647 lines
28 KiB
Python
647 lines
28 KiB
Python
# Copyright 2025 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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from copy import deepcopy
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from typing import Callable, List, Optional, Union
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import numpy as np
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import PIL.Image
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import torch
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import torch.nn.functional as F
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from packaging import version
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from PIL import Image
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from transformers import (
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XLMRobertaTokenizer,
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)
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from ... import __version__
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from ...models import UNet2DConditionModel, VQModel
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from ...schedulers import DDIMScheduler
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from ...utils import (
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is_torch_xla_available,
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logging,
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replace_example_docstring,
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)
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from ...utils.torch_utils import randn_tensor
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from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
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from .text_encoder import MultilingualCLIP
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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm
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XLA_AVAILABLE = True
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else:
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XLA_AVAILABLE = False
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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EXAMPLE_DOC_STRING = """
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Examples:
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```py
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>>> from diffusers import KandinskyInpaintPipeline, KandinskyPriorPipeline
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>>> from diffusers.utils import load_image
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>>> import torch
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>>> import numpy as np
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>>> pipe_prior = KandinskyPriorPipeline.from_pretrained(
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... "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
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... )
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>>> pipe_prior.to("cuda")
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>>> prompt = "a hat"
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>>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)
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>>> pipe = KandinskyInpaintPipeline.from_pretrained(
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... "kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16
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... )
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>>> pipe.to("cuda")
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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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... "/kandinsky/cat.png"
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... )
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>>> mask = np.zeros((768, 768), dtype=np.float32)
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>>> mask[:250, 250:-250] = 1
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>>> out = pipe(
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... prompt,
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... image=init_image,
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... mask_image=mask,
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... image_embeds=image_emb,
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... negative_image_embeds=zero_image_emb,
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... height=768,
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... width=768,
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... num_inference_steps=50,
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... )
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>>> image = out.images[0]
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>>> image.save("cat_with_hat.png")
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```
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"""
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def get_new_h_w(h, w, scale_factor=8):
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new_h = h // scale_factor**2
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if h % scale_factor**2 != 0:
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new_h += 1
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new_w = w // scale_factor**2
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if w % scale_factor**2 != 0:
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new_w += 1
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return new_h * scale_factor, new_w * scale_factor
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def prepare_mask(masks):
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prepared_masks = []
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for mask in masks:
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old_mask = deepcopy(mask)
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for i in range(mask.shape[1]):
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for j in range(mask.shape[2]):
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if old_mask[0][i][j] == 1:
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continue
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if i != 0:
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mask[:, i - 1, j] = 0
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if j != 0:
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mask[:, i, j - 1] = 0
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if i != 0 and j != 0:
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mask[:, i - 1, j - 1] = 0
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if i != mask.shape[1] - 1:
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mask[:, i + 1, j] = 0
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if j != mask.shape[2] - 1:
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mask[:, i, j + 1] = 0
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if i != mask.shape[1] - 1 and j != mask.shape[2] - 1:
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mask[:, i + 1, j + 1] = 0
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prepared_masks.append(mask)
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return torch.stack(prepared_masks, dim=0)
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def prepare_mask_and_masked_image(image, mask, height, width):
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r"""
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Prepares a pair (mask, image) to be consumed by the Kandinsky inpaint pipeline. This means that those inputs will
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be converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for
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the ``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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height (`int`, *optional*, defaults to 512):
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The height in pixels of the generated image.
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width (`int`, *optional*, defaults to 512):
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The width in pixels of the generated image.
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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, 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=Image.BICUBIC, reducing_gap=1) 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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mask = 1 - mask
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return mask, image
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class KandinskyInpaintPipeline(DiffusionPipeline):
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"""
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Pipeline for text-guided image inpainting using Kandinsky2.1
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Args:
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text_encoder ([`MultilingualCLIP`]):
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Frozen text-encoder.
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tokenizer ([`XLMRobertaTokenizer`]):
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Tokenizer of class
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scheduler ([`DDIMScheduler`]):
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A scheduler to be used in combination with `unet` to generate image latents.
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unet ([`UNet2DConditionModel`]):
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Conditional U-Net architecture to denoise the image embedding.
