# coding=utf-8 # Copyright 2024 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 copy import random import unittest import numpy as np import torch from PIL import Image from transformers import ( CLIPImageProcessor, CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionConfig, CLIPVisionModelWithProjection, ) from diffusers import ( AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, LCMScheduler, StableDiffusionXLInpaintPipeline, UNet2DConditionModel, UniPCMultistepScheduler, ) from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, slow, torch_device from ..pipeline_params import ( TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS, TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS, ) from ..test_pipelines_common import IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() class StableDiffusionXLInpaintPipelineFastTests( IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase ): pipeline_class = StableDiffusionXLInpaintPipeline params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS image_params = frozenset([]) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess image_latents_params = frozenset([]) callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union( { "add_text_embeds", "add_time_ids", "mask", "masked_image_latents", } ) def get_dummy_components(self, skip_first_text_encoder=False, time_cond_proj_dim=None): torch.manual_seed(0) unet = UNet2DConditionModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, time_cond_proj_dim=time_cond_proj_dim, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), # SD2-specific config below attention_head_dim=(2, 4), use_linear_projection=True, addition_embed_type="text_time", addition_time_embed_dim=8, transformer_layers_per_block=(1, 2), projection_class_embeddings_input_dim=72, # 5 * 8 + 32 cross_attention_dim=64 if not skip_first_text_encoder else 32, ) scheduler = EulerDiscreteScheduler( beta_start=0.00085, beta_end=0.012, steps_offset=1, beta_schedule="scaled_linear", timestep_spacing="leading", ) 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, sample_size=128, ) 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, # SD2-specific config below hidden_act="gelu", projection_dim=32, ) text_encoder = CLIPTextModel(text_encoder_config) tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config) tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") torch.manual_seed(0) image_encoder_config = CLIPVisionConfig( hidden_size=32, image_size=224, projection_dim=32, intermediate_size=37, num_attention_heads=4, num_channels=3, num_hidden_layers=5, patch_size=14, ) image_encoder = CLIPVisionModelWithProjection(image_encoder_config) feature_extractor = CLIPImageProcessor( crop_size=224, do_center_crop=True, do_normalize=True, do_resize=True, image_mean=[0.48145466, 0.4578275, 0.40821073], image_std=[0.26862954, 0.26130258, 0.27577711], resample=3, size=224, ) components = { "unet": unet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder if not skip_first_text_encoder else None, "tokenizer": tokenizer if not skip_first_text_encoder else None, "text_encoder_2": text_encoder_2, "tokenizer_2": tokenizer_2, "image_encoder": image_encoder, "feature_extractor": feature_extractor, "requires_aesthetics_score": True, } return components def get_dummy_inputs(self, device, seed=0): # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) image = image.cpu().permute(0, 2, 3, 1)[0] init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) # create mask image[8:, 8:, :] = 255 mask_image = Image.fromarray(np.uint8(image)).convert("L").resize((64, 64)) 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": init_image, "mask_image": mask_image, "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "strength": 1.0, "output_type": "np", } return inputs def get_dummy_inputs_2images(self, device, seed=0, img_res=64): # Get random floats in [0, 1] as image with spatial size (img_res, img_res) image1 = floats_tensor((1, 3, img_res, img_res), rng=random.Random(seed)).to(device) image2 = floats_tensor((1, 3, img_res, img_res), rng=random.Random(seed + 22)).to(device) # Convert images to [-1, 1] init_image1 = 2.0 * image1 - 1.0 init_image2 = 2.0 * image2 - 1.0 # empty mask mask_image = torch.zeros((1, 1, img_res, img_res), device=device) if str(device).startswith("mps"): generator1 = torch.manual_seed(seed) generator2 = torch.manual_seed(seed) else: generator1 = torch.Generator(device=device).manual_seed(seed) generator2 = torch.Generator(device=device).manual_seed(seed) inputs = { "prompt": ["A painting of a squirrel eating a burger"] * 2, "image": [init_image1, init_image2], "mask_image": [mask_image] * 