mirror of
https://github.com/huggingface/diffusers.git
synced 2026-10-24 20:59:27 +03:00
c2798cc785
* refactor the pipeline-level attention processor tests * simplify stuff. * remove utils.py
443 lines
17 KiB
Python
443 lines
17 KiB
Python
# coding=utf-8
|
|
# Copyright 2026 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 numpy as np
|
|
import pytest
|
|
import torch
|
|
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
|
|
|
|
from diffusers import (
|
|
AutoencoderKL,
|
|
DDIMScheduler,
|
|
EulerAncestralDiscreteScheduler,
|
|
EulerDiscreteScheduler,
|
|
LMSDiscreteScheduler,
|
|
PNDMScheduler,
|
|
StableDiffusionPipeline,
|
|
UNet2DConditionModel,
|
|
logging,
|
|
)
|
|
|
|
from ...testing_utils import (
|
|
CaptureLogger,
|
|
assert_tensors_close,
|
|
backend_empty_cache,
|
|
backend_max_memory_allocated,
|
|
backend_reset_peak_memory_stats,
|
|
enable_full_determinism,
|
|
load_numpy,
|
|
nightly,
|
|
numpy_cosine_similarity_distance,
|
|
require_torch_accelerator,
|
|
skip_mps,
|
|
slow,
|
|
torch_device,
|
|
)
|
|
from ..pipeline_params import (
|
|
TEXT_TO_IMAGE_BATCH_PARAMS,
|
|
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
|
|
TEXT_TO_IMAGE_PARAMS,
|
|
)
|
|
from ..testing_utils import (
|
|
BasePipelineTesterConfig,
|
|
MemoryTesterMixin,
|
|
PipelineTesterMixin,
|
|
)
|
|
|
|
|
|
enable_full_determinism()
|
|
|
|
|
|
class StableDiffusion2PipelineTesterConfig(BasePipelineTesterConfig):
|
|
pipeline_class = StableDiffusionPipeline
|
|
required_input_params_in_call_signature = TEXT_TO_IMAGE_PARAMS
|
|
batch_input_params = TEXT_TO_IMAGE_BATCH_PARAMS
|
|
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS
|
|
output_shape = (3, 64, 64)
|
|
|
|
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,
|
|
# SD2-specific config below
|
|
attention_head_dim=(2, 4),
|
|
use_linear_projection=True,
|
|
)
|
|
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,
|
|
sample_size=128,
|
|
)
|
|
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,
|
|
# SD2-specific config below
|
|
hidden_act="gelu",
|
|
projection_dim=512,
|
|
)
|
|
text_encoder = CLIPTextModel(text_encoder_config)
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
return {
|
|
"unet": unet,
|
|
"scheduler": scheduler,
|
|
"vae": vae,
|
|
"text_encoder": text_encoder,
|
|
"tokenizer": tokenizer,
|
|
"safety_checker": None,
|
|
"feature_extractor": None,
|
|
"image_encoder": None,
|
|
}
|
|
|
|
def get_dummy_inputs(self):
|
|
return {
|
|
"prompt": "A painting of a squirrel eating a burger",
|
|
"generator": self.get_generator(0),
|
|
"num_inference_steps": 2,
|
|
"guidance_scale": 6.0,
|
|
# Request torch outputs so tests compare torch tensors directly (see `BasePipelineTesterConfig`).
|
|
# Note `"pt"` images are `(batch, channels, height, width)`, unlike `"np"` (`(batch, h, w, c)`).
|
|
"output_type": "pt",
|
|
}
|
|
|
|
|
|
class TestStableDiffusion2Pipeline(StableDiffusion2PipelineTesterConfig, PipelineTesterMixin):
|
|
def test_stable_diffusion_ddim(self):
|
|
# Run on CPU: the expected slice below is CPU-specific.
|
|
sd_pipe = self.get_pipeline()
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -1, -3:, -3:]
|
|
|
|
assert image.shape == (1, *self.output_shape)
|
|
expected_slice = torch.tensor([0.5679, 0.6010, 0.5052, 0.5159, 0.5389, 0.4739, 0.5074, 0.4872, 0.4843])
|
|
|
|
assert_tensors_close(image_slice.flatten(), expected_slice, atol=1e-2)
|
|
|
|
def test_stable_diffusion_pndm(self):
