227 lines
7.6 KiB
Python
227 lines
7.6 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import unittest
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import numpy as np
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import torch
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from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
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from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
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from diffusers.utils import load_numpy, slow, torch_device
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from diffusers.utils.testing_utils import require_torch_gpu
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from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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torch.backends.cuda.matmul.allow_tf32 = False
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class VQDiffusionPipelineFastTests(unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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@property
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def num_embed(self):
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return 12
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@property
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def num_embeds_ada_norm(self):
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return 12
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@property
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def text_embedder_hidden_size(self):
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return 32
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@property
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def dummy_vqvae(self):
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torch.manual_seed(0)
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model = VQModel(
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block_out_channels=[32, 64],
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
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latent_channels=3,
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num_vq_embeddings=self.num_embed,
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vq_embed_dim=3,
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)
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return model
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@property
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def dummy_tokenizer(self):
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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return tokenizer
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@property
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def dummy_text_encoder(self):
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torch.manual_seed(0)
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config = CLIPTextConfig(
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bos_token_id=0,
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eos_token_id=2,
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hidden_size=self.text_embedder_hidden_size,
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intermediate_size=37,
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layer_norm_eps=1e-05,
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num_attention_heads=4,
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num_hidden_layers=5,
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pad_token_id=1,
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vocab_size=1000,
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)
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return CLIPTextModel(config)
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@property
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def dummy_transformer(self):
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torch.manual_seed(0)
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height = 12
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width = 12
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model_kwargs = {
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"attention_bias": True,
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"cross_attention_dim": 32,
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"attention_head_dim": height * width,
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"num_attention_heads": 1,
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"num_vector_embeds": self.num_embed,
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"num_embeds_ada_norm": self.num_embeds_ada_norm,
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"norm_num_groups": 32,
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"sample_size": width,
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"activation_fn": "geglu-approximate",
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}
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model = Transformer2DModel(**model_kwargs)
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return model
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def test_vq_diffusion(self):
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device = "cpu"
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vqvae = self.dummy_vqvae
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text_encoder = self.dummy_text_encoder
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tokenizer = self.dummy_tokenizer
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transformer = self.dummy_transformer
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scheduler = VQDiffusionScheduler(self.num_embed)
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learned_classifier_free_sampling_embeddings = LearnedClassifierFreeSamplingEmbeddings(learnable=False)
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pipe = VQDiffusionPipeline(
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vqvae=vqvae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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transformer=transformer,
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scheduler=scheduler,
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learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
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)
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pipe = pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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prompt = "teddy bear playing in the pool"
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generator = torch.Generator(device=device).manual_seed(0)
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output = pipe([prompt], generator=generator, num_inference_steps=2, output_type="np")
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image = output.images
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generator = torch.Generator(device=device).manual_seed(0)
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image_from_tuple = pipe(
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[prompt], generator=generator, output_type="np", return_dict=False, num_inference_steps=2
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)[0]
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image_slice = image[0, -3:, -3:, -1]
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image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
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assert image.shape == (1, 24, 24, 3)
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expected_slice = np.array([0.6583, 0.6410, 0.5325, 0.5635, 0.5563, 0.4234, 0.6008, 0.5491, 0.4880])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
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def test_vq_diffusion_classifier_free_sampling(self):
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device = "cpu"
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vqvae = self.dummy_vqvae
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text_encoder = self.dummy_text_encoder
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tokenizer = self.dummy_tokenizer
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transformer = self.dummy_transformer
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scheduler = VQDiffusionScheduler(self.num_embed)
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learned_classifier_free_sampling_embeddings = LearnedClassifierFreeSamplingEmbeddings(
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learnable=True, hidden_size=self.text_embedder_hidden_size, length=tokenizer.model_max_length
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)
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pipe = VQDiffusionPipeline(
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vqvae=vqvae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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transformer=transformer,
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scheduler=scheduler,
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learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
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)
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pipe = pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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prompt = "teddy bear playing in the pool"
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generator = torch.Generator(device=device).manual_seed(0)
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output = pipe([prompt], generator=generator, num_inference_steps=2, output_type="np")
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image = output.images
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generator = torch.Generator(device=device).manual_seed(0)
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image_from_tuple = pipe(
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[prompt], generator=generator, output_type="np", return_dict=False, num_inference_steps=2
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)[0]
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image_slice = image[0, -3:, -3:, -1]
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image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
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assert image.shape == (1, 24, 24, 3)
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expected_slice = np.array([0.6647, 0.6531, 0.5303, 0.5891, 0.5726, 0.4439, 0.6304, 0.5564, 0.4912])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
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@slow
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@require_torch_gpu
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class VQDiffusionPipelineIntegrationTests(unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def test_vq_diffusion_classifier_free_sampling(self):
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expected_image = load_numpy(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"
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)
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pipeline = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq")
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pipeline = pipeline.to(torch_device)
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pipeline.set_progress_bar_config(disable=None)
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generator = torch.Generator(device=torch_device).manual_seed(0)
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output = pipeline(
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"teddy bear playing in the pool",
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num_images_per_prompt=1,
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generator=generator,
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output_type="np",
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)
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image = output.images[0]
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assert image.shape == (256, 256, 3)
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assert np.abs(expected_image - image).max() < 1e-2
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