1511 lines
75 KiB
Python
1511 lines
75 KiB
Python
# Copyright 2023 DiffEdit Authors and Pix2Pix Zero Authors and 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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import inspect
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import warnings
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from dataclasses import dataclass
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from typing import Any, Callable, Dict, List, Optional, Union
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import numpy as np
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import PIL
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import torch
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from packaging import version
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from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
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from ...configuration_utils import FrozenDict
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from ...image_processor import VaeImageProcessor
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from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
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from ...models import AutoencoderKL, UNet2DConditionModel
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from ...schedulers import DDIMInverseScheduler, KarrasDiffusionSchedulers
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from ...utils import (
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PIL_INTERPOLATION,
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BaseOutput,
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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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replace_example_docstring,
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)
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from ..pipeline_utils import DiffusionPipeline
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from . import StableDiffusionPipelineOutput
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from .safety_checker import StableDiffusionSafetyChecker
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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@dataclass
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class DiffEditInversionPipelineOutput(BaseOutput):
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"""
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Output class for Stable Diffusion pipelines.
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Args:
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latents (`torch.FloatTensor`)
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inverted latents tensor
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images (`List[PIL.Image.Image]` or `np.ndarray`)
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List of denoised PIL images of length `num_timesteps * batch_size` or numpy array of shape `(num_timesteps,
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batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the
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diffusion pipeline.
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"""
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latents: torch.FloatTensor
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images: Union[List[PIL.Image.Image], np.ndarray]
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EXAMPLE_DOC_STRING = """
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```py
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>>> import PIL
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>>> import requests
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>>> import torch
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>>> from io import BytesIO
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>>> from diffusers import StableDiffusionDiffEditPipeline
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>>> def download_image(url):
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... response = requests.get(url)
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... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
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>>> img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
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>>> init_image = download_image(img_url).resize((768, 768))
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>>> pipe = StableDiffusionDiffEditPipeline.from_pretrained(
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... "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
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... )
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>>> pipe = pipe.to("cuda")
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>>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
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>>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
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>>> pipeline.enable_model_cpu_offload()
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>>> mask_prompt = "A bowl of fruits"
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>>> prompt = "A bowl of pears"
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>>> mask_image = pipe.generate_mask(image=init_image, source_prompt=prompt, target_prompt=mask_prompt)
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>>> image_latents = pipe.invert(image=init_image, prompt=mask_prompt).latents
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>>> image = pipe(prompt=prompt, mask_image=mask_image, image_latents=image_latents).images[0]
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```
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"""
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EXAMPLE_INVERT_DOC_STRING = """
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```py
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>>> import PIL
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>>> import requests
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>>> import torch
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>>> from io import BytesIO
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>>> from diffusers import StableDiffusionDiffEditPipeline
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>>> def download_image(url):
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... response = requests.get(url)
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... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
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>>> img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
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>>> init_image = download_image(img_url).resize((768, 768))
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>>> pipe = StableDiffusionDiffEditPipeline.from_pretrained(
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... "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
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... )
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>>> pipe = pipe.to("cuda")
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>>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
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>>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
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>>> pipeline.enable_model_cpu_offload()
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>>> prompt = "A bowl of fruits"
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>>> inverted_latents = pipe.invert(image=init_image, prompt=prompt).latents
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```
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"""
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def auto_corr_loss(hidden_states, generator=None):
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reg_loss = 0.0
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for i in range(hidden_states.shape[0]):
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for j in range(hidden_states.shape[1]):
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noise = hidden_states[i : i + 1, j : j + 1, :, :]
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while True:
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roll_amount = torch.randint(noise.shape[2] // 2, (1,), generator=generator).item()
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reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=2)).mean() ** 2
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reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=3)).mean() ** 2
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if noise.shape[2] <= 8:
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break
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noise = torch.nn.functional.avg_pool2d(noise, kernel_size=2)
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return reg_loss
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def kl_divergence(hidden_states):
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return hidden_states.var() + hidden_states.mean() ** 2 - 1 - torch.log(hidden_states.var() + 1e-7)
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
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def preprocess(image):
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warnings.warn(
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"The preprocess method is deprecated and will be removed in a future version. Please"
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" use VaeImageProcessor.preprocess instead",
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FutureWarning,
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)
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if isinstance(image, torch.Tensor):
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return image
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elif isinstance(image, PIL.Image.Image):
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image = [image]
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if isinstance(image[0], PIL.Image.Image):
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w, h = image[0].size
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w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
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image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
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image = np.concatenate(image, axis=0)
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image = np.array(image).astype(np.float32) / 255.0
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image = image.transpose(0, 3, 1, 2)
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image = 2.0 * image - 1.0
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image = torch.from_numpy(image)
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elif isinstance(image[0], torch.Tensor):
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image = torch.cat(image, dim=0)
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return image
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def preprocess_mask(mask, batch_size: int = 1):
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if not isinstance(mask, torch.Tensor):
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# preprocess mask
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if isinstance(mask, PIL.Image.Image) or isinstance(mask, np.ndarray):
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mask = [mask]
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if isinstance(mask, list):
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if isinstance(mask[0], PIL.Image.Image):
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mask = [np.array(m.convert("L")).astype(np.float32) / 255.0 for m in mask]
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if isinstance(mask[0], np.ndarray):
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mask = np.stack(mask, axis=0) if mask[0].ndim < 3 else np.concatenate(mask, axis=0)
