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| # coding=utf-8 | |
| # Copyright 2023 HuggingFace Inc. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import gc | |
| import tempfile | |
| import unittest | |
| import numpy as np | |
| import torch | |
| from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer | |
| from diffusers import ( | |
| AutoencoderKL, | |
| ControlNetModel, | |
| DDIMScheduler, | |
| StableDiffusionControlNetPipeline, | |
| UNet2DConditionModel, | |
| ) | |
| from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel | |
| from diffusers.utils import load_image, load_numpy, randn_tensor, slow, torch_device | |
| from diffusers.utils.import_utils import is_xformers_available | |
| from diffusers.utils.testing_utils import require_torch_gpu | |
| from ...pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS | |
| from ...test_pipelines_common import PipelineTesterMixin | |
| class StableDiffusionControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
| pipeline_class = StableDiffusionControlNetPipeline | |
| params = TEXT_TO_IMAGE_PARAMS | |
| batch_params = TEXT_TO_IMAGE_BATCH_PARAMS | |
| def get_dummy_components(self): | |
| torch.manual_seed(0) | |
| unet = UNet2DConditionModel( | |
| block_out_channels=(32, 64), | |
| layers_per_block=2, | |
| sample_size=32, | |
| in_channels=4, | |
| out_channels=4, | |
| down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), | |
| up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), | |
| cross_attention_dim=32, | |
| ) | |
| torch.manual_seed(0) | |
| controlnet = ControlNetModel( | |
| block_out_channels=(32, 64), | |
| layers_per_block=2, | |
| in_channels=4, | |
| down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), | |
| cross_attention_dim=32, | |
| conditioning_embedding_out_channels=(16, 32), | |
| ) | |
| torch.manual_seed(0) | |
| scheduler = DDIMScheduler( | |
| beta_start=0.00085, | |
| beta_end=0.012, | |
| beta_schedule="scaled_linear", | |
| clip_sample=False, | |
| set_alpha_to_one=False, | |
| ) | |
| torch.manual_seed(0) | |
| vae = AutoencoderKL( | |
| block_out_channels=[32, 64], | |
| in_channels=3, | |
| out_channels=3, | |
| down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], | |
| up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], | |
| latent_channels=4, | |
| ) | |
| torch.manual_seed(0) | |
| text_encoder_config = CLIPTextConfig( | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| hidden_size=32, | |
| intermediate_size=37, | |
| layer_norm_eps=1e-05, | |
| num_attention_heads=4, | |
| num_hidden_layers=5, | |
| pad_token_id=1, | |
| vocab_size=1000, | |
| ) | |
| text_encoder = CLIPTextModel(text_encoder_config) | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| components = { | |
| "unet": unet, | |
| "controlnet": controlnet, | |
| "scheduler": scheduler, | |
| "vae": vae, | |
| "text_encoder": text_encoder, | |
| "tokenizer": tokenizer, | |
| "safety_checker": None, | |
| "feature_extractor": None, | |
| } | |
| return components | |
| def get_dummy_inputs(self, device, seed=0): | |
| 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": "numpy", | |
| "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_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) | |
| class StableDiffusionMultiControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
| pipeline_class = StableDiffusionControlNetPipeline | |
| params = TEXT_TO_IMAGE_PARAMS | |
| batch_params = TEXT_TO_IMAGE_BATCH_PARAMS | |
| def get_dummy_components(self): | |
| torch.manual_seed(0) | |
| unet = UNet2DConditionModel( | |
| block_out_channels=(32, 64), | |
| layers_per_block=2, | |
| sample_size=32, | |
| in_channels=4, | |
| out_channels=4, | |
| down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), | |
| up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), | |
| cross_attention_dim=32, | |
| ) | |
| torch.manual_seed(0) | |
| controlnet1 = ControlNetModel( | |
| block_out_channels=(32, 64), | |
| layers_per_block=2, | |
| in_channels=4, | |
| down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), | |
| cross_attention_dim=32, | |
| conditioning_embedding_out_channels=(16, 32), | |
| ) | |
| torch.manual_seed(0) | |
| controlnet2 = ControlNetModel( | |
| block_out_channels=(32, 64), | |
| layers_per_block=2, | |
| in_channels=4, | |
| down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), | |
| cross_attention_dim=32, | |
| conditioning_embedding_out_channels=(16, 32), | |
| ) | |
| torch.manual_seed(0) | |
| scheduler = DDIMScheduler( | |
| beta_start=0.00085, | |
| beta_end=0.012, | |
| beta_schedule="scaled_linear", | |
