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* Use HF Papers * Apply style fixes --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
1470 lines
68 KiB
Python
1470 lines
68 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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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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# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
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import inspect
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import os
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import shutil
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from glob import glob
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from typing import Any, Callable, Dict, List, Optional, Union
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import cv2
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import numpy as np
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import PIL.Image
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import requests
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import torch
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from detectron2.config import get_cfg
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from detectron2.data import MetadataCatalog
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from detectron2.engine import DefaultPredictor
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from detectron2.projects import point_rend
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from detectron2.structures.instances import Instances
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from detectron2.utils.visualizer import ColorMode, Visualizer
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from packaging import version
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from tqdm import tqdm
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from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
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from diffusers.configuration_utils import FrozenDict
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from diffusers.image_processor import VaeImageProcessor
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from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
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from diffusers.models import AsymmetricAutoencoderKL, AutoencoderKL, UNet2DConditionModel
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
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from diffusers.schedulers import KarrasDiffusionSchedulers
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from diffusers.utils import (
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deprecate,
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is_accelerate_available,
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is_accelerate_version,
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logging,
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randn_tensor,
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)
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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AMI_INSTALL_MESSAGE = """
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Example Demo of Adaptive Mask Inpainting
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Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models
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Kim et al.
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ECCV-2024 (Oral)
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Please prepare the environment via
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```
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conda create --name ami python=3.9 -y
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conda activate ami
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conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge -y
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python -m pip install detectron2==0.6 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
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pip install easydict
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pip install diffusers==0.20.2 accelerate safetensors transformers
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pip install setuptools==59.5.0
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pip install opencv-python
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pip install numpy==1.24.1
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```
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Put the code inside the root of diffusers library (e.g., as '/home/username/diffusers/adaptive_mask_inpainting_example.py') and run the python code.
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"""
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EXAMPLE_DOC_STRING = """
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Examples:
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```py
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>>> # !pip install transformers accelerate
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>>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, DDIMScheduler
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>>> from diffusers.utils import load_image
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>>> import numpy as np
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>>> import torch
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>>> init_image = load_image(
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... "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"
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... )
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>>> init_image = init_image.resize((512, 512))
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>>> generator = torch.Generator(device="cpu").manual_seed(1)
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>>> mask_image = load_image(
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... "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"
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... )
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>>> mask_image = mask_image.resize((512, 512))
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>>> def make_inpaint_condition(image, image_mask):
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... image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
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... image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
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... assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
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... image[image_mask > 0.5] = -1.0 # set as masked pixel
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... image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
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... image = torch.from_numpy(image)
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... return image
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>>> control_image = make_inpaint_condition(init_image, mask_image)
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>>> controlnet = ControlNetModel.from_pretrained(
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... "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
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... )
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>>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
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... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
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... )
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>>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
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>>> pipe.enable_model_cpu_offload()
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>>> # generate image
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>>> image = pipe(
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... "a handsome man with ray-ban sunglasses",
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... num_inference_steps=20,
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... generator=generator,
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... eta=1.0,
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... image=init_image,
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... mask_image=mask_image,
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... control_image=control_image,
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... ).images[0]
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```
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"""
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def download_file(url, output_file, exist_ok: bool):
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if exist_ok and os.path.exists(output_file):
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return
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response = requests.get(url, stream=True)
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with open(output_file, "wb") as file:
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for chunk in tqdm(response.iter_content(chunk_size=8192), desc=f"Downloading '{output_file}'..."):
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if chunk:
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file.write(chunk)
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def generate_video_from_imgs(images_save_directory, fps=15.0, delete_dir=True):
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# delete videos if exists
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if os.path.exists(f"{images_save_directory}.mp4"):
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os.remove(f"{images_save_directory}.mp4")
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if os.path.exists(f"{images_save_directory}_before_process.mp4"):
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os.remove(f"{images_save_directory}_before_process.mp4")
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# assume there are "enumerated" images under "images_save_directory"
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assert os.path.isdir(images_save_directory)
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ImgPaths = sorted(glob(f"{images_save_directory}/*"))
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if len(ImgPaths) == 0:
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print("\tSkipping, since there must be at least one image to create mp4\n")
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else:
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# mp4 configuration
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video_path = images_save_directory + "_before_process.mp4"
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# Get height and width config
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images = sorted([ImgPath.split("/")[-1] for ImgPath in ImgPaths if ImgPath.endswith(".png")])
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frame = cv2.imread(os.path.join(images_save_directory, images[0]))
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height, width, channels = frame.shape
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# create mp4 video writer
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fourcc = cv2.VideoWriter_fourcc(*"mp4v")
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video = cv2.VideoWriter(video_path, fourcc, fps, (width, height))
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for image in images:
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video.write(cv2.imread(os.path.join(images_save_directory, image)))
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cv2.destroyAllWindows()
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video.release()
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# generated video is not compatible with HTML5. Post-process and change codec of video, so that it is applicable to HTML.
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os.system(
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f'ffmpeg -i "{images_save_directory}_before_process.mp4" -vcodec libx264 -f mp4 "{images_save_directory}.mp4" '
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)
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# remove group of images, and remove video before post-process.
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if delete_dir and os.path.exists(images_save_directory):
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shutil.rmtree(images_save_directory)
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# remove 'before-process' video
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if os.path.exists(f"{images_save_directory}_before_process.mp4"):
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os.remove(f"{images_save_directory}_before_process.mp4")
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.prepare_mask_and_masked_image
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def prepare_mask_and_masked_image(image, mask, height, width, return_image=False):
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"""
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Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
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converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
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``image`` and ``1`` for the ``mask``.
