# 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 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 transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer 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