679 lines
26 KiB
Python
679 lines
26 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 gc
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import random
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import unittest
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import numpy as np
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import torch
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from diffusers import (
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AutoencoderKL,
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LMSDiscreteScheduler,
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PNDMScheduler,
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StableDiffusionInpaintPipeline,
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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.utils import floats_tensor, load_image, load_numpy, slow, torch_device
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import prepare_mask_and_masked_image
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from diffusers.utils.testing_utils import require_torch_gpu
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from PIL import Image
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from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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from ...test_pipelines_common import PipelineTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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class StableDiffusionInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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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_cond_unet_inpaint(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=9,
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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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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_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_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_inpaint
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scheduler = PNDMScheduler(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.cpu().permute(0, 2, 3, 1)[0]
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init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128))
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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=None,
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feature_extractor=None,
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)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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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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image=init_image,
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mask_image=mask_image,
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)
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image = output.images
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generator = torch.Generator(device=device).manual_seed(0)
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image_from_tuple = 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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image=init_image,
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mask_image=mask_image,
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return_dict=False,
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)[0]
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image_slice = image[0, -3:, -3:, -1]
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image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
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assert image.shape == (1, 128, 128, 3)
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expected_slice = np.array([0.5075, 0.4485, 0.4558, 0.5369, 0.5369, 0.5236, 0.5127, 0.4983, 0.4776])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_inpaint_with_num_images_per_prompt(self):
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device = "cpu"
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unet = self.dummy_cond_unet_inpaint
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scheduler = PNDMScheduler(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.cpu().permute(0, 2, 3, 1)[0]
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init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128))
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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=None,
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feature_extractor=None,
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)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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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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images = 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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image=init_image,
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mask_image=mask_image,
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num_images_per_prompt=2,
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).images
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# check if the output is a list of 2 images
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assert len(images) == 2
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@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
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def test_stable_diffusion_inpaint_fp16(self):
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"""Test that stable diffusion inpaint_legacy works with fp16"""
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unet = self.dummy_cond_unet_inpaint
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scheduler = PNDMScheduler(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.cpu().permute(0, 2, 3, 1)[0]
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init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128))