|
|
# Run on CPU: the expected slice below is CPU-specific.
|
|
components = self.get_dummy_components()
|
|
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
|
|
sd_pipe = self.get_pipeline(**components)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -1, -3:, -3:]
|
|
|
|
assert image.shape == (1, *self.output_shape)
|
|
expected_slice = torch.tensor([0.5121, 0.5714, 0.4827, 0.5057, 0.5646, 0.4766, 0.5189, 0.4895, 0.4990])
|
|
|
|
assert_tensors_close(image_slice.flatten(), expected_slice, atol=1e-2)
|
|
|
|
def test_stable_diffusion_k_lms(self):
|
|
# Run on CPU: the expected slice below is CPU-specific.
|
|
components = self.get_dummy_components()
|
|
components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)
|
|
sd_pipe = self.get_pipeline(**components)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -1, -3:, -3:]
|
|
|
|
assert image.shape == (1, *self.output_shape)
|
|
expected_slice = torch.tensor([0.4767, 0.5389, 0.4766, 0.5015, 0.5585, 0.4859, 0.5341, 0.5041, 0.5073])
|
|
|
|
assert_tensors_close(image_slice.flatten(), expected_slice, atol=1e-2)
|
|
|
|
def test_stable_diffusion_k_euler_ancestral(self):
|
|
# Run on CPU: the expected slice below is CPU-specific.
|
|
components = self.get_dummy_components()
|
|
components["scheduler"] = EulerAncestralDiscreteScheduler.from_config(components["scheduler"].config)
|
|
sd_pipe = self.get_pipeline(**components)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -1, -3:, -3:]
|
|
|
|
assert image.shape == (1, *self.output_shape)
|
|
expected_slice = torch.tensor([0.4766, 0.5390, 0.4768, 0.5013, 0.5585, 0.4859, 0.5338, 0.5041, 0.5075])
|
|
|
|
assert_tensors_close(image_slice.flatten(), expected_slice, atol=1e-2)
|
|
|
|
def test_stable_diffusion_k_euler(self):
|
|
# Run on CPU: the expected slice below is CPU-specific.
|
|
components = self.get_dummy_components()
|
|
components["scheduler"] = EulerDiscreteScheduler.from_config(components["scheduler"].config)
|
|
sd_pipe = self.get_pipeline(**components)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -1, -3:, -3:]
|
|
|
|
assert image.shape == (1, *self.output_shape)
|
|
expected_slice = torch.tensor([0.4767, 0.5389, 0.4766, 0.5015, 0.5585, 0.4859, 0.5341, 0.5041, 0.5073])
|
|
|
|
assert_tensors_close(image_slice.flatten(), expected_slice, atol=1e-2)
|
|
|
|
def test_stable_diffusion_unflawed(self):
|
|
# Run on CPU: the expected slice below is CPU-specific.
|
|
components = self.get_dummy_components()
|
|
components["scheduler"] = DDIMScheduler.from_config(
|
|
components["scheduler"].config, timestep_spacing="trailing"
|
|
)
|
|
sd_pipe = self.get_pipeline(**components)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
inputs["guidance_rescale"] = 0.7
|
|
inputs["num_inference_steps"] = 10
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -1, -3:, -3:]
|
|
|
|
assert image.shape == (1, *self.output_shape)
|
|
expected_slice = torch.tensor([0.4736, 0.5405, 0.4705, 0.4955, 0.5675, 0.4812, 0.5310, 0.4967, 0.5064])
|
|
|
|
assert_tensors_close(image_slice.flatten(), expected_slice, atol=1e-2)
|
|
|
|
def test_stable_diffusion_long_prompt(self):
|
|
# Run on CPU: the expected slice below is CPU-specific.
|
|
components = self.get_dummy_components()
|
|
components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)
|
|
sd_pipe = self.get_pipeline(**components).to(torch_device)
|
|
|
|
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 = 25 * "@"
|
|
with CaptureLogger(logger) as cap_logger_3:
|
|
text_embeddings_3, negeative_text_embeddings_3 = sd_pipe.encode_prompt(
|
|
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
|
|
)
|
|
if negeative_text_embeddings_3 is not None:
|
|
text_embeddings_3 = torch.cat([negeative_text_embeddings_3, text_embeddings_3])
|
|
|
|
prompt = 100 * "@"
|
|
with CaptureLogger(logger) as cap_logger:
|
|
text_embeddings, negative_embeddings = sd_pipe.encode_prompt(
|
|
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
|
|
)
|
|
if negative_embeddings is not None:
|
|
text_embeddings = torch.cat([negative_embeddings, text_embeddings])
|
|
|
|
negative_prompt = "Hello"
|
|
with CaptureLogger(logger) as cap_logger_2:
|
|
text_embeddings_2, negative_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 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_inference_batch_single_identical(self, batch_size=3, expected_max_diff=3e-3):
|
|
super().test_inference_batch_single_identical(batch_size=batch_size, expected_max_diff=expected_max_diff)
|
|
|
|
def test_encode_prompt_works_in_isolation(self):
|
|
extra_required_param_value_dict = {
|
|
"device": torch.device(torch_device).type,
|
|
"do_classifier_free_guidance": self.get_dummy_inputs().get("guidance_scale", 1.0) > 1.0,
|
|
}
|
|
return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)
|
|
|
|
# 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, base_pipe_output):
|
|
sd_pipe = self.get_pipeline().to(torch_device)
|
|
|
|
sd_pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
|
|
torch.manual_seed(0)
|
|
output_freeu = sd_pipe(**self.get_dummy_inputs()).images
|
|
|
|
assert not torch.allclose(base_pipe_output[0, -1, -3:, -3:], output_freeu[0, -1, -3:, -3:]), (
|
|
"Enabling of FreeU should lead to different results."
