# 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 os import random import tempfile import unittest import numpy as np import torch import PIL from diffusers import ( AutoencoderKL, DDIMPipeline, DDIMScheduler, DDPMPipeline, DDPMScheduler, KarrasVePipeline, KarrasVeScheduler, LDMPipeline, LDMTextToImagePipeline, LMSDiscreteScheduler, PNDMPipeline, PNDMScheduler, ScoreSdeVePipeline, ScoreSdeVeScheduler, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipeline, StableDiffusionOnnxPipeline, StableDiffusionPipeline, UNet2DConditionModel, UNet2DModel, VQModel, ) from diffusers.pipeline_utils import DiffusionPipeline from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, load_image, slow, torch_device from diffusers.utils.testing_utils import get_tests_dir from PIL import Image from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer torch.backends.cuda.matmul.allow_tf32 = False def test_progress_bar(capsys): model = UNet2DModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=3, out_channels=3, down_block_types=("DownBlock2D", "AttnDownBlock2D"), up_block_types=("AttnUpBlock2D", "UpBlock2D"), ) scheduler = DDPMScheduler(num_train_timesteps=10) ddpm = DDPMPipeline(model, scheduler).to(torch_device) ddpm(output_type="numpy").images captured = capsys.readouterr() assert "10/10" in captured.err, "Progress bar has to be displayed" ddpm.set_progress_bar_config(disable=True) ddpm(output_type="numpy").images captured = capsys.readouterr() assert captured.err == "", "Progress bar should be disabled" class CustomPipelineTests(unittest.TestCase): def test_load_custom_pipeline(self): pipeline = DiffusionPipeline.from_pretrained( "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline" ) # NOTE that `"CustomPipeline"` is not a class that is defined in this library, but solely on the Hub # under https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L24 assert pipeline.__class__.__name__ == "CustomPipeline" def test_run_custom_pipeline(self): pipeline = DiffusionPipeline.from_pretrained( "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline" ) images, output_str = pipeline(num_inference_steps=2, output_type="np") assert images[0].shape == (1, 32, 32, 3) # compare output to https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L102 assert output_str == "This is a test" def test_local_custom_pipeline(self): local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline") pipeline = DiffusionPipeline.from_pretrained( "google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path ) images, output_str = pipeline(num_inference_steps=2, output_type="np") assert pipeline.__class__.__name__ == "CustomLocalPipeline" assert images[0].shape == (1, 32, 32, 3) # compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102 assert output_str == "This is a local test" @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_load_pipeline_from_git(self): clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K" feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id) clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16) pipeline = DiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", custom_pipeline="clip_guided_stable_diffusion", clip_model=clip_model, feature_extractor=feature_extractor, torch_dtype=torch.float16, revision="fp16", ) pipeline.enable_attention_slicing() pipeline = pipeline.to(torch_device) # NOTE that `"CLIPGuidedStableDiffusion"` is not a class that is defined in the pypi package of th e library, but solely on the community examples folder of GitHub under: # https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion.py assert pipeline.__class__.__name__ == "CLIPGuidedStableDiffusion" image = pipeline("a prompt", num_inference_steps=2, output_type="np").images[0] assert image.shape == (512, 512, 3) class PipelineFastTests(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() @property def dummy_image(self): batch_size = 1 num_channels = 3 sizes = (32, 32) image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device) return image @property def dummy_uncond_unet(self): torch.manual_seed(0) model = UNet2DModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=3, out_channels=3, down_block_types=("DownBlock2D", "AttnDownBlock2D"), up_block_types=("AttnUpBlock2D", "UpBlock2D"), ) return model @property 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, ) return model @property def dummy_vq_model(self): torch.manual_seed(0) model = VQModel( block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], latent_channels=3, ) return model @property 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, ) return model @property 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, ) return CLIPTextModel(config) @property def dummy_safety_checker(self): def check(images, *args, **kwargs): return images, [False] * len(images) return check @property def dummy_extractor(self): def extract(*args, **kwargs): class Out: def __init__(self): self.pixel_values = torch.ones([0]) def to(self, device): self.pixel_values.to(device) return self return Out() return extract def test_ddim(self): unet = self.dummy_uncond_unet scheduler = DDIMScheduler() ddpm = DDIMPipeline(unet=unet, scheduler=scheduler) ddpm.to(torch_device) ddpm.set_progress_bar_config(disable=None) # Warmup pass when using mps (see #372) if torch_device == "mps": _ = ddpm(num_inference_steps=1) generator = torch.manual_seed(0) image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images generator = torch.manual_seed(0) image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type="numpy", 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, 32, 32, 3) expected_slice = np.array( [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04] ) tolerance = 1e-2 if torch_device != "mps" else 3e-2 assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance def test_pndm_cifar10(self): unet = self.dummy_uncond_unet scheduler = PNDMScheduler() pndm = PNDMPipeline(unet=unet, scheduler=scheduler) pndm.to(torch_device) pndm.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) image = pndm(generator=generator, num_inference_steps=20, output_type="numpy").images generator = torch.manual_seed(0) image_from_tuple = pndm(generator=generator, num_inference_steps=20, output_type="numpy", 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, 32, 32, 3) expected_slice = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]) 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_ldm_text2img(self): unet = self.dummy_cond_unet scheduler = DDIMScheduler() vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") ldm = LDMTextToImagePipeline(vqvae=vae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler) ldm.to(torch_device) ldm.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" # Warmup pass when using mps (see #372) if torch_device == "mps": generator = torch.manual_seed(0) _ = ldm([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=1, output_type="numpy")[ "sample" ] generator = torch.manual_seed(0) image = ldm([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="numpy")[ "sample" ] generator = torch.manual_seed(0) image_from_tuple = ldm( [prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="numpy", 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([0.5074, 0.5026, 0.4998, 0.4056, 0.3523, 0.4649, 0.5289, 0.5299, 0.4897]) 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_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, ) 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=self.dummy_safety_checker, feature_extractor=self.dummy_extractor, ) 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, 128, 128, 3) expected_slice = np.array([0.5112, 0.4692, 0.4715, 0.5206, 0.4894, 0.5114, 0.5096, 0.4932, 0.4755]) 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_ddim_factor_8(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, ) 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=self.dummy_safety_checker, feature_extractor=self.dummy_extractor, ) 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, height=536, width=536, num_inference_steps=2, output_type="np", ) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 134, 134, 3) expected_slice = np.array([0.7834, 0.5488, 0.5781, 0.46, 0.3609, 0.5369, 0.542, 0.4855, 0.5557]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_pndm(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = PNDMScheduler(skip_prk_steps=True) 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=self.dummy_safety_checker, feature_extractor=self.dummy_extractor, ) 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, 128, 128, 3) expected_slice = np.array([0.4937, 0.4649, 0.4716, 0.5145, 0.4889, 0.513, 0.513, 0.4905, 0.4738]) 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_k_lms(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear") 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=self.dummy_safety_checker, feature_extractor=self.dummy_extractor, ) 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, 128, 128, 3) expected_slice = np.array([0.5067, 0.4689, 0.4614, 0.5233, 0.4903, 0.5112, 0.524, 0.5069, 0.4785]) 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_attention_chunk(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear") 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=self.dummy_safety_checker, feature_extractor=self.dummy_extractor, ) 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_1 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np") # make sure chunking the attention yields the same result sd_pipe.enable_attention_slicing(slice_size=1) generator = torch.Generator(device=device).manual_seed(0) output_2 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np") assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 1e-4 def test_score_sde_ve_pipeline(self): unet = self.dummy_uncond_unet scheduler = ScoreSdeVeScheduler() sde_ve = ScoreSdeVePipeline(unet=unet, scheduler=scheduler) sde_ve.to(torch_device) sde_ve.