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movq ([`VQModel`]):
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MoVQ image encoder and decoder
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"""
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model_cpu_offload_seq = "text_encoder->unet->movq"
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def __init__(
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self,
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text_encoder: MultilingualCLIP,
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movq: VQModel,
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tokenizer: XLMRobertaTokenizer,
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unet: UNet2DConditionModel,
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scheduler: DDIMScheduler,
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):
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super().__init__()
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self.register_modules(
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text_encoder=text_encoder,
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movq=movq,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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)
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self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
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self._warn_has_been_called = False
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# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
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def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
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if latents is None:
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latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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else:
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if latents.shape != shape:
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raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
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latents = latents.to(device)
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latents = latents * scheduler.init_noise_sigma
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return latents
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def _encode_prompt(
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self,
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prompt,
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device,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_prompt=None,
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):
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batch_size = len(prompt) if isinstance(prompt, list) else 1
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# get prompt text embeddings
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=77,
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truncation=True,
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return_attention_mask=True,
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add_special_tokens=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
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if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
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removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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text_input_ids = text_input_ids.to(device)
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text_mask = text_inputs.attention_mask.to(device)
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prompt_embeds, text_encoder_hidden_states = self.text_encoder(
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input_ids=text_input_ids, attention_mask=text_mask
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)
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prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
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text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
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text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
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if do_classifier_free_guidance:
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uncond_tokens: List[str]
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if negative_prompt is None:
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uncond_tokens = [""] * batch_size
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elif type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif isinstance(negative_prompt, str):
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uncond_tokens = [negative_prompt]
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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else:
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uncond_tokens = negative_prompt
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uncond_input = self.tokenizer(
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uncond_tokens,
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padding="max_length",
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max_length=77,
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truncation=True,
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return_attention_mask=True,
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add_special_tokens=True,
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return_tensors="pt",
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)
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uncond_text_input_ids = uncond_input.input_ids.to(device)
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uncond_text_mask = uncond_input.attention_mask.to(device)
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negative_prompt_embeds, uncond_text_encoder_hidden_states = self.text_encoder(
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input_ids=uncond_text_input_ids, attention_mask=uncond_text_mask
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)
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# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
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seq_len = negative_prompt_embeds.shape[1]
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negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
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negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
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seq_len = uncond_text_encoder_hidden_states.shape[1]
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uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
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uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
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batch_size * num_images_per_prompt, seq_len, -1
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)
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uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
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# done duplicates
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
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text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
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text_mask = torch.cat([uncond_text_mask, text_mask])
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return prompt_embeds, text_encoder_hidden_states, text_mask
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@torch.no_grad()
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@replace_example_docstring(EXAMPLE_DOC_STRING)
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def __call__(
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self,
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prompt: Union[str, List[str]],
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image: Union[torch.Tensor, PIL.Image.Image],
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mask_image: Union[torch.Tensor, PIL.Image.Image, np.ndarray],
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image_embeds: torch.Tensor,
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negative_image_embeds: torch.Tensor,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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height: int = 512,
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width: int = 512,
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num_inference_steps: int = 100,
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guidance_scale: float = 4.0,
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num_images_per_prompt: int = 1,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.Tensor] = None,
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output_type: Optional[str] = "pil",
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callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
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callback_steps: int = 1,
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return_dict: bool = True,
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):
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"""
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Function invoked when calling the pipeline for generation.
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Args:
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prompt (`str` or `List[str]`):
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The prompt or prompts to guide the image generation.
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image (`torch.Tensor`, `PIL.Image.Image` or `np.ndarray`):
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`Image`, or tensor representing an image batch, that will be used as the starting point for the
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process.
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mask_image (`PIL.Image.Image`,`torch.Tensor` or `np.ndarray`):
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`Image`, or a tensor representing an image batch, to mask `image`. White pixels in the mask will be
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repainted, while black pixels will be preserved. You can pass a pytorch tensor as mask only if the
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image you passed is a pytorch tensor, and it should contain one color channel (L) instead of 3, so the
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expected shape would be either `(B, 1, H, W,)`, `(B, H, W)`, `(1, H, W)` or `(H, W)` If image is an PIL
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image or numpy array, mask should also be a either PIL image or numpy array. If it is a PIL image, it
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will be converted to a single channel (luminance) before use. If it is a nummpy array, the expected
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shape is `(H, W)`.
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image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
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The clip image embeddings for text prompt, that will be used to condition the image generation.
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negative_image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
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The clip image embeddings for negative text prompt, will be used to condition the image generation.
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
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if `guidance_scale` is less than `1`).
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height (`int`, *optional*, defaults to 512):
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The height in pixels of the generated image.
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width (`int`, *optional*, defaults to 512):
|
|
The width in pixels of the generated image.
|
|
num_inference_steps (`int`, *optional*, defaults to 100):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference.
|
|
guidance_scale (`float`, *optional*, defaults to 4.0):
|
|
Guidance scale as defined in [Classifier-Free Diffusion
|
|
Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
|
|
of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
|
|
`guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
|
|
the text `prompt`, usually at the expense of lower image quality.
