351 lines
11 KiB
Python
351 lines
11 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 random
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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 AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNet2DConditionModel, UNet2DModel, VQModel
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from diffusers.utils import floats_tensor, load_image, 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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from ...test_pipelines_common import PipelineTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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class CycleDiffusionPipelineFastTests(PipelineTesterMixin, 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 dummy_image(self):
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batch_size = 1
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num_channels = 3
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sizes = (32, 32)
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image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
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return image
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@property
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def dummy_uncond_unet(self):
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torch.manual_seed(0)
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model = UNet2DModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=3,
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out_channels=3,
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down_block_types=("DownBlock2D", "AttnDownBlock2D"),
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up_block_types=("AttnUpBlock2D", "UpBlock2D"),
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)
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return model
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@property
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def dummy_cond_unet(self):
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torch.manual_seed(0)
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model = UNet2DConditionModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=4,
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out_channels=4,
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down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
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up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
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cross_attention_dim=32,
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)
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return model
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@property
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def dummy_cond_unet_inpaint(self):
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torch.manual_seed(0)
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model = UNet2DConditionModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=9,
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out_channels=4,
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down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
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up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
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cross_attention_dim=32,
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)
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return model
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@property
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def dummy_vq_model(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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)
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return model
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@property
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def dummy_vae(self):
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torch.manual_seed(0)
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model = AutoencoderKL(
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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=4,
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)
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return model
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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=32,
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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_extractor(self):
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def extract(*args, **kwargs):
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class Out:
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def __init__(self):
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self.pixel_values = torch.ones([0])
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def to(self, device):
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self.pixel_values.to(device)
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return self
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return Out()
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return extract
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def test_stable_diffusion_cycle(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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unet = self.dummy_cond_unet
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scheduler = DDIMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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num_train_timesteps=1000,
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clip_sample=False,
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set_alpha_to_one=False,
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)
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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# make sure here that pndm scheduler skips prk
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sd_pipe = CycleDiffusionPipeline(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=None,
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feature_extractor=self.dummy_extractor,
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)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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source_prompt = "An astronaut riding a horse"
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prompt = "An astronaut riding an elephant"
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init_image = self.dummy_image.to(device)
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generator = torch.Generator(device=device).manual_seed(0)
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output = sd_pipe(
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prompt=prompt,
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source_prompt=source_prompt,
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generator=generator,
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num_inference_steps=2,
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init_image=init_image,
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eta=0.1,
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strength=0.8,
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guidance_scale=3,
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source_guidance_scale=1,
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output_type="np",
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)
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images = output.images
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image_slice = images[0, -3:, -3:, -1]
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assert images.shape == (1, 32, 32, 3)
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expected_slice = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
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def test_stable_diffusion_cycle_fp16(self):
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unet = self.dummy_cond_unet
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scheduler = DDIMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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num_train_timesteps=1000,
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clip_sample=False,
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set_alpha_to_one=False,
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)
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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unet = unet.half()
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vae = vae.half()
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bert = bert.half()
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# make sure here that pndm scheduler skips prk
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sd_pipe = CycleDiffusionPipeline(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=None,
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feature_extractor=self.dummy_extractor,
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)
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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source_prompt = "An astronaut riding a horse"
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prompt = "An astronaut riding an elephant"
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init_image = self.dummy_image.to(torch_device)
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generator = torch.Generator(device=torch_device).manual_seed(0)
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output = sd_pipe(
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prompt=prompt,
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source_prompt=source_prompt,
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generator=generator,
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num_inference_steps=2,
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init_image=init_image,
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eta=0.1,
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strength=0.8,
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guidance_scale=3,
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source_guidance_scale=1,
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output_type="np",
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)
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images = output.images
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image_slice = images[0, -3:, -3:, -1]
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assert images.shape == (1, 32, 32, 3)
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expected_slice = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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@slow
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@require_torch_gpu
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class CycleDiffusionPipelineIntegrationTests(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_cycle_diffusion_pipeline_fp16(self):
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/cycle-diffusion/black_colored_car.png"
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)
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expected_image = load_numpy(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"
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)
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init_image = init_image.resize((512, 512))
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model_id = "CompVis/stable-diffusion-v1-4"
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scheduler = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler")
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pipe = CycleDiffusionPipeline.from_pretrained(
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model_id, scheduler=scheduler, safety_checker=None, torch_dtype=torch.float16, revision="fp16"
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)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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source_prompt = "A black colored car"
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prompt = "A blue colored car"
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generator = torch.Generator(device=torch_device).manual_seed(0)
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output = pipe(
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prompt=prompt,
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source_prompt=source_prompt,
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init_image=init_image,
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num_inference_steps=100,
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eta=0.1,
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strength=0.85,
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guidance_scale=3,
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source_guidance_scale=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
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# the values aren't exactly equal, but the images look the same visually
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assert np.abs(image - expected_image).max() < 5e-1
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def test_cycle_diffusion_pipeline(self):
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/cycle-diffusion/black_colored_car.png"
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)
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expected_image = load_numpy(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"
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)
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init_image = init_image.resize((512, 512))
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model_id = "CompVis/stable-diffusion-v1-4"
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scheduler = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler")
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pipe = CycleDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, safety_checker=None)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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source_prompt = "A black colored car"
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prompt = "A blue colored car"
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generator = torch.Generator(device=torch_device).manual_seed(0)
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output = pipe(
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prompt=prompt,
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source_prompt=source_prompt,
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init_image=init_image,
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num_inference_steps=100,
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eta=0.1,
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strength=0.85,
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guidance_scale=3,
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source_guidance_scale=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
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assert np.abs(image - expected_image).max() < 1e-2
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