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mask = torch.from_numpy(mask)
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elif isinstance(mask[0], torch.Tensor):
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mask = torch.stack(mask, dim=0) if mask[0].ndim < 3 else torch.cat(mask, dim=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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# Check mask shape
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if batch_size > 1:
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if mask.shape[0] == 1:
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mask = torch.cat([mask] * batch_size)
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elif mask.shape[0] > 1 and mask.shape[0] != batch_size:
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raise ValueError(
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f"`mask_image` with batch size {mask.shape[0]} cannot be broadcasted to batch size {batch_size} "
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f"inferred by prompt inputs"
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)
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if mask.shape[1] != 1:
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raise ValueError(f"`mask_image` must have 1 channel, but has {mask.shape[1]} channels")
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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_image` 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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return mask
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class StableDiffusionDiffEditPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
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r"""
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<Tip warning={true}>
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This is an experimental feature!
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</Tip>
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Pipeline for text-guided image inpainting using Stable Diffusion and DiffEdit.
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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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In addition the pipeline inherits the following loading methods:
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- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
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- *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]
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as well as the following saving methods:
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- *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]
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Args:
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vae ([`AutoencoderKL`]):
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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 (`CLIPTokenizer`):
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A [`~transformers.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.
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inverse_scheduler (`[DDIMInverseScheduler]`):
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A scheduler to be used in combination with `unet` to fill in the unmasked part of the input latents.
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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 ([`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", "inverse_scheduler"]
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def __init__(
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self,
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vae: AutoencoderKL,
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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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feature_extractor: CLIPImageProcessor,
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inverse_scheduler: DDIMInverseScheduler,
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requires_safety_checker: bool = True,
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):
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super().__init__()
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if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 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 hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps 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 = hasattr(unet.config, "_diffusers_version") and version.parse(
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version.parse(unet.config._diffusers_version).base_version
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) < version.parse("0.9.0.dev0")
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is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
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if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
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deprecation_message = (
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"The configuration file of the unet has set the default `sample_size` to smaller than"
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" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
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" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
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" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
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" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
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" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
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" in the config might lead to incorrect results in future versions. If you have downloaded this"
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" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
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" the `unet/config.json` file"
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)
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deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
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new_config = dict(unet.config)
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new_config["sample_size"] = 64
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unet._internal_dict = FrozenDict(new_config)
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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safety_checker=safety_checker,
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feature_extractor=feature_extractor,
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inverse_scheduler=inverse_scheduler,
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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self.register_to_config(requires_safety_checker=requires_safety_checker)
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
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def enable_vae_slicing(self):
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r"""
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Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
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compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
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"""
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self.vae.enable_slicing()
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
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def disable_vae_slicing(self):
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r"""
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Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
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computing decoding in one step.
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"""
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self.vae.disable_slicing()
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||
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
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def enable_vae_tiling(self):
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r"""
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Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
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compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
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processing larger images.
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"""
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self.vae.enable_tiling()
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||
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
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def disable_vae_tiling(self):
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r"""
|
||
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_tiling()
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
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def enable_model_cpu_offload(self, gpu_id=0):
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r"""
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||
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
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time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
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Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
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iterative execution of the `unet`.
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||
"""
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||
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
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||
from accelerate import cpu_offload_with_hook
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||
else:
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||
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
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||
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device = torch.device(f"cuda:{gpu_id}")
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if self.device.type != "cpu":
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self.to("cpu", silence_dtype_warnings=True)
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torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
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hook = None
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for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
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_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
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||
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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]
|
||
|
||
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.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=self.text_encoder.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://arxiv.org/abs/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
|
||
|
||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
|
||
def decode_latents(self, latents):
|
||
warnings.warn(
|
||
"The decode_latents method is deprecated and will be removed in a future version. Please"
|
||
" use VaeImageProcessor instead",
|
||
FutureWarning,
|
||
)
|
||
latents = 1 / self.vae.config.scaling_factor * latents
|
||
image = self.vae.decode(latents, return_dict=False)[0]
|
||
image = (image / 2 + 0.5).clamp(0, 1)
|
||
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
|
||
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
|
||
return image
|
||
|
||
def check_inputs(
|
||
self,
|
||
prompt,
|
||
strength,
|
||
callback_steps,
|
||
negative_prompt=None,
|
||
prompt_embeds=None,
|
||
negative_prompt_embeds=None,
|
||
):
|
||
if (strength is None) or (strength is not None and (strength < 0 or strength > 1)):
|
||
raise ValueError(
|
||
f"The value of `strength` should in [0.0, 1.0] but is, but is {strength} of type {type(strength)}."
