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# coding=utf-8
# Copyright 2024 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 traceback
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,
LCMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
logging,
)
from diffusers.utils.testing_utils import (
CaptureLogger,
enable_full_determinism,
load_image,
load_numpy,
nightly,
numpy_cosine_similarity_distance,
require_accelerate_version_greater,
require_python39_or_higher,
require_torch_2,
require_torch_gpu,
require_torch_multi_gpu,
run_test_in_subprocess,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import (
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
IPAdapterTesterMixin,
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
# Will be run via run_test_in_subprocess
def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
error = None
try:
inputs = in_queue.get(timeout=timeout)
torch_device = inputs.pop("torch_device")
seed = inputs.pop("seed")
inputs["generator"] = torch.Generator(device=torch_device).manual_seed(seed)
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.unet.to(memory_format=torch.channels_last)
sd_pipe.unet = torch.compile(sd_pipe.unet, mode="reduce-overhead", fullgraph=True)
sd_pipe.set_progress_bar_config(disable=None)
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() < 5e-3
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
class StableDiffusionPipelineFastTests(
IPAdapterTesterMixin,
PipelineLatentTesterMixin,
PipelineKarrasSchedulerTesterMixin,
PipelineTesterMixin,
unittest.TestCase,
):
pipeline_class = StableDiffusionPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS
def get_dummy_components(self, time_cond_proj_dim=None):
cross_attention_dim = 8
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(4, 8),
layers_per_block=1,
sample_size=32,
time_cond_proj_dim=time_cond_proj_dim,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=cross_attention_dim,
norm_num_groups=2,
)
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=[4, 8],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=cross_attention_dim,
intermediate_size=16,
layer_norm_eps=1e-05,
num_attention_heads=2,
num_hidden_layers=2,
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,
"image_encoder": 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": "np",
}
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([0.1763, 0.4776, 0.4986, 0.2566, 0.3802, 0.4596, 0.5363, 0.3277, 0.3949])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
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([0.2368, 0.4900, 0.5019, 0.2723, 0.4473, 0.4578, 0.4551, 0.3532, 0.4133])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
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([0.2368, 0.4900, 0.5019, 0.2723, 0.4473, 0.4578, 0.4551, 0.3532, 0.4133])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_ays(self):
from diffusers.schedulers import AysSchedules
timestep_schedule = AysSchedules["StableDiffusionTimesteps"]
sigma_schedule = AysSchedules["StableDiffusionSigmas"]
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["num_inference_steps"] = 10
output = sd_pipe(**inputs).images
inputs = self.get_dummy_inputs(device)
inputs["num_inference_steps"] = None
inputs["timesteps"] = timestep_schedule
output_ts = sd_pipe(**inputs).images
inputs = self.get_dummy_inputs(device)
inputs["num_inference_steps"] = None
inputs["sigmas"] = sigma_schedule
output_sigmas = sd_pipe(**inputs).images
assert (
np.abs(output_sigmas.flatten() - output_ts.flatten()).max() < 1e-3
), "ays timesteps and ays sigmas should have the same outputs"
assert (
np.abs(output.flatten() - output_ts.flatten()).max() > 1e-3
), "use ays timesteps should have different outputs"
assert (
np.abs(output.flatten() - output_sigmas.flatten()).max() > 1e-3
), "use ays sigmas should have different outputs"
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_prompt_embeds_no_text_encoder_or_tokenizer(self):
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(torch_device)
inputs["negative_prompt"] = "this is a negative prompt"
# forward
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
inputs = self.get_dummy_inputs(torch_device)
prompt = inputs.pop("prompt")
negative_prompt = "this is a negative prompt"
prompt_embeds, negative_prompt_embeds = sd_pipe.encode_prompt(
prompt,
torch_device,
1,
True,
negative_prompt=negative_prompt,
prompt_embeds=None,
negative_prompt_embeds=None,
)
inputs["prompt_embeds"] = prompt_embeds
inputs["negative_prompt_embeds"] = negative_prompt_embeds
sd_pipe.text_encoder = None
