# 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 random import unittest import numpy as np import torch from PIL import Image from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, StableDiffusionInstructPix2PixPipeline, UNet2DConditionModel, ) from diffusers.utils import floats_tensor, load_image, slow, torch_device from diffusers.utils.testing_utils import require_torch_gpu from ...pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS from ...test_pipelines_common import PipelineTesterMixin torch.backends.cuda.matmul.allow_tf32 = False class StableDiffusionInstructPix2PixPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = StableDiffusionInstructPix2PixPipeline params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"} batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS 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=8, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=32, ) scheduler = PNDMScheduler(skip_prk_steps=True) 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) image = image.cpu().permute(0, 2, 3, 1)[0] image = Image.fromarray(np.uint8(image)).convert("RGB") if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) inputs = { "prompt": "A painting of a squirrel eating a burger", "image": image, "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "image_guidance_scale": 1, "output_type": "numpy", } return inputs def test_stable_diffusion_pix2pix_default_case(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = StableDiffusionInstructPix2PixPipeline(**components) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) image = sd_pipe(**inputs).images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 32, 32, 3) expected_slice = np.array([0.7318, 0.3723, 0.4662, 0.623, 0.5770, 0.5014, 0.4281, 0.5550, 0.4813]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 def test_stable_diffusion_pix2pix_negative_prompt(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = StableDiffusionInstructPix2PixPipeline(**components) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) negative_prompt = "french fries" output = sd_pipe(**inputs, negative_prompt=negative_prompt) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 32, 32, 3) expected_slice = np.array([0.7323, 0.3688, 0.4611, 0.6255, 0.5746, 0.5017, 0.433, 0.5553, 0.4827]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 def test_stable_diffusion_pix2pix_multiple_init_images(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = StableDiffusionInstructPix2PixPipeline(**components) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) inputs["prompt"] = [inputs["prompt"]] * 2 image = np.array(inputs["image"]).astype(np.float32) / 255.0 image = torch.from_numpy(image).unsqueeze(0).to(device) image = image.permute(0, 3, 1, 2) inputs["image"] = image.repeat(2, 1, 1, 1) image = sd_pipe(**inputs).images image_slice = image[-1, -3:, -3:, -1] assert image.shape == (2, 32, 32, 3) expected_slice = np.array([0.606, 0.5712, 0.5099, 0.598, 0.5805, 0.7205, 0.6793, 0.554, 0.5607]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 def test_stable_diffusion_pix2pix_euler(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() components["scheduler"] = EulerAncestralDiscreteScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear" ) sd_pipe = StableDiffusionInstructPix2PixPipeline(**components) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) image = sd_pipe(**inputs).images image_slice = image[0, -3:, -3:, -1] slice = [round(x, 4) for x in image_slice.flatten().tolist()] print(",".join([str(x) for x in slice])) assert image.shape == (1, 32, 32, 3) expected_slice = np.array([0.726, 0.3902, 0.4868, 0.585, 0.5672, 0.511, 0.3906, 0.551, 0.4846]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 @slow @require_torch_gpu class StableDiffusionInstructPix2PixPipelineSlowTests(unittest.TestCase): def tearDown(self): super().tearDown() gc.collect() torch.cuda.empty_cache() def get_inputs(self, seed=0): generator = torch.manual_seed(seed) image = load_image( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg" ) inputs = { "prompt": "turn him into a cyborg", "image": image, "generator": generator, "num_inference_steps": 3, "guidance_scale": 7.5, "image_guidance_scale": 1.0, "output_type": "numpy", } return inputs def test_stable_diffusion_pix2pix_default(self): pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( "timbrooks/instruct-pix2pix", safety_checker=None ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() inputs = self.get_inputs() image = pipe(**inputs).images image_slice = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]) assert np.abs(expected_slice - image_slice).max() < 1e-3 def test_stable_diffusion_pix2pix_k_lms(self): pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( "timbrooks/instruct-pix2pix", safety_checker=None ) pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() inputs = self.get_inputs() image = pipe(**inputs).images image_slice = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]) assert np.abs(expected_slice - image_slice).max() < 1e-3 def test_stable_diffusion_pix2pix_ddim(self): pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( "timbrooks/instruct-pix2pix", safety_checker=None ) pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() inputs = self.get_inputs() image = pipe(**inputs).images image_slice = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]) assert np.abs(expected_slice - image_slice).max() < 1e-3 def test_stable_diffusion_pix2pix_intermediate_state(self): number_of_steps = 0 def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None: callback_fn.has_been_called = True nonlocal number_of_steps number_of_steps += 1 if step == 1: latents = latents.detach().cpu().numpy() assert latents.shape == (1, 4, 64, 64) latents_slice = latents[0, -3:, -3:, -1] expected_slice = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]) assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 elif step == 2: latents = latents.detach().cpu().numpy() assert latents.shape == (1, 4, 64, 64) latents_slice = latents[0, -3:, -3:, -1] expected_slice = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]) assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 callback_fn.has_been_called = False pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( "timbrooks/instruct-pix2pix", safety_checker=None, torch_dtype=torch.float16 ) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() inputs = self.get_inputs() pipe(**inputs, callback=callback_fn, callback_steps=1) assert callback_fn.has_been_called assert number_of_steps == 3 def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self): torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() torch.cuda.reset_peak_memory_stats() pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( "timbrooks/instruct-pix2pix", safety_checker=None, torch_dtype=torch.float16 ) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing(1) pipe.enable_sequential_cpu_offload() inputs = self.get_inputs() _ = pipe(**inputs) mem_bytes = torch.cuda.max_memory_allocated() # make sure that less than 2.2 GB is allocated assert mem_bytes < 2.2 * 10**9 def test_stable_diffusion_pix2pix_pipeline_multiple_of_8(self): inputs = self.get_inputs() # resize to resolution that is divisible by 8 but not 16 or 32 inputs["image"] = inputs["image"].resize((504, 504)) model_id = "timbrooks/instruct-pix2pix" pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( model_id, safety_checker=None, ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() output = pipe(**inputs) image = output.images[0] image_slice = image[255:258, 383:386, -1] assert image.shape == (504, 504, 3) expected_slice = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]) assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3