2, "generator": [generator1, generator2], "num_inference_steps": 2, "guidance_scale": 6.0, "output_type": "np", } return inputs def test_ip_adapter_single(self): expected_pipe_slice = None if torch_device == "cpu": expected_pipe_slice = np.array([0.7971, 0.5371, 0.5973, 0.5642, 0.6689, 0.6894, 0.5770, 0.6063, 0.5261]) return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice) def test_components_function(self): init_components = self.get_dummy_components() init_components.pop("requires_aesthetics_score") pipe = self.pipeline_class(**init_components) self.assertTrue(hasattr(pipe, "components")) self.assertTrue(set(pipe.components.keys()) == set(init_components.keys())) def test_stable_diffusion_xl_inpaint_euler(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = StableDiffusionXLInpaintPipeline(**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, 64, 64, 3) expected_slice = np.array([0.8029, 0.5523, 0.5825, 0.6003, 0.6702, 0.7018, 0.6369, 0.5955, 0.5123]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_xl_inpaint_euler_lcm(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components(time_cond_proj_dim=256) sd_pipe = StableDiffusionXLInpaintPipeline(**components) sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.config) 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, 64, 64, 3) expected_slice = np.array([0.6611, 0.5569, 0.5531, 0.5471, 0.5918, 0.6393, 0.5074, 0.5468, 0.5185]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_xl_inpaint_euler_lcm_custom_timesteps(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components(time_cond_proj_dim=256) sd_pipe = StableDiffusionXLInpaintPipeline(**components) sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.config) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) del inputs["num_inference_steps"] inputs["timesteps"] = [999, 499] image = sd_pipe(**inputs).images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) expected_slice = np.array([0.6611, 0.5569, 0.5531, 0.5471, 0.5918, 0.6393, 0.5074, 0.5468, 0.5185]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_attention_slicing_forward_pass(self): super().test_attention_slicing_forward_pass(expected_max_diff=3e-3) def test_inference_batch_single_identical(self): super().test_inference_batch_single_identical(expected_max_diff=3e-3) # TODO(Patrick, Sayak) - skip for now as this requires more refiner tests def test_save_load_optional_components(self): pass def test_stable_diffusion_xl_inpaint_negative_prompt_embeds(self): components = self.get_dummy_components() sd_pipe = StableDiffusionXLInpaintPipeline(**components) sd_pipe = sd_pipe.to(torch_device) sd_pipe = sd_pipe.to(torch_device) sd_pipe.set_progress_bar_config(disable=None) # forward without prompt embeds inputs = self.get_dummy_inputs(torch_device) negative_prompt = 3 * ["this is a negative prompt"] inputs["negative_prompt"] = negative_prompt inputs["prompt"] = 3 * [inputs["prompt"]] output = sd_pipe(**inputs) image_slice_1 = output.images[0, -3:, -3:, -1] # forward with prompt embeds inputs = self.get_dummy_inputs(torch_device) negative_prompt = 3 * ["this is a negative prompt"] prompt = 3 * [inputs.pop("prompt")] ( prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) = sd_pipe.encode_prompt(prompt, negative_prompt=negative_prompt) output = sd_pipe( **inputs, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, ) image_slice_2 = output.images[0, -3:, -3:, -1] # make sure that it's equal assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4 @require_torch_gpu def test_stable_diffusion_xl_offloads(self): pipes = [] components = self.get_dummy_components() sd_pipe = StableDiffusionXLInpaintPipeline(**components).to(torch_device) pipes.append(sd_pipe) components = self.get_dummy_components() sd_pipe = StableDiffusionXLInpaintPipeline(**components) sd_pipe.enable_model_cpu_offload() pipes.append(sd_pipe) components = self.get_dummy_components() sd_pipe = StableDiffusionXLInpaintPipeline(**components) sd_pipe.enable_sequential_cpu_offload() pipes.append(sd_pipe) image_slices = [] for pipe in pipes: pipe.unet.set_default_attn_processor() inputs = self.get_dummy_inputs(torch_device) image = pipe(**inputs).images image_slices.append(image[0, -3:, -3:, -1].flatten()) assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3 assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3 def test_stable_diffusion_xl_refiner(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components(skip_first_text_encoder=True) sd_pipe = self.pipeline_class(**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, 64, 64, 3) expected_slice = np.array([0.7045, 0.4838, 0.5454, 0.6270, 0.6168, 0.6717, 