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) image = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator).images generator = torch.manual_seed(0) image_from_tuple = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator, 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, 32, 32, 3) expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) 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_ldm_uncond(self): unet = self.dummy_uncond_unet scheduler = DDIMScheduler() vae = self.dummy_vq_model ldm = LDMPipeline(unet=unet, vqvae=vae, scheduler=scheduler) ldm.to(torch_device) ldm.set_progress_bar_config(disable=None) # Warmup pass when using mps (see #372) if torch_device == "mps": generator = torch.manual_seed(0) _ = ldm(generator=generator, num_inference_steps=1, output_type="numpy").images generator = torch.manual_seed(0) image = ldm(generator=generator, num_inference_steps=2, output_type="numpy").images generator = torch.manual_seed(0) image_from_tuple = ldm(generator=generator, num_inference_steps=2, output_type="numpy", 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([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]) 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_karras_ve_pipeline(self): unet = self.dummy_uncond_unet scheduler = KarrasVeScheduler() pipe = KarrasVePipeline(unet=unet, scheduler=scheduler) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) image = pipe(num_inference_steps=2, generator=generator, output_type="numpy").images generator = torch.manual_seed(0) image_from_tuple = pipe(num_inference_steps=2, generator=generator, output_type="numpy", 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, 32, 32, 3) expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) 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_img2img(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") init_image = self.dummy_image.to(device) # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionImg2ImgPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=self.dummy_safety_checker, feature_extractor=self.dummy_extractor, ) 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", init_image=init_image, ) 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", init_image=init_image, 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, 32, 32, 3) expected_slice = np.array([0.4492, 0.3865, 0.4222, 0.5854, 0.5139, 0.4379, 0.4193, 0.48, 0.4218]) 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_img2img_k_lms(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear") vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") init_image = self.dummy_image.to(device) # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionImg2ImgPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=self.dummy_safety_checker, feature_extractor=self.dummy_extractor, ) 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", init_image=init_image, ) image = output.images 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", init_image=init_image, return_dict=False, ) image_from_tuple = output[0] image_slice = image[0, -3:, -3:, -1] image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 32, 32, 3) expected_slice = np.array([0.4367, 0.4986, 0.4372, 0.6706, 0.5665, 0.444, 0.5864, 0.6019, 0.5203]) 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_inpaint(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] init_image = Image.fromarray(np.uint8(image)).convert("RGB") mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128)) # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionInpaintPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=self.dummy_safety_checker, feature_extractor=self.dummy_extractor, ) 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", init_image=init_image, mask_image=mask_image, ) 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", init_image=init_image, mask_image=mask_image, 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, 32, 32, 3) expected_slice = np.array([0.4731, 0.5346, 0.4531, 0.6251, 0.5446, 0.4057, 0.5527, 0.5896, 0.5153]) 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_num_images_per_prompt(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = PNDMScheduler(skip_prk_steps=True) 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=self.dummy_safety_checker, feature_extractor=self.dummy_extractor, ) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" # test num_images_per_prompt=1 (default) images = sd_pipe(prompt, num_inference_steps=2, output_type="np").images assert images.shape == (1, 128, 128, 3) # test num_images_per_prompt=1 (default) for batch of prompts batch_size = 2 images = sd_pipe([prompt] * batch_size, num_inference_steps=2, output_type="np").images assert images.shape == (batch_size, 128, 128, 3) # test num_images_per_prompt for single prompt num_images_per_prompt = 2 images = sd_pipe( prompt, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt ).images assert images.shape == (num_images_per_prompt, 128, 128, 3) # test num_images_per_prompt for batch of prompts batch_size = 2 images = sd_pipe( [prompt] * batch_size, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt ).images assert images.shape == (batch_size * num_images_per_prompt, 128, 128, 3) def test_stable_diffusion_img2img_num_images_per_prompt(self): device = "cpu" unet = self.dummy_cond_unet scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") init_image = self.dummy_image.to(device) # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionImg2ImgPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=self.dummy_safety_checker, feature_extractor=self.dummy_extractor, ) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" # test