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The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
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binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
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Args:
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image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
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It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
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``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
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mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
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It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
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``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
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Raises:
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ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
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should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
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TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
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(ot the other way around).
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Returns:
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tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
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dimensions: ``batch x channels x height x width``.
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"""
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if image is None:
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raise ValueError("`image` input cannot be undefined.")
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if mask is None:
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raise ValueError("`mask_image` input cannot be undefined.")
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if isinstance(image, torch.Tensor):
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if not isinstance(mask, torch.Tensor):
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raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")
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# Batch single image
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if image.ndim == 3:
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assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
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image = image.unsqueeze(0)
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# Batch and add channel dim for single mask
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if mask.ndim == 2:
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mask = mask.unsqueeze(0).unsqueeze(0)
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# Batch single mask or add channel dim
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if mask.ndim == 3:
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# Single batched mask, no channel dim or single mask not batched but channel dim
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if mask.shape[0] == 1:
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mask = mask.unsqueeze(0)
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# Batched masks no channel dim
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else:
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mask = mask.unsqueeze(1)
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assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
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assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
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assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
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# Check image is in [-1, 1]
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if image.min() < -1 or image.max() > 1:
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raise ValueError("Image should be in [-1, 1] range")
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# Check mask is in [0, 1]
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if mask.min() < 0 or mask.max() > 1:
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raise ValueError("Mask should be in [0, 1] range")
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# Binarize mask
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mask[mask < 0.5] = 0
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mask[mask >= 0.5] = 1
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# Image as float32
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image = image.to(dtype=torch.float32)
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elif isinstance(mask, torch.Tensor):
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raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
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else:
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# preprocess image
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if isinstance(image, (PIL.Image.Image, np.ndarray)):
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image = [image]
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if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
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# resize all images w.r.t passed height an width
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image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image]
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image = [np.array(i.convert("RGB"))[None, :] for i in image]
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image = np.concatenate(image, axis=0)
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elif isinstance(image, list) and isinstance(image[0], np.ndarray):
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image = np.concatenate([i[None, :] for i in image], axis=0)
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image = image.transpose(0, 3, 1, 2)
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image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
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# preprocess mask
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if isinstance(mask, (PIL.Image.Image, np.ndarray)):
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mask = [mask]
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if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
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mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]
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mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
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mask = mask.astype(np.float32) / 255.0
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elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
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mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
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mask[mask < 0.5] = 0
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mask[mask >= 0.5] = 1
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mask = torch.from_numpy(mask)
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masked_image = image * (mask < 0.5)
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# n.b. ensure backwards compatibility as old function does not return image
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if return_image:
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return mask, masked_image, image
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return mask, masked_image
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class AdaptiveMaskInpaintPipeline(
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DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
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):
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r"""
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Pipeline for text-guided image inpainting using Stable Diffusion.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
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implemented for all pipelines (downloading, saving, running on a particular device, etc.).
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The pipeline also inherits the following loading methods:
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- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
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- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
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- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
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Args:
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vae ([`AutoencoderKL`, `AsymmetricAutoencoderKL`]):
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Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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text_encoder ([`CLIPTextModel`]):
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Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
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tokenizer ([`~transformers.CLIPTokenizer`]):
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A `CLIPTokenizer` to tokenize text.
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unet ([`UNet2DConditionModel`]):
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A `UNet2DConditionModel` to denoise the encoded image latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
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safety_checker ([`StableDiffusionSafetyChecker`]):
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Classification module that estimates whether generated images could be considered offensive or harmful.
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Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
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about a model's potential harms.
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feature_extractor ([`~transformers.CLIPImageProcessor`]):
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A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
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"""
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_optional_components = ["safety_checker", "feature_extractor"]
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def __init__(
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self,
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vae: Union[AutoencoderKL, AsymmetricAutoencoderKL],
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text_encoder: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: KarrasDiffusionSchedulers,
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# safety_checker: StableDiffusionSafetyChecker,
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safety_checker,
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feature_extractor: CLIPImageProcessor,
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requires_safety_checker: bool = True,
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):
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super().__init__()
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self.register_adaptive_mask_model()
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self.register_adaptive_mask_settings()
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if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
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deprecation_message = (
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f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
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f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
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"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
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" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
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" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
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" file"
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)
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deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
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new_config = dict(scheduler.config)
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new_config["steps_offset"] = 1
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scheduler._internal_dict = FrozenDict(new_config)
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if scheduler is not None and getattr(scheduler.config, "skip_prk_steps", True) is False:
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deprecation_message = (
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f"The configuration file of this scheduler: {scheduler} has not set the configuration"
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" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
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" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
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" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
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" Hub, it would be very nice if you could open a Pull request for the"
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" `scheduler/scheduler_config.json` file"
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)
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deprecate("skip_prk_steps not set", "1.0.0", deprecation_message, standard_warn=False)
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new_config = dict(scheduler.config)
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new_config["skip_prk_steps"] = True
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scheduler._internal_dict = FrozenDict(new_config)
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if safety_checker is None and requires_safety_checker:
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logger.warning(
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f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
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" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
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" results in services or applications open to the public. Both the diffusers team and Hugging Face"
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" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
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" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
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" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
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)
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if safety_checker is not None and feature_extractor is None:
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raise ValueError(
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"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
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" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
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)
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is_unet_version_less_0_9_0 = (
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unet is not None
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and hasattr(unet.config, "_diffusers_version")
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and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
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)
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is_unet_sample_size_less_64 = (
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|
unet is not None and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
|
|
)
|
|
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
|
|
deprecation_message = (
|
|
"The configuration file of the unet has set the default `sample_size` to smaller than"
|
|
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
|
|
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
|
|
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
|
|
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
|
|
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
|
|
" in the config might lead to incorrect results in future versions. If you have downloaded this"
|
|
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
|
|
" the `unet/config.json` file"
|
|
)
|
|
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
|
|
new_config = dict(unet.config)
|
|
new_config["sample_size"] = 64
|
|
unet._internal_dict = FrozenDict(new_config)
|
|
|
|
# Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
|
|
if unet is not None and unet.config.in_channels != 9:
|
|
logger.info(f"You have loaded a UNet with {unet.config.in_channels} input channels which.")
