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# 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 unittest | |
import numpy as np | |
import torch | |
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer | |
from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel | |
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings | |
from diffusers.utils import load_numpy, slow, torch_device | |
from diffusers.utils.testing_utils import require_torch_gpu | |
torch.backends.cuda.matmul.allow_tf32 = False | |
class VQDiffusionPipelineFastTests(unittest.TestCase): | |
def tearDown(self): | |
# clean up the VRAM after each test | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def num_embed(self): | |
return 12 | |
def num_embeds_ada_norm(self): | |
return 12 | |
def text_embedder_hidden_size(self): | |
return 32 | |
def dummy_vqvae(self): | |
torch.manual_seed(0) | |
model = VQModel( | |
block_out_channels=[32, 64], | |
in_channels=3, | |
out_channels=3, | |
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], | |
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], | |
latent_channels=3, | |
num_vq_embeddings=self.num_embed, | |
vq_embed_dim=3, | |
) | |
return model | |
def dummy_tokenizer(self): | |
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
return tokenizer | |
def dummy_text_encoder(self): | |
torch.manual_seed(0) | |
config = CLIPTextConfig( | |
bos_token_id=0, | |
eos_token_id=2, | |
hidden_size=self.text_embedder_hidden_size, | |
intermediate_size=37, | |
layer_norm_eps=1e-05, | |
num_attention_heads=4, | |
num_hidden_layers=5, | |
pad_token_id=1, | |
vocab_size=1000, | |
) | |
return CLIPTextModel(config) | |
def dummy_transformer(self): | |
torch.manual_seed(0) | |
height = 12 | |
width = 12 | |
model_kwargs = { | |
"attention_bias": True, | |
"cross_attention_dim": 32, | |
"attention_head_dim": height * width, | |
"num_attention_heads": 1, | |
"num_vector_embeds": self.num_embed, | |
"num_embeds_ada_norm": self.num_embeds_ada_norm, | |
"norm_num_groups": 32, | |
"sample_size": width, | |
"activation_fn": "geglu-approximate", | |
} | |
model = Transformer2DModel(**model_kwargs) | |
return model | |
def test_vq_diffusion(self): | |
device = "cpu" | |
vqvae = self.dummy_vqvae | |
text_encoder = self.dummy_text_encoder | |
tokenizer = self.dummy_tokenizer | |
transformer = self.dummy_transformer | |
scheduler = VQDiffusionScheduler(self.num_embed) | |
learned_classifier_free_sampling_embeddings = LearnedClassifierFreeSamplingEmbeddings(learnable=False) | |
pipe = VQDiffusionPipeline( | |
vqvae=vqvae, | |
text_encoder=text_encoder, | |
tokenizer=tokenizer, | |
transformer=transformer, | |
scheduler=scheduler, | |
learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings, | |
) | |
pipe = pipe.to(device) | |
pipe.set_progress_bar_config(disable=None) | |
prompt = "teddy bear playing in the pool" | |
generator = torch.Generator(device=device).manual_seed(0) | |
output = pipe([prompt], generator=generator, num_inference_steps=2, output_type="np") | |
image = output.images | |
generator = torch.Generator(device=device).manual_seed(0) | |
image_from_tuple = pipe( | |
[prompt], generator=generator, output_type="np", return_dict=False, num_inference_steps=2 | |
)[0] | |
image_slice = image[0, -3:, -3:, -1] | |
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] | |
assert image.shape == (1, 24, 24, 3) | |
expected_slice = np.array([0.6583, 0.6410, 0.5325, 0.5635, 0.5563, 0.4234, 0.6008, 0.5491, 0.4880]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_vq_diffusion_classifier_free_sampling(self): | |
device = "cpu" | |
vqvae = self.dummy_vqvae | |
text_encoder = self.dummy_text_encoder | |
tokenizer = self.dummy_tokenizer | |
transformer = self.dummy_transformer | |
scheduler = VQDiffusionScheduler(self.num_embed) | |
learned_classifier_free_sampling_embeddings = LearnedClassifierFreeSamplingEmbeddings( | |
learnable=True, hidden_size=self.text_embedder_hidden_size, length=tokenizer.model_max_length | |
) | |
pipe = VQDiffusionPipeline( | |
vqvae=vqvae, | |
text_encoder=text_encoder, | |
tokenizer=tokenizer, | |
transformer=transformer, | |
scheduler=scheduler, | |
learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings, | |
) | |
pipe = pipe.to(device) | |
pipe.set_progress_bar_config(disable=None) | |
prompt = "teddy bear playing in the pool" | |
generator = torch.Generator(device=device).manual_seed(0) | |
output = pipe([prompt], generator=generator, num_inference_steps=2, output_type="np") | |
image = output.images | |
generator = torch.Generator(device=device).manual_seed(0) | |
image_from_tuple = pipe( | |
[prompt], generator=generator, output_type="np", return_dict=False, num_inference_steps=2 | |
)[0] | |
image_slice = image[0, -3:, -3:, -1] | |
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] | |
assert image.shape == (1, 24, 24, 3) | |
expected_slice = np.array([0.6647, 0.6531, 0.5303, 0.5891, 0.5726, 0.4439, 0.6304, 0.5564, 0.4912]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 | |
class VQDiffusionPipelineIntegrationTests(unittest.TestCase): | |
def tearDown(self): | |
# clean up the VRAM after each test | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def test_vq_diffusion_classifier_free_sampling(self): | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy" | |
) | |
pipeline = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq") | |
pipeline = pipeline.to(torch_device) | |
pipeline.set_progress_bar_config(disable=None) | |
# requires GPU generator for gumbel softmax | |
# don't use GPU generator in tests though | |
generator = torch.Generator(device=torch_device).manual_seed(0) | |
output = pipeline( | |
"teddy bear playing in the pool", | |
num_images_per_prompt=1, | |
generator=generator, | |
output_type="np", | |
) | |
image = output.images[0] | |
assert image.shape == (256, 256, 3) | |
assert np.abs(expected_image - image).max() < 1e-2 | |