# 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 from diffusers import ( AutoencoderKL, PNDMScheduler, StableDiffusionAdapterPipeline, T2IAdapter, UNet2DConditionModel, ) from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS from ..test_pipelines_common import PipelineTesterMixin 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) adapter = T2IAdapter( in_channels=3, channels=[32, 64], num_res_blocks=2, downscale_factor=2, adapter_type=adapter_type, ) 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): image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device) 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) @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) 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 @slow @require_torch_gpu 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