|
|
|
|
self.register_modules(
|
|
vae=vae,
|
|
text_encoder=text_encoder,
|
|
tokenizer=tokenizer,
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
safety_checker=safety_checker,
|
|
feature_extractor=feature_extractor,
|
|
)
|
|
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
|
|
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
|
|
self.register_to_config(requires_safety_checker=requires_safety_checker)
|
|
|
|
""" Preparation for Adaptive Mask inpainting """
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
|
|
def enable_model_cpu_offload(self, gpu_id=0):
|
|
r"""
|
|
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
|
|
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
|
|
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
|
|
iterative execution of the `unet`.
|
|
"""
|
|
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
|
|
from accelerate import cpu_offload_with_hook
|
|
else:
|
|
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
|
|
|
|
device = torch.device(f"cuda:{gpu_id}")
|
|
|
|
if self.device.type != "cpu":
|
|
self.to("cpu", silence_dtype_warnings=True)
|
|
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
|
|
|
|
hook = None
|
|
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
|
|
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
|
|
|
|
if self.safety_checker is not None:
|
|
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
|
|
|
|
# We'll offload the last model manually.
|
|
self.final_offload_hook = hook
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
|
|
def _encode_prompt(
|
|
self,
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt=None,
|
|
prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
lora_scale: Optional[float] = None,
|
|
):
|
|
r"""
|
|
Encodes the prompt into text encoder hidden states.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
prompt to be encoded
|
|
device: (`torch.device`):
|
|
torch device
|
|
num_images_per_prompt (`int`):
|
|
number of images that should be generated per prompt
|
|
do_classifier_free_guidance (`bool`):
|
|
whether to use classifier free guidance or not
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
|
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
|
less than `1`).
|
|
prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
|
provided, text embeddings will be generated from `prompt` input argument.
|
|
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
|
argument.
|
|
lora_scale (`float`, *optional*):
|
|
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
|
|
"""
|
|
# set lora scale so that monkey patched LoRA
|
|
# function of text encoder can correctly access it
|
|
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
|
|
self._lora_scale = lora_scale
|
|
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
if prompt_embeds is None:
|
|
# textual inversion: procecss multi-vector tokens if necessary
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
|
|
|
|
text_inputs = self.tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=self.tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids
|
|
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
|
|
|
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
|
|
text_input_ids, untruncated_ids
|
|
):
|
|
removed_text = self.tokenizer.batch_decode(
|
|
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
|
|
)
|
|
logger.warning(
|
|
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
|
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
|
)
|
|
|
|
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
|
attention_mask = text_inputs.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
prompt_embeds = self.text_encoder(
|
|
text_input_ids.to(device),
|
|
attention_mask=attention_mask,
|
|
)
|
|
prompt_embeds = prompt_embeds[0]
|
|
|
|
if self.text_encoder is not None:
|
|
prompt_embeds_dtype = self.text_encoder.dtype
|
|
elif self.unet is not None:
|
|
prompt_embeds_dtype = self.unet.dtype
|
|
else:
|
|
prompt_embeds_dtype = prompt_embeds.dtype
|
|
|
|
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
|
|
|
bs_embed, seq_len, _ = prompt_embeds.shape
|
|
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
|
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
|
|
|
# get unconditional embeddings for classifier free guidance
|
|
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
|
uncond_tokens: List[str]
|
|
if negative_prompt is None:
|
|
uncond_tokens = [""] * batch_size
|
|
elif prompt is not None and type(prompt) is not type(negative_prompt):
|
|
raise TypeError(
|
|
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
|
f" {type(prompt)}."
|
|
)
|
|
elif isinstance(negative_prompt, str):
|
|
uncond_tokens = [negative_prompt]
|
|
elif batch_size != len(negative_prompt):
|
|
raise ValueError(
|
|
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
|
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
|
" the batch size of `prompt`."
