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ldm3d-inpainting
/
diffuserslocal
/tests
/pipelines
/stable_diffusion
/test_stable_diffusion_adapter.py
| # coding=utf-8 | |
| # Copyright 2022 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 random | |
| import unittest | |
| import numpy as np | |
| import torch | |
| from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer | |
| import diffusers | |
| from diffusers import ( | |
| AutoencoderKL, | |
| MultiAdapter, | |
| PNDMScheduler, | |
| StableDiffusionAdapterPipeline, | |
| T2IAdapter, | |
| UNet2DConditionModel, | |
| ) | |
| from diffusers.utils import logging | |
| from diffusers.utils.import_utils import is_xformers_available | |
| from diffusers.utils.testing_utils import ( | |
| enable_full_determinism, | |
| floats_tensor, | |
| load_image, | |
| load_numpy, | |
| require_torch_gpu, | |
| slow, | |
| torch_device, | |
| ) | |
| from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS | |
| from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference | |
| enable_full_determinism() | |
| class AdapterTests: | |
| pipeline_class = StableDiffusionAdapterPipeline | |
| params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS | |
| batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS | |
| def get_dummy_components(self, adapter_type): | |
| 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=("CrossAttnDownBlock2D", "DownBlock2D"), | |
| up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), | |
| cross_attention_dim=32, | |
| ) | |
| scheduler = PNDMScheduler(skip_prk_steps=True) | |
| 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") | |
| torch.manual_seed(0) | |
| if adapter_type == "full_adapter" or adapter_type == "light_adapter": | |
| adapter = T2IAdapter( | |
| in_channels=3, | |
| channels=[32, 64], | |
| num_res_blocks=2, | |
| downscale_factor=2, | |
| adapter_type=adapter_type, | |
| ) | |
| elif adapter_type == "multi_adapter": | |
| adapter = MultiAdapter( | |
| [ | |
| T2IAdapter( | |
| in_channels=3, | |
| channels=[32, 64], | |
| num_res_blocks=2, | |
| downscale_factor=2, | |
| adapter_type="full_adapter", | |
| ), | |
| T2IAdapter( | |
| in_channels=3, | |
| channels=[32, 64], | |
| num_res_blocks=2, | |
| downscale_factor=2, | |
| adapter_type="full_adapter", | |
| ), | |
| ] | |
| ) | |
| else: | |
| raise ValueError( | |
| f"Unknown adapter type: {adapter_type}, must be one of 'full_adapter', 'light_adapter', or 'multi_adapter''" | |
| ) | |
| components = { | |
| "adapter": adapter, | |
| "unet": unet, | |
| "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, num_images=1): | |
| if num_images == 1: | |
| image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device) | |
| else: | |
| image = [floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device) for _ in range(num_images)] | |
| 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": image, | |
| "generator": generator, | |
| "num_inference_steps": 2, | |
| "guidance_scale": 6.0, | |
| "output_type": "numpy", | |
| } | |
| 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 StableDiffusionFullAdapterPipelineFastTests(AdapterTests, PipelineTesterMixin, unittest.TestCase): | |
| def get_dummy_components(self): | |
| return super().get_dummy_components("full_adapter") | |
| def test_stable_diffusion_adapter_default_case(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| sd_pipe = StableDiffusionAdapterPipeline(**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([0.4858, 0.5500, 0.4278, 0.4669, 0.6184, 0.4322, 0.5010, 0.5033, 0.4746]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3 | |
| class StableDiffusionLightAdapterPipelineFastTests(AdapterTests, PipelineTesterMixin, unittest.TestCase): | |
| def get_dummy_components(self): | |
| return super().get_dummy_components("light_adapter") | |
| def test_stable_diffusion_adapter_default_case(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| sd_pipe = StableDiffusionAdapterPipeline(**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([0.4965, 0.5548, 0.4330, 0.4771, 0.6226, 0.4382, 0.5037, 0.5071, 0.4782]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3 | |
| class StableDiffusionMultiAdapterPipelineFastTests(AdapterTests, PipelineTesterMixin, unittest.TestCase): | |
| def get_dummy_components(self): | |
| return super().get_dummy_components("multi_adapter") | |
| def get_dummy_inputs(self, device, seed=0): | |
| inputs = super().get_dummy_inputs(device, seed, num_images=2) | |
| inputs["adapter_conditioning_scale"] = [0.5, 0.5] | |
| return inputs | |
| def test_stable_diffusion_adapter_default_case(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| sd_pipe = StableDiffusionAdapterPipeline(**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([0.4902, 0.5539, 0.4317, 0.4682, 0.6190, 0.4351, 0.5018, 0.5046, 0.4772]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3 | |
