321 lines
17 KiB
Python
321 lines
17 KiB
Python
from typing import Callable, List, Optional, Union
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import torch
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import PIL
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from diffusers.configuration_utils import FrozenDict
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from diffusers.models import AutoencoderKL, UNet2DConditionModel
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from diffusers.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
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from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
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from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
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from diffusers.utils import deprecate, is_accelerate_available, logging
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from transformers import (
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CLIPFeatureExtractor,
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CLIPSegForImageSegmentation,
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CLIPSegProcessor,
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CLIPTextModel,
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CLIPTokenizer,
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)
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class TextInpainting(DiffusionPipeline):
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r"""
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Pipeline for text based inpainting using Stable Diffusion.
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Uses CLIPSeg to get a mask from the given text, then calls the Inpainting pipeline with the generated mask
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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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segmentation_model ([`CLIPSegForImageSegmentation`]):
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CLIPSeg Model to generate mask from the given text. Please refer to the [model card]() for details.
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segmentation_processor ([`CLIPSegProcessor`]):
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CLIPSeg processor to get image, text features to translate prompt to English, if necessary. Please refer to the
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[model card](https://huggingface.co/docs/transformers/model_doc/clipseg) for details.
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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. Stable Diffusion uses the text portion of
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
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the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
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tokenizer (`CLIPTokenizer`):
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Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
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unet ([`UNet2DConditionModel`]): Conditional U-Net architecture 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 latens. Can be one of
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
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safety_checker ([`StableDiffusionSafetyChecker`]):
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Classification module that estimates whether generated images could be considered offensive or harmful.
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Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
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feature_extractor ([`CLIPFeatureExtractor`]):
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Model that extracts features from generated images to be used as inputs for the `safety_checker`.
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"""
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def __init__(
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self,
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segmentation_model: CLIPSegForImageSegmentation,
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segmentation_processor: CLIPSegProcessor,
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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: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
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safety_checker: StableDiffusionSafetyChecker,
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feature_extractor: CLIPFeatureExtractor,
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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:
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logger.warn(
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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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self.register_modules(
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segmentation_model=segmentation_model,
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segmentation_processor=segmentation_processor,
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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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)
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def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
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r"""
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Enable sliced attention computation.
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When this option is enabled, the attention module will split the input tensor in slices, to compute attention
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in several steps. This is useful to save some memory in exchange for a small speed decrease.
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Args:
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slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
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When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
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a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
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`attention_head_dim` must be a multiple of `slice_size`.
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"""
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if slice_size == "auto":
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# half the attention head size is usually a good trade-off between
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# speed and memory
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slice_size = self.unet.config.attention_head_dim // 2
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self.unet.set_attention_slice(slice_size)
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def disable_attention_slicing(self):
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r"""
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Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
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back to computing attention in one step.
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"""
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# set slice_size = `None` to disable `attention slicing`
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self.enable_attention_slicing(None)
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def enable_sequential_cpu_offload(self):
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r"""
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Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
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text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
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`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
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"""
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if is_accelerate_available():
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from accelerate import cpu_offload
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else:
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raise ImportError("Please install accelerate via `pip install accelerate`")
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device = torch.device("cuda")
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for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
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if cpu_offloaded_model is not None:
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cpu_offload(cpu_offloaded_model, device)
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@property
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
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def _execution_device(self):
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r"""
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Returns the device on which the pipeline's models will be executed. After calling
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`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
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hooks.
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"""
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if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
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return self.device
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for module in self.unet.modules():
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if (
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hasattr(module, "_hf_hook")
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and hasattr(module._hf_hook, "execution_device")
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and module._hf_hook.execution_device is not None
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):
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return torch.device(module._hf_hook.execution_device)
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return self.device
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def enable_xformers_memory_efficient_attention(self):
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r"""
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Enable memory efficient attention as implemented in xformers.
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When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
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time. Speed up at training time is not guaranteed.
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Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
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is used.
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"""
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self.unet.set_use_memory_efficient_attention_xformers(True)
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def disable_xformers_memory_efficient_attention(self):
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r"""
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Disable memory efficient attention as implemented in xformers.
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"""
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self.unet.set_use_memory_efficient_attention_xformers(False)
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@torch.no_grad()
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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.FloatTensor, PIL.Image.Image],
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text: str,
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height: int = 512,
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width: int = 512,
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num_inference_steps: int = 50,
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guidance_scale: float = 7.5,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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eta: float = 0.0,
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generator: Optional[torch.Generator] = None,
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latents: Optional[torch.FloatTensor] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
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callback_steps: Optional[int] = 1,
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**kwargs,
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):
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r"""
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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 (`PIL.Image.Image`):
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`Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
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be masked out with `mask_image` and repainted according to `prompt`.
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text (`str``):
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The text to use to generate the mask.
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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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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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guidance_scale (`float`, *optional*, defaults to 7.5):
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
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`guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
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1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
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usually at the expense of lower image quality.
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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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num_images_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
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eta (`float`, *optional*, defaults to 0.0):
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Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
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[`schedulers.DDIMScheduler`], will be ignored for others.
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generator (`torch.Generator`, *optional*):
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A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
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deterministic.
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latents (`torch.FloatTensor`, *optional*):
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor will ge generated by sampling using the supplied random `generator`.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generate image. Choose between
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
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plain tuple.
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callback (`Callable`, *optional*):
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A function that will be called every `callback_steps` steps during inference. The function will be
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called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
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callback_steps (`int`, *optional*, defaults to 1):
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The frequency at which the `callback` function will be called. If not specified, the callback will be
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called at every step.
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Returns:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
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When returning a tuple, the first element is a list with the generated images, and the second element is a
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list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
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(nsfw) content, according to the `safety_checker`.
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"""
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# We use the input text to generate the mask
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inputs = self.segmentation_processor(
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text=[text], images=[image], padding="max_length", return_tensors="pt"
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).to(self.device)
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outputs = self.segmentation_model(**inputs)
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mask = torch.sigmoid(outputs.logits).cpu().detach().unsqueeze(-1).numpy()
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mask_pil = self.numpy_to_pil(mask)[0].resize(image.size)
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# Run inpainting pipeline with the generated mask
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inpainting_pipeline = StableDiffusionInpaintPipeline(
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vae=self.vae,
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text_encoder=self.text_encoder,
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tokenizer=self.tokenizer,
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unet=self.unet,
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scheduler=self.scheduler,
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safety_checker=self.safety_checker,
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feature_extractor=self.feature_extractor,
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)
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return inpainting_pipeline(
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prompt=prompt,
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image=image,
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mask_image=mask_pil,
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height=height,
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width=width,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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negative_prompt=negative_prompt,
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num_images_per_prompt=num_images_per_prompt,
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eta=eta,
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generator=generator,
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latents=latents,
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output_type=output_type,
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return_dict=return_dict,
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callback=callback,
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callback_steps=callback_steps,
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)
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