|
|
)
|
|
else:
|
|
uncond_tokens = negative_prompt
|
|
|
|
# textual inversion: procecss multi-vector tokens if necessary
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
|
|
|
|
max_length = prompt_embeds.shape[1]
|
|
uncond_input = self.tokenizer(
|
|
uncond_tokens,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
|
attention_mask = uncond_input.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
negative_prompt_embeds = self.text_encoder(
|
|
uncond_input.input_ids.to(device),
|
|
attention_mask=attention_mask,
|
|
)
|
|
negative_prompt_embeds = negative_prompt_embeds[0]
|
|
|
|
if do_classifier_free_guidance:
|
|
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
|
seq_len = negative_prompt_embeds.shape[1]
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
|
|
|
|
# For classifier free guidance, we need to do two forward passes.
|
|
# Here we concatenate the unconditional and text embeddings into a single batch
|
|
# to avoid doing two forward passes
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
|
|
|
return prompt_embeds
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
|
|
def run_safety_checker(self, image, device, dtype):
|
|
if self.safety_checker is None:
|
|
has_nsfw_concept = None
|
|
else:
|
|
if torch.is_tensor(image):
|
|
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
|
|
else:
|
|
feature_extractor_input = self.image_processor.numpy_to_pil(image)
|
|
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
|
|
image, has_nsfw_concept = self.safety_checker(
|
|
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
|
|
)
|
|
return image, has_nsfw_concept
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
|
def prepare_extra_step_kwargs(self, generator, eta):
|
|
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
|
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
|
# eta corresponds to η in DDIM paper: https://huggingface.co/papers/2010.02502
|
|
# and should be between [0, 1]
|
|
|
|
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
extra_step_kwargs = {}
|
|
if accepts_eta:
|
|
extra_step_kwargs["eta"] = eta
|
|
|
|
# check if the scheduler accepts generator
|
|
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
if accepts_generator:
|
|
extra_step_kwargs["generator"] = generator
|
|
return extra_step_kwargs
|
|
|
|
def check_inputs(
|
|
self,
|
|
prompt,
|
|
height,
|
|
width,
|
|
strength,
|
|
callback_steps,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
):
|
|
if strength < 0 or strength > 1:
|
|
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
|
|
|
|
if height % 8 != 0 or width % 8 != 0:
|
|
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
|
|
|
if (callback_steps is None) or (
|
|
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
|
|
):
|
|
raise ValueError(
|
|
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
|
f" {type(callback_steps)}."
|
|
)
|
|
|
|
if prompt is not None and prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
|
" only forward one of the two."
|
|
)
|
|
elif prompt is None and prompt_embeds is None:
|
|
raise ValueError(
|
|
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
|
)
|
|
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
|
|
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
|
|
|
if negative_prompt is not None and negative_prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
|
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
|
)
|
|
|
|
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
|
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
|
raise ValueError(
|
|
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
|
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
|
f" {negative_prompt_embeds.shape}."
|
|
)
|
|
|
|
def prepare_latents(
|
|
self,
|
|
batch_size,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
latents=None,
|
|
image=None,
|
|
timestep=None,
|
|
is_strength_max=True,
|
|
return_noise=False,
|
|
return_image_latents=False,
|
|
):
|
|
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
|
|
if isinstance(generator, list) and len(generator) != batch_size:
|
|
raise ValueError(
|
|
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
|
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
|
)
|
|
|
|
if (image is None or timestep is None) and not is_strength_max:
|
|
raise ValueError(
|
|
"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
|
|
"However, either the image or the noise timestep has not been provided."
|
|
)
|
|
|
|
if return_image_latents or (latents is None and not is_strength_max):
|
|
image = image.to(device=device, dtype=dtype)
|
|
image_latents = self._encode_vae_image(image=image, generator=generator)
|
|
|
|
if latents is None:
|
|
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
# if strength is 1. then initialise the latents to noise, else initial to image + noise
|
|
latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
|
|
# if pure noise then scale the initial latents by the Scheduler's init sigma
|
|
latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
|
|
else:
|
|
noise = latents.to(device)
|
|
latents = noise * self.scheduler.init_noise_sigma
|
|
|
|
outputs = (latents,)
|
|
|
|
if return_noise:
|
|
outputs += (noise,)
|
|
|
|
if return_image_latents:
|
|
outputs += (image_latents,)
|
|
|
|
return outputs
|
|
|
|
def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
|
|
if isinstance(generator, list):
|
|
image_latents = [
|
|
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
|
|
for i in range(image.shape[0])
|
|
]
|
|
image_latents = torch.cat(image_latents, dim=0)
|
|
else:
|
|
image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
|
|
|
|
image_latents = self.vae.config.scaling_factor * image_latents
|
|
|
|
return image_latents
|
|
|
|
def prepare_mask_latents(
|
|
self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
|
|
):
|
|
# resize the mask to latents shape as we concatenate the mask to the latents
|
|
# we do that before converting to dtype to avoid breaking in case we're using cpu_offload
|
|
# and half precision
|
|
mask = torch.nn.functional.interpolate(
|
|
mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
|
|
)
|
|
mask = mask.to(device=device, dtype=dtype)
|
|
|
|
masked_image = masked_image.to(device=device, dtype=dtype)
|
|
masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
|
|
|
|
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
|
|
if mask.shape[0] < batch_size:
|
|
if not batch_size % mask.shape[0] == 0:
|
|
raise ValueError(
|
|
"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
|
|
f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
|
|
" of masks that you pass is divisible by the total requested batch size."
|
|
)
|
|
mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
|
|
if masked_image_latents.shape[0] < batch_size:
|
|
if not batch_size % masked_image_latents.shape[0] == 0:
|
|
raise ValueError(
|
|
"The passed images and the required batch size don't match. Images are supposed to be duplicated"
|
|
f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
|
|
" Make sure the number of images that you pass is divisible by the total requested batch size."
