# 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 copy import tempfile import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, StableDiffusionXLImg2ImgPipeline, StableDiffusionXLPipeline, UNet2DConditionModel, UniPCMultistepScheduler, ) from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin enable_full_determinism() class StableDiffusionXLPipelineFastTests( PipelineLatentTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase ): pipeline_class = StableDiffusionXLPipeline params = TEXT_TO_IMAGE_PARAMS batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = TEXT_TO_IMAGE_IMAGE_PARAMS image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS def get_dummy_components(self): torch.manual_seed(0) unet = UNet2DConditionModel( block_out_channels=(2, 4), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, 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=80, # 6 * 8 + 32 cross_attention_dim=64, norm_num_groups=1, ) 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") components = { "unet": unet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "text_encoder_2": text_encoder_2, "tokenizer_2": tokenizer_2, } return components def get_dummy_inputs(self, device, seed=0): 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", "generator": generator, "num_inference_steps": 2, "guidance_scale": 5.0, "output_type": "np", } return inputs def test_stable_diffusion_xl_euler(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = StableDiffusionXLPipeline(**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([0.5552, 0.5569, 0.4725, 0.4348, 0.4994, 0.4632, 0.5142, 0.5012, 0.47]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_xl_prompt_embeds(self): components = self.get_dummy_components() sd_pipe = StableDiffusionXLPipeline(**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) inputs["prompt"] = 2 * [inputs["prompt"]] inputs["num_images_per_prompt"] = 2 output = sd_pipe(**inputs) image_slice_1 = output.images[0, -3:, -3:, -1] # forward with prompt embeds inputs = self.get_dummy_inputs(torch_device) prompt = 2 * [inputs.pop("prompt")] ( prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) = sd_pipe.encode_prompt(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 def test_stable_diffusion_xl_negative_prompt_embeds(self): components = self.get_dummy_components() sd_pipe = StableDiffusionXLPipeline(**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 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) def test_save_load_optional_components(self): self._test_save_load_optional_components() @require_torch_gpu def test_stable_diffusion_xl_offloads(self): pipes = [] components = self.get_dummy_components() sd_pipe = StableDiffusionXLPipeline(**components).to(torch_device) pipes.append(sd_pipe) components = self.get_dummy_components() sd_pipe = StableDiffusionXLPipeline(**components) sd_pipe.enable_model_cpu_offload() pipes.append(sd_pipe) components = self.get_dummy_components() sd_pipe = StableDiffusionXLPipeline(**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_img2img_prompt_embeds_only(self): components = self.get_dummy_components() sd_pipe = StableDiffusionXLPipeline(**components) sd_pipe = sd_pipe.to(torch_device) sd_pipe.set_progress_bar_config(disable=None) # forward without prompt embeds generator_device = "cpu" inputs = self.get_dummy_inputs(generator_device) inputs["prompt"] = 3 * [inputs["prompt"]] output = sd_pipe(**inputs) image_slice_1 = output.images[0, -3:, -3:, -1] # forward with prompt embeds generator_device = "cpu" inputs = self.get_dummy_inputs(generator_device) prompt = 3 * [inputs.pop("prompt")] ( prompt_embeds, _, pooled_prompt_embeds, _, ) = sd_pipe.encode_prompt(prompt) output = sd_pipe( **inputs, prompt_embeds=prompt_embeds, pooled_prompt_embeds=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 def test_stable_diffusion_two_xl_mixture_of_denoiser_fast(self): components = self.get_dummy_components() pipe_1 = StableDiffusionXLPipeline(**components).to(torch_device) pipe_1.unet.set_default_attn_processor() pipe_2 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device) pipe_2.unet.set_default_attn_processor() def assert_run_mixture( num_steps, split, scheduler_cls_orig, expected_tss, 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() if pipe_1.scheduler.order == 2: expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss)) expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split, expected_tss)) expected_steps = expected_steps_1 + expected_steps_2 else: expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss)) expected_steps_2 = list(filter(lambda ts: ts < split, expected_tss)) # 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": 1.0 - (split / num_train_timesteps), "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": 1.0 - (split / num_train_timesteps), "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}" steps = 10 for split in [300, 700]: for scheduler_cls_timesteps in [ (EulerDiscreteScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]), ( HeunDiscreteScheduler, [ 901.0, 801.0, 801.0, 701.0, 701.0, 601.0, 601.0, 501.0, 501.0, 401.0, 401.0, 301.0, 301.0, 201.0, 201.0, 101.0, 101.0, 1.0, 1.0, ], ), ]: assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1]) @slow def test_stable_diffusion_two_xl_mixture_of_denoiser(self): components = self.get_dummy_components() pipe_1 = StableDiffusionXLPipeline(**components).to(torch_device) pipe_1.unet.set_default_attn_processor() pipe_2 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device) pipe_2.unet.set_default_attn_processor() def assert_run_mixture( num_steps, split, scheduler_cls_orig, expected_tss, 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() if pipe_1.scheduler.order == 2: expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss)) expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split, expected_tss)) expected_steps = expected_steps_1 + expected_steps_2 else: expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss)) expected_steps_2 = list(filter(lambda ts: ts < split, expected_tss)) # 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": 1.0 - (split / num_train_timesteps), "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": 1.0 - (split / num_train_timesteps), "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}" steps = 10 for split in [300, 500, 700]: for scheduler_cls_timesteps in [ (DDIMScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]), (EulerDiscreteScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]), (DPMSolverMultistepScheduler, [901, 811, 721, 631, 541, 451, 361, 271, 181, 91]), (UniPCMultistepScheduler, [901, 811, 721, 631, 541, 451, 361, 271, 181, 91]), ( HeunDiscreteScheduler, [ 901.0, 801.0, 801.0, 701.0, 701.0, 601.0, 601.0, 501.0, 501.0, 401.0, 401.0, 301.0, 301.0, 201.0, 201.0, 101.0, 101.0, 1.0, 1.0, ], ), ]: assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1]) steps = 25 for split in [300, 500, 700]: for scheduler_cls_timesteps in [ ( DDIMScheduler, [ 961, 921, 881, 841, 801, 761, 721, 681, 641, 601, 561, 521, 481, 441, 401, 361, 321, 281, 241, 201, 161, 121, 81, 41, 1, ], ), ( EulerDiscreteScheduler, [ 961.0, 921.0, 881.0, 841.0, 801.0, 761.0, 721.0, 681.0, 641.0, 601.0, 561.0, 521.0, 481.0, 441.0, 401.0, 361.0, 321.0, 281.0, 241.0, 201.0, 161.0, 121.0, 81.0, 41.0, 1.0, ], ), ( DPMSolverMultistepScheduler, [ 951, 913, 875, 837, 799, 761, 723, 685, 647, 609, 571, 533, 495, 457, 419, 381, 343, 305, 267, 229, 191, 153, 115, 77, 39, ], ), ( UniPCMultistepScheduler, [ 951, 913, 875, 837, 799, 761, 723, 685, 647, 609, 571, 533, 495, 457, 419, 381, 343, 305, 267, 229, 191, 153, 115, 77, 39, ], ), ( HeunDiscreteScheduler, [ 961.0, 921.0, 921.0, 881.0, 881.0, 841.0, 841.0, 801.0, 801.0, 761.0, 761.0, 721.0, 721.0, 681.0, 681.0, 641.0, 641.0, 601.0, 601.0, 561.0, 561.0, 521.0, 521.0, 481.0, 481.0, 441.0, 441.0, 401.0, 401.0, 361.0, 361.0, 321.0, 321.0, 281.0, 281.0, 241.0, 241.0, 201.0, 201.0, 161.0, 161.0, 121.0, 121.0, 81.0, 81.0, 41.0, 41.0, 1.0, 1.0, ], ), ]: assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1]) @slow def test_stable_diffusion_three_xl_mixture_of_denoiser(self): components = self.get_dummy_components() pipe_1 = StableDiffusionXLPipeline(**components).to(torch_device) pipe_1.unet.set_default_attn_processor() pipe_2 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device) pipe_2.unet.set_default_attn_processor() pipe_3 = StableDiffusionXLImg2ImgPipeline(**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}" with self.assertRaises(ValueError) as cm: inputs_2 = { **inputs, **{ "denoising_start": split_2, "denoising_end": split_1, "image": latents, "output_type": "latent", }, } pipe_2(**inputs_2).images[0] assert "cannot be larger than or equal to `denoising_end`" in str(cm.exception) 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) 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["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["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["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["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["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_negative_conditions(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = StableDiffusionXLPipeline(**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_cond = 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_cond = image[0, -3:, -3:, -1] self.assertTrue(np.abs(image_slice_with_no_neg_cond - image_slice_with_neg_cond).max() > 1e-2) def test_stable_diffusion_xl_save_from_pretrained(self): pipes = [] components = self.get_dummy_components() sd_pipe = StableDiffusionXLPipeline(**components).to(torch_device) pipes.append(sd_pipe) with tempfile.TemporaryDirectory() as tmpdirname: sd_pipe.save_pretrained(tmpdirname) sd_pipe = StableDiffusionXLPipeline.from_pretrained(tmpdirname).to(torch_device) 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