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ldm3d-inpainting
/
diffuserslocal
/tests
/pipelines
/stable_diffusion_2
/test_stable_diffusion_diffedit.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 random | |
import tempfile | |
import unittest | |
import numpy as np | |
import torch | |
from PIL import Image | |
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer | |
from diffusers import ( | |
AutoencoderKL, | |
DDIMInverseScheduler, | |
DDIMScheduler, | |
DPMSolverMultistepInverseScheduler, | |
DPMSolverMultistepScheduler, | |
StableDiffusionDiffEditPipeline, | |
UNet2DConditionModel, | |
) | |
from diffusers.utils.testing_utils import ( | |
enable_full_determinism, | |
floats_tensor, | |
load_image, | |
nightly, | |
require_torch_gpu, | |
slow, | |
torch_device, | |
) | |
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS | |
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin | |
enable_full_determinism() | |
class StableDiffusionDiffEditPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase): | |
pipeline_class = StableDiffusionDiffEditPipeline | |
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"} | |
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"} | |
image_params = frozenset( | |
[] | |
) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess | |
image_latents_params = frozenset([]) | |
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, | |
) | |
inverse_scheduler = DDIMInverseScheduler( | |
beta_start=0.00085, | |
beta_end=0.012, | |
beta_schedule="scaled_linear", | |
clip_sample=False, | |
set_alpha_to_zero=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") | |
components = { | |
"unet": unet, | |
"scheduler": scheduler, | |
"inverse_scheduler": inverse_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): | |
mask = floats_tensor((1, 16, 16), rng=random.Random(seed)).to(device) | |
latents = floats_tensor((1, 2, 4, 16, 16), rng=random.Random(seed)).to(device) | |
if str(device).startswith("mps"): | |
generator = torch.manual_seed(seed) | |
else: | |
generator = torch.Generator(device=device).manual_seed(seed) | |
inputs = { | |
"prompt": "a dog and a newt", | |
"mask_image": mask, | |
"image_latents": latents, | |
"generator": generator, | |
"num_inference_steps": 2, | |
"inpaint_strength": 1.0, | |
"guidance_scale": 6.0, | |
"output_type": "numpy", | |
} | |
return inputs | |
def get_dummy_mask_inputs(self, device, seed=0): | |
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) | |
image = image.cpu().permute(0, 2, 3, 1)[0] | |
image = Image.fromarray(np.uint8(image)).convert("RGB") | |
if str(device).startswith("mps"): | |
generator = torch.manual_seed(seed) | |
else: | |
generator = torch.Generator(device=device).manual_seed(seed) | |
inputs = { | |
"image": image, | |
"source_prompt": "a cat and a frog", | |
"target_prompt": "a dog and a newt", | |
"generator": generator, | |
"num_inference_steps": 2, | |
"num_maps_per_mask": 2, | |
"mask_encode_strength": 1.0, | |
"guidance_scale": 6.0, | |
"output_type": "numpy", | |
} | |
return inputs | |
def get_dummy_inversion_inputs(self, device, seed=0): | |
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) | |
image = image.cpu().permute(0, 2, 3, 1)[0] | |
image = Image.fromarray(np.uint8(image)).convert("RGB") | |
if str(device).startswith("mps"): | |
generator = torch.manual_seed(seed) | |
else: | |
generator = torch.Generator(device=device).manual_seed(seed) | |
inputs = { | |
"image": image, | |
"prompt": "a cat and a frog", | |
"generator": generator, | |
"num_inference_steps": 2, | |
"inpaint_strength": 1.0, | |
"guidance_scale": 6.0, | |
"decode_latents": True, | |
"output_type": "numpy", | |
} | |
return inputs | |
def test_save_load_optional_components(self): | |
if not hasattr(self.pipeline_class, "_optional_components"): | |
return | |
components = self.get_dummy_components() | |
pipe = self.pipeline_class(**components) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
# set all optional components to None and update pipeline config accordingly | |
for optional_component in pipe._optional_components: | |
setattr(pipe, optional_component, None) | |
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}) | |
inputs = self.get_dummy_inputs(torch_device) | |
output = pipe(**inputs)[0] | |
with tempfile.TemporaryDirectory() as tmpdir: | |
pipe.save_pretrained(tmpdir) | |
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) | |
pipe_loaded.to(torch_device) | |
pipe_loaded.set_progress_bar_config(disable=None) | |
for optional_component in pipe._optional_components: | |
self.assertTrue( | |
getattr(pipe_loaded, optional_component) is None, | |
f"`{optional_component}` did not stay set to None after loading.", | |
) | |
inputs = self.get_dummy_inputs(torch_device) | |
output_loaded = pipe_loaded(**inputs)[0] | |
max_diff = np.abs(output - output_loaded).max() | |
self.assertLess(max_diff, 1e-4) | |
def test_mask(self): | |
device = "cpu" | |
components = self.get_dummy_components() | |
pipe = self.pipeline_class(**components) | |
pipe.to(device) | |
pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_dummy_mask_inputs(device) | |
mask = pipe.generate_mask(**inputs) | |