sd_pipe.tokenizer = None
# 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_prompt_embeds_with_plain_negative_prompt_list(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)
inputs["negative_prompt"] = negative_prompt
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_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([0.4720, 0.5426, 0.5160, 0.3961, 0.4696, 0.4296, 0.5738, 0.5888, 0.5481])
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([0.1941, 0.4748, 0.4880, 0.2222, 0.4221, 0.4545, 0.5604, 0.3488, 0.3902])
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([0.2681, 0.4785, 0.4857, 0.2426, 0.4473, 0.4481, 0.5610, 0.3676, 0.3855])
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([0.2682, 0.4782, 0.4855, 0.2424, 0.4472, 0.4479, 0.5612, 0.3676, 0.3854])
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([0.2681, 0.4785, 0.4857, 0.2426, 0.4473, 0.4481, 0.5610, 0.3676, 0.3855])
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([0.1907, 0.4709, 0.4858, 0.2224, 0.4223, 0.4539, 0.5606, 0.3489, 0.3900])
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")
logger.setLevel(logging.WARNING)
prompt = 100 * "@"
with CaptureLogger(logger) as cap_logger:
negative_text_embeddings, text_embeddings = sd_pipe.encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
if negative_text_embeddings is not None:
text_embeddings = torch.cat([negative_text_embeddings, text_embeddings])
# 100 - 77 + 1 (BOS token) + 1 (EOS token) = 25
assert cap_logger.out.count("@") == 25
negative_prompt = "Hello"
with CaptureLogger(logger) as cap_logger_2:
negative_text_embeddings_2, text_embeddings_2 = sd_pipe.encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
if negative_text_embeddings_2 is not None:
text_embeddings_2 = torch.cat([negative_text_embeddings_2, text_embeddings_2])
assert cap_logger.out == cap_logger_2.out
prompt = 25 * "@"
with CaptureLogger(logger) as cap_logger_3:
negative_text_embeddings_3, text_embeddings_3 = sd_pipe.encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
if negative_text_embeddings_3 is not None:
text_embeddings_3 = torch.cat([negative_text_embeddings_3, text_embeddings_3])
assert text_embeddings_3.shape == text_embeddings_2.shape == text_embeddings.shape
assert text_embeddings.shape[1] == 77
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)
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
# MPS currently doesn't support ComplexFloats, which are required for freeU - see https://github.com/huggingface/diffusers/issues/7569.
@skip_mps
def test_freeu_enabled(self):
components = self.get_dummy_components()
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", generator=torch.manual_seed(0)).images
sd_pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
output_freeu = sd_pipe(prompt, num_inference_steps=1, output_type="np", generator=torch.manual_seed(0)).images
assert not np.allclose(
output[0, -3:, -3:, -1], output_freeu[0, -3:, -3:, -1]
), "Enabling of FreeU should lead to different results."
def test_freeu_disabled(self):
components = self.get_dummy_components()
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", generator=torch.manual_seed(0)).images
sd_pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
sd_pipe.disable_freeu()
freeu_keys = {"s1", "s2", "b1", "b2"}
for upsample_block in sd_pipe.unet.up_blocks:
for key in freeu_keys:
assert getattr(upsample_block, key) is None, f"Disabling of FreeU should have set {key} to None."
output_no_freeu = sd_pipe(
prompt, num_inference_steps=1, output_type="np", generator=torch.manual_seed(0)
).images
assert np.allclose(
output[0, -3:, -3:, -1], output_no_freeu[0, -3:, -3:, -1]
), "Disabling of FreeU should lead to results similar to the default pipeline results."
def test_fused_qkv_projections(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)
image = sd_pipe(**inputs).images
original_image_slice = image[0, -3:, -3:, -1]
sd_pipe.fuse_qkv_projections()
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice_fused = image[0, -3:, -3:, -1]
sd_pipe.unfuse_qkv_projections()
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice_disabled = image[0, -3:, -3:, -1]
assert np.allclose(
original_image_slice, image_slice_fused, atol=1e-2, rtol=1e-2
), "Fusion of QKV projections shouldn't affect the outputs."
assert np.allclose(
image_slice_fused, image_slice_disabled, atol=1e-2, rtol=1e-2
), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
assert np.allclose(
original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
), "Original outputs should match when fused QKV projections are disabled."
