899 lines
33 KiB
Python
899 lines
33 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import tempfile
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import unittest
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import numpy as np
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import torch
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import PIL
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from datasets import load_dataset
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from diffusers import (
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AutoencoderKL,
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DDIMPipeline,
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DDIMScheduler,
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DDPMPipeline,
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DDPMScheduler,
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KarrasVePipeline,
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KarrasVeScheduler,
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LDMPipeline,
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LDMTextToImagePipeline,
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LMSDiscreteScheduler,
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PNDMPipeline,
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PNDMScheduler,
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ScoreSdeVePipeline,
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ScoreSdeVeScheduler,
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StableDiffusionImg2ImgPipeline,
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StableDiffusionInpaintPipeline,
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StableDiffusionPipeline,
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UNet2DConditionModel,
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UNet2DModel,
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VQModel,
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)
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from diffusers.pipeline_utils import DiffusionPipeline
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from diffusers.testing_utils import floats_tensor, slow, torch_device
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from PIL import Image
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from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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torch.backends.cuda.matmul.allow_tf32 = False
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def test_progress_bar(capsys):
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model = UNet2DModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=3,
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out_channels=3,
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down_block_types=("DownBlock2D", "AttnDownBlock2D"),
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up_block_types=("AttnUpBlock2D", "UpBlock2D"),
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)
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scheduler = DDPMScheduler(num_train_timesteps=10)
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ddpm = DDPMPipeline(model, scheduler).to(torch_device)
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ddpm(output_type="numpy")["sample"]
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captured = capsys.readouterr()
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assert "10/10" in captured.err, "Progress bar has to be displayed"
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ddpm.set_progress_bar_config(disable=True)
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ddpm(output_type="numpy")["sample"]
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captured = capsys.readouterr()
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assert captured.err == "", "Progress bar should be disabled"
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class PipelineFastTests(unittest.TestCase):
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@property
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def dummy_image(self):
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batch_size = 1
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num_channels = 3
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sizes = (32, 32)
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image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
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return image
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@property
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def dummy_uncond_unet(self):
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torch.manual_seed(0)
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model = UNet2DModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=3,
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out_channels=3,
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down_block_types=("DownBlock2D", "AttnDownBlock2D"),
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up_block_types=("AttnUpBlock2D", "UpBlock2D"),
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)
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return model
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@property
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def dummy_cond_unet(self):
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torch.manual_seed(0)
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model = UNet2DConditionModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=4,
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out_channels=4,
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down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
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up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
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cross_attention_dim=32,
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)
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return model
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@property
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def dummy_vq_model(self):
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torch.manual_seed(0)
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model = VQModel(
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block_out_channels=[32, 64],
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
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latent_channels=3,
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)
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return model
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@property
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def dummy_vae(self):
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torch.manual_seed(0)
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model = AutoencoderKL(
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block_out_channels=[32, 64],
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
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latent_channels=4,
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)
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return model
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@property
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def dummy_text_encoder(self):
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torch.manual_seed(0)
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config = CLIPTextConfig(
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bos_token_id=0,
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chunk_size_feed_forward=0,
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eos_token_id=2,
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hidden_size=32,
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intermediate_size=37,
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layer_norm_eps=1e-05,
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num_attention_heads=4,
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num_hidden_layers=5,
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pad_token_id=1,
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vocab_size=1000,
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)
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return CLIPTextModel(config)
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@property
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def dummy_safety_checker(self):
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def check(images, *args, **kwargs):
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return images, False
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return check
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@property
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def dummy_extractor(self):
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def extract(*args, **kwargs):
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class Out:
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def __init__(self):
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self.pixel_values = torch.ones([0])
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def to(self, device):
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self.pixel_values.to(device)
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return self
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return Out()
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return extract
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def test_ddim(self):
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unet = self.dummy_uncond_unet
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scheduler = DDIMScheduler(tensor_format="pt")
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ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
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ddpm.to(torch_device)
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generator = torch.manual_seed(0)
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image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy")["sample"]
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 32, 32, 3)
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expected_slice = np.array(
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[1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]
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)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_pndm_cifar10(self):
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unet = self.dummy_uncond_unet
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scheduler = PNDMScheduler(tensor_format="pt")
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pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
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pndm.to(torch_device)
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generator = torch.manual_seed(0)
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image = pndm(generator=generator, num_inference_steps=20, output_type="numpy")["sample"]
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 32, 32, 3)
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expected_slice = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_ldm_text2img(self):
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unet = self.dummy_cond_unet
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scheduler = DDIMScheduler(tensor_format="pt")
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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ldm = LDMTextToImagePipeline(vqvae=vae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
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ldm.to(torch_device)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.manual_seed(0)