|
||
)
|
||
|
||
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 check_source_inputs(
|
||
self,
|
||
source_prompt=None,
|
||
source_negative_prompt=None,
|
||
source_prompt_embeds=None,
|
||
source_negative_prompt_embeds=None,
|
||
):
|
||
if source_prompt is not None and source_prompt_embeds is not None:
|
||
raise ValueError(
|
||
f"Cannot forward both `source_prompt`: {source_prompt} and `source_prompt_embeds`: {source_prompt_embeds}."
|
||
" Please make sure to only forward one of the two."
|
||
)
|
||
elif source_prompt is None and source_prompt_embeds is None:
|
||
raise ValueError(
|
||
"Provide either `source_image` or `source_prompt_embeds`. Cannot leave all both of the arguments undefined."
|
||
)
|
||
elif source_prompt is not None and (
|
||
not isinstance(source_prompt, str) and not isinstance(source_prompt, list)
|
||
):
|
||
raise ValueError(f"`source_prompt` has to be of type `str` or `list` but is {type(source_prompt)}")
|
||
|
||
if source_negative_prompt is not None and source_negative_prompt_embeds is not None:
|
||
raise ValueError(
|
||
f"Cannot forward both `source_negative_prompt`: {source_negative_prompt} and `source_negative_prompt_embeds`:"
|
||
f" {source_negative_prompt_embeds}. Please make sure to only forward one of the two."
|
||
)
|
||
|
||
if source_prompt_embeds is not None and source_negative_prompt_embeds is not None:
|
||
if source_prompt_embeds.shape != source_negative_prompt_embeds.shape:
|
||
raise ValueError(
|
||
"`source_prompt_embeds` and `source_negative_prompt_embeds` must have the same shape when passed"
|
||
f" directly, but got: `source_prompt_embeds` {source_prompt_embeds.shape} !="
|
||
f" `source_negative_prompt_embeds` {source_negative_prompt_embeds.shape}."
|
||
)
|
||
|
||
# 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
|
||
|
||
def get_inverse_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)
|
||
|
||
# safety for t_start overflow to prevent empty timsteps slice
|
||
if t_start == 0:
|
||
return self.inverse_scheduler.timesteps, num_inference_steps
|
||
timesteps = self.inverse_scheduler.timesteps[:-t_start]
|
||
|
||
return timesteps, num_inference_steps - t_start
|
||
|
||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
|
||
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
|
||
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 latents is None:
|
||
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
||
else:
|
||
latents = latents.to(device)
|
||
|
||
# scale the initial noise by the standard deviation required by the scheduler
|
||
latents = latents * self.scheduler.init_noise_sigma
|
||
return latents
|
||
|
||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.StableDiffusionPix2PixZeroPipeline.prepare_image_latents
|
||
def prepare_image_latents(self, image, batch_size, dtype, device, generator=None):
|
||
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
|
||
raise ValueError(
|
||
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
|
||
)
|
||
|
||
image = image.to(device=device, dtype=dtype)
|
||
|
||
if image.shape[1] == 4:
|
||
latents = image
|
||
|
||
else:
|
||
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 isinstance(generator, list):
|
||
latents = [
|
||
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
|
||
]
|
||
latents = torch.cat(latents, dim=0)
|
||
else:
|
||
latents = self.vae.encode(image).latent_dist.sample(generator)
|
||
|
||
latents = self.vae.config.scaling_factor * latents
|
||
|
||
if batch_size != latents.shape[0]:
|
||
if batch_size % latents.shape[0] == 0:
|
||
# expand image_latents for batch_size
|
||
deprecation_message = (
|
||
f"You have passed {batch_size} text prompts (`prompt`), but only {latents.shape[0]} initial"
|
||
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
|
||
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
|
||
" your script to pass as many initial images as text prompts to suppress this warning."
|
||
)
|
||
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
|
||
additional_latents_per_image = batch_size // latents.shape[0]
|
||
latents = torch.cat([latents] * additional_latents_per_image, dim=0)
|
||
else:
|
||
raise ValueError(
|
||
f"Cannot duplicate `image` of batch size {latents.shape[0]} to {batch_size} text prompts."