0.6484, 0.5681, 0.4922]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_two_xl_mixture_of_denoiser_fast(self): components = self.get_dummy_components() pipe_1 = StableDiffusionXLInpaintPipeline(**components).to(torch_device) pipe_1.unet.set_default_attn_processor() pipe_2 = StableDiffusionXLInpaintPipeline(**components).to(torch_device) pipe_2.unet.set_default_attn_processor() def assert_run_mixture( num_steps, split, scheduler_cls_orig, num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps ): inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = num_steps class scheduler_cls(scheduler_cls_orig): pass pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config) pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config) # Let's retrieve the number of timesteps we want to use pipe_1.scheduler.set_timesteps(num_steps) expected_steps = pipe_1.scheduler.timesteps.tolist() split_ts = num_train_timesteps - int(round(num_train_timesteps * split)) if pipe_1.scheduler.order == 2: expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps)) expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split_ts, expected_steps)) expected_steps = expected_steps_1 + expected_steps_2 else: expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps)) expected_steps_2 = list(filter(lambda ts: ts < split_ts, expected_steps)) # now we monkey patch step `done_steps` # list into the step function for testing done_steps = [] old_step = copy.copy(scheduler_cls.step) def new_step(self, *args, **kwargs): done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t` return old_step(self, *args, **kwargs) scheduler_cls.step = new_step inputs_1 = {**inputs, **{"denoising_end": split, "output_type": "latent"}} latents = pipe_1(**inputs_1).images[0] assert expected_steps_1 == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}" inputs_2 = {**inputs, **{"denoising_start": split, "image": latents}} pipe_2(**inputs_2).images[0] assert expected_steps_2 == done_steps[len(expected_steps_1) :] assert expected_steps == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}" for steps in [7, 20]: assert_run_mixture(steps, 0.33, EulerDiscreteScheduler) assert_run_mixture(steps, 0.33, HeunDiscreteScheduler) @slow def test_stable_diffusion_two_xl_mixture_of_denoiser(self): components = self.get_dummy_components() pipe_1 = StableDiffusionXLInpaintPipeline(**components).to(torch_device) pipe_1.unet.set_default_attn_processor() pipe_2 = StableDiffusionXLInpaintPipeline(**components).to(torch_device) pipe_2.unet.set_default_attn_processor() def assert_run_mixture( num_steps, split, scheduler_cls_orig, num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps ): inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = num_steps class scheduler_cls(scheduler_cls_orig): pass pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config) pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config) # Let's retrieve the number of timesteps we want to use pipe_1.scheduler.set_timesteps(num_steps) expected_steps = pipe_1.scheduler.timesteps.tolist() split_ts = num_train_timesteps - int(round(num_train_timesteps * split)) if pipe_1.scheduler.order == 2: expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps)) expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split_ts, expected_steps)) expected_steps = expected_steps_1 + expected_steps_2 else: expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps)) expected_steps_2 = list(filter(lambda ts: ts < split_ts, expected_steps)) # now we monkey patch step `done_steps` # list into the step function for testing done_steps = [] old_step = copy.copy(scheduler_cls.step) def new_step(self, *args, **kwargs): done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t` return old_step(self, *args, **kwargs) scheduler_cls.step = new_step inputs_1 = {**inputs, **{"denoising_end": split, "output_type": "latent"}} latents = pipe_1(**inputs_1).images[0] assert expected_steps_1 == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}" inputs_2 = {**inputs, **{"denoising_start": split, "image": latents}} pipe_2(**inputs_2).images[0] assert expected_steps_2 == done_steps[len(expected_steps_1) :] assert expected_steps == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}" for steps in [5, 8, 20]: for split in [0.33, 0.49, 0.71]: for scheduler_cls in [ DDIMScheduler, EulerDiscreteScheduler, DPMSolverMultistepScheduler, UniPCMultistepScheduler, HeunDiscreteScheduler, ]: assert_run_mixture(steps, split, scheduler_cls) @slow def test_stable_diffusion_three_xl_mixture_of_denoiser(self): components = self.get_dummy_components() pipe_1 = StableDiffusionXLInpaintPipeline(**components).to(torch_device) pipe_1.unet.set_default_attn_processor() pipe_2 = StableDiffusionXLInpaintPipeline(**components).to(torch_device) pipe_2.unet.set_default_attn_processor() pipe_3 = StableDiffusionXLInpaintPipeline(**components).to(torch_device) pipe_3.unet.set_default_attn_processor() def assert_run_mixture( num_steps, split_1, split_2, scheduler_cls_orig, num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps, ): inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = num_steps class scheduler_cls(scheduler_cls_orig): pass pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config) pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config) pipe_3.scheduler = scheduler_cls.from_config(pipe_3.scheduler.config) # Let's retrieve the number of timesteps we want to use pipe_1.scheduler.set_timesteps(num_steps) expected_steps = pipe_1.scheduler.timesteps.tolist() split_1_ts = num_train_timesteps - int(round(num_train_timesteps * split_1)) split_2_ts = num_train_timesteps - int(round(num_train_timesteps * split_2)) if pipe_1.scheduler.order == 2: expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps)) expected_steps_2 = expected_steps_1[-1:] + list( filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps) ) expected_steps_3 = expected_steps_2[-1:] + list(filter(lambda ts: ts < split_2_ts, expected_steps)) expected_steps = expected_steps_1 + expected_steps_2 + expected_steps_3 else: expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps)) expected_steps_2 = list(filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps)) expected_steps_3 = list(filter(lambda ts: ts < split_2_ts, expected_steps)) # now we monkey patch step `done_steps` # list into the step function for testing done_steps = [] old_step = copy.copy(scheduler_cls.step) def new_step(self, *args, **kwargs): done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t` return old_step(self, *args, **kwargs) scheduler_cls.step = new_step inputs_1 = {**inputs, **{"denoising_end": split_1, "output_type": "latent"}} latents = pipe_1(**inputs_1).images[0] assert ( expected_steps_1 == done_steps ), f"Failure with {scheduler_cls.__name__} and {num_steps} and {split_1} and {split_2}" inputs_2 = { **inputs, **{"denoising_start": split_1, "denoising_end": split_2, "image": latents, "output_type": "latent"}, } pipe_2(**inputs_2).images[0] assert expected_steps_2 == done_steps[len(expected_steps_1) :] inputs_3 = {**inputs, **{"denoising_start": split_2, "image": latents}} pipe_3(**inputs_3).images[0] assert expected_steps_3 == done_steps[len(expected_steps_1) + len(expected_steps_2) :] assert ( expected_steps == done_steps ), f"Failure with {scheduler_cls.__name__} and {num_steps} and {split_1} and {split_2}" for steps in [7, 11, 20]: for split_1, split_2 in zip([0.19, 0.32], [0.81, 0.68]): for scheduler_cls in [ DDIMScheduler, EulerDiscreteScheduler, DPMSolverMultistepScheduler, UniPCMultistepScheduler, HeunDiscreteScheduler, ]: assert_run_mixture(steps, split_1, split_2, scheduler_cls) def test_stable_diffusion_xl_multi_prompts(self): components = self.get_dummy_components() sd_pipe = self.pipeline_class(**components).to(torch_device) # forward with single prompt inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = 5 output = sd_pipe(**inputs) image_slice_1 = output.images[0, -3:, -3:, -1] # forward with same prompt duplicated inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = 5 inputs["prompt_2"] = inputs["prompt"] output = sd_pipe(**inputs) image_slice_2 = output.images[0, -3:, -3:, -1] # ensure the results are equal assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4 # forward with different prompt inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = 5 inputs["prompt_2"] = "different prompt" output = sd_pipe(**inputs) image_slice_3 = output.images[0, -3:, -3:, -1] # ensure the results are not equal assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4 # manually set a negative_prompt inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = 5 inputs["negative_prompt"] = "negative prompt" output = sd_pipe(**inputs) image_slice_1 = output.images[0, -3:, -3:, -1] # forward with same negative_prompt duplicated inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = 5 inputs["negative_prompt"] = "negative prompt" inputs["negative_prompt_2"] = inputs["negative_prompt"] output = sd_pipe(**inputs) image_slice_2 = output.images[0, -3:, -3:, -1] # ensure the results are equal assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4 # forward with different negative_prompt inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = 5 inputs["negative_prompt"] = "negative prompt" inputs["negative_prompt_2"] = "different