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image = ldm([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="numpy")[
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"sample"
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]
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.5074, 0.5026, 0.4998, 0.4056, 0.3523, 0.4649, 0.5289, 0.5299, 0.4897])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_ddim(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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unet = self.dummy_cond_unet
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scheduler = DDIMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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clip_sample=False,
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set_alpha_to_one=False,
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)
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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# make sure here that pndm scheduler skips prk
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sd_pipe = StableDiffusionPipeline(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=self.dummy_safety_checker,
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feature_extractor=self.dummy_extractor,
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)
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sd_pipe = sd_pipe.to(device)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=device).manual_seed(0)
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output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
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image = output["sample"]
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 128, 128, 3)
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expected_slice = np.array([0.5112, 0.4692, 0.4715, 0.5206, 0.4894, 0.5114, 0.5096, 0.4932, 0.4755])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_pndm(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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unet = self.dummy_cond_unet
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scheduler = PNDMScheduler(tensor_format="pt", skip_prk_steps=True)
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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# make sure here that pndm scheduler skips prk
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sd_pipe = StableDiffusionPipeline(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=self.dummy_safety_checker,
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feature_extractor=self.dummy_extractor,
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)
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sd_pipe = sd_pipe.to(device)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=device).manual_seed(0)
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output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
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image = output["sample"]
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 128, 128, 3)
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expected_slice = np.array([0.4937, 0.4649, 0.4716, 0.5145, 0.4889, 0.513, 0.513, 0.4905, 0.4738])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_k_lms(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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unet = self.dummy_cond_unet
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scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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# make sure here that pndm scheduler skips prk
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sd_pipe = StableDiffusionPipeline(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=self.dummy_safety_checker,
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feature_extractor=self.dummy_extractor,
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)
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sd_pipe = sd_pipe.to(device)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=device).manual_seed(0)
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output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
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image = output["sample"]
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 128, 128, 3)
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expected_slice = np.array([0.5067, 0.4689, 0.4614, 0.5233, 0.4903, 0.5112, 0.524, 0.5069, 0.4785])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_score_sde_ve_pipeline(self):
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unet = self.dummy_uncond_unet
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scheduler = ScoreSdeVeScheduler(tensor_format="pt")
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sde_ve = ScoreSdeVePipeline(unet=unet, scheduler=scheduler)
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sde_ve.to(torch_device)
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torch.manual_seed(0)
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image = sde_ve(num_inference_steps=2, output_type="numpy")["sample"]
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 32, 32, 3)
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expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_ldm_uncond(self):
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unet = self.dummy_uncond_unet
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scheduler = DDIMScheduler(tensor_format="pt")
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vae = self.dummy_vq_model
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ldm = LDMPipeline(unet=unet, vqvae=vae, scheduler=scheduler)
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ldm.to(torch_device)
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generator = torch.manual_seed(0)
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image = ldm(generator=generator, num_inference_steps=2, output_type="numpy")["sample"]
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_karras_ve_pipeline(self):
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unet = self.dummy_uncond_unet
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scheduler = KarrasVeScheduler(tensor_format="pt")
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pipe = KarrasVePipeline(unet=unet, scheduler=scheduler)
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pipe.to(torch_device)
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generator = torch.manual_seed(0)
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image = pipe(num_inference_steps=2, generator=generator, output_type="numpy")["sample"]
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 32, 32, 3)
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expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_img2img(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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unet = self.dummy_cond_unet
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scheduler = PNDMScheduler(tensor_format="pt", skip_prk_steps=True)
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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init_image = self.dummy_image.to(device)
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# make sure here that pndm scheduler skips prk
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sd_pipe = StableDiffusionImg2ImgPipeline(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=self.dummy_safety_checker,
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feature_extractor=self.dummy_extractor,
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)
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sd_pipe = sd_pipe.to(device)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=device).manual_seed(0)
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output = sd_pipe(
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[prompt],
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generator=generator,
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guidance_scale=6.0,
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num_inference_steps=2,
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output_type="np",
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init_image=init_image,
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)
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image = output["sample"]
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 32, 32, 3)
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expected_slice = np.array([0.4492, 0.3865, 0.4222, 0.5854, 0.5139, 0.4379, 0.4193, 0.48, 0.4218])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_inpaint(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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unet = self.dummy_cond_unet
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scheduler = PNDMScheduler(tensor_format="pt", skip_prk_steps=True)
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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image = self.dummy_image.to(device).permute(0, 2, 3, 1)[0]
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init_image = Image.fromarray(np.uint8(image)).convert("RGB")
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mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
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# make sure here that pndm scheduler skips prk
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sd_pipe = StableDiffusionInpaintPipeline(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=self.dummy_safety_checker,
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feature_extractor=self.dummy_extractor,
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)
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sd_pipe = sd_pipe.to(device)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=device).manual_seed(0)
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output = sd_pipe(
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[prompt],
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generator=generator,
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guidance_scale=6.0,
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num_inference_steps=2,
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output_type="np",
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init_image=init_image,
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mask_image=mask_image,
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|
)
|
|
|
|
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"
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pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, use_auth_token=True)
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pipe.to(torch_device)
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prompt = "A red cat sitting on a parking bench"
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generator = torch.Generator(device=torch_device).manual_seed(0)
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image = pipe(
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prompt=prompt,
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init_image=init_image,
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mask_image=mask_image,
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strength=0.75,
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guidance_scale=7.5,
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generator=generator,
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)["sample"][0]
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expected_array = np.array(output_image)
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sampled_array = np.array(image)
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assert sampled_array.shape == (512, 768, 3)
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assert np.max(np.abs(sampled_array - expected_array)) < 1e-3
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