|
||
)
|
||
else:
|
||
latents = torch.cat([latents], dim=0)
|
||
|
||
return latents
|
||
|
||
def get_epsilon(self, model_output: torch.Tensor, sample: torch.Tensor, timestep: int):
|
||
pred_type = self.inverse_scheduler.config.prediction_type
|
||
alpha_prod_t = self.inverse_scheduler.alphas_cumprod[timestep]
|
||
|
||
beta_prod_t = 1 - alpha_prod_t
|
||
|
||
if pred_type == "epsilon":
|
||
return model_output
|
||
elif pred_type == "sample":
|
||
return (sample - alpha_prod_t ** (0.5) * model_output) / beta_prod_t ** (0.5)
|
||
elif pred_type == "v_prediction":
|
||
return (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
|
||
else:
|
||
raise ValueError(
|
||
f"prediction_type given as {pred_type} must be one of `epsilon`, `sample`, or `v_prediction`"
|
||
)
|
||
|
||
@torch.no_grad()
|
||
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
||
def generate_mask(
|
||
self,
|
||
image: Union[torch.FloatTensor, PIL.Image.Image] = None,
|
||
target_prompt: Optional[Union[str, List[str]]] = None,
|
||
target_negative_prompt: Optional[Union[str, List[str]]] = None,
|
||
target_prompt_embeds: Optional[torch.FloatTensor] = None,
|
||
target_negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
||
source_prompt: Optional[Union[str, List[str]]] = None,
|
||
source_negative_prompt: Optional[Union[str, List[str]]] = None,
|
||
source_prompt_embeds: Optional[torch.FloatTensor] = None,
|
||
source_negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
||
num_maps_per_mask: Optional[int] = 10,
|
||
mask_encode_strength: Optional[float] = 0.5,
|
||
mask_thresholding_ratio: Optional[float] = 3.0,
|
||
num_inference_steps: int = 50,
|
||
guidance_scale: float = 7.5,
|
||
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
||
output_type: Optional[str] = "np",
|
||
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||
):
|
||
r"""
|
||
Generate a latent mask given a mask prompt, a target prompt, and an image.
|
||
|
||
Args:
|
||
image (`PIL.Image.Image`):
|
||
`Image` or tensor representing an image batch to be used for computing the mask.
|
||
target_prompt (`str` or `List[str]`, *optional*):
|
||
The prompt or prompts to guide semantic mask generation. If not defined, you need to pass
|
||
`prompt_embeds`.
|
||
target_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`).
|
||
target_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.
|
||
target_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.
|
||
source_prompt (`str` or `List[str]`, *optional*):
|
||
The prompt or prompts to guide semantic mask generation using DiffEdit. If not defined, you need to
|
||
pass `source_prompt_embeds` or `source_image` instead.
|
||
source_negative_prompt (`str` or `List[str]`, *optional*):
|
||
The prompt or prompts to guide semantic mask generation away from using DiffEdit. If not defined, you
|
||
need to pass `source_negative_prompt_embeds` or `source_image` instead.
|
||
source_prompt_embeds (`torch.FloatTensor`, *optional*):
|
||
Pre-generated text embeddings to guide the semantic mask generation. Can be used to easily tweak text
|
||
inputs (prompt weighting). If not provided, text embeddings are generated from `source_prompt` input
|
||
argument.
|
||
source_negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
||
Pre-generated text embeddings to negatively guide the semantic mask generation. Can be used to easily
|
||
tweak text inputs (prompt weighting). If not provided, text embeddings are generated from
|
||
`source_negative_prompt` input argument.
|
||
num_maps_per_mask (`int`, *optional*, defaults to 10):
|
||
The number of noise maps sampled to generate the semantic mask using DiffEdit.
|
||
mask_encode_strength (`float`, *optional*, defaults to 0.5):
|
||
The strength of the noise maps sampled to generate the semantic mask using DiffEdit. Must be between 0
|
||
and 1.
|
||
mask_thresholding_ratio (`float`, *optional*, defaults to 3.0):
|
||
The maximum multiple of the mean absolute difference used to clamp the semantic guidance map before
|
||
mask binarization.
|
||
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.
|
||
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`.
|
||
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
||
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
||
generation deterministic.
|
||
output_type (`str`, *optional*, defaults to `"pil"`):
|
||
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
||
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/cross_attention.py).
|
||
|
||
Examples:
|
||
|
||
Returns:
|
||
`List[PIL.Image.Image]` or `np.array`:
|
||
When returning a `List[PIL.Image.Image]`, the list consists of a batch of single-channel binary images
|
||
with dimensions `(height // self.vae_scale_factor, width // self.vae_scale_factor)`. If it's
|
||
`np.array`, the shape is `(batch_size, height // self.vae_scale_factor, width //
|
||
self.vae_scale_factor)`.
