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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()
@property
def num_embed(self):
return 12
@property
def num_embeds_ada_norm(self):
return 12
@property
def text_embedder_hidden_size(self):
return 32
@property
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
@property
def dummy_tokenizer(self):
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
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)
@property
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
@slow
@require_torch_gpu
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
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