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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 time | |
| import unittest | |
| import numpy as np | |
| import torch | |
| from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer | |
| from diffusers import ( | |
| AutoencoderKL, | |
| DDIMScheduler, | |
| DPMSolverMultistepScheduler, | |
| EulerDiscreteScheduler, | |
| StableDiffusionPipeline, | |
| UNet2DConditionModel, | |
| ) | |
| from diffusers.utils import load_numpy, slow, torch_device | |
| from diffusers.utils.testing_utils import require_torch_gpu | |
| torch.backends.cuda.matmul.allow_tf32 = False | |
| class StableDiffusion2VPredictionPipelineFastTests(unittest.TestCase): | |
| def tearDown(self): | |
| # clean up the VRAM after each test | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def dummy_cond_unet(self): | |
| torch.manual_seed(0) | |
| model = 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, | |
| # SD2-specific config below | |
| attention_head_dim=(2, 4), | |
| use_linear_projection=True, | |
| ) | |
| return model | |
| def dummy_vae(self): | |
| torch.manual_seed(0) | |
| model = 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, | |
| sample_size=128, | |
| ) | |
| return model | |
| def dummy_text_encoder(self): | |
| torch.manual_seed(0) | |
| 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=64, | |
| ) | |
| return CLIPTextModel(config) | |
| def test_stable_diffusion_v_pred_ddim(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| unet = self.dummy_cond_unet | |
| scheduler = DDIMScheduler( | |
| beta_start=0.00085, | |
| beta_end=0.012, | |
| beta_schedule="scaled_linear", | |
| clip_sample=False, | |
| set_alpha_to_one=False, | |
| prediction_type="v_prediction", | |
| ) | |
| vae = self.dummy_vae | |
| bert = self.dummy_text_encoder | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| # make sure here that pndm scheduler skips prk | |
| sd_pipe = StableDiffusionPipeline( | |
| unet=unet, | |
| scheduler=scheduler, | |
| vae=vae, | |
| text_encoder=bert, | |
| tokenizer=tokenizer, | |
| safety_checker=None, | |
| feature_extractor=None, | |
| requires_safety_checker=False, | |
| ) | |
| sd_pipe = sd_pipe.to(device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| prompt = "A painting of a squirrel eating a burger" | |
| generator = torch.Generator(device=device).manual_seed(0) | |
| output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np") | |
| image = output.images | |
| generator = torch.Generator(device=device).manual_seed(0) | |
| image_from_tuple = sd_pipe( | |
| [prompt], | |
| generator=generator, | |
| guidance_scale=6.0, | |
| num_inference_steps=2, | |
| output_type="np", | |
| return_dict=False, | |
| )[0] | |
| image_slice = image[0, -3:, -3:, -1] | |
| image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] | |
| assert image.shape == (1, 64, 64, 3) | |
| expected_slice = np.array([0.6424, 0.6109, 0.494, 0.5088, 0.4984, 0.4525, 0.5059, 0.5068, 0.4474]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
| assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 | |
| def test_stable_diffusion_v_pred_k_euler(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| unet = self.dummy_cond_unet | |
| scheduler = EulerDiscreteScheduler( | |
| beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", prediction_type="v_prediction" | |
| ) | |
| vae = self.dummy_vae | |
| bert = self.dummy_text_encoder | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| # make sure here that pndm scheduler skips prk | |
| sd_pipe = StableDiffusionPipeline( | |
| unet=unet, | |
| scheduler=scheduler, | |
| vae=vae, | |
| text_encoder=bert, | |
| tokenizer=tokenizer, | |
| safety_checker=None, | |
| feature_extractor=None, | |
| requires_safety_checker=False, | |
| ) | |
| sd_pipe = sd_pipe.to(device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| prompt = "A painting of a squirrel eating a burger" | |
| generator = torch.Generator(device=device).manual_seed(0) | |
| output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np") | |
| image = output.images | |
| generator = torch.Generator(device=device).manual_seed(0) | |
| image_from_tuple = sd_pipe( | |
| [prompt], | |
| generator=generator, | |
| guidance_scale=6.0, | |
| num_inference_steps=2, | |
| output_type="np", | |
| return_dict=False, | |
| )[0] | |
| image_slice = image[0, -3:, -3:, -1] | |
| image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] | |
| assert image.shape == (1, 64, 64, 3) | |
| expected_slice = np.array([0.4616, 0.5184, 0.4887, 0.5111, 0.4839, 0.48, 0.5119, 0.5263, 0.4776]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
| assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 | |
| def test_stable_diffusion_v_pred_fp16(self): | |
| """Test that stable diffusion v-prediction works with fp16""" | |
| unet = self.dummy_cond_unet | |