|
||
"""
|
||
|
||
# 1. Check inputs (Provide dummy argument for callback_steps)
|
||
self.check_inputs(
|
||
target_prompt,
|
||
mask_encode_strength,
|
||
1,
|
||
target_negative_prompt,
|
||
target_prompt_embeds,
|
||
target_negative_prompt_embeds,
|
||
)
|
||
|
||
self.check_source_inputs(
|
||
source_prompt,
|
||
source_negative_prompt,
|
||
source_prompt_embeds,
|
||
source_negative_prompt_embeds,
|
||
)
|
||
|
||
if (num_maps_per_mask is None) or (
|
||
num_maps_per_mask is not None and (not isinstance(num_maps_per_mask, int) or num_maps_per_mask <= 0)
|
||
):
|
||
raise ValueError(
|
||
f"`num_maps_per_mask` has to be a positive integer but is {num_maps_per_mask} of type"
|
||
f" {type(num_maps_per_mask)}."
|
||
)
|
||
|
||
if mask_thresholding_ratio is None or mask_thresholding_ratio <= 0:
|
||
raise ValueError(
|
||
f"`mask_thresholding_ratio` has to be positive but is {mask_thresholding_ratio} of type"
|
||
f" {type(mask_thresholding_ratio)}."
|
||
)
|
||
|
||
# 2. Define call parameters
|
||
if target_prompt is not None and isinstance(target_prompt, str):
|
||
batch_size = 1
|
||
elif target_prompt is not None and isinstance(target_prompt, list):
|
||
batch_size = len(target_prompt)
|
||
else:
|
||
batch_size = target_prompt_embeds.shape[0]
|
||
if cross_attention_kwargs is None:
|
||
cross_attention_kwargs = {}
|
||
|
||
device = self._execution_device
|
||
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
||
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
||
# corresponds to doing no classifier free guidance.
|
||
do_classifier_free_guidance = guidance_scale > 1.0
|
||
|
||
# 3. Encode input prompts
|
||
(cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None)
|
||
target_prompt_embeds = self._encode_prompt(
|
||
target_prompt,
|
||
device,
|
||
num_maps_per_mask,
|
||
do_classifier_free_guidance,
|
||
target_negative_prompt,
|
||
prompt_embeds=target_prompt_embeds,
|
||
negative_prompt_embeds=target_negative_prompt_embeds,
|
||
)
|
||
|
||
source_prompt_embeds = self._encode_prompt(
|
||
source_prompt,
|
||
device,
|
||
num_maps_per_mask,
|
||
do_classifier_free_guidance,
|
||
source_negative_prompt,
|
||
prompt_embeds=source_prompt_embeds,
|
||
negative_prompt_embeds=source_negative_prompt_embeds,
|
||
)
|
||
|
||
# 4. Preprocess image
|
||
image = self.image_processor.preprocess(image).repeat_interleave(num_maps_per_mask, dim=0)
|
||
|
||
# 5. Set timesteps
|
||
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
||
timesteps, _ = self.get_timesteps(num_inference_steps, mask_encode_strength, device)
|
||
encode_timestep = timesteps[0]
|
||
|
||
# 6. Prepare image latents and add noise with specified strength
|
||
image_latents = self.prepare_image_latents(
|
||
image, batch_size * num_maps_per_mask, self.vae.dtype, device, generator
|
||
)
|
||
noise = randn_tensor(image_latents.shape, generator=generator, device=device, dtype=self.vae.dtype)
|
||
image_latents = self.scheduler.add_noise(image_latents, noise, encode_timestep)
|
||
|
||
latent_model_input = torch.cat([image_latents] * (4 if do_classifier_free_guidance else 2))
|
||
latent_model_input = self.scheduler.scale_model_input(latent_model_input, encode_timestep)
|
||
|
||
# 7. Predict the noise residual
|
||
prompt_embeds = torch.cat([source_prompt_embeds, target_prompt_embeds])
|
||
noise_pred = self.unet(
|
||
latent_model_input,
|
||
encode_timestep,
|
||
encoder_hidden_states=prompt_embeds,
|
||
cross_attention_kwargs=cross_attention_kwargs,
|
||
).sample
|
||
|
||
if do_classifier_free_guidance:
|
||
noise_pred_neg_src, noise_pred_source, noise_pred_uncond, noise_pred_target = noise_pred.chunk(4)
|
||
noise_pred_source = noise_pred_neg_src + guidance_scale * (noise_pred_source - noise_pred_neg_src)
|
||
noise_pred_target = noise_pred_uncond + guidance_scale * (noise_pred_target - noise_pred_uncond)
|
||
else:
|
||
noise_pred_source, noise_pred_target = noise_pred.chunk(2)
|
||
|
||
# 8. Compute the mask from the absolute difference of predicted noise residuals
|
||
# TODO: Consider smoothing mask guidance map
|
||
mask_guidance_map = (
|
||
torch.abs(noise_pred_target - noise_pred_source)
|
||
.reshape(batch_size, num_maps_per_mask, *noise_pred_target.shape[-3:])
|
||
.mean([1, 2])
|
||
)
|
||
clamp_magnitude = mask_guidance_map.mean() * mask_thresholding_ratio
|
||
semantic_mask_image = mask_guidance_map.clamp(0, clamp_magnitude) / clamp_magnitude
|
||
semantic_mask_image = torch.where(semantic_mask_image <= 0.5, 0, 1)
|
||
mask_image = semantic_mask_image.cpu().numpy()
