# 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 gc import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from diffusers import ( AutoencoderKL, ControlNetModel, EulerDiscreteScheduler, HeunDiscreteScheduler, LCMScheduler, StableDiffusionXLControlNetPipeline, StableDiffusionXLImg2ImgPipeline, UNet2DConditionModel, ) from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D from diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.testing_utils import ( enable_full_determinism, load_image, require_torch_gpu, slow, torch_device, ) from diffusers.utils.torch_utils import randn_tensor from ..pipeline_params import ( IMAGE_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS, ) from ..test_pipelines_common import ( IPAdapterTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, ) enable_full_determinism() class StableDiffusionXLControlNetPipelineFastTests( IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase, ): pipeline_class = StableDiffusionXLControlNetPipeline params = TEXT_TO_IMAGE_PARAMS batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS def get_dummy_components(self, 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, 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, time_cond_proj_dim=time_cond_proj_dim, ) torch.manual_seed(0) controlnet = ControlNetModel( block_out_channels=(32, 64), layers_per_block=2, in_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), conditioning_embedding_out_channels=(16, 32), # 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, ) torch.manual_seed(0) 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, ) 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, "controlnet": controlnet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "text_encoder_2": text_encoder_2, "tokenizer_2": tokenizer_2, "feature_extractor": None, "image_encoder": None, } 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) controlnet_embedder_scale_factor = 2 image = randn_tensor( (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor), generator=generator, device=torch.device(device), ) inputs = { "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "output_type": "np", "image": image, } return inputs def test_attention_slicing_forward_pass(self): return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3) def test_ip_adapter_single(self, from_ssd1b=False, expected_pipe_slice=None): if not from_ssd1b: expected_pipe_slice = None if torch_device == "cpu": expected_pipe_slice = np.array( [0.7331, 0.5907, 0.5667, 0.6029, 0.5679, 0.5968, 0.4033, 0.4761, 0.5090] ) return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice) @unittest.skipIf( torch_device != "cuda" or not is_xformers_available(), reason="XFormers attention is only available with CUDA and `xformers` installed", ) def test_xformers_attention_forwardGenerator_pass(self): self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3) def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical(expected_max_diff=2e-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 = self.pipeline_class(**components).to(torch_device) pipes.append(sd_pipe) components = self.get_dummy_components() sd_pipe = self.pipeline_class(**components) sd_pipe.enable_model_cpu_offload() pipes.append(sd_pipe) components = self.get_dummy_components() sd_pipe = self.pipeline_class(**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_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 # Copied from test_stable_diffusion_xl.py def test_stable_diffusion_xl_prompt_embeds(self): components = self.get_dummy_components() sd_pipe = self.pipeline_class(**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_controlnet_sdxl_guess(self): device = "cpu" 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) inputs["guess_mode"] = True output = sd_pipe(**inputs) image_slice = output.images[0, -3:, -3:, -1] expected_slice = np.array( [0.7330834, 0.590667, 0.5667336, 0.6029023, 0.5679491, 0.5968194, 0.4032986, 0.47612396, 0.5089609] ) # make sure that it's equal assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4 def test_controlnet_sdxl_lcm(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components(time_cond_proj_dim=256) sd_pipe = StableDiffusionXLControlNetPipeline(**components) sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config) sd_pipe = sd_pipe.to(torch_device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) output = sd_pipe(**inputs) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) expected_slice = np.array([0.7799, 0.614, 0.6162, 0.7082, 0.6662, 0.5833, 0.4148, 0.5182, 0.4866]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 # Copied from test_stable_diffusion_xl.py:test_stable_diffusion_two_xl_mixture_of_denoiser_fast # with `StableDiffusionXLControlNetPipeline` instead of `StableDiffusionXLPipeline` def test_controlnet_sdxl_two_mixture_of_denoiser_fast(self): components = self.get_dummy_components() pipe_1 = StableDiffusionXLControlNetPipeline(**components).to(torch_device) pipe_1.unet.set_default_attn_processor() components_without_controlnet = {k: v for k, v in components.items() if k != "controlnet"} pipe_2 = StableDiffusionXLImg2ImgPipeline(**components_without_controlnet).