num_images_per_prompt=1 (default) images = sd_pipe( prompt, num_inference_steps=2, output_type="np", init_image=init_image, ).images assert images.shape == (1, 32, 32, 3) # test num_images_per_prompt=1 (default) for batch of prompts batch_size = 2 images = sd_pipe( [prompt] * batch_size, num_inference_steps=2, output_type="np", init_image=init_image, ).images assert images.shape == (batch_size, 32, 32, 3) # test num_images_per_prompt for single prompt num_images_per_prompt = 2 images = sd_pipe( prompt, num_inference_steps=2, output_type="np", init_image=init_image, num_images_per_prompt=num_images_per_prompt, ).images assert images.shape == (num_images_per_prompt, 32, 32, 3) # test num_images_per_prompt for batch of prompts batch_size = 2 images = sd_pipe( [prompt] * batch_size, num_inference_steps=2, output_type="np", init_image=init_image, num_images_per_prompt=num_images_per_prompt, ).images assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3) def test_stable_diffusion_inpaint_num_images_per_prompt(self): device = "cpu" unet = self.dummy_cond_unet scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] init_image = Image.fromarray(np.uint8(image)).convert("RGB") mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128)) # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionInpaintPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=self.dummy_safety_checker, feature_extractor=self.dummy_extractor, ) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" # test num_images_per_prompt=1 (default) images = sd_pipe( prompt, num_inference_steps=2, output_type="np", init_image=init_image, mask_image=mask_image, ).images assert images.shape == (1, 32, 32, 3) # test num_images_per_prompt=1 (default) for batch of prompts batch_size = 2 images = sd_pipe( [prompt] * batch_size, num_inference_steps=2, output_type="np", init_image=init_image, mask_image=mask_image, ).images assert images.shape == (batch_size, 32, 32, 3) # test num_images_per_prompt for single prompt num_images_per_prompt = 2 images = sd_pipe( prompt, num_inference_steps=2, output_type="np", init_image=init_image, mask_image=mask_image, num_images_per_prompt=num_images_per_prompt, ).images assert images.shape == (num_images_per_prompt, 32, 32, 3) # test num_images_per_prompt for batch of prompts batch_size = 2 images = sd_pipe( [prompt] * batch_size, num_inference_steps=2, output_type="np", init_image=init_image, mask_image=mask_image, num_images_per_prompt=num_images_per_prompt, ).images assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3) class PipelineTesterMixin(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def test_smart_download(self): model_id = "hf-internal-testing/unet-pipeline-dummy" with tempfile.TemporaryDirectory() as tmpdirname: _ = DiffusionPipeline.from_pretrained(model_id, cache_dir=tmpdirname, force_download=True) local_repo_name = "--".join(["models"] + model_id.split("/")) snapshot_dir = os.path.join(tmpdirname, local_repo_name, "snapshots") snapshot_dir = os.path.join(snapshot_dir, os.listdir(snapshot_dir)[0]) # inspect all downloaded files to make sure that everything is included assert os.path.isfile(os.path.join(snapshot_dir, DiffusionPipeline.config_name)) assert os.path.isfile(os.path.join(snapshot_dir, CONFIG_NAME)) assert os.path.isfile(os.path.join(snapshot_dir, SCHEDULER_CONFIG_NAME)) assert os.path.isfile(os.path.join(snapshot_dir, WEIGHTS_NAME)) assert os.path.isfile(os.path.join(snapshot_dir, "scheduler", SCHEDULER_CONFIG_NAME)) assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME)) assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME)) # let's make sure the super large numpy file: # https://huggingface.co/hf-internal-testing/unet-pipeline-dummy/blob/main/big_array.npy # is not downloaded, but all the expected ones assert not os.path.isfile(os.path.join(snapshot_dir, "big_array.npy")) @property def dummy_safety_checker(self): def check(images, *args, **kwargs): return images, [False] * len(images) return check def test_from_pretrained_save_pretrained(self): # 1. Load models model = UNet2DModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=3, out_channels=3, down_block_types=("DownBlock2D", "AttnDownBlock2D"), up_block_types=("AttnUpBlock2D", "UpBlock2D"), ) schedular = DDPMScheduler(num_train_timesteps=10) ddpm = DDPMPipeline(model, schedular) ddpm.to(torch_device) ddpm.set_progress_bar_config(disable=None) with tempfile.TemporaryDirectory() as tmpdirname: ddpm.save_pretrained(tmpdirname) new_ddpm = DDPMPipeline.from_pretrained(tmpdirname) new_ddpm.to(torch_device) generator = torch.manual_seed(0) image = ddpm(generator=generator, output_type="numpy").images generator = generator.manual_seed(0) new_image = new_ddpm(generator=generator, output_type="numpy").images assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass" @slow def test_from_pretrained_hub(self): model_path = "google/ddpm-cifar10-32" scheduler = DDPMScheduler(num_train_timesteps=10) ddpm = DDPMPipeline.from_pretrained(model_path, scheduler=scheduler) ddpm.to(torch_device) ddpm.set_progress_bar_config(disable=None) ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler) ddpm_from_hub.to(torch_device) ddpm_from_hub.