images
with torch.autocast(torch_device):
inputs = self.get_inputs(torch_device)
image_autocast = pipe(**inputs).images
# Make sure results are close enough
diff = np.abs(image_fp16.flatten() - image_autocast.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_intermediate_state(self):
number_of_steps = 0
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.5693, -0.3018, -0.9746, 0.0518, -0.8770, 0.7559, -1.7402, 0.1022, 1.1582]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 2:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.1958, -0.2993, -1.0166, -0.5005, -0.4810, 0.6162, -0.9492, 0.6621, 1.4492]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
callback_fn.has_been_called = False
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
pipe(**inputs, callback=callback_fn, callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == inputs["num_inference_steps"]
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, 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, torch_dtype=torch.float16, 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()
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.8 GB is allocated
assert mem_bytes < 2.8 * 10**9
def test_stable_diffusion_pipeline_with_model_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
# Normal inference
pipe = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
)
pipe.unet.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
outputs = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# With model offloading
# Reload but don't move to cuda
pipe = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
)
pipe.unet.set_default_attn_processor()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device, dtype=torch.float16)
outputs_offloaded = pipe(**inputs)
mem_bytes_offloaded = torch.cuda.max_memory_allocated()
assert np.abs(outputs.images - outputs_offloaded.images).max() < 1e-3
assert mem_bytes_offloaded < mem_bytes
assert mem_bytes_offloaded < 3.5 * 10**9
for module in pipe.text_encoder, pipe.unet, pipe.vae, pipe.safety_checker:
assert module.device == torch.device("cpu")
# With attention slicing
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe.enable_attention_slicing()
_ = pipe(**inputs)
mem_bytes_slicing = torch.cuda.max_memory_allocated()
assert mem_bytes_slicing < mem_bytes_offloaded
assert mem_bytes_slicing < 3 * 10**9
def test_stable_diffusion_textual_inversion(self):
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe.load_textual_inversion("sd-concepts-library/low-poly-hd-logos-icons")
a111_file = hf_hub_download("hf-internal-testing/text_inv_embedding_a1111_format", "winter_style.pt")
a111_file_neg = hf_hub_download(
"hf-internal-testing/text_inv_embedding_a1111_format", "winter_style_negative.pt"
)
pipe.load_textual_inversion(a111_file)
pipe.load_textual_inversion(a111_file_neg)
pipe.to("cuda")
generator = torch.Generator(device="cpu").manual_seed(1)
prompt = "An logo of a turtle in strong Style-Winter with <low-poly-hd-logos-icons>"
neg_prompt = "Style-Winter-neg"
image = pipe(prompt=prompt, negative_prompt=neg_prompt, generator=generator, output_type="np").images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_inv/winter_logo_style.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 5e-2
@nightly
@require_torch_gpu
class StableDiffusionPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_1_4_pndm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_text2img/stable_diffusion_1_4_pndm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_1_5_pndm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_text2img/stable_diffusion_1_5_pndm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_ddim(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_text2img/stable_diffusion_1_4_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_lms(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_text2img/stable_diffusion_1_4_lms.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_euler(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_text2img/stable_diffusion_1_4_euler.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_dpm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 25
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_text2img/stable_diffusion_1_4_dpm_multi.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3