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import gc
import inspect
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
LatentConsistencyModelImg2ImgPipeline,
LCMScheduler,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class LatentConsistencyModelImg2ImgPipelineFastTests(
IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = LatentConsistencyModelImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "negative_prompt", "negative_prompt_embeds"}
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents", "negative_prompt"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(4, 8),
layers_per_block=1,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
norm_num_groups=2,
time_cond_proj_dim=32,
)
scheduler = LCMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[4, 8],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=64,
layer_norm_eps=1e-05,
num_attention_heads=8,
num_hidden_layers=3,
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,
"image_encoder": None,
"requires_safety_checker": False,
}
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 / 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,
"output_type": "np",
}
return inputs
def test_ip_adapter_single(self):
expected_pipe_slice = None
if torch_device == "cpu":
expected_pipe_slice = np.array([0.4003, 0.3718, 0.2863, 0.5500, 0.5587, 0.3772, 0.4617, 0.4961, 0.4417])
return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
def test_lcm_onestep(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["num_inference_steps"] = 1
output = pipe(**inputs)
image = output.images
assert image.shape == (1, 32, 32, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.4388, 0.3717, 0.2202, 0.7213, 0.6370, 0.3664, 0.5815, 0.6080, 0.4977])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_lcm_multistep(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = pipe(**inputs)
image = output.images
assert image.shape == (1, 32, 32, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.4150, 0.3719, 0.2479, 0.6333, 0.6024, 0.3778, 0.5036, 0.5420, 0.4678])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
output = pipe(**inputs)
image = output.images
assert image.shape == (1, 32, 32, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.3994, 0.3471, 0.2540, 0.7030, 0.6193, 0.3645, 0.5777, 0.5850, 0.4965])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=5e-4)
# override default test because the final latent variable is "denoised" instead of "latents"
def test_callback_inputs(self):
sig = inspect.signature(self.pipeline_class.__call__)
if not ("callback_on_step_end_tensor_inputs" in sig.parameters and "callback_on_step_end" in sig.parameters):
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
self.assertTrue(
hasattr(pipe, "_callback_tensor_inputs"),
f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
)
def callback_inputs_test(pipe, i, t, callback_kwargs):
missing_callback_inputs = set()
for v in pipe._callback_tensor_inputs:
if v not in callback_kwargs:
missing_callback_inputs.add(v)
self.assertTrue(
len(missing_callback_inputs) == 0, f"Missing callback tensor inputs: {missing_callback_inputs}"
)
last_i = pipe.num_timesteps - 1
if i == last_i:
callback_kwargs["denoised"] = torch.zeros_like(callback_kwargs["denoised"])
return callback_kwargs
inputs = self.get_dummy_inputs(torch_device)
inputs["callback_on_step_end"] = callback_inputs_test
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
inputs["output_type"] = "latent"
output = pipe(**inputs)[0]
assert output.abs().sum() == 0
@slow
@require_torch_gpu
class LatentConsistencyModelImg2ImgPipelineSlowTests(unittest.TestCase):
def setUp(self):
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/sketch-mountains-input.png"
)
init_image = init_image.resize((512, 512))
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "np",
"image": init_image,
}
return inputs
def test_lcm_onestep(self):
pipe = LatentConsistencyModelImg2ImgPipeline.from_pretrained(
"SimianLuo/LCM_Dreamshaper_v7", safety_checker=None
)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 1
image = pipe(**inputs).images
assert image.shape == (1, 512, 512, 3)
image_slice = image[0, -3:, -3:, -1].flatten()
expected_slice = np.array([0.1950, 0.1961, 0.2308, 0.1786, 0.1837, 0.2320, 0.1898, 0.1885, 0.2309])
assert np.abs(image_slice - expected_slice).max() < 1e-3
def test_lcm_multistep(self):
pipe = LatentConsistencyModelImg2ImgPipeline.from_pretrained(
"SimianLuo/LCM_Dreamshaper_v7", safety_checker=None
)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
assert image.shape == (1, 512, 512, 3)
image_slice = image[0, -3:, -3:, -1].flatten()
expected_slice = np.array([0.3756, 0.3816, 0.3767, 0.3718, 0.3739, 0.3735, 0.3863, 0.3803, 0.3563])
assert np.abs(image_slice - expected_slice).max() < 1e-3
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