def test_pipeline_interrupt(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "hey"
num_inference_steps = 3
# store intermediate latents from the generation process
class PipelineState:
def __init__(self):
self.state = []
def apply(self, pipe, i, t, callback_kwargs):
self.state.append(callback_kwargs["latents"])
return callback_kwargs
pipe_state = PipelineState()
sd_pipe(
prompt,
num_inference_steps=num_inference_steps,
output_type="np",
generator=torch.Generator("cpu").manual_seed(0),
callback_on_step_end=pipe_state.apply,
).images
# interrupt generation at step index
interrupt_step_idx = 1
def callback_on_step_end(pipe, i, t, callback_kwargs):
if i == interrupt_step_idx:
pipe._interrupt = True
return callback_kwargs
output_interrupted = sd_pipe(
prompt,
num_inference_steps=num_inference_steps,
output_type="latent",
generator=torch.Generator("cpu").manual_seed(0),
callback_on_step_end=callback_on_step_end,
).images
# fetch intermediate latents at the interrupted step
# from the completed generation process
intermediate_latent = pipe_state.state[interrupt_step_idx]
# compare the intermediate latent to the output of the interrupted process
# they should be the same
assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)
@slow
@require_torch_gpu
class StableDiffusionPipelineSlowTests(unittest.TestCase):
def setUp(self):
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": "np",
}
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([0.4363, 0.4355, 0.3667, 0.4066, 0.3970, 0.3866, 0.4394, 0.4356, 0.4059])
assert np.abs(image_slice - expected_slice).max() < 3e-3
def test_stable_diffusion_v1_4_with_freeu(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)
inputs["num_inference_steps"] = 25
sd_pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
image = sd_pipe(**inputs).images
image = image[0, -3:, -3:, -1].flatten()
expected_image = [0.0721, 0.0588, 0.0268, 0.0384, 0.0636, 0.0, 0.0429, 0.0344, 0.0309]
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
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([0.5740, 0.4784, 0.3162, 0.6358, 0.5831, 0.5505, 0.5082, 0.5631, 0.5575])
assert np.abs(image_slice - expected_slice).max() < 3e-3
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() < 3e-3
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,
final_sigmas_type="sigma_min",
)
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() < 3e-3
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.unet.set_default_attn_processor()
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()
pipe.unet.set_default_attn_processor()
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
max_diff = numpy_cosine_similarity_distance(image_sliced.flatten(), image.flatten())
assert max_diff < 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.
max_diff = numpy_cosine_similarity_distance(image_sliced.flatten(), image.flatten())
assert max_diff < 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, safety_checker=None
)
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="np",
)
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="np",
)
image = output.images
assert mem_bytes < 1e10
max_diff = numpy_cosine_similarity_distance(image_chunked.flatten(), image.flatten())
assert max_diff < 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.Tensor) -> 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.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()
images = outputs.images
offloaded_images = outputs_offloaded.images
max_diff = numpy_cosine_similarity_distance(images.flatten(), offloaded_images.flatten())
assert max_diff < 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:
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 < 8e-1
def test_stable_diffusion_textual_inversion_with_model_cpu_offload(self):
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe.enable_model_cpu_offload()
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)
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 < 8e-1
def test_stable_diffusion_textual_inversion_with_sequential_cpu_offload(self):
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe.enable_sequential_cpu_offload()
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)
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 < 8e-1
@require_python39_or_higher
@require_torch_2
def test_stable_diffusion_compile(self):
seed = 0
inputs = self.get_inputs(torch_device, seed=seed)
# Can't pickle a Generator object
del inputs["generator"]
inputs["torch_device"] = torch_device
inputs["seed"] = seed
run_test_in_subprocess(test_case=self, target_func=_test_stable_diffusion_compile, inputs=inputs)
def test_stable_diffusion_lcm(self):
unet = UNet2DConditionModel.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", subfolder="unet")
sd_pipe = StableDiffusionPipeline.from_pretrained("Lykon/dreamshaper-7", unet=unet).to(torch_device)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 6
inputs["output_type"] = "pil"
image = sd_pipe(**inputs).images[0]
expected_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/lcm_full/stable_diffusion_lcm.png"
)
image = sd_pipe.image_processor.pil_to_numpy(image)
expected_image = sd_pipe.image_processor.pil_to_numpy(expected_image)
max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
assert max_diff < 1e-2
@slow
@require_torch_gpu
class StableDiffusionPipelineCkptTests(unittest.TestCase):
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_download_from_hub(self):
ckpt_paths = [
"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors",
"https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors",
]
for ckpt_path in ckpt_paths:
pipe = StableDiffusionPipeline.from_single_file(ckpt_path, torch_dtype=torch.float16)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
image_out = pipe("test", num_inference_steps=1, output_type="np").images[0]
assert image_out.shape == (512, 512, 3)
def test_download_local(self):
ckpt_filename = hf_hub_download("runwayml/stable-diffusion-v1-5", filename="v1-5-pruned-emaonly.safetensors")
config_filename = hf_hub_download("runwayml/stable-diffusion-v1-5", filename="v1-inference.yaml")
pipe = StableDiffusionPipeline.from_single_file(
ckpt_filename, config_files={"v1": config_filename}, torch_dtype=torch.float16
)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
image_out = pipe("test", num_inference_steps=1, output_type="np").images[0]
assert image_out.shape == (512, 512, 3)
@nightly
@require_torch_gpu
class StableDiffusionPipelineNightlyTests(unittest.TestCase):
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
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": "np",
}
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 < 3e-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
