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add PAG support for SD Img2Img (#9463)
* added pag to sd img2img pipeline --------- Co-authored-by: YiYi Xu <yixu310@gmail.com>
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src/diffusers/pipelines/pag/pipeline_pag_sd_img2img.py
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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 inspect | ||
import random | ||
import unittest | ||
|
||
import numpy as np | ||
import torch | ||
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer | ||
|
||
from diffusers import ( | ||
AutoencoderKL, | ||
AutoencoderTiny, | ||
AutoPipelineForImage2Image, | ||
EulerDiscreteScheduler, | ||
StableDiffusionImg2ImgPipeline, | ||
StableDiffusionPAGImg2ImgPipeline, | ||
UNet2DConditionModel, | ||
) | ||
from diffusers.utils.testing_utils import ( | ||
enable_full_determinism, | ||
floats_tensor, | ||
load_image, | ||
require_torch_gpu, | ||
slow, | ||
torch_device, | ||
) | ||
|
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from ..pipeline_params import ( | ||
IMAGE_TO_IMAGE_IMAGE_PARAMS, | ||
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, | ||
TEXT_GUIDED_IMAGE_VARIATION_PARAMS, | ||
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS, | ||
) | ||
from ..test_pipelines_common import ( | ||
IPAdapterTesterMixin, | ||
PipelineKarrasSchedulerTesterMixin, | ||
PipelineLatentTesterMixin, | ||
PipelineTesterMixin, | ||
) | ||
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enable_full_determinism() | ||
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class StableDiffusionPAGImg2ImgPipelineFastTests( | ||
IPAdapterTesterMixin, | ||
PipelineLatentTesterMixin, | ||
PipelineKarrasSchedulerTesterMixin, | ||
PipelineTesterMixin, | ||
unittest.TestCase, | ||
): | ||
pipeline_class = StableDiffusionPAGImg2ImgPipeline | ||
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"pag_scale", "pag_adaptive_scale"}) - {"height", "width"} | ||
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"} | ||
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS | ||
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS | ||
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS | ||
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS | ||
|
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def get_dummy_components(self, time_cond_proj_dim=None): | ||
torch.manual_seed(0) | ||
unet = UNet2DConditionModel( | ||
block_out_channels=(32, 64), | ||
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"), | ||
cross_attention_dim=32, | ||
) | ||
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, | ||
) | ||
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") | ||
|
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components = { | ||
"unet": unet, | ||
"scheduler": scheduler, | ||
"vae": vae, | ||
"text_encoder": text_encoder, | ||
"tokenizer": tokenizer, | ||
"safety_checker": None, | ||
"feature_extractor": None, | ||
"image_encoder": None, | ||
} | ||
return components | ||
|
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def get_dummy_tiny_autoencoder(self): | ||
return AutoencoderTiny(in_channels=3, out_channels=3, latent_channels=4) | ||
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def get_dummy_inputs(self, device, seed=0): | ||
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) | ||
image = image / 2 + 0.5 | ||
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", | ||
"image": image, | ||
"generator": generator, | ||
"num_inference_steps": 2, | ||
"guidance_scale": 6.0, | ||
"pag_scale": 0.9, | ||
"output_type": "np", | ||
} | ||
return inputs | ||
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def test_pag_disable_enable(self): | ||
device = "cpu" # ensure determinism for the device-dependent torch.Generator | ||
components = self.get_dummy_components() | ||
|
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# base pipeline (expect same output when pag is disabled) | ||
pipe_sd = StableDiffusionImg2ImgPipeline(**components) | ||
pipe_sd = pipe_sd.to(device) | ||
pipe_sd.set_progress_bar_config(disable=None) | ||
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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] | ||
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# 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) | ||
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inputs = self.get_dummy_inputs(device) | ||
inputs["pag_scale"] = 0.0 | ||
out_pag_disabled = pipe_pag(**inputs).images[0, -3:, -3:, -1] | ||