| def test_inference_batch_consistent( | |
| self, batch_sizes=[2, 4, 13], additional_params_copy_to_batched_inputs=["num_inference_steps"] | |
| ): | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(torch_device) | |
| logger = logging.get_logger(pipe.__module__) | |
| logger.setLevel(level=diffusers.logging.FATAL) | |
| # batchify inputs | |
| for batch_size in batch_sizes: | |
| batched_inputs = {} | |
| for name, value in inputs.items(): | |
| if name in self.batch_params: | |
| # prompt is string | |
| if name == "prompt": | |
| len_prompt = len(value) | |
| # make unequal batch sizes | |
| batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)] | |
| # make last batch super long | |
| batched_inputs[name][-1] = 100 * "very long" | |
| elif name == "image": | |
| batched_images = [] | |
| for image in value: | |
| batched_images.append(batch_size * [image]) | |
| batched_inputs[name] = batched_images | |
| else: | |
| batched_inputs[name] = batch_size * [value] | |
| elif name == "batch_size": | |
| batched_inputs[name] = batch_size | |
| else: | |
| batched_inputs[name] = value | |
| for arg in additional_params_copy_to_batched_inputs: | |
| batched_inputs[arg] = inputs[arg] | |
| batched_inputs["output_type"] = "np" | |
| if self.pipeline_class.__name__ == "DanceDiffusionPipeline": | |
| batched_inputs.pop("output_type") | |
| output = pipe(**batched_inputs) | |
| assert len(output[0]) == batch_size | |
| batched_inputs["output_type"] = "np" | |
| if self.pipeline_class.__name__ == "DanceDiffusionPipeline": | |
| batched_inputs.pop("output_type") | |
| output = pipe(**batched_inputs)[0] | |
| assert output.shape[0] == batch_size | |
| logger.setLevel(level=diffusers.logging.WARNING) | |
| def test_num_images_per_prompt(self): | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| batch_sizes = [1, 2] | |
| num_images_per_prompts = [1, 2] | |
| for batch_size in batch_sizes: | |
| for num_images_per_prompt in num_images_per_prompts: | |
| inputs = self.get_dummy_inputs(torch_device) | |
| for key in inputs.keys(): | |
| if key in self.batch_params: | |
| if key == "image": | |
| batched_images = [] | |
| for image in inputs[key]: | |
| batched_images.append(batch_size * [image]) | |
| inputs[key] = batched_images | |
| else: | |
| inputs[key] = batch_size * [inputs[key]] | |
| images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0] | |
| assert images.shape[0] == batch_size * num_images_per_prompt | |
| def test_inference_batch_single_identical( | |
| self, | |
| batch_size=3, | |
| test_max_difference=None, | |
| test_mean_pixel_difference=None, | |
| relax_max_difference=False, | |
| expected_max_diff=2e-3, | |
| additional_params_copy_to_batched_inputs=["num_inference_steps"], | |
| ): | |
| if test_max_difference is None: | |
| # TODO(Pedro) - not sure why, but not at all reproducible at the moment it seems | |
| # make sure that batched and non-batched is identical | |
| test_max_difference = torch_device != "mps" | |
| if test_mean_pixel_difference is None: | |
| # TODO same as above | |
| test_mean_pixel_difference = torch_device != "mps" | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(torch_device) | |
| logger = logging.get_logger(pipe.__module__) | |
| logger.setLevel(level=diffusers.logging.FATAL) | |
| # batchify inputs | |
| batched_inputs = {} | |
| batch_size = batch_size | |
| for name, value in inputs.items(): | |
| if name in self.batch_params: | |
| # prompt is string | |
| if name == "prompt": | |
| len_prompt = len(value) | |
| # make unequal batch sizes | |
| batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)] | |
| # make last batch super long | |
| batched_inputs[name][-1] = 100 * "very long" | |
| elif name == "image": | |
| batched_images = [] | |
| for image in value: | |
| batched_images.append(batch_size * [image]) | |
| batched_inputs[name] = batched_images | |
| else: | |
| batched_inputs[name] = batch_size * [value] | |
| elif name == "batch_size": | |
| batched_inputs[name] = batch_size | |
| elif name == "generator": | |
| batched_inputs[name] = [self.get_generator(i) for i in range(batch_size)] | |
| else: | |
| batched_inputs[name] = value | |
| for arg in additional_params_copy_to_batched_inputs: | |
| batched_inputs[arg] = inputs[arg] | |
| if self.pipeline_class.__name__ != "DanceDiffusionPipeline": | |
| batched_inputs["output_type"] = "np" | |
| output_batch = pipe(**batched_inputs) | |
| assert output_batch[0].shape[0] == batch_size | |
| inputs["generator"] = self.get_generator(0) | |
| output = pipe(**inputs) | |
| logger.setLevel(level=diffusers.logging.WARNING) | |
| if test_max_difference: | |
| if relax_max_difference: | |
| # Taking the median of the largest <n> differences | |
| # is resilient to outliers | |