| scheduler = DDIMScheduler( | |
| beta_start=0.00085, | |
| beta_end=0.012, | |
| beta_schedule="scaled_linear", | |
| clip_sample=False, | |
| set_alpha_to_one=False, | |
| prediction_type="v_prediction", | |
| ) | |
| vae = self.dummy_vae | |
| bert = self.dummy_text_encoder | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| # put models in fp16 | |
| unet = unet.half() | |
| vae = vae.half() | |
| bert = bert.half() | |
| # make sure here that pndm scheduler skips prk | |
| sd_pipe = StableDiffusionPipeline( | |
| unet=unet, | |
| scheduler=scheduler, | |
| vae=vae, | |
| text_encoder=bert, | |
| tokenizer=tokenizer, | |
| safety_checker=None, | |
| feature_extractor=None, | |
| requires_safety_checker=False, | |
| ) | |
| sd_pipe = sd_pipe.to(torch_device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| prompt = "A painting of a squirrel eating a burger" | |
| generator = torch.manual_seed(0) | |
| image = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="np").images | |
| assert image.shape == (1, 64, 64, 3) | |
| class StableDiffusion2VPredictionPipelineIntegrationTests(unittest.TestCase): | |
| def tearDown(self): | |
| # clean up the VRAM after each test | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def test_stable_diffusion_v_pred_default(self): | |
| sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2") | |
| sd_pipe = sd_pipe.to(torch_device) | |
| sd_pipe.enable_attention_slicing() | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| prompt = "A painting of a squirrel eating a burger" | |
| generator = torch.manual_seed(0) | |
| output = sd_pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=20, output_type="np") | |
| image = output.images | |
| image_slice = image[0, 253:256, 253:256, -1] | |
| assert image.shape == (1, 768, 768, 3) | |
| expected_slice = np.array([0.1868, 0.1922, 0.1527, 0.1921, 0.1908, 0.1624, 0.1779, 0.1652, 0.1734]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
| def test_stable_diffusion_v_pred_upcast_attention(self): | |
| sd_pipe = StableDiffusionPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16 | |
| ) | |
| sd_pipe = sd_pipe.to(torch_device) | |
| sd_pipe.enable_attention_slicing() | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| prompt = "A painting of a squirrel eating a burger" | |
| generator = torch.manual_seed(0) | |
| output = sd_pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=20, output_type="np") | |
| image = output.images | |
| image_slice = image[0, 253:256, 253:256, -1] | |
| assert image.shape == (1, 768, 768, 3) | |
| expected_slice = np.array([0.4209, 0.4087, 0.4097, 0.4209, 0.3860, 0.4329, 0.4280, 0.4324, 0.4187]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2 | |
| def test_stable_diffusion_v_pred_euler(self): | |
| scheduler = EulerDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-2", subfolder="scheduler") | |
| sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=scheduler) | |
| sd_pipe = sd_pipe.to(torch_device) | |
| sd_pipe.enable_attention_slicing() | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| prompt = "A painting of a squirrel eating a burger" | |
| generator = torch.manual_seed(0) | |
| output = sd_pipe([prompt], generator=generator, num_inference_steps=5, output_type="numpy") | |
| image = output.images | |
| image_slice = image[0, 253:256, 253:256, -1] | |
| assert image.shape == (1, 768, 768, 3) | |
| expected_slice = np.array([0.1781, 0.1695, 0.1661, 0.1705, 0.1588, 0.1699, 0.2005, 0.1589, 0.1677]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
| def test_stable_diffusion_v_pred_dpm(self): | |
| """ | |
| TODO: update this test after making DPM compatible with V-prediction! | |
| """ | |
| scheduler = DPMSolverMultistepScheduler.from_pretrained( | |
| "stabilityai/stable-diffusion-2", subfolder="scheduler" | |
| ) | |
| sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=scheduler) | |
| sd_pipe = sd_pipe.to(torch_device) | |
| sd_pipe.enable_attention_slicing() | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| prompt = "a photograph of an astronaut riding a horse" | |
| generator = torch.manual_seed(0) | |
| image = sd_pipe( | |
| [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=5, output_type="numpy" | |
| ).images | |
| image_slice = image[0, 253:256, 253:256, -1] | |
| assert image.shape == (1, 768, 768, 3) | |
| expected_slice = np.array([0.3303, 0.3184, 0.3291, 0.3300, 0.3256, 0.3113, 0.2965, 0.3134, 0.3192]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
| def test_stable_diffusion_attention_slicing_v_pred(self): | |
| torch.cuda.reset_peak_memory_stats() | |
| model_id = "stabilityai/stable-diffusion-2" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| prompt = "a photograph of an astronaut riding a horse" | |
| # make attention efficient | |
| pipe.enable_attention_slicing() | |