| clip_sample=False, | |
| set_alpha_to_one=False, | |
| ) | |
| torch.manual_seed(0) | |
| vae = AutoencoderKL( | |
| block_out_channels=[32, 64], | |
| in_channels=3, | |
| out_channels=3, | |
| down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], | |
| up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], | |
| latent_channels=4, | |
| ) | |
| torch.manual_seed(0) | |
| text_encoder_config = CLIPTextConfig( | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| hidden_size=32, | |
| intermediate_size=37, | |
| layer_norm_eps=1e-05, | |
| num_attention_heads=4, | |
| num_hidden_layers=5, | |
| pad_token_id=1, | |
| vocab_size=1000, | |
| ) | |
| text_encoder = CLIPTextModel(text_encoder_config) | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| controlnet = MultiControlNetModel([controlnet1, controlnet2]) | |
| components = { | |
| "unet": unet, | |
| "controlnet": controlnet, | |
| "scheduler": scheduler, | |
| "vae": vae, | |
| "text_encoder": text_encoder, | |
| "tokenizer": tokenizer, | |
| "safety_checker": None, | |
| "feature_extractor": None, | |
| } | |
| return components | |
| def get_dummy_inputs(self, device, seed=0): | |
| 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": "numpy", | |
| "image": images, | |
| } | |
| return inputs | |
| def test_attention_slicing_forward_pass(self): | |
| return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3) | |
| 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_pretrained_raise_not_implemented_exception(self): | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| with tempfile.TemporaryDirectory() as tmpdir: | |
| try: | |
| # save_pretrained is not implemented for Multi-ControlNet | |
| pipe.save_pretrained(tmpdir) | |
| except NotImplementedError: | |
| pass | |
| # override PipelineTesterMixin | |
| def test_save_load_float16(self): | |
| ... | |
| # override PipelineTesterMixin | |
| def test_save_load_local(self): | |
| ... | |
| # override PipelineTesterMixin | |
| def test_save_load_optional_components(self): | |
| ... | |
| class StableDiffusionControlNetPipelineSlowTests(unittest.TestCase): | |
| def tearDown(self): | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def test_canny(self): | |
| controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny") | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet | |
| ) | |
| pipe.enable_model_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" | |
| ) | |
| output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) | |
| image = output.images[0] | |
| assert image.shape == (768, 512, 3) | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out.npy" | |
| ) | |
| assert np.abs(expected_image - image).max() < 5e-3 | |
| def test_depth(self): | |
| controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-depth") | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet | |
| ) | |
| pipe.enable_model_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" | |
| ) | |
| output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) | |
| image = output.images[0] | |
| assert image.shape == (512, 512, 3) | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth_out.npy" | |
| ) | |
| assert np.abs(expected_image - image).max() < 5e-3 | |
| def test_hed(self): | |
| controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-hed") | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet | |
| ) | |
| pipe.enable_model_cpu_offload() | |
| pipe.set_progress_bar_config(disable=None) | |
| generator = torch.Generator(device="cpu").manual_seed(0) | |
| prompt = "oil painting of handsome old man, masterpiece" | |
| image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed.png" | |
| ) | |
| output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) | |
| image = output.images[0] | |
| assert image.shape == (704, 512, 3) | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed_out.npy" | |
| ) | |
| assert np.abs(expected_image - image).max() < 5e-3 | |
| def test_mlsd(self): | |
| controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-mlsd") | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet | |
| ) | |
| pipe.enable_model_cpu_offload() | |
| pipe.set_progress_bar_config(disable=None) | |
| generator = torch.Generator(device="cpu").manual_seed(0) | |
| prompt = "room" | |
| image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd.png" | |
| ) | |
| output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) | |
| image = output.images[0] | |
| assert image.shape == (704, 512, 3) | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd_out.npy" | |
| ) | |
| assert np.abs(expected_image - image).max() < 5e-3 | |
