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mustango
/
diffusers
/tests
/pipelines
/stable_diffusion
/test_stable_diffusion_instruction_pix2pix.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 unittest | |
import numpy as np | |
import torch | |
from PIL import Image | |
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer | |
from diffusers import ( | |
AutoencoderKL, | |
DDIMScheduler, | |
EulerAncestralDiscreteScheduler, | |
LMSDiscreteScheduler, | |
PNDMScheduler, | |
StableDiffusionInstructPix2PixPipeline, | |
UNet2DConditionModel, | |
) | |
from diffusers.utils import floats_tensor, load_image, slow, torch_device | |
from diffusers.utils.testing_utils import require_torch_gpu | |
from ...pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS | |
from ...test_pipelines_common import PipelineTesterMixin | |
torch.backends.cuda.matmul.allow_tf32 = False | |
class StableDiffusionInstructPix2PixPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
pipeline_class = StableDiffusionInstructPix2PixPipeline | |
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"} | |
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_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=8, | |
out_channels=4, | |
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), | |
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), | |
cross_attention_dim=32, | |
) | |
scheduler = PNDMScheduler(skip_prk_steps=True) | |
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): | |
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 = { | |
"prompt": "A painting of a squirrel eating a burger", | |
"image": image, | |
"generator": generator, | |
"num_inference_steps": 2, | |
"guidance_scale": 6.0, | |
"image_guidance_scale": 1, | |
"output_type": "numpy", | |
} | |
return inputs | |
def test_stable_diffusion_pix2pix_default_case(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
components = self.get_dummy_components() | |
sd_pipe = StableDiffusionInstructPix2PixPipeline(**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, 32, 32, 3) | |
expected_slice = np.array([0.7318, 0.3723, 0.4662, 0.623, 0.5770, 0.5014, 0.4281, 0.5550, 0.4813]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
def test_stable_diffusion_pix2pix_negative_prompt(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
components = self.get_dummy_components() | |
sd_pipe = StableDiffusionInstructPix2PixPipeline(**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, 32, 32, 3) | |
expected_slice = np.array([0.7323, 0.3688, 0.4611, 0.6255, 0.5746, 0.5017, 0.433, 0.5553, 0.4827]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
def test_stable_diffusion_pix2pix_multiple_init_images(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
components = self.get_dummy_components() | |
sd_pipe = StableDiffusionInstructPix2PixPipeline(**components) | |
sd_pipe = sd_pipe.to(device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_dummy_inputs(device) | |
inputs["prompt"] = [inputs["prompt"]] * 2 | |
image = np.array(inputs["image"]).astype(np.float32) / 255.0 | |
image = torch.from_numpy(image).unsqueeze(0).to(device) | |
image = image.permute(0, 3, 1, 2) | |
inputs["image"] = image.repeat(2, 1, 1, 1) | |
image = sd_pipe(**inputs).images | |
image_slice = image[-1, -3:, -3:, -1] | |
assert image.shape == (2, 32, 32, 3) | |
expected_slice = np.array([0.606, 0.5712, 0.5099, 0.598, 0.5805, 0.7205, 0.6793, 0.554, 0.5607]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
def test_stable_diffusion_pix2pix_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 = StableDiffusionInstructPix2PixPipeline(**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] | |
slice = [round(x, 4) for x in image_slice.flatten().tolist()] | |
print(",".join([str(x) for x in slice])) | |
assert image.shape == (1, 32, 32, 3) | |
expected_slice = np.array([0.726, 0.3902, 0.4868, 0.585, 0.5672, 0.511, 0.3906, 0.551, 0.4846]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
class StableDiffusionInstructPix2PixPipelineSlowTests(unittest.TestCase): | |
def tearDown(self): | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def get_inputs(self, seed=0): | |
generator = torch.manual_seed(seed) | |
image = load_image( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg" | |
) | |
inputs = { | |
"prompt": "turn him into a cyborg", | |
"image": image, | |
"generator": generator, | |
"num_inference_steps": 3, | |
"guidance_scale": 7.5, | |
"image_guidance_scale": 1.0, | |
"output_type": "numpy", | |
} | |
return inputs | |
def test_stable_diffusion_pix2pix_default(self): | |
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( | |
"timbrooks/instruct-pix2pix", 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([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]) | |
assert np.abs(expected_slice - image_slice).max() < 1e-3 | |
def test_stable_diffusion_pix2pix_k_lms(self): | |
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( | |
"timbrooks/instruct-pix2pix", safety_checker=None | |
) | |
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) | |
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([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]) | |
assert np.abs(expected_slice - image_slice).max() < 1e-3 | |
def test_stable_diffusion_pix2pix_ddim(self): | |
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( | |
"timbrooks/instruct-pix2pix", safety_checker=None | |
) | |
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) | |
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([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]) | |
assert np.abs(expected_slice - image_slice).max() < 1e-3 | |
def test_stable_diffusion_pix2pix_intermediate_state(self): | |
number_of_steps = 0 | |
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None: | |
callback_fn.has_been_called = True | |
nonlocal number_of_steps | |
number_of_steps += 1 | |
if step == 1: | |
latents = latents.detach().cpu().numpy() | |
assert latents.shape == (1, 4, 64, 64) | |
latents_slice = latents[0, -3:, -3:, -1] | |
expected_slice = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]) | |
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 | |
elif step == 2: | |
latents = latents.detach().cpu().numpy() | |
assert latents.shape == (1, 4, 64, 64) | |
latents_slice = latents[0, -3:, -3:, -1] | |
expected_slice = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]) | |
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 | |
callback_fn.has_been_called = False | |
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( | |
"timbrooks/instruct-pix2pix", safety_checker=None, torch_dtype=torch.float16 | |
) | |
pipe = pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing() | |
inputs = self.get_inputs() | |
pipe(**inputs, callback=callback_fn, callback_steps=1) | |
assert callback_fn.has_been_called | |
assert number_of_steps == 3 | |
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self): | |
torch.cuda.empty_cache() | |
torch.cuda.reset_max_memory_allocated() | |
torch.cuda.reset_peak_memory_stats() | |
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( | |
"timbrooks/instruct-pix2pix", safety_checker=None, torch_dtype=torch.float16 | |
) | |
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 2.2 GB is allocated | |
assert mem_bytes < 2.2 * 10**9 | |
def test_stable_diffusion_pix2pix_pipeline_multiple_of_8(self): | |
inputs = self.get_inputs() | |
# resize to resolution that is divisible by 8 but not 16 or 32 | |
inputs["image"] = inputs["image"].resize((504, 504)) | |
model_id = "timbrooks/instruct-pix2pix" | |
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( | |
model_id, | |
safety_checker=None, | |
) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing() | |
output = pipe(**inputs) | |
image = output.images[0] | |
image_slice = image[255:258, 383:386, -1] | |
assert image.shape == (504, 504, 3) | |
expected_slice = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3 | |