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) image = ddpm(generator=generator, output_type="numpy").images generator = generator.manual_seed(0) new_image = ddpm_from_hub(generator=generator, output_type="numpy").images assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass" @slow def test_from_pretrained_hub_pass_model(self): model_path = "google/ddpm-cifar10-32" scheduler = DDPMScheduler(num_train_timesteps=10) # pass unet into DiffusionPipeline unet = UNet2DModel.from_pretrained(model_path) ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet, scheduler=scheduler) ddpm_from_hub_custom_model.to(torch_device) ddpm_from_hub_custom_model.set_progress_bar_config(disable=None) ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler) ddpm_from_hub.to(torch_device) ddpm_from_hub_custom_model.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) image = ddpm_from_hub_custom_model(generator=generator, output_type="numpy").images generator = generator.manual_seed(0) new_image = ddpm_from_hub(generator=generator, output_type="numpy").images assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass" @slow def test_output_format(self): model_path = "google/ddpm-cifar10-32" pipe = DDIMPipeline.from_pretrained(model_path) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) images = pipe(generator=generator, output_type="numpy").images assert images.shape == (1, 32, 32, 3) assert isinstance(images, np.ndarray) images = pipe(generator=generator, output_type="pil").images assert isinstance(images, list) assert len(images) == 1 assert isinstance(images[0], PIL.Image.Image) # use PIL by default images = pipe(generator=generator).images assert isinstance(images, list) assert isinstance(images[0], PIL.Image.Image) @slow def test_ddpm_cifar10(self): model_id = "google/ddpm-cifar10-32" unet = UNet2DModel.from_pretrained(model_id) scheduler = DDPMScheduler.from_config(model_id) ddpm = DDPMPipeline(unet=unet, scheduler=scheduler) ddpm.to(torch_device) ddpm.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) image = ddpm(generator=generator, output_type="numpy").images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 32, 32, 3) expected_slice = np.array([0.41995, 0.35885, 0.19385, 0.38475, 0.3382, 0.2647, 0.41545, 0.3582, 0.33845]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @slow def test_ddim_lsun(self): model_id = "google/ddpm-ema-bedroom-256" unet = UNet2DModel.from_pretrained(model_id) scheduler = DDIMScheduler.from_config(model_id) ddpm = DDIMPipeline(unet=unet, scheduler=scheduler) ddpm.to(torch_device) ddpm.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) image = ddpm(generator=generator, output_type="numpy").images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 256, 256, 3) expected_slice = np.array([0.00605, 0.0201, 0.0344, 0.00235, 0.00185, 0.00025, 0.00215, 0.0, 0.00685]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @slow def test_ddim_cifar10(self): model_id = "google/ddpm-cifar10-32" unet = UNet2DModel.from_pretrained(model_id) scheduler = DDIMScheduler() ddim = DDIMPipeline(unet=unet, scheduler=scheduler) ddim.to(torch_device) ddim.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) image = ddim(generator=generator, eta=0.0, output_type="numpy").images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 32, 32, 3) expected_slice = np.array([0.17235, 0.16175, 0.16005, 0.16255, 0.1497, 0.1513, 0.15045, 0.1442, 0.1453]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @slow def test_pndm_cifar10(self): model_id = "google/ddpm-cifar10-32" unet = UNet2DModel.from_pretrained(model_id) scheduler = PNDMScheduler() pndm = PNDMPipeline(unet=unet, scheduler=scheduler) pndm.to(torch_device) pndm.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) image = pndm(generator=generator, output_type="numpy").images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 32, 32, 3) expected_slice = np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @slow def test_ldm_text2img(self): ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256") ldm.to(torch_device) ldm.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" generator = torch.manual_seed(0) image = ldm([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy")[ "sample" ] image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 256, 256, 3) expected_slice = np.array([0.9256, 0.9340, 0.8933, 0.9361, 0.9113, 0.8727, 0.9122, 0.8745, 0.8099]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @slow def test_ldm_text2img_fast(self): ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256") ldm.to(torch_device) ldm.