|
||
|
||
# 9. Convert to Numpy array or PIL.
|
||
if output_type == "pil":
|
||
mask_image = self.image_processor.numpy_to_pil(mask_image)
|
||
|
||
# Offload last model to CPU
|
||
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
||
self.final_offload_hook.offload()
|
||
|
||
return mask_image
|
||
|
||
@torch.no_grad()
|
||
@replace_example_docstring(EXAMPLE_INVERT_DOC_STRING)
|
||
def invert(
|
||
self,
|
||
prompt: Optional[Union[str, List[str]]] = None,
|
||
image: Union[torch.FloatTensor, PIL.Image.Image] = None,
|
||
num_inference_steps: int = 50,
|
||
inpaint_strength: float = 0.8,
|
||
guidance_scale: float = 7.5,
|
||
negative_prompt: Optional[Union[str, List[str]]] = None,
|
||
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
||
prompt_embeds: Optional[torch.FloatTensor] = None,
|
||
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
||
decode_latents: bool = False,
|
||
output_type: Optional[str] = "pil",
|
||
return_dict: bool = True,
|
||
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
||
callback_steps: Optional[int] = 1,
|
||
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||
lambda_auto_corr: float = 20.0,
|
||
lambda_kl: float = 20.0,
|
||
num_reg_steps: int = 0,
|
||
num_auto_corr_rolls: int = 5,
|
||
):
|
||
r"""
|
||
Generate inverted latents given a prompt and image.
|
||
|
||
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 produce the inverted latents guided by `prompt`.
|
||
inpaint_strength (`float`, *optional*, defaults to 0.8):
|
||
Indicates extent of the noising process to run latent inversion. Must be between 0 and 1. When
|
||
`strength` is 1, the inversion process iss ru for the full number of iterations specified in
|
||
`num_inference_steps`. `image` is used as a reference for the inversion process, adding more noise the
|
||
larger the `strength`. If `strength` is 0, no inpainting occurs.
|
||
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.
|
||
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`).
|
||
generator (`torch.Generator`, *optional*):
|
||
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
||
generation deterministic.
|
||
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.
|
||
decode_latents (`bool`, *optional*, defaults to `False`):
|
||
Whether or not to decode the inverted latents into a generated image. Setting this argument to `True`
|
||
decodes all inverted latents for each timestep into a list of generated images.
|
||
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.DiffEditInversionPipelineOutput`] 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/cross_attention.py).
|
||
lambda_auto_corr (`float`, *optional*, defaults to 20.0):
|
||
Lambda parameter to control auto correction.
|
||
lambda_kl (`float`, *optional*, defaults to 20.0):
|
||
Lambda parameter to control Kullback–Leibler divergence output.
|
||
num_reg_steps (`int`, *optional*, defaults to 0):
|
||
Number of regularization loss steps.
|
||
num_auto_corr_rolls (`int`, *optional*, defaults to 5):
|
||
Number of auto correction roll steps.
|
||
|
||
Examples:
|
||
|
||
Returns:
|
||
[`~pipelines.stable_diffusion.pipeline_stable_diffusion_diffedit.DiffEditInversionPipelineOutput`] or
|
||
`tuple`:
|
||
If `return_dict` is `True`,
|
||
[`~pipelines.stable_diffusion.pipeline_stable_diffusion_diffedit.DiffEditInversionPipelineOutput`] is
|
||
returned, otherwise a `tuple` is returned where the first element is the inverted latents tensors
|
||
ordered by increasing noise, and the second is the corresponding decoded images if `decode_latents` is
|
||
`True`, otherwise `None`.