| diff = np.abs(output_batch[0][0] - output[0][0]) | |
| diff = diff.flatten() | |
| diff.sort() | |
| max_diff = np.median(diff[-5:]) | |
| else: | |
| max_diff = np.abs(output_batch[0][0] - output[0][0]).max() | |
| assert max_diff < expected_max_diff | |
| if test_mean_pixel_difference: | |
| assert_mean_pixel_difference(output_batch[0][0], output[0][0]) | |
| class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase): | |
| def tearDown(self): | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def test_stable_diffusion_adapter(self): | |
| test_cases = [ | |
| ( | |
| "TencentARC/t2iadapter_color_sd14v1", | |
| "CompVis/stable-diffusion-v1-4", | |
| "snail", | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/color.png", | |
| 3, | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_color_sd14v1.npy", | |
| ), | |
| ( | |
| "TencentARC/t2iadapter_depth_sd14v1", | |
| "CompVis/stable-diffusion-v1-4", | |
| "desk", | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/desk_depth.png", | |
| 3, | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_depth_sd14v1.npy", | |
| ), | |
| ( | |
| "TencentARC/t2iadapter_depth_sd15v2", | |
| "runwayml/stable-diffusion-v1-5", | |
| "desk", | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/desk_depth.png", | |
| 3, | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_depth_sd15v2.npy", | |
| ), | |
| ( | |
| "TencentARC/t2iadapter_keypose_sd14v1", | |
| "CompVis/stable-diffusion-v1-4", | |
| "person", | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/person_keypose.png", | |
| 3, | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_keypose_sd14v1.npy", | |
| ), | |
| ( | |
| "TencentARC/t2iadapter_openpose_sd14v1", | |
| "CompVis/stable-diffusion-v1-4", | |
| "person", | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/iron_man_pose.png", | |
| 3, | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_openpose_sd14v1.npy", | |
| ), | |
| ( | |
| "TencentARC/t2iadapter_seg_sd14v1", | |
| "CompVis/stable-diffusion-v1-4", | |
| "motorcycle", | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motor.png", | |
| 3, | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_seg_sd14v1.npy", | |
| ), | |
| ( | |
| "TencentARC/t2iadapter_zoedepth_sd15v1", | |
| "runwayml/stable-diffusion-v1-5", | |
| "motorcycle", | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motorcycle.png", | |
| 3, | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_zoedepth_sd15v1.npy", | |
| ), | |
| ( | |
| "TencentARC/t2iadapter_canny_sd14v1", | |
| "CompVis/stable-diffusion-v1-4", | |
| "toy", | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png", | |
| 1, | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_canny_sd14v1.npy", | |
| ), | |
| ( | |
| "TencentARC/t2iadapter_canny_sd15v2", | |
| "runwayml/stable-diffusion-v1-5", | |
| "toy", | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png", | |
| 1, | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_canny_sd15v2.npy", | |
| ), | |
| ( | |
| "TencentARC/t2iadapter_sketch_sd14v1", | |
| "CompVis/stable-diffusion-v1-4", | |
| "cat", | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/edge.png", | |
| 1, | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_sketch_sd14v1.npy", | |
| ), | |
| ( | |
| "TencentARC/t2iadapter_sketch_sd15v2", | |
| "runwayml/stable-diffusion-v1-5", | |
| "cat", | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/edge.png", | |
| 1, | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_sketch_sd15v2.npy", | |
| ), | |
| ] | |
| for adapter_model, sd_model, prompt, image_url, input_channels, out_url in test_cases: | |
| image = load_image(image_url) | |
| expected_out = load_numpy(out_url) | |
| if input_channels == 1: | |
| image = image.convert("L") | |
| adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16) | |
| pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| generator = torch.Generator(device="cpu").manual_seed(0) | |
| out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images | |
| self.assertTrue(np.allclose(out, expected_out)) | |
| def test_stable_diffusion_adapter_pipeline_with_sequential_cpu_offloading(self): | |
| torch.cuda.empty_cache() | |
| torch.cuda.reset_max_memory_allocated() | |
| torch.cuda.reset_peak_memory_stats() | |
| adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_seg_sd14v1") | |
| pipe = StableDiffusionAdapterPipeline.from_pretrained( | |
| "CompVis/stable-diffusion-v1-4", adapter=adapter, safety_checker=None | |
| ) | |
| pipe = pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing(1) | |
| pipe.enable_sequential_cpu_offload() | |
| image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motor.png" | |
| ) | |
| pipe(prompt="foo", image=image, num_inference_steps=2) | |
| mem_bytes = torch.cuda.max_memory_allocated() | |
| assert mem_bytes < 5 * 10**9 | |