|
|
)
|
|
masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
|
|
|
|
mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
|
|
masked_image_latents = (
|
|
torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
|
|
)
|
|
|
|
# aligning device to prevent device errors when concating it with the latent model input
|
|
masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
|
|
return mask, masked_image_latents
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
|
|
def get_timesteps(self, num_inference_steps, strength, device):
|
|
# get the original timestep using init_timestep
|
|
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
|
|
|
|
t_start = max(num_inference_steps - init_timestep, 0)
|
|
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
|
|
|
|
return timesteps, num_inference_steps - t_start
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
image: Union[torch.FloatTensor, PIL.Image.Image] = None,
|
|
default_mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
|
|
height: Optional[int] = None,
|
|
width: Optional[int] = None,
|
|
strength: float = 1.0,
|
|
num_inference_steps: int = 50,
|
|
guidance_scale: float = 7.5,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
eta: float = 0.0,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.FloatTensor] = None,
|
|
prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
|
callback_steps: int = 1,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
use_adaptive_mask: bool = True,
|
|
enforce_full_mask_ratio: float = 0.5,
|
|
human_detection_thres: float = 0.008,
|
|
visualization_save_dir: str = None,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
|
image (`PIL.Image.Image`):
|
|
`Image` or tensor representing an image batch to be inpainted (which parts of the image to be masked
|
|
out with `default_mask_image` and repainted according to `prompt`).
|
|
default_mask_image (`PIL.Image.Image`):
|
|
`Image` or tensor representing an image batch to mask `image`. White pixels in the mask are repainted
|
|
while black pixels are preserved. If `default_mask_image` is a PIL image, it is converted to a single channel
|
|
(luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the
|
|
expected shape would be `(B, H, W, 1)`.
|
|
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The height in pixels of the generated image.
|
|
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The width in pixels of the generated image.
|
|
strength (`float`, *optional*, defaults to 1.0):
|
|
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
|
|
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
|
|
on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
|
|
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
|
|
essentially ignores `image`.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference. This parameter is modulated by `strength`.
|
|
guidance_scale (`float`, *optional*, defaults to 7.5):
|
|
A higher guidance scale value encourages the model to generate images closely linked to the text
|
|
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
|
|
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only applies
|
|
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
|
generation deterministic.
|
|
latents (`torch.FloatTensor`, *optional*):
|
|
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
|
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
|
tensor is generated by sampling using the supplied random `generator`.
|
|
prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
|
|
provided, text embeddings are generated from the `prompt` input argument.
|
|
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
|
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
callback (`Callable`, *optional*):
|
|
A function that calls every `callback_steps` steps during inference. The function is called with the
|
|
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
|
callback_steps (`int`, *optional*, defaults to 1):
|
|
The frequency at which the `callback` function is called. If not specified, the callback is called at
|
|
every step.
|
|
cross_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
|
|
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
|
|
Examples:
|
|
|
|
```py
|
|
>>> import PIL
|
|
>>> import requests
|
|
>>> import torch
|
|
>>> from io import BytesIO
|
|
|
|
>>> from diffusers import AdaptiveMaskInpaintPipeline
|
|
|
|
|
|
>>> def download_image(url):
|
|
... response = requests.get(url)
|
|
... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
|
|
|
|
|
|
>>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
|
|
>>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
|
|
|
|
>>> init_image = download_image(img_url).resize((512, 512))
|
|
>>> default_mask_image = download_image(mask_url).resize((512, 512))
|
|
|
|
>>> pipe = AdaptiveMaskInpaintPipeline.from_pretrained(
|
|
... "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16
|
|
... )
|
|
>>> pipe = pipe.to("cuda")
|
|
|
|
>>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
|
|
>>> image = pipe(prompt=prompt, image=init_image, default_mask_image=default_mask_image).images[0]
|
|
```
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
|
|
otherwise a `tuple` is returned where the first element is a list with the generated images and the
|
|
second element is a list of `bool`s indicating whether the corresponding generated image contains
|
|
"not-safe-for-work" (nsfw) content.
