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import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import load_numpy, require_torch_gpu, slow, torch_device
from ...pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ...test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class StableUnCLIPPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableUnCLIPPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
test_xformers_attention = False
def get_dummy_components(self):
embedder_hidden_size = 32
embedder_projection_dim = embedder_hidden_size
# prior components
torch.manual_seed(0)
prior_tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
torch.manual_seed(0)
prior_text_encoder = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=embedder_hidden_size,
projection_dim=embedder_projection_dim,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
)
torch.manual_seed(0)
prior = PriorTransformer(
num_attention_heads=2,
attention_head_dim=12,
embedding_dim=embedder_projection_dim,
num_layers=1,
)
torch.manual_seed(0)
prior_scheduler = DDPMScheduler(
variance_type="fixed_small_log",
prediction_type="sample",
num_train_timesteps=1000,
clip_sample=True,
clip_sample_range=5.0,
beta_schedule="squaredcos_cap_v2",
)
# regular denoising components
torch.manual_seed(0)
image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedder_hidden_size)
image_noising_scheduler = DDPMScheduler(beta_schedule="squaredcos_cap_v2")
torch.manual_seed(0)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
torch.manual_seed(0)
text_encoder = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=embedder_hidden_size,
projection_dim=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
)
torch.manual_seed(0)
unet = UNet2DConditionModel(
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),
up_block_types=("UpBlock2D", "CrossAttnUpBlock2D"),
block_out_channels=(32, 64),
attention_head_dim=(2, 4),
class_embed_type="projection",
# The class embeddings are the noise augmented image embeddings.
# I.e. the image embeddings concated with the noised embeddings of the same dimension
projection_class_embeddings_input_dim=embedder_projection_dim * 2,
cross_attention_dim=embedder_hidden_size,
layers_per_block=1,
upcast_attention=True,
use_linear_projection=True,
)
torch.manual_seed(0)
scheduler = DDIMScheduler(
beta_schedule="scaled_linear",
beta_start=0.00085,
beta_end=0.012,
prediction_type="v_prediction",
set_alpha_to_one=False,
steps_offset=1,
)
torch.manual_seed(0)
vae = AutoencoderKL()
components = {
# prior components
"prior_tokenizer": prior_tokenizer,
"prior_text_encoder": prior_text_encoder,
"prior": prior,
"prior_scheduler": prior_scheduler,
# image noising components
"image_normalizer": image_normalizer,
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder,
"unet": unet,
"scheduler": scheduler,
"vae": vae,
}
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,
"prior_num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
# Overriding PipelineTesterMixin::test_attention_slicing_forward_pass
# because UnCLIP GPU undeterminism requires a looser check.
def test_attention_slicing_forward_pass(self):
test_max_difference = torch_device == "cpu"
self._test_attention_slicing_forward_pass(test_max_difference=test_max_difference)
# Overriding PipelineTesterMixin::test_inference_batch_single_identical
# because UnCLIP undeterminism requires a looser check.
def test_inference_batch_single_identical(self):
test_max_difference = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=test_max_difference)
@slow
@require_torch_gpu
class StableUnCLIPPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_stable_unclip(self):
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"
)
pipe = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l", torch_dtype=torch.float16)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
generator = torch.Generator(device="cpu").manual_seed(0)
output = pipe("anime turle", generator=generator, output_type="np")
image = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(image, expected_image)
def test_stable_unclip_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l", torch_dtype=torch.float16)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_ = pipe(
"anime turtle",
prior_num_inference_steps=2,
num_inference_steps=2,
output_type="np",
)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
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