# 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 inspect
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
AutoPipelineForText2Image,
EulerDiscreteScheduler,
StableDiffusionXLPAGPipeline,
StableDiffusionXLPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
require_torch_gpu,
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,
PipelineFromPipeTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
SDXLOptionalComponentsTesterMixin,
)
enable_full_determinism()
class StableDiffusionXLPAGPipelineFastTests(
PipelineTesterMixin,
IPAdapterTesterMixin,
PipelineLatentTesterMixin,
PipelineFromPipeTesterMixin,
SDXLOptionalComponentsTesterMixin,
unittest.TestCase,
):
pipeline_class = StableDiffusionXLPAGPipeline
params = TEXT_TO_IMAGE_PARAMS.union({"pag_scale", "pag_adaptive_scale"})
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.union({"add_text_embeds", "add_time_ids"})
def get_dummy_components(self, time_cond_proj_dim=None):
# Copied from tests.pipelines.stable_diffusion_xl.test_stable_diffusion_xl.StableDiffusionXLPipelineFastTests.get_dummy_components
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(2, 4),
layers_per_block=2,
time_cond_proj_dim=time_cond_proj_dim,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
norm_num_groups=1,
)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
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=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
"image_encoder": 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": 5.0,
"pag_scale": 0.9,
"output_type": "np",
}
return inputs
def test_pag_disable_enable(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
# base pipeline (expect same output when pag is disabled)
pipe_sd = StableDiffusionXLPipeline(**components)
pipe_sd = pipe_sd.to(device)
pipe_sd.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["pag_scale"]
assert (
"pag_scale" not in inspect.signature(pipe_sd.__call__).parameters
), f"`pag_scale` should not be a call parameter of the base pipeline {pipe_sd.__class__.__name__}."
out = pipe_sd(**inputs).images[0, -3:, -3:, -1]
# pag disabled with pag_scale=0.0
pipe_pag = self.pipeline_class(**components)
pipe_pag = pipe_pag.to(device)
pipe_pag.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["pag_scale"] = 0.0
out_pag_disabled = pipe_pag(**inputs).images[0, -3:, -3:, -1]
# pag enabled
pipe_pag = self.pipeline_class(**components, pag_applied_layers=["mid", "up", "down"])
pipe_pag = pipe_pag.to(device)
pipe_pag.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
out_pag_enabled = pipe_pag(**inputs).images[0, -3:, -3:, -1]
assert np.abs(out.flatten() - out_pag_disabled.flatten()).max() < 1e-3
assert np.abs(out.flatten() - out_pag_enabled.flatten()).max() > 1e-3
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
def test_pag_applied_layers(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
# base pipeline
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
# pag_applied_layers = ["mid","up","down"] should apply to all self-attention layers
all_self_attn_layers = [k for k in pipe.unet.attn_processors.keys() if "attn1" in k]
original_attn_procs = pipe.unet.attn_processors
pag_layers = ["mid", "down", "up"]
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
assert set(pipe.pag_attn_processors) == set(all_self_attn_layers)
# pag_applied_layers = ["mid"], or ["mid.block_0"] or ["mid.block_0.attentions_0"] should apply to all self-attention layers in mid_block, i.e.
# mid_block.attentions.0.transformer_blocks.0.attn1.processor
# mid_block.attentions.0.transformer_blocks.1.attn1.processor
all_self_attn_mid_layers = [
"mid_block.attentions.0.transformer_blocks.0.attn1.processor",
"mid_block.attentions.0.transformer_blocks.1.attn1.processor",
]
pipe.unet.set_attn_processor(original_attn_procs.copy())
pag_layers = ["mid"]
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
assert set(pipe.pag_attn_processors) == set(all_self_attn_mid_layers)
pipe.unet.set_attn_processor(original_attn_procs.copy())
pag_layers = ["mid_block"]
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
assert set(pipe.pag_attn_processors) == set(all_self_attn_mid_layers)
pipe.unet.set_attn_processor(original_attn_procs.copy())
pag_layers = ["mid_block.attentions.0"]
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
assert set(pipe.pag_attn_processors) == set(all_self_attn_mid_layers)
# pag_applied_layers = ["mid.block_0.attentions_1"] does not exist in the model
pipe.unet.set_attn_processor(original_attn_procs.copy())
pag_layers = ["mid_block.attentions.1"]
with self.assertRaises(ValueError):
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
# pag_applied_layers = "down" should apply to all self-attention layers in down_blocks
# down_blocks.1.attentions.0.transformer_blocks.0.attn1.processor
# down_blocks.1.attentions.0.transformer_blocks.1.attn1.processor
# down_blocks.1.attentions.1.transformer_blocks.0.attn1.processor
# down_blocks.1.attentions.1.transformer_blocks.1.attn1.processor
pipe.unet.set_attn_processor(original_attn_procs.copy())
pag_layers = ["down"]
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
assert len(pipe.pag_attn_processors) == 4
pipe.unet.set_attn_processor(original_attn_procs.copy())
pag_layers = ["down_blocks.0"]
with self.assertRaises(ValueError):
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
pipe.unet.set_attn_processor(original_attn_procs.copy())
pag_layers = ["down_blocks.1"]
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
assert len(pipe.pag_attn_processors) == 4
pipe.unet.set_attn_processor(original_attn_procs.copy())
pag_layers = ["down_blocks.1.attentions.1"]
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
assert len(pipe.pag_attn_processors) == 2
def test_pag_inference(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe_pag = self.pipeline_class(**components, pag_applied_layers=["mid", "up", "down"])
pipe_pag = pipe_pag.to(device)
pipe_pag.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe_pag(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (
1,
64,
64,
3,
), f"the shape of the output image should be (1, 64, 64, 3) but got {image.shape}"
expected_slice = np.array([0.5382, 0.5439, 0.4704, 0.4569, 0.5234, 0.4834, 0.5289, 0.5039, 0.4764])
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
@slow
@require_torch_gpu
class StableDiffusionXLPAGPipelineIntegrationTests(unittest.TestCase):
pipeline_class = StableDiffusionXLPAGPipeline
repo_id = "stabilityai/stable-diffusion-xl-base-1.0"
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", seed=0, guidance_scale=7.0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
inputs = {
"prompt": "a polar bear sitting in a chair drinking a milkshake",
"negative_prompt": "deformed, ugly, wrong proportion, low res, bad anatomy, worst quality, low quality",
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": guidance_scale,
"pag_scale": 3.0,
"output_type": "np",
}
return inputs
def test_pag_cfg(self):
pipeline = AutoPipelineForText2Image.from_pretrained(self.repo_id, enable_pag=True, torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()
pipeline.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = pipeline(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 1024, 1024, 3)
expected_slice = np.array(
[0.3123679, 0.31725878, 0.32026544, 0.327533, 0.3266391, 0.3303998, 0.33544615, 0.34181812, 0.34102726]
)
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
), f"output is different from expected, {image_slice.flatten()}"
def test_pag_uncond(self):
pipeline = AutoPipelineForText2Image.from_pretrained(self.repo_id, enable_pag=True, torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()
pipeline.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device, guidance_scale=0.0)
image = pipeline(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 1024, 1024, 3)
expected_slice = np.array(
[0.47400922, 0.48650584, 0.4839625, 0.4724013, 0.4890427, 0.49544555, 0.51707107, 0.54299414, 0.5224372]
)
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
), f"output is different from expected, {image_slice.flatten()}"