|
|
"""
|
|
# 0. Default height and width to unet
|
|
width, height = image.size
|
|
# height = height or self.unet.config.sample_size * self.vae_scale_factor
|
|
# width = width or self.unet.config.sample_size * self.vae_scale_factor
|
|
|
|
# 1. Check inputs
|
|
self.check_inputs(
|
|
prompt,
|
|
height,
|
|
width,
|
|
strength,
|
|
callback_steps,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
)
|
|
|
|
# 2. Define call parameters
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
device = self._execution_device
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
|
|
# 3. Encode input prompt
|
|
text_encoder_lora_scale = (
|
|
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
|
|
)
|
|
prompt_embeds = self._encode_prompt(
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
lora_scale=text_encoder_lora_scale,
|
|
)
|
|
|
|
# 4. set timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps, num_inference_steps = self.get_timesteps(
|
|
num_inference_steps=num_inference_steps, strength=strength, device=device
|
|
)
|
|
# check that number of inference steps is not < 1 - as this doesn't make sense
|
|
if num_inference_steps < 1:
|
|
raise ValueError(
|
|
f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
|
|
f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
|
|
)
|
|
# at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
|
|
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
|
|
# create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
|
|
is_strength_max = strength == 1.0
|
|
|
|
# 5. Preprocess mask and image (will be used later, once again)
|
|
mask, masked_image, init_image = prepare_mask_and_masked_image(
|
|
image, default_mask_image, height, width, return_image=True
|
|
)
|
|
default_mask_image_np = np.array(default_mask_image).astype(np.uint8) / 255
|
|
mask_condition = mask.clone()
|
|
|
|
# 6. Prepare latent variables
|
|
num_channels_latents = self.vae.config.latent_channels
|
|
num_channels_unet = self.unet.config.in_channels
|
|
return_image_latents = num_channels_unet == 4
|
|
|
|
latents_outputs = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
image=init_image,
|
|
timestep=latent_timestep,
|
|
is_strength_max=is_strength_max,
|
|
return_noise=True,
|
|
return_image_latents=return_image_latents,
|
|
)
|
|
|
|
if return_image_latents:
|
|
latents, noise, image_latents = latents_outputs
|
|
else:
|
|
latents, noise = latents_outputs
|
|
|
|
# 7. Prepare mask latent variables
|
|
mask, masked_image_latents = self.prepare_mask_latents(
|
|
mask,
|
|
masked_image,
|
|
batch_size * num_images_per_prompt,
|
|
height,
|
|
width,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
do_classifier_free_guidance,
|
|
)
|
|
|
|
# 8. Check that sizes of mask, masked image and latents match
|
|
if num_channels_unet == 9:
|
|
# default case for runwayml/stable-diffusion-inpainting
|
|
num_channels_mask = mask.shape[1]
|
|
num_channels_masked_image = masked_image_latents.shape[1]
|
|
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
|
|
raise ValueError(
|
|
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
|
|
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
|
|
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
|
|
f" = {num_channels_latents + num_channels_masked_image + num_channels_mask}. Please verify the config of"
|
|
" `pipeline.unet` or your `default_mask_image` or `image` input."
|
|
)
|
|
elif num_channels_unet != 4:
|
|
raise ValueError(
|
|
f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
|
|
)
|
|
|
|
# 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
|
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
|
|
|
# 10. Denoising loop
|
|
mask_image_np = default_mask_image_np
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
|
|
|
# concat latents, mask, masked_image_latents in the channel dimension
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
|
|
if num_channels_unet == 9:
|
|
latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1 & predicted original sample x_0
|
|
outputs = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=True)
|
|
latents = outputs["prev_sample"] # x_t-1
|
|
pred_orig_latents = outputs["pred_original_sample"] # x_0
|
|
|
|
# run segmentation
|
|
if use_adaptive_mask:
|
|
if enforce_full_mask_ratio > 0.0:
|
|
use_default_mask = t < self.scheduler.config.num_train_timesteps * enforce_full_mask_ratio
|
|
elif enforce_full_mask_ratio == 0.0:
|
|
use_default_mask = False
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
pred_orig_image = self.decode_to_npuint8_image(pred_orig_latents)
|
|
dilate_num = self.adaptive_mask_settings.dilate_scheduler(i)
|
|
do_adapt_mask = self.adaptive_mask_settings.provoke_scheduler(i)
|
|
if do_adapt_mask:
|
|
mask, masked_image_latents, mask_image_np, vis_np = self.adapt_mask(
|
|
init_image,
|
|
pred_orig_image,
|
|
default_mask_image_np,
|
|
dilate_num=dilate_num,
|
|
use_default_mask=use_default_mask,
|
|
height=height,
|
|
width=width,
|
|
batch_size=batch_size,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
device=device,
|
|
generator=generator,
|
|
do_classifier_free_guidance=do_classifier_free_guidance,
|
|
i=i,
|
|
human_detection_thres=human_detection_thres,
|
|
mask_image_np=mask_image_np,
|
|
)
|
|
|
|
if self.adaptive_mask_model.use_visualizer:
|
|
import matplotlib.pyplot as plt
|
|
|
|
# mask_image_new_colormap = np.clip(0.6 + (1.0 - mask_image_np), a_min=0.0, a_max=1.0) * 255
|
|
|
|
os.makedirs(visualization_save_dir, exist_ok=True)
|
|
|
|
# Image.fromarray(mask_image_new_colormap).convert("L").save(f"{visualization_save_dir}/masks/{i:05}.png")
|
|
plt.axis("off")
|
|
plt.subplot(1, 2, 1)
|
|
plt.imshow(mask_image_np)
|
|
plt.subplot(1, 2, 2)
|
|
plt.imshow(pred_orig_image)
|
|
plt.savefig(f"{visualization_save_dir}/{i:05}.png", bbox_inches="tight")
|
|
plt.close("all")
|
|
|
|
if num_channels_unet == 4:
|
|
init_latents_proper = image_latents[:1]
|
|
init_mask = mask[:1]
|
|
|
|
if i < len(timesteps) - 1:
|
|
noise_timestep = timesteps[i + 1]
|
|
init_latents_proper = self.scheduler.add_noise(
|
|
init_latents_proper, noise, torch.tensor([noise_timestep])
|
|
)
|
|
|
|
latents = (1 - init_mask) * init_latents_proper + init_mask * latents
|
|
|
|
# call the callback, if provided
|
|
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
progress_bar.update()
|
|
if callback is not None and i % callback_steps == 0:
|
|
callback(i, t, latents)
|
|
|
|
if not output_type == "latent":
|
|
condition_kwargs = {}
|
|
if isinstance(self.vae, AsymmetricAutoencoderKL):
|
|
init_image = init_image.to(device=device, dtype=masked_image_latents.dtype)
|
|
init_image_condition = init_image.clone()
|
|
init_image = self._encode_vae_image(init_image, generator=generator)
|
|
mask_condition = mask_condition.to(device=device, dtype=masked_image_latents.dtype)
|
|
condition_kwargs = {"image": init_image_condition, "mask": mask_condition}
|
|
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, **condition_kwargs)[0]
|
|
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
|
|
else:
|
|
image = latents
|
|
has_nsfw_concept = None
|
|
|
|
if has_nsfw_concept is None:
|
|
do_denormalize = [True] * image.shape[0]
|
|
else:
|
|
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
|
|
|
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
|
|
|
|
# Offload last model to CPU
|
|
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
|
self.final_offload_hook.offload()
|
|
|
|
if self.adaptive_mask_model.use_visualizer:
|
|
generate_video_from_imgs(images_save_directory=visualization_save_dir, fps=10, delete_dir=True)
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
|
|
|
def decode_to_npuint8_image(self, latents):
|
|
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, **{})[
|
|
0
|
|
] # torch, float32, -1.~1.
