# coding=utf-8 # Copyright 2022 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 random import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNet2DConditionModel from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device from diffusers.utils.testing_utils import require_torch_gpu from ...test_pipelines_common import PipelineTesterMixin torch.backends.cuda.matmul.allow_tf32 = False class CycleDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = CycleDiffusionPipeline def get_dummy_components(self): torch.manual_seed(0) unet = UNet2DConditionModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=32, ) scheduler = DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False, set_alpha_to_one=False, ) torch.manual_seed(0) vae = AutoencoderKL( block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], latent_channels=4, ) torch.manual_seed(0) text_encoder_config = CLIPTextConfig( bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1000, ) text_encoder = CLIPTextModel(text_encoder_config) tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") components = { "unet": unet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "safety_checker": None, "feature_extractor": None, } return components def get_dummy_inputs(self, device, seed=0): image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) inputs = { "prompt": "An astronaut riding an elephant", "source_prompt": "An astronaut riding a horse", "image": image, "generator": generator, "num_inference_steps": 2, "eta": 0.1, "strength": 0.8, "guidance_scale": 3, "source_guidance_scale": 1, "output_type": "numpy", } return inputs def test_stable_diffusion_cycle(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() pipe = CycleDiffusionPipeline(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) output = pipe(**inputs) images = output.images image_slice = images[0, -3:, -3:, -1] assert images.shape == (1, 32, 32, 3) expected_slice = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @unittest.skipIf(torch_device != "cuda", "This test requires a GPU") def test_stable_diffusion_cycle_fp16(self): components = self.get_dummy_components() for name, module in components.items(): if hasattr(module, "half"): components[name] = module.half() pipe = CycleDiffusionPipeline(**components) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) output = pipe(**inputs) images = output.images image_slice = images[0, -3:, -3:, -1] assert images.shape == (1, 32, 32, 3) expected_slice = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @slow @require_torch_gpu class CycleDiffusionPipelineIntegrationTests(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def test_cycle_diffusion_pipeline_fp16(self): init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/cycle-diffusion/black_colored_car.png" ) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy" ) init_image = init_image.resize((512, 512)) model_id = "CompVis/stable-diffusion-v1-4" scheduler = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = CycleDiffusionPipeline.from_pretrained( model_id, scheduler=scheduler, safety_checker=None, torch_dtype=torch.float16, revision="fp16" ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() source_prompt = "A black colored car" prompt = "A blue colored car" generator = torch.manual_seed(0) output = pipe( prompt=prompt, source_prompt=source_prompt, image=init_image, num_inference_steps=100, eta=0.1, strength=0.85, guidance_scale=3, source_guidance_scale=1, generator=generator, output_type="np", ) image = output.images # the values aren't exactly equal, but the images look the same visually assert np.abs(image - expected_image).max() < 5e-1 def test_cycle_diffusion_pipeline(self): init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/cycle-diffusion/black_colored_car.png" ) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy" ) init_image = init_image.resize((512, 512)) model_id = "CompVis/stable-diffusion-v1-4" scheduler = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = CycleDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, safety_checker=None) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() source_prompt = "A black colored car" prompt = "A blue colored car" generator = torch.manual_seed(0) output = pipe( prompt=prompt, source_prompt=source_prompt, image=init_image, num_inference_steps=100, eta=0.1, strength=0.85, guidance_scale=3, source_guidance_scale=1, generator=generator, output_type="np", ) image = output.images assert np.abs(image - expected_image).max() < 1e-2