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mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
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# put models in fp16
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unet = unet.half()
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vae = vae.half()
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bert = bert.half()
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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=None,
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feature_extractor=None,
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)
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=torch_device).manual_seed(0)
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image = sd_pipe(
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[prompt],
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generator=generator,
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num_inference_steps=2,
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output_type="np",
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image=init_image,
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mask_image=mask_image,
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).images
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assert image.shape == (1, 128, 128, 3)
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@slow
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@require_torch_gpu
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class StableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def test_stable_diffusion_inpaint_pipeline(self):
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/in_paint/overture-creations-5sI6fQgYIuo.png"
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)
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mask_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
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)
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expected_image = load_numpy(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint"
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"/yellow_cat_sitting_on_a_park_bench.npy"
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)
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model_id = "runwayml/stable-diffusion-inpainting"
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pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
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generator = torch.Generator(device=torch_device).manual_seed(0)
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output = pipe(
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prompt=prompt,
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image=init_image,
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mask_image=mask_image,
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generator=generator,
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output_type="np",
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)
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image = output.images[0]
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assert image.shape == (512, 512, 3)
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assert np.abs(expected_image - image).max() < 1e-3
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def test_stable_diffusion_inpaint_pipeline_fp16(self):
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/in_paint/overture-creations-5sI6fQgYIuo.png"
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)
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mask_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
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)
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expected_image = load_numpy(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint"
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"/yellow_cat_sitting_on_a_park_bench_fp16.npy"
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)
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model_id = "runwayml/stable-diffusion-inpainting"
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pipe = StableDiffusionInpaintPipeline.from_pretrained(
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model_id,
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revision="fp16",
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torch_dtype=torch.float16,
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safety_checker=None,
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)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
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generator = torch.Generator(device=torch_device).manual_seed(0)
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output = pipe(
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prompt=prompt,
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image=init_image,
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mask_image=mask_image,
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generator=generator,
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output_type="np",
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)
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image = output.images[0]
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assert image.shape == (512, 512, 3)
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assert np.abs(expected_image - image).max() < 5e-1
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def test_stable_diffusion_inpaint_pipeline_pndm(self):
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/in_paint/overture-creations-5sI6fQgYIuo.png"
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)
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mask_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
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)
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expected_image = load_numpy(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint"