|
|
image = self.image_processor.postprocess(image, output_type="pt", do_denormalize=[True] * image.shape[0])
|
|
image = (image.squeeze().permute(1, 2, 0).detach().cpu().numpy() * 255).astype(np.uint8) # np, uint8, 0~255
|
|
return image
|
|
|
|
def register_adaptive_mask_settings(self):
|
|
from easydict import EasyDict
|
|
|
|
num_steps = 50
|
|
|
|
step_num = int(num_steps * 0.1)
|
|
final_step_num = num_steps - step_num * 7
|
|
# adaptive mask settings
|
|
self.adaptive_mask_settings = EasyDict(
|
|
dilate_scheduler=MaskDilateScheduler(
|
|
max_dilate_num=20,
|
|
num_inference_steps=num_steps,
|
|
schedule=[20] * step_num
|
|
+ [10] * step_num
|
|
+ [5] * step_num
|
|
+ [4] * step_num
|
|
+ [3] * step_num
|
|
+ [2] * step_num
|
|
+ [1] * step_num
|
|
+ [0] * final_step_num,
|
|
),
|
|
dilate_kernel=np.ones((3, 3), dtype=np.uint8),
|
|
provoke_scheduler=ProvokeScheduler(
|
|
num_inference_steps=num_steps,
|
|
schedule=list(range(2, 10 + 1, 2)) + list(range(12, 40 + 1, 2)) + [45],
|
|
is_zero_indexing=False,
|
|
),
|
|
)
|
|
|
|
def register_adaptive_mask_model(self):
|
|
# declare segmentation model used for mask adaptation
|
|
use_visualizer = True
|
|
# assert not use_visualizer, \
|
|
# """
|
|
# If you plan to 'use_visualizer', USE WITH CAUTION.
|
|
# It creates a directory of images and masks, which is used for merging into a video.
|
|
# The procedure involves deleting the directory of images, which means that
|
|
# if you set the directory wrong you can have other important files blown away.
|
|
# """
|
|
|
|
self.adaptive_mask_model = PointRendPredictor(
|
|
# pointrend_thres=0.2,
|
|
pointrend_thres=0.9,
|
|
device="cuda" if torch.cuda.is_available() else "cpu",
|
|
use_visualizer=use_visualizer,
|
|
config_pth="pointrend_rcnn_R_50_FPN_3x_coco.yaml",
|
|
weights_pth="model_final_edd263.pkl",
|
|
)
|
|
|
|
def adapt_mask(self, init_image, pred_orig_image, default_mask_image, dilate_num, use_default_mask, **kwargs):
|
|
## predict mask to use for adaptation
|
|
adapt_output = self.adaptive_mask_model(pred_orig_image) # vis can be None if 'use_visualizer' is False
|
|
mask = adapt_output["mask"]
|
|
vis = adapt_output["vis"]
|
|
|
|
## if mask is empty or too small, use default_mask_image. else, use dilate and intersect with default_mask_image
|
|
if use_default_mask or mask.sum() < 512 * 512 * kwargs["human_detection_thres"]: # 0.005
|
|
# set mask as default mask
|
|
mask = default_mask_image # HxW
|
|
|
|
else:
|
|
## timestep-adaptive mask
|
|
mask = cv2.dilate(
|
|
mask, self.adaptive_mask_settings.dilate_kernel, iterations=dilate_num
|
|
) # dilate_kernel: np.ones((3,3), np.uint8)
|
|
mask = np.logical_and(mask, default_mask_image) # HxW
|
|
|
|
## prepare mask as pt tensor format
|
|
mask = torch.tensor(mask, dtype=torch.float32).to(kwargs["device"])[None, None] # 1 x 1 x H x W
|
|
mask, masked_image = prepare_mask_and_masked_image(
|
|
init_image.to(kwargs["device"]), mask, kwargs["height"], kwargs["width"], return_image=False
|
|
)
|
|
|
|
mask_image_np = mask.clone().squeeze().detach().cpu().numpy()
|
|
|
|
mask, masked_image_latents = self.prepare_mask_latents(
|
|
mask,
|
|
masked_image,
|
|
kwargs["batch_size"] * kwargs["num_images_per_prompt"],
|
|
kwargs["height"],
|
|
kwargs["width"],
|
|
kwargs["prompt_embeds"].dtype,
|
|
kwargs["device"],
|
|
kwargs["generator"],
|
|
kwargs["do_classifier_free_guidance"],
|
|
)
|
|
|
|
return mask, masked_image_latents, mask_image_np, vis
|
|
|
|
|
|
def seg2bbox(seg_mask: np.ndarray):
|
|
nonzero_i, nonzero_j = seg_mask.nonzero()
|
|
min_i, max_i = nonzero_i.min(), nonzero_i.max()
|
|
min_j, max_j = nonzero_j.min(), nonzero_j.max()
|
|
|
|
return np.array([min_j, min_i, max_j + 1, max_i + 1])