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# 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) | ||
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inputs = self.get_dummy_inputs(device) | ||
out_pag_enabled = pipe_pag(**inputs).images[0, -3:, -3:, -1] | ||
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assert np.abs(out.flatten() - out_pag_disabled.flatten()).max() < 1e-3 | ||
assert np.abs(out.flatten() - out_pag_enabled.flatten()).max() > 1e-3 | ||
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def test_pag_inference(self): | ||
device = "cpu" # ensure determinism for the device-dependent torch.Generator | ||
components = self.get_dummy_components() | ||
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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) | ||
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inputs = self.get_dummy_inputs(device) | ||
image = pipe_pag(**inputs).images | ||
image_slice = image[0, -3:, -3:, -1] | ||
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assert image.shape == ( | ||
1, | ||
32, | ||
32, | ||
3, | ||
), f"the shape of the output image should be (1, 32, 32, 3) but got {image.shape}" | ||
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expected_slice = np.array( | ||
[0.44203848, 0.49598145, 0.42248967, 0.6707724, 0.5683791, 0.43603387, 0.58316565, 0.60077155, 0.5174199] | ||
) | ||
max_diff = np.abs(image_slice.flatten() - expected_slice).max() | ||
self.assertLessEqual(max_diff, 1e-3) | ||
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@slow | ||
@require_torch_gpu | ||
class StableDiffusionPAGImg2ImgPipelineIntegrationTests(unittest.TestCase): | ||
pipeline_class = StableDiffusionPAGImg2ImgPipeline | ||
repo_id = "Jiali/stable-diffusion-1.5" | ||
|
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def setUp(self): | ||
super().setUp() | ||
gc.collect() | ||
torch.cuda.empty_cache() | ||
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def tearDown(self): | ||
super().tearDown() | ||
gc.collect() | ||
torch.cuda.empty_cache() | ||
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def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): | ||
generator = torch.Generator(device=generator_device).manual_seed(seed) | ||
init_image = load_image( | ||
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | ||
"/stable_diffusion_img2img/sketch-mountains-input.png" | ||
) | ||
inputs = { | ||
"prompt": "a fantasy landscape, concept art, high resolution", | ||
"image": init_image, | ||
"generator": generator, | ||
"num_inference_steps": 3, | ||
"strength": 0.75, | ||
"guidance_scale": 7.5, | ||
"pag_scale": 3.0, | ||
"output_type": "np", | ||
} | ||
return inputs | ||
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def test_pag_cfg(self): | ||
pipeline = AutoPipelineForImage2Image.from_pretrained(self.repo_id, enable_pag=True, torch_dtype=torch.float16) | ||
pipeline.enable_model_cpu_offload() | ||
pipeline.set_progress_bar_config(disable=None) | ||
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inputs = self.get_inputs(torch_device) | ||
image = pipeline(**inputs).images | ||
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image_slice = image[0, -3:, -3:, -1].flatten() | ||
assert image.shape == (1, 512, 512, 3) | ||
print(image_slice.flatten()) | ||
expected_slice = np.array( | ||
[0.58251953, 0.5722656, 0.5683594, 0.55029297, 0.52001953, 0.52001953, 0.49951172, 0.45410156, 0.50146484] | ||
) | ||
assert ( | ||
np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | ||
), f"output is different from expected, {image_slice.flatten()}" | ||
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def test_pag_uncond(self): | ||
pipeline = AutoPipelineForImage2Image.from_pretrained(self.repo_id, enable_pag=True, torch_dtype=torch.float16) | ||
pipeline.enable_model_cpu_offload() | ||
pipeline.set_progress_bar_config(disable=None) | ||
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inputs = self.get_inputs(torch_device, guidance_scale=0.0) | ||
image = pipeline(**inputs).images | ||
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image_slice = image[0, -3:, -3:, -1].flatten() | ||
assert image.shape == (1, 512, 512, 3) | ||
expected_slice = np.array( | ||
[0.5986328, 0.52441406, 0.3972168, 0.4741211, 0.34985352, 0.22705078, 0.4128418, 0.2866211, 0.31713867] | ||
) | ||
print(image_slice.flatten()) | ||
assert ( | ||
np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | ||
), f"output is different from expected, {image_slice.flatten()}" |