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"/yellow_cat_sitting_on_a_park_bench_pndm.npy"
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)
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model_id = "runwayml/stable-diffusion-inpainting"
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pndm = PNDMScheduler.from_pretrained(model_id, subfolder="scheduler")
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pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None, scheduler=pndm)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
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generator = torch.Generator(device=torch_device).manual_seed(0)
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output = pipe(
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prompt=prompt,
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image=init_image,
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mask_image=mask_image,
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generator=generator,
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output_type="np",
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)
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image = output.images[0]
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assert image.shape == (512, 512, 3)
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assert np.abs(expected_image - image).max() < 1e-2
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def test_stable_diffusion_inpaint_pipeline_k_lms(self):
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/in_paint/overture-creations-5sI6fQgYIuo.png"
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)
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mask_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
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)
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expected_image = load_numpy(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint"
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"/yellow_cat_sitting_on_a_park_bench_k_lms.npy"
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)
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model_id = "runwayml/stable-diffusion-inpainting"
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pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None)
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pipe.to(torch_device)
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# switch to LMS
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pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
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generator = torch.Generator(device=torch_device).manual_seed(0)
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output = pipe(
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prompt=prompt,
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image=init_image,
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mask_image=mask_image,
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generator=generator,
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output_type="np",
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)
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image = output.images[0]
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assert image.shape == (512, 512, 3)
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assert np.abs(expected_image - image).max() < 1e-2
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@unittest.skipIf(torch_device == "cpu", "This test is supposed to run on GPU")
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def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
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torch.cuda.empty_cache()
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torch.cuda.reset_max_memory_allocated()
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torch.cuda.reset_peak_memory_stats()
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/in_paint/overture-creations-5sI6fQgYIuo.png"
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)
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mask_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
|
|
)
|
|
|
|
model_id = "runwayml/stable-diffusion-inpainting"
|
|
pndm = PNDMScheduler.from_pretrained(model_id, subfolder="scheduler")
|
|
pipe = StableDiffusionInpaintPipeline.from_pretrained(
|
|
model_id,
|
|
safety_checker=None,
|
|
scheduler=pndm,
|
|
device_map="auto",
|
|
revision="fp16",
|
|
torch_dtype=torch.float16,
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing(1)
|
|
pipe.enable_sequential_cpu_offload()
|
|
|
|
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
_ = pipe(
|
|
prompt=prompt,
|
|
image=init_image,
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|
mask_image=mask_image,
|
|
generator=generator,
|
|
num_inference_steps=5,
|
|
output_type="np",
|
|
)
|
|
|
|
mem_bytes = torch.cuda.max_memory_allocated()
|
|
# make sure that less than 2.2 GB is allocated
|
|
assert mem_bytes < 2.2 * 10**9
|
|
|
|
class StableDiffusionInpaintingPrepareMaskAndMaskedImageTests(unittest.TestCase):
|
|
def test_pil_inputs(self):
|
|
im = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
|
|
im = Image.fromarray(im)
|
|
mask = np.random.randint(0, 255, (32, 32), dtype=np.uint8) > 127.5
|
|
mask = Image.fromarray((mask * 255).astype(np.uint8))
|
|
|
|
t_mask, t_masked = prepare_mask_and_masked_image(im, mask)
|
|
|
|
self.assertTrue(isinstance(t_mask, torch.Tensor))
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|
self.assertTrue(isinstance(t_masked, torch.Tensor))
|
|
|
|
self.assertEqual(t_mask.ndim, 4)
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|
self.assertEqual(t_masked.ndim, 4)
|
|
|
|
self.assertEqual(t_mask.shape, (1, 1, 32, 32))
|
|
self.assertEqual(t_masked.shape, (1, 3, 32, 32))
|
|
|
|
self.assertTrue(t_mask.dtype == torch.float32)
|
|
self.assertTrue(t_masked.dtype == torch.float32)
|
|
|
|
self.assertTrue(t_mask.min() >= 0.0)
|
|
self.assertTrue(t_mask.max() <= 1.0)
|
|
self.assertTrue(t_masked.min() >= -1.0)
|
|
