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diffusers
/tests
/pipelines
/stable_diffusion
/test_stable_diffusion_model_editing.py
# coding=utf-8 | |
# Copyright 2023 HuggingFace Inc. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import gc | |
import unittest | |
import numpy as np | |
import torch | |
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer | |
from diffusers import ( | |
AutoencoderKL, | |
DDIMScheduler, | |
EulerAncestralDiscreteScheduler, | |
PNDMScheduler, | |
StableDiffusionModelEditingPipeline, | |
UNet2DConditionModel, | |
) | |
from diffusers.utils import slow, torch_device | |
from diffusers.utils.testing_utils import require_torch_gpu, skip_mps | |
from ...pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS | |
from ...test_pipelines_common import PipelineTesterMixin | |
torch.backends.cuda.matmul.allow_tf32 = False | |
class StableDiffusionModelEditingPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
pipeline_class = StableDiffusionModelEditingPipeline | |
params = TEXT_TO_IMAGE_PARAMS | |
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS | |
def get_dummy_components(self): | |
torch.manual_seed(0) | |
unet = UNet2DConditionModel( | |
block_out_channels=(32, 64), | |
layers_per_block=2, | |
sample_size=32, | |
in_channels=4, | |
out_channels=4, | |
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), | |
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), | |
cross_attention_dim=32, | |
) | |
scheduler = DDIMScheduler() | |
torch.manual_seed(0) | |
vae = AutoencoderKL( | |
block_out_channels=[32, 64], | |
in_channels=3, | |
out_channels=3, | |
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], | |
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], | |
latent_channels=4, | |
) | |
torch.manual_seed(0) | |
text_encoder_config = CLIPTextConfig( | |
bos_token_id=0, | |
eos_token_id=2, | |
hidden_size=32, | |
intermediate_size=37, | |
layer_norm_eps=1e-05, | |
num_attention_heads=4, | |
num_hidden_layers=5, | |
pad_token_id=1, | |
vocab_size=1000, | |
) | |
text_encoder = CLIPTextModel(text_encoder_config) | |
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
components = { | |
"unet": unet, | |
"scheduler": scheduler, | |
"vae": vae, | |
"text_encoder": text_encoder, | |
"tokenizer": tokenizer, | |
"safety_checker": None, | |
"feature_extractor": None, | |
} | |
return components | |
def get_dummy_inputs(self, device, seed=0): | |
generator = torch.manual_seed(seed) | |
inputs = { | |
"prompt": "A field of roses", | |
"generator": generator, | |
# Setting height and width to None to prevent OOMs on CPU. | |
"height": None, | |
"width": None, | |
"num_inference_steps": 2, | |
"guidance_scale": 6.0, | |
"output_type": "numpy", | |
} | |
return inputs | |
def test_stable_diffusion_model_editing_default_case(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
components = self.get_dummy_components() | |
sd_pipe = StableDiffusionModelEditingPipeline(**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 | |
image_slice = image[0, -3:, -3:, -1] | |
assert image.shape == (1, 64, 64, 3) | |
expected_slice = np.array( | |
[0.5217179, 0.50658035, 0.5003239, 0.41109088, 0.3595158, 0.46607107, 0.5323504, 0.5335255, 0.49187922] | |
) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_stable_diffusion_model_editing_negative_prompt(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
components = self.get_dummy_components() | |
sd_pipe = StableDiffusionModelEditingPipeline(**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( | |
[0.546259, 0.5108156, 0.50897664, 0.41931948, 0.3748669, 0.4669299, 0.5427151, 0.54561913, 0.49353] | |
) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_stable_diffusion_model_editing_euler(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
components = self.get_dummy_components() | |
components["scheduler"] = EulerAncestralDiscreteScheduler( | |
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear" | |
) | |
sd_pipe = StableDiffusionModelEditingPipeline(**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 | |
image_slice = image[0, -3:, -3:, -1] | |
assert image.shape == (1, 64, 64, 3) | |
expected_slice = np.array( | |
[0.47106352, 0.53579676, 0.45798016, 0.514294, 0.56856745, 0.4788605, 0.54380214, 0.5046455, 0.50404465] | |
) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_stable_diffusion_model_editing_pndm(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
components = self.get_dummy_components() | |
components["scheduler"] = PNDMScheduler() | |
sd_pipe = StableDiffusionModelEditingPipeline(**components) | |
sd_pipe = sd_pipe.to(device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_dummy_inputs(device) | |
# the pipeline does not expect pndm so test if it raises error. | |
with self.assertRaises(ValueError): | |
_ = sd_pipe(**inputs).images | |
class StableDiffusionModelEditingSlowTests(unittest.TestCase): | |
def tearDown(self): | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def get_inputs(self, seed=0): | |
generator = torch.manual_seed(seed) | |
inputs = { | |
"prompt": "A field of roses", | |
"generator": generator, | |
"num_inference_steps": 3, | |
"guidance_scale": 7.5, | |
"output_type": "numpy", | |
} | |
return inputs | |
def test_stable_diffusion_model_editing_default(self): | |
model_ckpt = "CompVis/stable-diffusion-v1-4" | |
pipe = StableDiffusionModelEditingPipeline.from_pretrained(model_ckpt, safety_checker=None) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing() | |
inputs = self.get_inputs() | |
image = pipe(**inputs).images | |
image_slice = image[0, -3:, -3:, -1].flatten() | |
assert image.shape == (1, 512, 512, 3) | |
expected_slice = np.array( | |
[0.6749496, 0.6386453, 0.51443267, 0.66094905, 0.61921215, 0.5491332, 0.5744417, 0.58075106, 0.5174658] | |
) | |
assert np.abs(expected_slice - image_slice).max() < 1e-2 | |
# make sure image changes after editing | |
pipe.edit_model("A pack of roses", "A pack of blue roses") | |
image = pipe(**inputs).images | |
image_slice = image[0, -3:, -3:, -1].flatten() | |
assert image.shape == (1, 512, 512, 3) | |
assert np.abs(expected_slice - image_slice).max() > 1e-1 | |
def test_stable_diffusion_model_editing_pipeline_with_sequential_cpu_offloading(self): | |
torch.cuda.empty_cache() | |
torch.cuda.reset_max_memory_allocated() | |
torch.cuda.reset_peak_memory_stats() | |
model_ckpt = "CompVis/stable-diffusion-v1-4" | |
scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler") | |
pipe = StableDiffusionModelEditingPipeline.from_pretrained( | |
model_ckpt, scheduler=scheduler, safety_checker=None | |
) | |
pipe = pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing(1) | |
pipe.enable_sequential_cpu_offload() | |
inputs = self.get_inputs() | |
_ = pipe(**inputs) | |
mem_bytes = torch.cuda.max_memory_allocated() | |
# make sure that less than 4.4 GB is allocated | |
assert mem_bytes < 4.4 * 10**9 | |