# 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 random import tempfile import unittest import numpy as np import torch import PIL from datasets import load_dataset from diffusers import ( AutoencoderKL, DDIMPipeline, DDIMScheduler, DDPMPipeline, DDPMScheduler, KarrasVePipeline, KarrasVeScheduler, LDMPipeline, LDMTextToImagePipeline, LMSDiscreteScheduler, PNDMPipeline, PNDMScheduler, ScoreSdeVePipeline, ScoreSdeVeScheduler, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipeline, StableDiffusionPipeline, UNet2DConditionModel, UNet2DModel, VQModel, ) from diffusers.pipeline_utils import DiffusionPipeline from diffusers.testing_utils import floats_tensor, slow, torch_device from PIL import Image from transformers import 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")["sample"] 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")["sample"] captured = capsys.readouterr() assert captured.err == "", "Progress bar should be disabled" class PipelineFastTests(unittest.TestCase): @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, chunk_size_feed_forward=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 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(tensor_format="pt") ddpm = DDIMPipeline(unet=unet, scheduler=scheduler) ddpm.to(torch_device) generator = torch.manual_seed(0) image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy")["sample"] image_slice = image[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] ) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_pndm_cifar10(self): unet = self.dummy_uncond_unet scheduler = PNDMScheduler(tensor_format="pt") pndm = PNDMPipeline(unet=unet, scheduler=scheduler) pndm.to(torch_device) generator = torch.manual_seed(0) image = pndm(generator=generator, num_inference_steps=20, output_type="numpy")["sample"] image_slice = image[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 def test_ldm_text2img(self): unet = self.dummy_cond_unet scheduler = DDIMScheduler(tensor_format="pt") 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) 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=2, output_type="numpy")[ "sample" ] image_slice = image[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 def test_stable_diffusion_ddim(self): 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(torch_device) 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=2, output_type="np" ) image = output["sample"] image_slice = image[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 def test_stable_diffusion_pndm(self): unet = self.dummy_cond_unet scheduler = PNDMScheduler(tensor_format="pt", 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(torch_device) 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=2, output_type="np" ) image = output["sample"] image_slice = image[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 def test_stable_diffusion_k_lms(self): unet = self.dummy_cond_unet scheduler = PNDMScheduler(tensor_format="pt", skip_prk_steps=True) 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(torch_device) 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=2, output_type="np" ) image = output["sample"] image_slice = image[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 def test_score_sde_ve_pipeline(self): unet = self.dummy_uncond_unet scheduler = ScoreSdeVeScheduler(tensor_format="pt") sde_ve = ScoreSdeVePipeline(unet=unet, scheduler=scheduler) sde_ve.to(torch_device) torch.manual_seed(0) image = sde_ve(num_inference_steps=2, output_type="numpy")["sample"] image_slice = image[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 def test_ldm_uncond(self): unet = self.dummy_uncond_unet scheduler = DDIMScheduler(tensor_format="pt") vae = self.dummy_vq_model ldm = LDMPipeline(unet=unet, vqvae=vae, scheduler=scheduler) ldm.to(torch_device) generator = torch.manual_seed(0) image = ldm(generator=generator, num_inference_steps=2, output_type="numpy")["sample"] image_slice = image[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 def test_karras_ve_pipeline(self): unet = self.dummy_uncond_unet scheduler = KarrasVeScheduler(tensor_format="pt") pipe = KarrasVePipeline(unet=unet, scheduler=scheduler) pipe.to(torch_device) generator = torch.manual_seed(0) image = pipe(num_inference_steps=2, generator=generator, output_type="numpy")["sample"] image_slice = image[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 def test_stable_diffusion_img2img(self): unet = self.dummy_cond_unet scheduler = PNDMScheduler(tensor_format="pt", 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 # 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(torch_device) 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=2, output_type="np", init_image=init_image, ) image = output["sample"] image_slice = image[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 def test_stable_diffusion_inpaint(self): unet = self.dummy_cond_unet scheduler = PNDMScheduler(tensor_format="pt", 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.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(torch_device) 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=2, output_type="np", init_image=init_image, mask_image=mask_image, ) image = output["sample"] image_slice = image[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 class PipelineTesterMixin(unittest.TestCase): 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) 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")["sample"] generator = generator.manual_seed(0) new_image = new_ddpm(generator=generator, output_type="numpy")["sample"] 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_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler) ddpm_from_hub.to(torch_device) generator = torch.manual_seed(0) image = ddpm(generator=generator, output_type="numpy")["sample"] generator = generator.manual_seed(0) new_image = ddpm_from_hub(generator=generator, output_type="numpy")["sample"] 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 = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler) ddpm_from_hub.to(torch_device) generator = torch.manual_seed(0) image = ddpm_from_hub_custom_model(generator=generator, output_type="numpy")["sample"] generator = generator.manual_seed(0) new_image = ddpm_from_hub(generator=generator, output_type="numpy")["sample"] 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) generator = torch.manual_seed(0) images = pipe(generator=generator, output_type="numpy")["sample"] assert images.shape == (1, 32, 32, 3) assert isinstance(images, np.ndarray) images = pipe(generator=generator, output_type="pil")["sample"] assert isinstance(images, list) assert len(images) == 1 assert isinstance(images[0], PIL.Image.Image) # use PIL by default images = pipe(generator=generator)["sample"] 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) scheduler = scheduler.set_format("pt") ddpm = DDPMPipeline(unet=unet, scheduler=scheduler) ddpm.to(torch_device) generator = torch.manual_seed(0) image = ddpm(generator=generator, output_type="numpy")["sample"] 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) generator = torch.manual_seed(0) image = ddpm(generator=generator, output_type="numpy")["sample"] 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(tensor_format="pt") ddim = DDIMPipeline(unet=unet, scheduler=scheduler) ddim.to(torch_device) generator = torch.manual_seed(0) image = ddim(generator=generator, eta=0.0, output_type="numpy")["sample"] 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(tensor_format="pt") pndm = PNDMPipeline(unet=unet, scheduler=scheduler) pndm.to(torch_device) generator = torch.manual_seed(0) image = pndm(generator=generator, output_type="numpy")["sample"] 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) 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) 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")["sample"] 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", use_auth_token=True) sd_pipe = sd_pipe.to(torch_device) 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["sample"] 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", use_auth_token=True) sd_pipe = sd_pipe.to(torch_device) 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["sample"] image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.8354, 0.83, 0.866, 0.838, 0.8315, 0.867, 0.836, 0.8584, 0.869]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 @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) torch.manual_seed(0) image = sde_ve(num_inference_steps=300, output_type="numpy")["sample"] image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 256, 256, 3) expected_slice = np.array([0.64363, 0.5868, 0.3031, 0.2284, 0.7409, 0.3216, 0.25643, 0.6557, 0.2633]) 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) generator = torch.manual_seed(0) image = ldm(generator=generator, num_inference_steps=5, output_type="numpy")["sample"] 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(tensor_format="pt") ddim_scheduler = DDIMScheduler(tensor_format="pt") ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler) ddpm.to(torch_device) ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler) ddim.to(torch_device) generator = torch.manual_seed(0) ddpm_image = ddpm(generator=generator, output_type="numpy")["sample"] generator = torch.manual_seed(0) ddim_image = ddim(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_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(tensor_format="pt") ddim_scheduler = DDIMScheduler(tensor_format="pt") ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler) ddpm.to(torch_device) ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler) ddim.to(torch_device) generator = torch.manual_seed(0) ddpm_images = ddpm(batch_size=4, generator=generator, output_type="numpy")["sample"] 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(tensor_format="pt") pipe = KarrasVePipeline(unet=model, scheduler=scheduler) pipe.to(torch_device) generator = torch.manual_seed(0) image = pipe(num_inference_steps=20, generator=generator, output_type="numpy")["sample"] image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 256, 256, 3) expected_slice = np.array([0.26815, 0.1581, 0.2658, 0.23248, 0.1550, 0.2539, 0.1131, 0.1024, 0.0837]) 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, use_auth_token=True).to(torch_device) scheduler = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler", use_auth_token=True) 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_img2img_pipeline(self): ds = load_dataset("hf-internal-testing/diffusers-images", split="train") init_image = ds[1]["image"].resize((768, 512)) output_image = ds[0]["image"].resize((768, 512)) model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, use_auth_token=True) pipe.to(torch_device) prompt = "A fantasy landscape, trending on artstation" generator = torch.Generator(device=torch_device).manual_seed(0) image = pipe(prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5, generator=generator)[ "sample" ][0] expected_array = np.array(output_image) sampled_array = np.array(image) assert sampled_array.shape == (512, 768, 3) assert np.max(np.abs(sampled_array - expected_array)) < 1e-4 @slow @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU") def test_stable_diffusion_in_paint_pipeline(self): ds = load_dataset("hf-internal-testing/diffusers-images", split="train") init_image = ds[2]["image"].resize((768, 512)) mask_image = ds[3]["image"].resize((768, 512)) output_image = ds[4]["image"].resize((768, 512)) model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, use_auth_token=True) pipe.to(torch_device) prompt = "A red cat sitting on a parking bench" generator = torch.Generator(device=torch_device).manual_seed(0) image = pipe( prompt=prompt, init_image=init_image, mask_image=mask_image, strength=0.75, guidance_scale=7.5, generator=generator, )["sample"][0] expected_array = np.array(output_image) sampled_array = np.array(image) assert sampled_array.shape == (512, 768, 3) assert np.max(np.abs(sampled_array - expected_array)) < 1e-3