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# 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) | |
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 | |
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 | |