# coding=utf-8 # Copyright 2024 Harutatsu Akiyama, Jinbin Bai, and 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 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, ControlNetModel, EulerDiscreteScheduler, StableDiffusionXLControlNetInpaintPipeline, UNet2DConditionModel, ) from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device from ..pipeline_params import ( IMAGE_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS, ) from ..test_pipelines_common import ( PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, ) enable_full_determinism() class ControlNetPipelineSDXLFastTests( PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase ): pipeline_class = StableDiffusionXLControlNetInpaintPipeline params = TEXT_TO_IMAGE_PARAMS batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = frozenset(IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"mask_image", "control_image"})) image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS 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): 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) 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, ) 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") torch.manual_seed(0) text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config) tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") 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, "controlnet": controlnet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "text_encoder_2": text_encoder_2, "tokenizer_2": tokenizer_2, "image_encoder": image_encoder, "feature_extractor": feature_extractor, } return components def get_dummy_inputs(self, device, seed=0, img_res=64): if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) # Get random floats in [0, 1] as image image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) image = image.cpu().permute(0, 2, 3, 1)[0] mask_image = torch.ones_like(image) controlnet_embedder_scale_factor = 2 control_image = ( floats_tensor( (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor), rng=random.Random(seed), ) .to(device) .cpu() ) control_image = control_image.cpu().permute(0, 2, 3, 1)[0] # Convert image and mask_image to [0, 255] image = 255 * image mask_image = 255 * mask_image control_image = 255 * control_image # Convert to PIL image init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((img_res, img_res)) mask_image = Image.fromarray(np.uint8(mask_image)).convert("L").resize((img_res, img_res)) control_image = Image.fromarray(np.uint8(control_image)).convert("RGB").resize((img_res, img_res)) inputs = { "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "output_type": "np", "image": init_image, "mask_image": mask_image, "control_image": control_image, } return inputs def test_attention_slicing_forward_pass(self): return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3) def test_dict_tuple_outputs_equivalent(self): expected_slice = None if torch_device == "cpu": expected_slice = np.array([0.5490, 0.5053, 0.4676, 0.5816, 0.5364, 0.4830, 0.5937, 0.5719, 0.4318]) super().test_dict_tuple_outputs_equivalent(expected_slice=expected_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) @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 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([0.549, 0.5053, 0.4676, 0.5816, 0.5364, 0.483, 0.5937, 0.5719, 0.4318]) # make sure that it's equal assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4 # TODO(Patrick, Sayak) - skip for now as this requires more refiner tests def test_save_load_optional_components(self): pass def test_float16_inference(self): super().test_float16_inference(expected_max_diff=5e-1)