File size: 10,193 Bytes
43b7e92 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 |
import gc
import inspect
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
LatentConsistencyModelPipeline,
LCMScheduler,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class LatentConsistencyModelPipelineFastTests(
IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = LatentConsistencyModelPipeline
params = TEXT_TO_IMAGE_PARAMS - {"negative_prompt", "negative_prompt_embeds"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS - {"negative_prompt"}
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_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):
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",
"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([0.1403, 0.5072, 0.5316, 0.1202, 0.3865, 0.4211, 0.5363, 0.3557, 0.3645])
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 = LatentConsistencyModelPipeline(**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, 64, 64, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.1441, 0.5304, 0.5452, 0.1361, 0.4011, 0.4370, 0.5326, 0.3492, 0.3637])
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 = LatentConsistencyModelPipeline(**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, 64, 64, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.1403, 0.5072, 0.5316, 0.1202, 0.3865, 0.4211, 0.5363, 0.3557, 0.3645])
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 = LatentConsistencyModelPipeline(**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, 64, 64, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.1403, 0.5072, 0.5316, 0.1202, 0.3865, 0.4211, 0.5363, 0.3557, 0.3645])
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)
# skip because lcm pipeline apply cfg differently
def test_callback_cfg(self):
pass
# 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 LatentConsistencyModelPipelineSlowTests(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)
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",
}
return inputs
def test_lcm_onestep(self):
pipe = LatentConsistencyModelPipeline.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([0.1025, 0.0911, 0.0984, 0.0981, 0.0901, 0.0918, 0.1055, 0.0940, 0.0730])
assert np.abs(image_slice - expected_slice).max() < 1e-3
def test_lcm_multistep(self):
pipe = LatentConsistencyModelPipeline.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([0.01855, 0.01855, 0.01489, 0.01392, 0.01782, 0.01465, 0.01831, 0.02539, 0.0])
assert np.abs(image_slice - expected_slice).max() < 1e-3
|