self.assertTrue(t_masked.min() <= 1.0)
|
|
|
|
self.assertTrue(t_mask.sum() > 0.0)
|
|
|
|
def test_np_inputs(self):
|
|
im_np = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
|
|
im_pil = Image.fromarray(im_np)
|
|
mask_np = np.random.randint(0, 255, (32, 32), dtype=np.uint8) > 127.5
|
|
mask_pil = Image.fromarray((mask_np * 255).astype(np.uint8))
|
|
|
|
t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
|
|
t_mask_pil, t_masked_pil = prepare_mask_and_masked_image(im_pil, mask_pil)
|
|
|
|
self.assertTrue((t_mask_np == t_mask_pil).all())
|
|
self.assertTrue((t_masked_np == t_masked_pil).all())
|
|
|
|
def test_torch_3D_2D_inputs(self):
|
|
im_tensor = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8)
|
|
mask_tensor = torch.randint(0, 255, (32, 32), dtype=torch.uint8) > 127.5
|
|
im_np = im_tensor.numpy().transpose(1, 2, 0)
|
|
mask_np = mask_tensor.numpy()
|
|
|
|
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
|
|
t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
|
|
|
|
self.assertTrue((t_mask_tensor == t_mask_np).all())
|
|
self.assertTrue((t_masked_tensor == t_masked_np).all())
|
|
|
|
def test_torch_3D_3D_inputs(self):
|
|
im_tensor = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8)
|
|
mask_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8) > 127.5
|
|
im_np = im_tensor.numpy().transpose(1, 2, 0)
|
|
mask_np = mask_tensor.numpy()[0]
|
|
|
|
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
|
|
t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
|
|
|
|
self.assertTrue((t_mask_tensor == t_mask_np).all())
|
|
self.assertTrue((t_masked_tensor == t_masked_np).all())
|
|
|
|
def test_torch_4D_2D_inputs(self):
|
|
im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8)
|
|
mask_tensor = torch.randint(0, 255, (32, 32), dtype=torch.uint8) > 127.5
|
|
im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
|
|
mask_np = mask_tensor.numpy()
|
|
|
|
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
|
|
t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
|
|
|
|
self.assertTrue((t_mask_tensor == t_mask_np).all())
|
|
self.assertTrue((t_masked_tensor == t_masked_np).all())
|
|
|
|
def test_torch_4D_3D_inputs(self):
|
|
im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8)
|
|
mask_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8) > 127.5
|
|
im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
|
|
mask_np = mask_tensor.numpy()[0]
|
|
|
|
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
|
|
t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
|
|
|
|
self.assertTrue((t_mask_tensor == t_mask_np).all())
|
|
self.assertTrue((t_masked_tensor == t_masked_np).all())
|
|
|
|
def test_torch_4D_4D_inputs(self):
|
|
im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8)
|
|
mask_tensor = torch.randint(0, 255, (1, 1, 32, 32), dtype=torch.uint8) > 127.5
|
|
im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
|
|
mask_np = mask_tensor.numpy()[0][0]
|
|
|
|
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
|
|
t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
|
|
|
|
self.assertTrue((t_mask_tensor == t_mask_np).all())
|
|
self.assertTrue((t_masked_tensor == t_masked_np).all())
|
|
|
|
def test_torch_batch_4D_3D(self):
|
|
im_tensor = torch.randint(0, 255, (2, 3, 32, 32), dtype=torch.uint8)
|
|
mask_tensor = torch.randint(0, 255, (2, 32, 32), dtype=torch.uint8) > 127.5
|
|
|
|
im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor]
|
|
mask_nps = [mask.numpy() for mask in mask_tensor]
|
|
|
|
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
|
|
nps = [prepare_mask_and_masked_image(i, m) for i, m in zip(im_nps, mask_nps)]
|
|
t_mask_np = torch.cat([n[0] for n in nps])
|
|
t_masked_np = torch.cat([n[1] for n in nps])
|
|
|
|
self.assertTrue((t_mask_tensor == t_mask_np).all())
|
|
self.assertTrue((t_masked_tensor == t_masked_np).all())
|
|
|
|
def test_torch_batch_4D_4D(self):
|
|
im_tensor = torch.randint(0, 255, (2, 3, 32, 32), dtype=torch.uint8)
|
|
mask_tensor = torch.randint(0, 255, (2, 1, 32, 32), dtype=torch.uint8) > 127.5
|
|
|
|
im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor]
|
|
mask_nps = [mask.numpy()[0] for mask in mask_tensor]
|
|
|
|
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
|
|
nps = [prepare_mask_and_masked_image(i, m) for i, m in zip(im_nps, mask_nps)]
|
|
t_mask_np = torch.cat([n[0] for n in nps])
|
|
t_masked_np = torch.cat([n[1] for n in nps])
|
|
|
|
self.assertTrue((t_mask_tensor == t_mask_np).all())
|
|
self.assertTrue((t_masked_tensor == t_masked_np).all())
|
|
|
|
def test_shape_mismatch(self):
|
|
# test height and width
|
|
with self.assertRaises(AssertionError):
|
|
prepare_mask_and_masked_image(torch.randn(3, 32, 32), torch.randn(64, 64))
|
|
# test batch dim
|
|
with self.assertRaises(AssertionError):
|
|
prepare_mask_and_masked_image(torch.randn(2, 3, 32, 32), torch.randn(4, 64, 64))
|
|
# test batch dim
|
|
with self.assertRaises(AssertionError):
|
|
prepare_mask_and_masked_image(torch.randn(2, 3, 32, 32), torch.randn(4, 1, 64, 64))
|
|
|
|
def test_type_mismatch(self):
|
|
# test tensors-only
|
|
with self.assertRaises(TypeError):
|
|
prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.rand(3, 32, 32).numpy())
|
|
# test tensors-only
|
|
with self.assertRaises(TypeError):
|
|
prepare_mask_and_masked_image(torch.rand(3, 32, 32).numpy(), torch.rand(3, 32, 32))
|
|
|
|
def test_channels_first(self):
|
|
# test channels first for 3D tensors
|
|
with self.assertRaises(AssertionError):
|
|
prepare_mask_and_masked_image(torch.rand(32, 32, 3), torch.rand(3, 32, 32))
|
|
|
|
def test_tensor_range(self):
|
|
# test im <= 1
|
|
with self.assertRaises(ValueError):
|
|
prepare_mask_and_masked_image(torch.ones(3, 32, 32) * 2, torch.rand(32, 32))
|
|
# test im >= -1
|
|
with self.assertRaises(ValueError):
|
|
prepare_mask_and_masked_image(torch.ones(3, 32, 32) * (-2), torch.rand(32, 32))
|
|
# test mask <= 1
|
|
with self.assertRaises(ValueError):
|
|
prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.ones(32, 32) * 2)
|
|
# test mask >= 0
|
|
with self.assertRaises(ValueError):
|
|
prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.ones(32, 32) * -1) |