negative prompt" output = sd_pipe(**inputs) image_slice_3 = output.images[0, -3:, -3:, -1] # ensure the results are not equal assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4 def test_stable_diffusion_xl_img2img_negative_conditions(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = self.pipeline_class(**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_with_no_neg_conditions = image[0, -3:, -3:, -1] image = sd_pipe( **inputs, negative_original_size=(512, 512), negative_crops_coords_top_left=( 0, 0, ), negative_target_size=(1024, 1024), ).images image_slice_with_neg_conditions = image[0, -3:, -3:, -1] assert ( np.abs(image_slice_with_no_neg_conditions.flatten() - image_slice_with_neg_conditions.flatten()).max() > 1e-4 ) def test_stable_diffusion_xl_inpaint_mask_latents(self): device = "cpu" components = self.get_dummy_components() sd_pipe = self.pipeline_class(**components).to(device) sd_pipe.set_progress_bar_config(disable=None) # normal mask + normal image ## `image`: pil, `mask_image``: pil, `masked_image_latents``: None inputs = self.get_dummy_inputs(device) inputs["strength"] = 0.9 out_0 = sd_pipe(**inputs).images # image latents + mask latents inputs = self.get_dummy_inputs(device) image = sd_pipe.image_processor.preprocess(inputs["image"]).to(sd_pipe.device) mask = sd_pipe.mask_processor.preprocess(inputs["mask_image"]).to(sd_pipe.device) masked_image = image * (mask < 0.5) generator = torch.Generator(device=device).manual_seed(0) image_latents = sd_pipe._encode_vae_image(image, generator=generator) torch.randn((1, 4, 32, 32), generator=generator) mask_latents = sd_pipe._encode_vae_image(masked_image, generator=generator) inputs["image"] = image_latents inputs["masked_image_latents"] = mask_latents inputs["mask_image"] = mask inputs["strength"] = 0.9 generator = torch.Generator(device=device).manual_seed(0) torch.randn((1, 4, 32, 32), generator=generator) inputs["generator"] = generator out_1 = sd_pipe(**inputs).images assert np.abs(out_0 - out_1).max() < 1e-2 def test_stable_diffusion_xl_inpaint_2_images(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = self.pipeline_class(**components) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) # test to confirm if we pass two same image, we will get same output inputs = self.get_dummy_inputs(device) gen1 = torch.Generator(device=device).manual_seed(0) gen2 = torch.Generator(device=device).manual_seed(0) for name in ["prompt", "image", "mask_image"]: inputs[name] = [inputs[name]] * 2 inputs["generator"] = [gen1, gen2] images = sd_pipe(**inputs).images assert images.shape == (2, 64, 64, 3) image_slice1 = images[0, -3:, -3:, -1] image_slice2 = images[1, -3:, -3:, -1] assert np.abs(image_slice1.flatten() - image_slice2.flatten()).max() < 1e-4 # test to confirm that if we pass two different images, we will get different output inputs = self.get_dummy_inputs_2images(device) images = sd_pipe(**inputs).images assert images.shape == (2, 64, 64, 3) image_slice1 = images[0, -3:, -3:, -1] image_slice2 = images[1, -3:, -3:, -1] assert np.abs(image_slice1.flatten() - image_slice2.flatten()).max() > 1e-2 def test_pipeline_interrupt(self): components = self.get_dummy_components() sd_pipe = StableDiffusionXLInpaintPipeline(**components) sd_pipe = sd_pipe.to(torch_device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) prompt = "hey" num_inference_steps = 5 # store intermediate latents from the generation process class PipelineState: def __init__(self): self.state = [] def apply(self, pipe, i, t, callback_kwargs): self.state.append(callback_kwargs["latents"]) return callback_kwargs pipe_state = PipelineState() sd_pipe( prompt, image=inputs["image"], mask_image=inputs["mask_image"], strength=0.8, num_inference_steps=num_inference_steps, output_type="np", generator=torch.Generator("cpu").manual_seed(0), callback_on_step_end=pipe_state.apply, ).images # interrupt generation at step index interrupt_step_idx = 1 def callback_on_step_end(pipe, i, t, callback_kwargs): if i == interrupt_step_idx: pipe._interrupt = True return callback_kwargs output_interrupted = sd_pipe( prompt, image=inputs["image"], mask_image=inputs["mask_image"], strength=0.8, num_inference_steps=num_inference_steps, output_type="latent", generator=torch.Generator("cpu").manual_seed(0), callback_on_step_end=callback_on_step_end, ).images # fetch intermediate latents at the interrupted step # from the completed generation process intermediate_latent = pipe_state.state[interrupt_step_idx] # compare the intermediate latent to the output of the interrupted process # they should be the same assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)