2023-04-15 16:44:20 +00:00
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# This file contains a mix of Apache and GPL code and should be treated as a GPL resource
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#
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# Original attribution:
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#
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2023-04-12 00:29:25 +00:00
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# Copyright 2023 The InstructPix2Pix Authors and The HuggingFace Team.
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# Converted for use with ONNX as part of https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16
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import inspect
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from typing import Callable, List, Optional, Union
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import numpy as np
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import PIL
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import torch
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from transformers import CLIPFeatureExtractor, CLIPTokenizer
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try:
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from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE
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except ImportError:
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ORT_TO_NP_TYPE = {
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"tensor(bool)": np.bool_,
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"tensor(int8)": np.int8,
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"tensor(uint8)": np.uint8,
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"tensor(int16)": np.int16,
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"tensor(uint16)": np.uint16,
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"tensor(int32)": np.int32,
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"tensor(uint32)": np.uint32,
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"tensor(int64)": np.int64,
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"tensor(uint64)": np.uint64,
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"tensor(float16)": np.float16,
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"tensor(float)": np.float32,
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"tensor(double)": np.float64,
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}
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from diffusers import OnnxRuntimeModel
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from diffusers.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
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from diffusers.schedulers import (
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DDIMScheduler,
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KarrasDiffusionSchedulers,
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LMSDiscreteScheduler,
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PNDMScheduler,
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)
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from diffusers.utils import PIL_INTERPOLATION, logging
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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# Simplified and ONNX specific version (only allows 1 image, np over torch)
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def preprocess(image):
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if isinstance(image, np.ndarray):
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return image
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w, h = image.size
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w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8
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image = np.array(image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[
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None, :
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]
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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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return image
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class OnnxStableDiffusionInstructPix2PixPipeline(DiffusionPipeline):
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r"""
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Pipeline for pixel-level image editing by following text instructions. Based on Stable Diffusion.
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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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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 latents. Can be one of
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
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safety_checker ([`StableDiffusionSafetyChecker`]):
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Classification module that estimates whether generated images could be considered offensive or harmful.
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Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for 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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vae_encoder: OnnxRuntimeModel
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vae_decoder: OnnxRuntimeModel
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text_encoder: OnnxRuntimeModel
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tokenizer: CLIPTokenizer
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unet: OnnxRuntimeModel
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scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
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safety_checker: OnnxRuntimeModel
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feature_extractor: CLIPFeatureExtractor
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_optional_components = ["safety_checker", "feature_extractor"]
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def __init__(
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self,
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vae_encoder: OnnxRuntimeModel,
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vae_decoder: OnnxRuntimeModel,
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text_encoder: OnnxRuntimeModel,
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tokenizer: CLIPTokenizer,
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unet: OnnxRuntimeModel,
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scheduler: KarrasDiffusionSchedulers,
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safety_checker: OnnxRuntimeModel,
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feature_extractor: CLIPFeatureExtractor,
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requires_safety_checker: bool = True,
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):
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super().__init__()
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self.unet_in_channels = 8
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self.vae_scale_factor = 8
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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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self.register_modules(
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vae_encoder=vae_encoder,
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vae_decoder=vae_decoder,
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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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# self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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self.register_to_config(requires_safety_checker=requires_safety_checker)
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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]] = None,
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image: Union[np.ndarray, PIL.Image.Image] = None,
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num_inference_steps: int = 100,
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guidance_scale: float = 7.5,
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image_guidance_scale: float = 1.5,
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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[np.random.RandomState] = None,
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latents: Optional[np.ndarray] = 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, np.ndarray], None]] = None,
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callback_steps: int = 1,
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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]`, *optional*):
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The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
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instead.
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image (`PIL.Image.Image`):
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`Image`, or tensor representing an image batch which will be repainted according to `prompt`.
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num_inference_steps (`int`, *optional*, defaults to 100):
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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. This pipeline requires a value of at least `1`.
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image_guidance_scale (`float`, *optional*, defaults to 1.5):
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Image guidance scale is to push the generated image towards the inital image `image`. Image guidance
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scale is enabled by setting `image_guidance_scale > 1`. Higher image guidance scale encourages to
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generate images that are closely linked to the source image `image`, usually at the expense of lower
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image quality. This pipeline requires a value of at least `1`.