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" generator = torch.manual_seed(0) image = ldm(prompt, generator=generator, num_inference_steps=1, output_type="numpy").images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 256, 256, 3) expected_slice = np.array([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_stable_diffusion(self): # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1") 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.Generator(device=torch_device).manual_seed(0) with torch.autocast("cuda"): output = sd_pipe( [prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np" ) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.8887, 0.915, 0.91, 0.894, 0.909, 0.912, 0.919, 0.925, 0.883]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_stable_diffusion_fast_ddim(self): sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1") sd_pipe = sd_pipe.to(torch_device) sd_pipe.set_progress_bar_config(disable=None) scheduler = DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, ) sd_pipe.scheduler = scheduler prompt = "A painting of a squirrel eating a burger" generator = torch.Generator(device=torch_device).manual_seed(0) with torch.autocast("cuda"): output = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="numpy") image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.9326, 0.923, 0.951, 0.9365, 0.9214, 0.951, 0.9365, 0.9414, 0.918]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @slow def test_score_sde_ve_pipeline(self): model_id = "google/ncsnpp-church-256" model = UNet2DModel.from_pretrained(model_id) scheduler = ScoreSdeVeScheduler.from_config(model_id) sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler) sde_ve.to(torch_device) sde_ve.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) image = sde_ve(num_inference_steps=10, output_type="numpy", generator=generator).images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 256, 256, 3) expected_slice = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @slow def test_ldm_uncond(self): ldm = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256") ldm.to(torch_device) ldm.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) image = ldm(generator=generator, num_inference_steps=5, output_type="numpy").images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 256, 256, 3) expected_slice = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @slow def test_ddpm_ddim_equality(self): model_id = "google/ddpm-cifar10-32" unet = UNet2DModel.from_pretrained(model_id) ddpm_scheduler = DDPMScheduler() ddim_scheduler = DDIMScheduler() ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler) ddpm.to(torch_device) ddpm.set_progress_bar_config(disable=None) ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler) ddim.to(torch_device) ddim.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) ddpm_image = ddpm(generator=generator, output_type="numpy").images generator = torch.manual_seed(0) ddim_image = ddim(generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy").images # the values aren't exactly equal, but the images look the same visually assert np.abs(ddpm_image - ddim_image).max() < 1e-1 @unittest.skip("(Anton) The test is failing for large batch sizes, needs investigation") def test_ddpm_ddim_equality_batched(self): model_id = "google/ddpm-cifar10-32" unet = UNet2DModel.from_pretrained(model_id) ddpm_scheduler = DDPMScheduler() ddim_scheduler = DDIMScheduler() ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler) ddpm.to(torch_device) ddpm.set_progress_bar_config(disable=None) ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler) ddim.to(torch_device) ddim.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) ddpm_images = ddpm(batch_size=4, generator=generator, output_type="numpy").images generator = torch.manual_seed(0) ddim_images = ddim(batch_size=4, generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy")[ "sample" ] # the values aren't exactly equal, but the images look the same visually assert np.abs(ddpm_images - ddim_images).max() < 1e-1 @slow def test_karras_ve_pipeline(self): model_id = "google/ncsnpp-celebahq-256" model = UNet2DModel.from_pretrained(model_id) scheduler = KarrasVeScheduler() pipe = KarrasVePipeline(unet=model, scheduler=scheduler) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) generator = torch.manual_seed(0) image = pipe(num_inference_steps=20, generator=generator, output_type="numpy").images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 256, 256, 3) expected_slice = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_lms_stable_diffusion_pipeline(self): model_id = "CompVis/stable-diffusion-v1-1" pipe = StableDiffusionPipeline.from_pretrained(model_id).to(torch_device) pipe.set_progress_bar_config(disable=None) scheduler = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler") pipe.scheduler = scheduler prompt = "a photograph of an astronaut riding a horse" generator = torch.Generator(device=torch_device).manual_seed(0) image = pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy")[ "sample" ] image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.9077, 0.9254, 0.9181, 0.9227, 0.9213, 0.9367, 0.9399, 0.9406, 0.9024]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_stable_diffusion_memory_chunking(self): torch.cuda.reset_peak_memory_stats() model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16).