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]) class StableDiffusionXLMultiControlNetPipelineFastTests( PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase ): pipeline_class = StableDiffusionXLControlNetPipeline params = TEXT_TO_IMAGE_PARAMS batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess 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"), # 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, ) torch.manual_seed(0) def init_weights(m): if isinstance(m, torch.nn.Conv2d): torch.nn.init.normal_(m.weight) m.bias.data.fill_(1.0) controlnet1 = ControlNetModel( block_out_channels=(32, 64), layers_per_block=2, in_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), conditioning_embedding_out_channels=(16, 32), # 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, ) controlnet1.controlnet_down_blocks.apply(init_weights) torch.manual_seed(0) controlnet2 = ControlNetModel( block_out_channels=(32, 64), layers_per_block=2, in_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), conditioning_embedding_out_channels=(16, 32), # 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, ) controlnet2.controlnet_down_blocks.apply(init_weights) torch.manual_seed(0) 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, ) 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") controlnet = MultiControlNetModel([controlnet1, controlnet2]) components = { "unet": unet, "controlnet": controlnet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "text_encoder_2": text_encoder_2, "tokenizer_2": tokenizer_2, "feature_extractor": None, "image_encoder": None, } 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) controlnet_embedder_scale_factor = 2 images = [ randn_tensor( (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor), generator=generator, device=torch.device(device), ), randn_tensor( (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor), generator=generator, device=torch.device(device), ), ] inputs = { "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "output_type": "np", "image": images, } return inputs def test_control_guidance_switch(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) scale = 10.0 steps = 4 inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_1 = pipe(**inputs)[0] inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0] inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_3 = pipe(**inputs, control_guidance_start=[0.1, 0.3], control_guidance_end=[0.2, 0.7])[0] inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5, 0.8])[0] # make sure that all outputs are different assert np.sum(np.abs(output_1 - output_2)) > 1e-3 assert np.sum(np.abs(output_1 - output_3)) > 1e-3 assert np.sum(np.abs(output_1 - output_4)) > 1e-3 def test_attention_slicing_forward_pass(self): return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3) @unittest.skipIf( torch_device != "cuda" or not is_xformers_available(), reason="XFormers attention is only available with CUDA and `xformers` installed", ) def test_xformers_attention_forwardGenerator_pass(self): self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3) def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical(expected_max_diff=2e-3) def test_save_load_optional_components(self): return self._test_save_load_optional_components() class StableDiffusionXLMultiControlNetOneModelPipelineFastTests( PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase ): pipeline_class = StableDiffusionXLControlNetPipeline params = TEXT_TO_IMAGE_PARAMS batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess 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"), # 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, ) torch.manual_seed(0) def init_weights(m): if isinstance(m, torch.nn.Conv2d): torch.nn.init.normal_(m.weight) m.bias.data.fill_(1.0) controlnet = ControlNetModel( block_out_channels=(32, 64), layers_per_block=2, in_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), conditioning_embedding_out_channels=(16, 32), # 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, ) controlnet.controlnet_down_blocks.apply(init_weights) torch.manual_seed(0) 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, ) 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") controlnet = MultiControlNetModel([controlnet]) components = { "unet": unet, "controlnet": controlnet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "text_encoder_2": text_encoder_2, "tokenizer_2": tokenizer_2, "feature_extractor": None, "image_encoder": None, } 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) controlnet_embedder_scale_factor = 2 images = [ randn_tensor( (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor), generator=generator, device=torch.device(device), ), ] inputs = { "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "output_type": "np", "image": images, } return inputs def test_control_guidance_switch(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) scale = 10.0 steps = 4 inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_1 = pipe(**inputs)[0] inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0] inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_3 = pipe( **inputs, control_guidance_start=[0.1], control_guidance_end=[0.2], )[0] inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5])[0] # make sure that all