| generator = torch.manual_seed(0) | |
| output_chunked = pipe( | |
| [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy" | |
| ) | |
| image_chunked = output_chunked.images | |
| mem_bytes = torch.cuda.max_memory_allocated() | |
| torch.cuda.reset_peak_memory_stats() | |
| # make sure that less than 5.5 GB is allocated | |
| assert mem_bytes < 5.5 * 10**9 | |
| # disable slicing | |
| pipe.disable_attention_slicing() | |
| generator = torch.manual_seed(0) | |
| output = pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy") | |
| image = output.images | |
| # make sure that more than 5.5 GB is allocated | |
| mem_bytes = torch.cuda.max_memory_allocated() | |
| assert mem_bytes > 5.5 * 10**9 | |
| assert np.abs(image_chunked.flatten() - image.flatten()).max() < 1e-3 | |
| def test_stable_diffusion_text2img_pipeline_v_pred_default(self): | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/" | |
| "sd2-text2img/astronaut_riding_a_horse_v_pred.npy" | |
| ) | |
| pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2") | |
| pipe.to(torch_device) | |
| pipe.enable_attention_slicing() | |
| pipe.set_progress_bar_config(disable=None) | |
| prompt = "astronaut riding a horse" | |
| generator = torch.manual_seed(0) | |
| output = pipe(prompt=prompt, guidance_scale=7.5, generator=generator, output_type="np") | |
| image = output.images[0] | |
| assert image.shape == (768, 768, 3) | |
| assert np.abs(expected_image - image).max() < 7.5e-2 | |
| def test_stable_diffusion_text2img_pipeline_v_pred_fp16(self): | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/" | |
| "sd2-text2img/astronaut_riding_a_horse_v_pred_fp16.npy" | |
| ) | |
| pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", torch_dtype=torch.float16) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| prompt = "astronaut riding a horse" | |
| generator = torch.manual_seed(0) | |
| output = pipe(prompt=prompt, guidance_scale=7.5, generator=generator, output_type="np") | |
| image = output.images[0] | |
| assert image.shape == (768, 768, 3) | |
| assert np.abs(expected_image - image).max() < 7.5e-1 | |
| def test_stable_diffusion_text2img_intermediate_state_v_pred(self): | |
| number_of_steps = 0 | |
| def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None: | |
| test_callback_fn.has_been_called = True | |
| nonlocal number_of_steps | |
| number_of_steps += 1 | |
| if step == 0: | |
| latents = latents.detach().cpu().numpy() | |
| assert latents.shape == (1, 4, 96, 96) | |
| latents_slice = latents[0, -3:, -3:, -1] | |
| expected_slice = np.array([0.7749, 0.0325, 0.5088, 0.1619, 0.3372, 0.3667, -0.5186, 0.6860, 1.4326]) | |
| assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 | |
| elif step == 19: | |
| latents = latents.detach().cpu().numpy() | |
| assert latents.shape == (1, 4, 96, 96) | |
| latents_slice = latents[0, -3:, -3:, -1] | |
| expected_slice = np.array([1.3887, 1.0273, 1.7266, 0.0726, 0.6611, 0.1598, -1.0547, 0.1522, 0.0227]) | |
| assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 | |
| test_callback_fn.has_been_called = False | |
| pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", torch_dtype=torch.float16) | |
| pipe = pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| prompt = "Andromeda galaxy in a bottle" | |
| generator = torch.manual_seed(0) | |
| pipe( | |
| prompt=prompt, | |
| num_inference_steps=20, | |
| guidance_scale=7.5, | |
| generator=generator, | |
| callback=test_callback_fn, | |
| callback_steps=1, | |
| ) | |
| assert test_callback_fn.has_been_called | |
| assert number_of_steps == 20 | |
| def test_stable_diffusion_low_cpu_mem_usage_v_pred(self): | |
| pipeline_id = "stabilityai/stable-diffusion-2" | |
| start_time = time.time() | |
| pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16) | |
| pipeline_low_cpu_mem_usage.to(torch_device) | |
| low_cpu_mem_usage_time = time.time() - start_time | |
| start_time = time.time() | |
| _ = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16, low_cpu_mem_usage=False) | |
| normal_load_time = time.time() - start_time | |
| assert 2 * low_cpu_mem_usage_time < normal_load_time | |
| def test_stable_diffusion_pipeline_with_sequential_cpu_offloading_v_pred(self): | |
| torch.cuda.empty_cache() | |
| torch.cuda.reset_max_memory_allocated() | |
| torch.cuda.reset_peak_memory_stats() | |
| pipeline_id = "stabilityai/stable-diffusion-2" | |
| prompt = "Andromeda galaxy in a bottle" | |
| pipeline = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16) | |
| pipeline = pipeline.to(torch_device) | |
| pipeline.enable_attention_slicing(1) | |
| pipeline.enable_sequential_cpu_offload() | |
| generator = torch.manual_seed(0) | |
| _ = pipeline(prompt, generator=generator, num_inference_steps=5) | |
| mem_bytes = torch.cuda.max_memory_allocated() | |
| # make sure that less than 2.8 GB is allocated | |
| assert mem_bytes < 2.8 * 10**9 | |