# (sayakpaul): This test suite was run in the DGX with two GPUs (1, 2).
@slow
@require_torch_multi_gpu
@require_accelerate_version_greater("0.27.0")
class StableDiffusionPipelineDeviceMapTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, generator_device="cpu", seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def get_pipeline_output_without_device_map(self):
sd_pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
).to(torch_device)
sd_pipe.set_progress_bar_config(disable=True)
inputs = self.get_inputs()
no_device_map_image = sd_pipe(**inputs).images
del sd_pipe
return no_device_map_image
def test_forward_pass_balanced_device_map(self):
no_device_map_image = self.get_pipeline_output_without_device_map()
sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", device_map="balanced", torch_dtype=torch.float16
)
sd_pipe_with_device_map.set_progress_bar_config(disable=True)
inputs = self.get_inputs()
device_map_image = sd_pipe_with_device_map(**inputs).images
max_diff = np.abs(device_map_image - no_device_map_image).max()
assert max_diff < 1e-3
def test_components_put_in_right_devices(self):
sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", device_map="balanced", torch_dtype=torch.float16
)
assert len(set(sd_pipe_with_device_map.hf_device_map.values())) >= 2
def test_max_memory(self):
no_device_map_image = self.get_pipeline_output_without_device_map()
sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
device_map="balanced",
max_memory={0: "1GB", 1: "1GB"},
torch_dtype=torch.float16,
)
sd_pipe_with_device_map.set_progress_bar_config(disable=True)
inputs = self.get_inputs()
device_map_image = sd_pipe_with_device_map(**inputs).images
max_diff = np.abs(device_map_image - no_device_map_image).max()
assert max_diff < 1e-3
def test_reset_device_map(self):
sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", device_map="balanced", torch_dtype=torch.float16
)
sd_pipe_with_device_map.reset_device_map()
assert sd_pipe_with_device_map.hf_device_map is None
for name, component in sd_pipe_with_device_map.components.items():
if isinstance(component, torch.nn.Module):
assert component.device.type == "cpu"
def test_reset_device_map_to(self):
sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", device_map="balanced", torch_dtype=torch.float16
)
sd_pipe_with_device_map.reset_device_map()
assert sd_pipe_with_device_map.hf_device_map is None
# Make sure `to()` can be used and the pipeline can be called.
pipe = sd_pipe_with_device_map.to("cuda")
_ = pipe("hello", num_inference_steps=2)
def test_reset_device_map_enable_model_cpu_offload(self):
sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", device_map="balanced", torch_dtype=torch.float16
)
sd_pipe_with_device_map.reset_device_map()
assert sd_pipe_with_device_map.hf_device_map is None
# Make sure `enable_model_cpu_offload()` can be used and the pipeline can be called.
sd_pipe_with_device_map.enable_model_cpu_offload()
_ = sd_pipe_with_device_map("hello", num_inference_steps=2)
def test_reset_device_map_enable_sequential_cpu_offload(self):
sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", device_map="balanced", torch_dtype=torch.float16
)
sd_pipe_with_device_map.reset_device_map()
assert sd_pipe_with_device_map.hf_device_map is None
# Make sure `enable_sequential_cpu_offload()` can be used and the pipeline can be called.
sd_pipe_with_device_map.enable_sequential_cpu_offload()
_ = sd_pipe_with_device_map("hello", num_inference_steps=2)
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