outputs are different assert np.sum(np.abs(output_1 - output_2)) > 1e-3 assert np.sum(np.abs(output_1 - output_3)) > 1e-3 assert np.sum(np.abs(output_1 - output_4)) > 1e-3 def test_attention_slicing_forward_pass(self): return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3) @unittest.skipIf( torch_device != "cuda" or not is_xformers_available(), reason="XFormers attention is only available with CUDA and `xformers` installed", ) def test_xformers_attention_forwardGenerator_pass(self): self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3) def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical(expected_max_diff=2e-3) def test_save_load_optional_components(self): self._test_save_load_optional_components() def test_negative_conditions(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) inputs = self.get_dummy_inputs(torch_device) image = pipe(**inputs).images image_slice_without_neg_cond = image[0, -3:, -3:, -1] image = 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_without_neg_cond - image_slice_with_neg_cond).max() > 1e-2) @slow @require_torch_gpu class ControlNetSDXLPipelineSlowTests(unittest.TestCase): def setUp(self): super().setUp() gc.collect() torch.cuda.empty_cache() def tearDown(self): super().tearDown() gc.collect() torch.cuda.empty_cache() def test_canny(self): controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-canny-sdxl-1.0") pipe = StableDiffusionXLControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet ) pipe.enable_sequential_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "bird" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" ) images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images assert images[0].shape == (768, 512, 3) original_image = images[0, -3:, -3:, -1].flatten() expected_image = np.array([0.4185, 0.4127, 0.4089, 0.4046, 0.4115, 0.4096, 0.4081, 0.4112, 0.3913]) assert np.allclose(original_image, expected_image, atol=1e-04) def test_depth(self): controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-depth-sdxl-1.0") pipe = StableDiffusionXLControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet ) pipe.enable_sequential_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "Stormtrooper's lecture" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png" ) images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images assert images[0].shape == (512, 512, 3) original_image = images[0, -3:, -3:, -1].flatten() expected_image = np.array([0.4399, 0.5112, 0.5478, 0.4314, 0.472, 0.4823, 0.4647, 0.4957, 0.4853]) assert np.allclose(original_image, expected_image, atol=1e-04) class StableDiffusionSSD1BControlNetPipelineFastTests(StableDiffusionXLControlNetPipelineFastTests): def test_controlnet_sdxl_guess(self): device = "cpu" 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) inputs["guess_mode"] = True output = sd_pipe(**inputs) image_slice = output.images[0, -3:, -3:, -1] expected_slice = np.array( [0.6831671, 0.5702532, 0.5459845, 0.6299793, 0.58563006, 0.6033695, 0.4493941, 0.46132287, 0.5035841] ) # make sure that it's equal assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4 def test_ip_adapter_single(self): expected_pipe_slice = None if torch_device == "cpu": expected_pipe_slice = np.array([0.6832, 0.5703, 0.5460, 0.6300, 0.5856, 0.6034, 0.4494, 0.4613, 0.5036]) return super().test_ip_adapter_single(from_ssd1b=True, expected_pipe_slice=expected_pipe_slice) def test_controlnet_sdxl_lcm(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components(time_cond_proj_dim=256) sd_pipe = StableDiffusionXLControlNetPipeline(**components) sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config) sd_pipe = sd_pipe.to(torch_device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) output = sd_pipe(**inputs) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) expected_slice = np.array([0.6850, 0.5135, 0.5545, 0.7033, 0.6617, 0.5971, 0.4165, 0.5480, 0.5070]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_conditioning_channels(self): 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"), mid_block_type="UNetMidBlock2D", # 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, time_cond_proj_dim=None, ) controlnet = ControlNetModel.from_unet(unet, conditioning_channels=4) assert type(controlnet.mid_block) == UNetMidBlock2D assert controlnet.conditioning_channels == 4 def get_dummy_components(self, 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, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), mid_block_type="UNetMidBlock2D", # 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, time_cond_proj_dim=time_cond_proj_dim, ) torch.manual_seed(0) controlnet = ControlNetModel( block_out_channels=(32, 64), layers_per_block=2, in_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), conditioning_embedding_out_channels=(16, 32), mid_block_type="UNetMidBlock2D", # 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, ) torch.manual_seed(0) 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, ) 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, "controlnet": controlnet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "text_encoder_2": text_encoder_2, "tokenizer_2": tokenizer_2, "feature_extractor": None, "image_encoder": None, } return components