| def test_normal(self): | |
| controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-normal") | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet | |
| ) | |
| pipe.enable_model_cpu_offload() | |
| pipe.set_progress_bar_config(disable=None) | |
| generator = torch.Generator(device="cpu").manual_seed(0) | |
| prompt = "cute toy" | |
| image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/cute_toy_normal.png" | |
| ) | |
| output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) | |
| image = output.images[0] | |
| assert image.shape == (512, 512, 3) | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/cute_toy_normal_out.npy" | |
| ) | |
| assert np.abs(expected_image - image).max() < 5e-3 | |
| def test_openpose(self): | |
| controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose") | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet | |
| ) | |
| pipe.enable_model_cpu_offload() | |
| pipe.set_progress_bar_config(disable=None) | |
| generator = torch.Generator(device="cpu").manual_seed(0) | |
| prompt = "Chef in the kitchen" | |
| image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" | |
| ) | |
| output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) | |
| image = output.images[0] | |
| assert image.shape == (768, 512, 3) | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/chef_pose_out.npy" | |
| ) | |
| assert np.abs(expected_image - image).max() < 5e-3 | |
| def test_scribble(self): | |
| controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble") | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet | |
| ) | |
| pipe.enable_model_cpu_offload() | |
| pipe.set_progress_bar_config(disable=None) | |
| generator = torch.Generator(device="cpu").manual_seed(5) | |
| prompt = "bag" | |
| image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble.png" | |
| ) | |
| output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) | |
| image = output.images[0] | |
| assert image.shape == (640, 512, 3) | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble_out.npy" | |
| ) | |
| assert np.abs(expected_image - image).max() < 5e-3 | |
| def test_seg(self): | |
| controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg") | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet | |
| ) | |
| pipe.enable_model_cpu_offload() | |
| pipe.set_progress_bar_config(disable=None) | |
| generator = torch.Generator(device="cpu").manual_seed(5) | |
| prompt = "house" | |
| image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png" | |
| ) | |
| output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) | |
| image = output.images[0] | |
| assert image.shape == (512, 512, 3) | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg_out.npy" | |
| ) | |
| assert np.abs(expected_image - image).max() < 5e-3 | |
| def test_sequential_cpu_offloading(self): | |
| torch.cuda.empty_cache() | |
| torch.cuda.reset_max_memory_allocated() | |
| torch.cuda.reset_peak_memory_stats() | |
| controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg") | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet | |
| ) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| pipe.enable_sequential_cpu_offload() | |
| prompt = "house" | |
| image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png" | |
| ) | |
| _ = pipe( | |
| prompt, | |
| image, | |
| num_inference_steps=2, | |
| output_type="np", | |
| ) | |
| mem_bytes = torch.cuda.max_memory_allocated() | |
| # make sure that less than 7 GB is allocated | |
| assert mem_bytes < 4 * 10**9 | |
| class StableDiffusionMultiControlNetPipelineSlowTests(unittest.TestCase): | |
| def tearDown(self): | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def test_pose_and_canny(self): | |
| controlnet_canny = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny") | |
| controlnet_pose = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose") | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=[controlnet_pose, controlnet_canny] | |
| ) | |
| pipe.enable_model_cpu_offload() | |
| pipe.set_progress_bar_config(disable=None) | |
| generator = torch.Generator(device="cpu").manual_seed(0) | |
| prompt = "bird and Chef" | |
| image_canny = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" | |
| ) | |
| image_pose = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" | |
| ) | |
| output = pipe(prompt, [image_pose, image_canny], generator=generator, output_type="np", num_inference_steps=3) | |
| image = output.images[0] | |
| assert image.shape == (768, 512, 3) | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose_canny_out.npy" | |
| ) | |
| assert np.abs(expected_image - image).max() < 5e-2 | |