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
|
to make generation deterministic.
|
|
latents (`torch.Tensor`, *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 will ge generated by sampling using the supplied random `generator`.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
|
|
(`np.array`) or `"pt"` (`torch.Tensor`).
|
|
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.Tensor)`.
|
|
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.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.ImagePipelineOutput`] or `tuple`
|
|
"""
|
|
if not self._warn_has_been_called and version.parse(version.parse(__version__).base_version) < version.parse(
|
|
"0.23.0.dev0"
|
|
):
|
|
logger.warning(
|
|
"Please note that the expected format of `mask_image` has recently been changed. "
|
|
"Before diffusers == 0.19.0, Kandinsky Inpainting pipelines repainted black pixels and preserved black pixels. "
|
|
"As of diffusers==0.19.0 this behavior has been inverted. Now white pixels are repainted and black pixels are preserved. "
|
|
"This way, Kandinsky's masking behavior is aligned with Stable Diffusion. "
|
|
"THIS means that you HAVE to invert the input mask to have the same behavior as before as explained in https://github.com/huggingface/diffusers/pull/4207. "
|
|
"This warning will be suppressed after the first inference call and will be removed in diffusers>0.23.0"
|
|
)
|
|
self._warn_has_been_called = True
|
|
|
|
# Define call parameters
|
|
if isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
|
|
|
device = self._execution_device
|
|
|
|
batch_size = batch_size * num_images_per_prompt
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
|
|
prompt_embeds, text_encoder_hidden_states, _ = self._encode_prompt(
|
|
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
|
|
)
|
|
|
|
if isinstance(image_embeds, list):
|
|
image_embeds = torch.cat(image_embeds, dim=0)
|
|
if isinstance(negative_image_embeds, list):
|
|
negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
|
|
|
|
if do_classifier_free_guidance:
|
|
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
|
|
negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
|
|
|
|
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
|
|
dtype=prompt_embeds.dtype, device=device
|
|
)
|
|
|
|
# preprocess image and mask
|
|
mask_image, image = prepare_mask_and_masked_image(image, mask_image, height, width)
|
|
|
|
image = image.to(dtype=prompt_embeds.dtype, device=device)
|
|
image = self.movq.encode(image)["latents"]
|
|
|
|
mask_image = mask_image.to(dtype=prompt_embeds.dtype, device=device)
|
|
|
|
image_shape = tuple(image.shape[-2:])
|
|
mask_image = F.interpolate(
|
|
mask_image,
|
|
image_shape,
|
|
mode="nearest",
|
|
)
|
|
mask_image = prepare_mask(mask_image)
|
|
masked_image = image * mask_image
|
|
|
|
mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0)
|
|
masked_image = masked_image.repeat_interleave(num_images_per_prompt, dim=0)
|
|
if do_classifier_free_guidance:
|
|
mask_image = mask_image.repeat(2, 1, 1, 1)
|
|
masked_image = masked_image.repeat(2, 1, 1, 1)
|
|
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps_tensor = self.scheduler.timesteps
|
|
|
|
num_channels_latents = self.movq.config.latent_channels
|
|
|
|
# get h, w for latents
|
|
sample_height, sample_width = get_new_h_w(height, width, self.movq_scale_factor)
|
|
|
|
# create initial latent
|
|
latents = self.prepare_latents(
|
|
(batch_size, num_channels_latents, sample_height, sample_width),
|
|
text_encoder_hidden_states.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
self.scheduler,
|
|
)
|
|
|
|
# Check that sizes of mask, masked image and latents match with expected
|
|
num_channels_mask = mask_image.shape[1]
|
|
num_channels_masked_image = masked_image.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 `mask_image` or `image` input."
|
|
)
|
|
|
|
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
|
latent_model_input = torch.cat([latent_model_input, masked_image, mask_image], dim=1)
|
|
|
|
added_cond_kwargs = {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
|
|
noise_pred = self.unet(
|
|
sample=latent_model_input,
|
|
timestep=t,
|
|
encoder_hidden_states=text_encoder_hidden_states,
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
if do_classifier_free_guidance:
|
|
noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
_, variance_pred_text = variance_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)
|
|
|
|
if not (
|
|
hasattr(self.scheduler.config, "variance_type")
|
|
and self.scheduler.config.variance_type in ["learned", "learned_range"]
|
|
):
|
|
noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents = self.scheduler.step(
|
|
noise_pred,
|
|
t,
|
|
latents,
|
|
generator=generator,
|
|
).prev_sample
|
|
|
|
if callback is not None and i % callback_steps == 0:
|
|
step_idx = i // getattr(self.scheduler, "order", 1)
|
|
callback(step_idx, t, latents)
|
|
|
|
if XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
# post-processing
|
|
image = self.movq.decode(latents, force_not_quantize=True)["sample"]
|
|
|
|
self.maybe_free_model_hooks()
|
|
|
|
if output_type not in ["pt", "np", "pil"]:
|
|
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
|
|
|
|
if output_type in ["np", "pil"]:
|
|
image = image * 0.5 + 0.5
|
|
image = image.clamp(0, 1)
|
|
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
|
|
|
|
if output_type == "pil":
|
|
image = self.numpy_to_pil(image)
|
|
|
|
if not return_dict:
|
|
return (image,)
|
|
|
|
return ImagePipelineOutput(images=image)
|