|
||
"""
|
||
|
||
# 1. Check inputs
|
||
self.check_inputs(
|
||
prompt,
|
||
inpaint_strength,
|
||
callback_steps,
|
||
negative_prompt,
|
||
prompt_embeds,
|
||
negative_prompt_embeds,
|
||
)
|
||
|
||
if image is None:
|
||
raise ValueError("`image` input cannot be undefined.")
|
||
|
||
# 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]
|
||
if cross_attention_kwargs is None:
|
||
cross_attention_kwargs = {}
|
||
|
||
device = self._execution_device
|
||
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
||
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
||
# corresponds to doing no classifier free guidance.
|
||
do_classifier_free_guidance = guidance_scale > 1.0
|
||
|
||
# 3. Preprocess image
|
||
image = self.image_processor.preprocess(image)
|
||
|
||
# 4. Prepare latent variables
|
||
num_images_per_prompt = 1
|
||
latents = self.prepare_image_latents(
|
||
image, batch_size * num_images_per_prompt, self.vae.dtype, device, generator
|
||
)
|
||
|
||
# 5. Encode input prompt
|
||
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,
|
||
)
|
||
|
||
# 6. Prepare timesteps
|
||
self.inverse_scheduler.set_timesteps(num_inference_steps, device=device)
|
||
timesteps, num_inference_steps = self.get_inverse_timesteps(num_inference_steps, inpaint_strength, device)
|
||
|
||
# 7. Noising loop where we obtain the intermediate noised latent image for each timestep.
|
||
num_warmup_steps = len(timesteps) - num_inference_steps * self.inverse_scheduler.order
|
||
inverted_latents = []
|
||
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
|
||
latent_model_input = self.inverse_scheduler.scale_model_input(latent_model_input, t)
|
||
|
||
# predict the noise residual
|
||
noise_pred = self.unet(
|
||
latent_model_input,
|
||
t,
|
||
encoder_hidden_states=prompt_embeds,
|
||
cross_attention_kwargs=cross_attention_kwargs,
|
||
).sample
|
||
|
||
# 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)
|
||
|
||
# regularization of the noise prediction (not in original code or paper but borrowed from Pix2PixZero)
|
||
if num_reg_steps > 0:
|
||
with torch.enable_grad():
|
||
for _ in range(num_reg_steps):
|
||
if lambda_auto_corr > 0:
|
||
for _ in range(num_auto_corr_rolls):
|
||
var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)
|
||
|
||
# Derive epsilon from model output before regularizing to IID standard normal
|
||
var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)
|
||
|
||
l_ac = auto_corr_loss(var_epsilon, generator=generator)
|
||
l_ac.backward()
|
||
|
||
grad = var.grad.detach() / num_auto_corr_rolls
|
||
noise_pred = noise_pred - lambda_auto_corr * grad
|
||
|
||
if lambda_kl > 0:
|
||
var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)
|
||
|
||
# Derive epsilon from model output before regularizing to IID standard normal
|
||
var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)
|
||
|
||
l_kld = kl_divergence(var_epsilon)
|
||
l_kld.backward()
|
||
|
||
grad = var.grad.detach()
|
||
noise_pred = noise_pred - lambda_kl * grad
|
||
|
||
noise_pred = noise_pred.detach()
|
||
|
||
# compute the previous noisy sample x_t -> x_t-1
|
||
latents = self.inverse_scheduler.step(noise_pred, t, latents).prev_sample
|
||
inverted_latents.append(latents.detach().clone())
|
||
|
||
# call the callback, if provided
|
||
if i == len(timesteps) - 1 or (
|
||
(i + 1) > num_warmup_steps and (i + 1) % self.inverse_scheduler.order == 0
|
||
):
|
||
progress_bar.update()
|
||
if callback is not None and i % callback_steps == 0:
|
||
callback(i, t, latents)
|
||
|
||
assert len(inverted_latents) == len(timesteps)
|
||
latents = torch.stack(list(reversed(inverted_latents)), 1)
|
||
|
||
# 8. Post-processing
|
||
image = None
|
||
if decode_latents:
|
||
image = self.decode_latents(latents.flatten(0, 1))
|
||
|
||
# 9. Convert to PIL.
|
||
if decode_latents and output_type == "pil":
|
||
image = self.image_processor.numpy_to_pil(image)
|
||
|
||
# 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 not return_dict:
|
||
return (latents, image)
|
||
|
||
return DiffEditInversionPipelineOutput(latents=latents, images=image)
|
||
|
||
@torch.no_grad()
|
||
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
||
def __call__(
|
||
self,
|
||
prompt: Optional[Union[str, List[str]]] = None,
|
||
mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
|
||
image_latents: Union[torch.FloatTensor, PIL.Image.Image] = None,
|
||
inpaint_strength: Optional[float] = 0.8,
|
||
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,
|
||
):
|
||
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`.