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.Generator(device=torch_device).manual_seed(0) with torch.autocast(torch_device): 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 3.75 GB is allocated assert mem_bytes < 3.75 * 10**9 # disable chunking pipe.disable_attention_slicing() generator = torch.Generator(device=torch_device).manual_seed(0) with torch.autocast(torch_device): output = pipe( [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy" ) image = output.images # make sure that more than 3.75 GB is allocated mem_bytes = torch.cuda.max_memory_allocated() assert mem_bytes > 3.75 * 10**9 assert np.abs(image_chunked.flatten() - image.flatten()).max() < 1e-3 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_stable_diffusion_text2img_pipeline_fp16(self): torch.cuda.reset_peak_memory_stats() model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16).to( torch_device ) pipe.set_progress_bar_config(disable=None) prompt = "a photograph of an astronaut riding a horse" generator = torch.Generator(device=torch_device).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 generator = torch.Generator(device=torch_device).manual_seed(0) with torch.autocast(torch_device): output = pipe( [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy" ) image = output.images # Make sure results are close enough diff = np.abs(image_chunked.flatten() - image.flatten()) # They ARE different since ops are not run always at the same precision # however, they should be extremely close. assert diff.mean() < 2e-2 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_stable_diffusion_text2img_pipeline(self): expected_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/text2img/astronaut_riding_a_horse.png" ) expected_image = np.array(expected_image, dtype=np.float32) / 255.0 model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionPipeline.from_pretrained( model_id, safety_checker=self.dummy_safety_checker, ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "astronaut riding a horse" generator = torch.Generator(device=torch_device).manual_seed(0) output = pipe(prompt=prompt, strength=0.75, guidance_scale=7.5, generator=generator, output_type="np") image = output.images[0] assert image.shape == (512, 512, 3) assert np.abs(expected_image - image).max() < 1e-2 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_stable_diffusion_img2img_pipeline(self): init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/img2img/sketch-mountains-input.jpg" ) expected_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/img2img/fantasy_landscape.png" ) init_image = init_image.resize((768, 512)) expected_image = np.array(expected_image, dtype=np.float32) / 255.0 model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionImg2ImgPipeline.from_pretrained( model_id, safety_checker=self.dummy_safety_checker, ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "A fantasy landscape, trending on artstation" generator = torch.Generator(device=torch_device).manual_seed(0) output = pipe( prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5, generator=generator, output_type="np", ) image = output.images[0] assert image.shape == (512, 768, 3) # img2img is flaky across GPUs even in fp32, so using MAE here assert np.abs(expected_image - image).mean() < 1e-2 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_stable_diffusion_img2img_pipeline_k_lms(self): init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/img2img/sketch-mountains-input.jpg" ) expected_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/img2img/fantasy_landscape_k_lms.png" ) init_image = init_image.resize((768, 512)) expected_image = np.array(expected_image, dtype=np.float32) / 255.0 lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear") model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionImg2ImgPipeline.from_pretrained( model_id, scheduler=lms, safety_checker=self.dummy_safety_checker, ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "A fantasy landscape, trending on artstation" generator = torch.Generator(device=torch_device).manual_seed(0) output = pipe( prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5, generator=generator, output_type="np", ) image = output.images[0] assert image.shape == (512, 768, 3) # img2img is flaky across GPUs even in fp32, so using MAE here assert np.abs(expected_image - image).mean() < 1e-2 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_stable_diffusion_inpaint_pipeline(self): init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo.png" ) mask_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" ) expected_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/red_cat_sitting_on_a_park_bench.png" ) expected_image = np.array(expected_image, dtype=np.float32) / 255.0 model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionInpaintPipeline.from_pretrained( model_id, safety_checker=self.dummy_safety_checker, ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "A red cat sitting on a park bench" generator = torch.Generator(device=torch_device).manual_seed(0) output = pipe( prompt=prompt, init_image=init_image, mask_image=mask_image, strength=0.75, guidance_scale=7.5, generator=generator, output_type="np", ) image = output.images[0] assert image.shape == (512, 512, 3) assert np.abs(expected_image - image).max() < 1e-2 