|
|
)
|
|
|
|
def test_freeu_disabled(self, base_pipe_output):
|
|
sd_pipe = self.get_pipeline().to(torch_device)
|
|
|
|
sd_pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
|
|
sd_pipe.disable_freeu()
|
|
|
|
for upsample_block in sd_pipe.unet.up_blocks:
|
|
for key in {"s1", "s2", "b1", "b2"}:
|
|
assert getattr(upsample_block, key) is None, f"Disabling of FreeU should have set {key} to None."
|
|
|
|
torch.manual_seed(0)
|
|
output_no_freeu = sd_pipe(**self.get_dummy_inputs()).images
|
|
|
|
assert torch.allclose(base_pipe_output[0, -1, -3:, -3:], output_no_freeu[0, -1, -3:, -3:]), (
|
|
"Disabling of FreeU should lead to results similar to the default pipeline results."
|
|
)
|
|
|
|
|
|
class TestStableDiffusion2PipelineMemory(StableDiffusion2PipelineTesterConfig, MemoryTesterMixin):
|
|
"""Memory optimization tests (CPU offload, group offload, layerwise casting) for the SD2 pipeline."""
|
|
|
|
|
|
@slow
|
|
@require_torch_accelerator
|
|
@skip_mps
|
|
class TestStableDiffusion2PipelineSlow:
|
|
@pytest.fixture(autouse=True)
|
|
def cleanup(self):
|
|
yield
|
|
gc.collect()
|
|
backend_empty_cache(torch_device)
|
|
|
|
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
|
|
if not str(device).startswith("mps"):
|
|
generator = torch.Generator(device=generator_device).manual_seed(seed)
|
|
else:
|
|
generator = torch.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_default_ddim(self):
|
|
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base")
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_inputs(torch_device)
|
|
image = pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1].flatten()
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506])
|
|
assert np.abs(image_slice - expected_slice).max() < 7e-3
|
|
|
|
@require_torch_accelerator
|
|
def test_stable_diffusion_attention_slicing(self):
|
|
backend_reset_peak_memory_stats(torch_device)
|
|
pipe = StableDiffusionPipeline.from_pretrained(
|
|
"stabilityai/stable-diffusion-2-base", 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 = backend_max_memory_allocated(torch_device)
|
|
backend_reset_peak_memory_stats(torch_device)
|
|
# make sure that less than 3.3 GB is allocated
|
|
assert mem_bytes < 3.3 * 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 3.3 GB is allocated
|
|
mem_bytes = backend_max_memory_allocated(torch_device)
|
|
assert mem_bytes > 3.3 * 10**9
|
|
max_diff = numpy_cosine_similarity_distance(image.flatten(), image_sliced.flatten())
|
|
assert max_diff < 5e-3
|
|
|
|
|
|
@nightly
|
|
@require_torch_accelerator
|
|
@skip_mps
|
|
class TestStableDiffusion2PipelineNightly:
|
|
@pytest.fixture(autouse=True)
|
|
def cleanup(self):
|
|
yield
|
|
gc.collect()
|
|
backend_empty_cache(torch_device)
|
|
|
|
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
|
|
_generator_device = "cpu" if not generator_device.startswith("cuda") else "cuda"
|
|
if not str(device).startswith("mps"):
|
|
generator = torch.Generator(device=_generator_device).manual_seed(seed)
|
|
else:
|
|
generator = torch.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": 2,
|
|
"guidance_scale": 7.5,
|
|
"output_type": "np",
|
|
}
|
|
return inputs
|
|
|
|
def test_stable_diffusion_2_1_default(self):
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").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_2_text2img/stable_diffusion_2_0_pndm.npy"
|
|
)
|
|
max_diff = np.abs(expected_image - image).max()
|
|
assert max_diff < 1e-3
|