|
||
mask_image (`PIL.Image.Image`):
|
||
`Image` or tensor representing an image batch to mask the generated image. White pixels in the mask are
|
||
repainted, while black pixels are preserved. If `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, 1, H, W)`.
|
||
image_latents (`PIL.Image.Image` or `torch.FloatTensor`):
|
||
Partially noised image latents from the inversion process to be used as inputs for image generation.
|
||
inpaint_strength (`float`, *optional*, defaults to 0.8):
|
||
Indicates extent to inpaint the masked area. Must be between 0 and 1. When `strength` is 1, the
|
||
denoising process is run on the masked area for the full number of iterations specified in
|
||
`num_inference_steps`. `image_latents` is used as a reference for the masked area, adding more noise to
|
||
that region the larger the `strength`. If `strength` is 0, no inpainting occurs.
|
||
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.
|
||
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://arxiv.org/abs/2010.02502) paper. Only applies
|
||
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
||
generator (`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/cross_attention.py).
|
||
|
||
Examples:
|
||
|
||
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.
|
||
"""
|
||
|
||
# 1. Check inputs
|
||
self.check_inputs(
|
||
prompt,
|
||
inpaint_strength,
|
||
callback_steps,
|
||
negative_prompt,
|
||
prompt_embeds,
|
||
negative_prompt_embeds,
|
||
)
|
||
|
||
if mask_image is None:
|
||
raise ValueError(
|
||
"`mask_image` input cannot be undefined. Use `generate_mask()` to compute `mask_image` from text prompts."
|
||
)
|
||
if image_latents is None:
|
||
raise ValueError(
|
||
"`image_latents` input cannot be undefined. Use `invert()` to compute `image_latents` from input images."
|
||
)
|
||
|
||
# 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]
|
||
if cross_attention_kwargs is None:
|
||
cross_attention_kwargs = {}
|
||
|
||
device = self._execution_device
|
||
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
||
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `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. Preprocess mask
|
||
mask_image = preprocess_mask(mask_image, batch_size)
|
||
latent_height, latent_width = mask_image.shape[-2:]
|
||
mask_image = torch.cat([mask_image] * num_images_per_prompt)
|
||
mask_image = mask_image.to(device=device, dtype=prompt_embeds.dtype)
|
||
|
||
# 5. Set timesteps
|
||
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
||
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, inpaint_strength, device)
|
||
|
||
# 6. Preprocess image latents
|
||
if isinstance(image_latents, list) and any(isinstance(l, torch.Tensor) and l.ndim == 5 for l in image_latents):
|
||
image_latents = torch.cat(image_latents).detach()
|
||
elif isinstance(image_latents, torch.Tensor) and image_latents.ndim == 5:
|
||
image_latents = image_latents.detach()
|
||
else:
|
||
image_latents = self.image_processor.preprocess(image_latents).detach()
|
||
|
||
latent_shape = (self.vae.config.latent_channels, latent_height, latent_width)
|
||
if image_latents.shape[-3:] != latent_shape:
|
||
raise ValueError(
|
||
f"Each latent image in `image_latents` must have shape {latent_shape}, "
|
||
f"but has shape {image_latents.shape[-3:]}"
|
||
)
|
||
if image_latents.ndim == 4:
|
||
image_latents = image_latents.reshape(batch_size, len(timesteps), *latent_shape)
|
||
if image_latents.shape[:2] != (batch_size, len(timesteps)):
|
||
raise ValueError(
|
||
f"`image_latents` must have batch size {batch_size} with latent images from {len(timesteps)}"
|
||
f" timesteps, but has batch size {image_latents.shape[0]} with latent images from"
|
||
f" {image_latents.shape[1]} timesteps."
|
||
)
|
||
image_latents = image_latents.transpose(0, 1).repeat_interleave(num_images_per_prompt, dim=1)
|
||
image_latents = image_latents.to(device=device, dtype=prompt_embeds.dtype)
|
||
|
||
# 7. 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)
|
||
|
||
# 8. Denoising loop
|
||
latents = image_latents[0].clone()
|
||
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
|
||
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
||
|
||
# predict the noise residual
|
||
noise_pred = self.unet(
|
||
latent_model_input,
|
||
t,
|
||
encoder_hidden_states=prompt_embeds,
|
||
cross_attention_kwargs=cross_attention_kwargs,
|
||
).sample
|
||
|
||
# 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
|
||
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
||
|
||
# mask with inverted latents from appropriate timestep - use original image latent for last step
|
||
latents = latents * mask_image + image_latents[i] * (1 - mask_image)
|
||
|
||
# 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":
|
||
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[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 not return_dict:
|
||
return (image, has_nsfw_concept)
|
||
|
||
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|