|
|
|
|
|
|
def merge_bbox(bboxes: list):
|
|
assert len(bboxes) > 0
|
|
|
|
all_bboxes = np.stack(bboxes, axis=0) # shape: N_bbox X 4
|
|
merged_bbox = np.zeros_like(all_bboxes[0]) # shape: 4,
|
|
|
|
merged_bbox[0] = all_bboxes[:, 0].min()
|
|
merged_bbox[1] = all_bboxes[:, 1].min()
|
|
merged_bbox[2] = all_bboxes[:, 2].max()
|
|
merged_bbox[3] = all_bboxes[:, 3].max()
|
|
|
|
return merged_bbox
|
|
|
|
|
|
class PointRendPredictor:
|
|
def __init__(
|
|
self,
|
|
cat_id_to_focus=0,
|
|
pointrend_thres=0.9,
|
|
device="cuda",
|
|
use_visualizer=False,
|
|
merge_mode="merge",
|
|
config_pth=None,
|
|
weights_pth=None,
|
|
):
|
|
super().__init__()
|
|
|
|
# category id to focus (default: 0, which is human)
|
|
self.cat_id_to_focus = cat_id_to_focus
|
|
|
|
# setup coco metadata
|
|
self.coco_metadata = MetadataCatalog.get("coco_2017_val")
|
|
self.cfg = get_cfg()
|
|
|
|
# get segmentation model config
|
|
point_rend.add_pointrend_config(self.cfg) # --> Add PointRend-specific config
|
|
self.cfg.merge_from_file(config_pth)
|
|
self.cfg.MODEL.WEIGHTS = weights_pth
|
|
self.cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = pointrend_thres
|
|
self.cfg.MODEL.DEVICE = device
|
|
|
|
# get segmentation model
|
|
self.pointrend_seg_model = DefaultPredictor(self.cfg)
|
|
|
|
# settings for visualizer
|
|
self.use_visualizer = use_visualizer
|
|
|
|
# mask-merge mode
|
|
assert merge_mode in ["merge", "max-confidence"], f"'merge_mode': {merge_mode} not implemented."
|
|
self.merge_mode = merge_mode
|
|
|
|
def merge_mask(self, masks, scores=None):
|
|
if self.merge_mode == "merge":
|
|
mask = np.any(masks, axis=0)
|
|
elif self.merge_mode == "max-confidence":
|
|
mask = masks[np.argmax(scores)]
|
|
return mask
|
|
|
|
def vis_seg_on_img(self, image, mask):
|
|
if type(mask) == np.ndarray:
|
|
mask = torch.tensor(mask)
|
|
v = Visualizer(image, self.coco_metadata, scale=0.5, instance_mode=ColorMode.IMAGE_BW)
|
|
instances = Instances(image_size=image.shape[:2], pred_masks=mask if len(mask.shape) == 3 else mask[None])
|
|
vis = v.draw_instance_predictions(instances.to("cpu")).get_image()
|
|
return vis
|
|
|
|
def __call__(self, image):
|
|
# run segmentation
|
|
outputs = self.pointrend_seg_model(image)
|
|
instances = outputs["instances"]
|
|
|
|
# merge instances for the category-id to focus
|
|
is_class = instances.pred_classes == self.cat_id_to_focus
|
|
masks = instances.pred_masks[is_class]
|
|
masks = masks.detach().cpu().numpy() # [N, img_size, img_size]
|
|
mask = self.merge_mask(masks, scores=instances.scores[is_class])
|
|
|
|
return {
|
|
"asset_mask": None,
|
|
"mask": mask.astype(np.uint8),
|
|
"vis": self.vis_seg_on_img(image, mask) if self.use_visualizer else None,
|
|
}
|
|
|
|
|
|
class MaskDilateScheduler:
|
|
def __init__(self, max_dilate_num=15, num_inference_steps=50, schedule=None):
|
|
super().__init__()
|
|
self.max_dilate_num = max_dilate_num
|
|
self.schedule = [num_inference_steps - i for i in range(num_inference_steps)] if schedule is None else schedule
|
|
assert len(self.schedule) == num_inference_steps
|
|
|
|
def __call__(self, i):
|
|
return min(self.max_dilate_num, self.schedule[i])
|
|
|
|
|
|
class ProvokeScheduler:
|
|
def __init__(self, num_inference_steps=50, schedule=None, is_zero_indexing=False):
|
|
super().__init__()
|
|
if len(schedule) > 0:
|
|
if is_zero_indexing:
|
|
assert max(schedule) <= num_inference_steps - 1
|
|
else:
|
|
assert max(schedule) <= num_inference_steps
|
|
|
|
# register as self
|
|
self.is_zero_indexing = is_zero_indexing
|
|
self.schedule = schedule
|
|
|
|
def __call__(self, i):
|
|
if self.is_zero_indexing:
|
|
return i in self.schedule
|
|
else:
|
|
return i + 1 in self.schedule
|