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_stable_diffusion_inpaint_pipeline_k_lms(self): init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo.png" ) mask_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" ) expected_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/red_cat_sitting_on_a_park_bench_k_lms.png" ) expected_image = np.array(expected_image, dtype=np.float32) / 255.0 lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear") model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionInpaintPipeline.from_pretrained( model_id, scheduler=lms, safety_checker=self.dummy_safety_checker, ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "A red cat sitting on a park bench" generator = torch.Generator(device=torch_device).manual_seed(0) output = pipe( prompt=prompt, init_image=init_image, mask_image=mask_image, strength=0.75, guidance_scale=7.5, generator=generator, output_type="np", ) image = output.images[0] assert image.shape == (512, 512, 3) assert np.abs(expected_image - image).max() < 1e-2 @slow def test_stable_diffusion_onnx(self): sd_pipe = StableDiffusionOnnxPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider" ) prompt = "A painting of a squirrel eating a burger" np.random.seed(0) output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=5, output_type="np") image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.3602, 0.3688, 0.3652, 0.3895, 0.3782, 0.3747, 0.3927, 0.4241, 0.4327]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_stable_diffusion_text2img_intermediate_state(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, 64, 64) latents_slice = latents[0, -3:, -3:, -1] expected_slice = np.array( [1.8285, 1.2857, -0.1024, 1.2406, -2.3068, 1.0747, -0.0818, -0.6520, -2.9506] ) assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3 test_callback_fn.has_been_called = False pipe = StableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16 ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "Andromeda galaxy in a bottle" generator = torch.Generator(device=torch_device).manual_seed(0) with torch.autocast(torch_device): pipe( prompt=prompt, num_inference_steps=50, guidance_scale=7.5, generator=generator, callback=test_callback_fn, callback_steps=1, ) assert test_callback_fn.has_been_called assert number_of_steps == 51 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_stable_diffusion_img2img_intermediate_state(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, 64, 96) latents_slice = latents[0, -3:, -3:, -1] expected_slice = np.array([0.9052, -0.0184, 0.4810, 0.2898, 0.5851, 1.4920, 0.5362, 1.9838, 0.0530]) assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3 test_callback_fn.has_been_called = False init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/img2img/sketch-mountains-input.jpg" ) init_image = init_image.resize((768, 512)) pipe = StableDiffusionImg2ImgPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16 ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "A fantasy landscape, trending on artstation" generator = torch.Generator(device=torch_device).manual_seed(0) with torch.autocast(torch_device): pipe( prompt=prompt, init_image=init_image, strength=0.75, num_inference_steps=50, guidance_scale=7.5, generator=generator, callback=test_callback_fn, callback_steps=1, ) assert test_callback_fn.has_been_called assert number_of_steps == 38 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_stable_diffusion_inpaint_intermediate_state(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, 64, 64) latents_slice = latents[0, -3:, -3:, -1] expected_slice = np.array( [-0.5472, 1.1218, -0.5505, -0.9390, -1.0794, 0.4063, 0.5158, 0.6429, -1.5246] ) assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3 test_callback_fn.has_been_called = False init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo.png" ) mask_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" ) pipe = StableDiffusionInpaintPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16 ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "A red cat sitting on a park bench" generator = torch.Generator(device=torch_device).manual_seed(0) with torch.autocast(torch_device): pipe( prompt=prompt, init_image=init_image, mask_image=mask_image, strength=0.75, num_inference_steps=50, guidance_scale=7.5, generator=generator, callback=test_callback_fn, callback_steps=1, ) assert test_callback_fn.has_been_called assert number_of_steps == 38 @slow def test_stable_diffusion_onnx_intermediate_state(self): number_of_steps = 0 def test_callback_fn(step: int, timestep: int, latents: np.ndarray) -> None: test_callback_fn.has_been_called = True nonlocal number_of_steps number_of_steps += 1 if step == 0: assert latents.shape == (1, 4, 64, 64) latents_slice = latents[0, -3:, -3:, -1] expected_slice = np.array( [-0.5950, -0.3039, -1.1672, 0.1594, -1.1572, 0.6719, -1.9712, -0.0403, 0.9592] ) assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3 test_callback_fn.has_been_called = False pipe = StableDiffusionOnnxPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider" ) pipe.set_progress_bar_config(disable=None) prompt = "Andromeda galaxy in a bottle" np.random.seed(0) pipe(prompt=prompt, num_inference_steps=5, guidance_scale=7.5, callback=test_callback_fn, callback_steps=1) assert test_callback_fn.has_been_called assert number_of_steps == 6