mask_slice = mask[0, -3:, -3:] | |
self.assertEqual(mask.shape, (1, 16, 16)) | |
expected_slice = np.array([0] * 9) | |
max_diff = np.abs(mask_slice.flatten() - expected_slice).max() | |
self.assertLessEqual(max_diff, 1e-3) | |
self.assertEqual(mask[0, -3, -4], 0) | |
def test_inversion(self): | |
device = "cpu" | |
components = self.get_dummy_components() | |
pipe = self.pipeline_class(**components) | |
pipe.to(device) | |
pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_dummy_inversion_inputs(device) | |
image = pipe.invert(**inputs).images | |
image_slice = image[0, -1, -3:, -3:] | |
self.assertEqual(image.shape, (2, 32, 32, 3)) | |
expected_slice = np.array( | |
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5105, 0.5015, 0.4407, 0.4799], | |
) | |
max_diff = np.abs(image_slice.flatten() - expected_slice).max() | |
self.assertLessEqual(max_diff, 1e-3) | |
def test_inference_batch_single_identical(self): | |
super().test_inference_batch_single_identical(expected_max_diff=5e-3) | |
def test_inversion_dpm(self): | |
device = "cpu" | |
components = self.get_dummy_components() | |
scheduler_args = {"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"} | |
components["scheduler"] = DPMSolverMultistepScheduler(**scheduler_args) | |
components["inverse_scheduler"] = DPMSolverMultistepInverseScheduler(**scheduler_args) | |
pipe = self.pipeline_class(**components) | |
pipe.to(device) | |
pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_dummy_inversion_inputs(device) | |
image = pipe.invert(**inputs).images | |
image_slice = image[0, -1, -3:, -3:] | |
self.assertEqual(image.shape, (2, 32, 32, 3)) | |
expected_slice = np.array( | |
[0.5305, 0.4673, 0.5314, 0.5308, 0.4886, 0.5279, 0.5142, 0.4724, 0.4892], | |
) | |
max_diff = np.abs(image_slice.flatten() - expected_slice).max() | |
self.assertLessEqual(max_diff, 1e-3) | |
class StableDiffusionDiffEditPipelineIntegrationTests(unittest.TestCase): | |
def tearDown(self): | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def setUpClass(cls): | |
raw_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png" | |
) | |
raw_image = raw_image.convert("RGB").resize((768, 768)) | |
cls.raw_image = raw_image | |
def test_stable_diffusion_diffedit_full(self): | |
generator = torch.manual_seed(0) | |
pipe = StableDiffusionDiffEditPipeline.from_pretrained( | |
"stabilityai/stable-diffusion-2-1", safety_checker=None, torch_dtype=torch.float16 | |
) | |
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) | |
pipe.inverse_scheduler = DDIMInverseScheduler.from_config(pipe.scheduler.config) | |
pipe.enable_model_cpu_offload() | |
pipe.set_progress_bar_config(disable=None) | |
source_prompt = "a bowl of fruit" | |
target_prompt = "a bowl of pears" | |
mask_image = pipe.generate_mask( | |
image=self.raw_image, | |
source_prompt=source_prompt, | |
target_prompt=target_prompt, | |
generator=generator, | |
) | |
inv_latents = pipe.invert( | |
prompt=source_prompt, image=self.raw_image, inpaint_strength=0.7, generator=generator | |
).latents | |
image = pipe( | |
prompt=target_prompt, | |
mask_image=mask_image, | |
image_latents=inv_latents, | |
generator=generator, | |
negative_prompt=source_prompt, | |
inpaint_strength=0.7, | |
output_type="numpy", | |
).images[0] | |
expected_image = ( | |
np.array( | |
load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/diffedit/pears.png" | |
).resize((768, 768)) | |
) | |
/ 255 | |
) | |
assert np.abs((expected_image - image).max()) < 5e-1 | |
class StableDiffusionDiffEditPipelineNightlyTests(unittest.TestCase): | |
def tearDown(self): | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def setUpClass(cls): | |
raw_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png" | |
) | |
raw_image = raw_image.convert("RGB").resize((768, 768)) | |
cls.raw_image = raw_image | |
def test_stable_diffusion_diffedit_dpm(self): | |
generator = torch.manual_seed(0) | |
pipe = StableDiffusionDiffEditPipeline.from_pretrained( | |
"stabilityai/stable-diffusion-2-1", safety_checker=None, torch_dtype=torch.float16 | |
) | |
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
pipe.inverse_scheduler = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config) | |
pipe.enable_model_cpu_offload() | |
pipe.set_progress_bar_config(disable=None) | |
source_prompt = "a bowl of fruit" | |
target_prompt = "a bowl of pears" | |
mask_image = pipe.generate_mask( | |
image=self.raw_image, | |
source_prompt=source_prompt, | |
target_prompt=target_prompt, | |
generator=generator, | |
) | |
inv_latents = pipe.invert( | |
prompt=source_prompt, | |
image=self.raw_image, | |
inpaint_strength=0.7, | |
generator=generator, | |
num_inference_steps=25, | |
).latents | |
image = pipe( | |
prompt=target_prompt, | |
mask_image=mask_image, | |
image_latents=inv_latents, | |
generator=generator, | |
negative_prompt=source_prompt, | |
inpaint_strength=0.7, | |
num_inference_steps=25, | |
output_type="numpy", | |
).images[0] | |
expected_image = ( | |
np.array( | |
load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/diffedit/pears.png" | |
).resize((768, 768)) | |
) | |
/ 255 | |
) | |
assert np.abs((expected_image - image).max()) < 5e-1 | |