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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. If not defined, one has to pass
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`negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`
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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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One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
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to make generation 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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prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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negative_prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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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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Examples:
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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 StableDiffusionInstructPix2PixPipeline
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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://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
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>>> image = download_image(img_url).resize((512, 512))
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>>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
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... "timbrooks/instruct-pix2pix", torch_dtype=torch.float16
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... )
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>>> pipe = pipe.to("cuda")
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>>> prompt = "make the mountains snowy"
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>>> image = pipe(prompt=prompt, image=image).images[0]
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```
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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 need a deterministic torch generator for schedulers if a (likely seeded) generator was provided
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if generator:
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torch_seed = generator.randint(2147483647)
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torch_gen = torch.Generator().manual_seed(torch_seed)
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else:
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generator = np.random
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torch_gen = None
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# 0. Check inputs
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self.check_inputs(prompt, callback_steps)
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if image is None:
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raise ValueError("`image` input cannot be undefined.")
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# 1. Define call parameters
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if isinstance(prompt, str):
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batch_size = 1
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elif isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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raise ValueError(
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f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
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)
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = (
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guidance_scale > 1.0 and image_guidance_scale >= 1.0
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)
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# check if scheduler is in sigmas space
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scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas")
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# 2. Encode input prompt
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prompt_embeds = self._encode_prompt(
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prompt,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_prompt,
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)
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# 3. Preprocess image
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image = preprocess(image)
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height, width = image.shape[-2:]
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# 4. set timesteps
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self.scheduler.set_timesteps(num_inference_steps)
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timesteps = self.scheduler.timesteps
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# 5. Prepare Image latents
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latents_dtype = prompt_embeds.dtype
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image = image.astype(latents_dtype)
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# encode the init image into latents and scale the latents
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image_latents = self.vae_encoder(sample=image)[0]
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if do_classifier_free_guidance:
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uncond_image_latents = np.zeros_like(image_latents)
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image_latents = np.concatenate(
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(image_latents, image_latents, uncond_image_latents), axis=0
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)
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# 6. Prepare latent variables
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latents_dtype = prompt_embeds.dtype
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latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
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if latents is None:
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latents = generator.randn(*latents_shape).astype(latents_dtype)
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elif latents.shape != latents_shape:
|
|
|
|
raise ValueError(
|
|
|
|
f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"
|
|
|
|
)
|
2023-04-13 04:44:08 +00:00
|
|
|
|
|
|
|
init_noise_sigma = self.scheduler.init_noise_sigma
|
|
|
|
if torch.is_tensor(init_noise_sigma):
|
|
|
|
init_noise_sigma = init_noise_sigma.numpy()
|
|
|
|
|
|
|
|
latents = latents * init_noise_sigma
|
2023-04-12 00:29:25 +00:00
|
|
|
|
|
|
|
# 7. Check that shapes of latents and image match the UNet channels
|
|
|
|
num_channels_image = image_latents.shape[1]
|
|
|
|
if 4 + num_channels_image != self.unet_in_channels:
|
|
|
|
raise ValueError(
|
|
|
|
f"Incorrect configuration settings! The config of `pipeline.unet`: expects"
|
|
|
|
f" {self.unet_in_channels} but received `num_channels_latents`: 4 +"
|
|
|
|
f" `num_channels_image`: {num_channels_image} "
|
|
|
|
f" = {4+num_channels_image}. Please verify the config of"
|
|
|
|
" `pipeline.unet` or your `image` input."
|
|
|
|
)
|
|
|
|
|
|
|
|
timestep_dtype = next(
|
|
|
|
(
|
|
|
|
input.type
|
|
|
|
for input in self.unet.model.get_inputs()
|
|
|
|
if input.name == "timestep"
|
|
|
|
),
|
|
|
|
"tensor(float)",
|
|
|
|
)
|
|
|
|
timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
|
|
|
|
|
|
|
|
# 8. 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, torch_gen)
|
|
|
|
|
|
|
|
# 9. Denoising loop
|
|
|
|
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.
|
|
|
|
# The latents are expanded 3 times because for pix2pix the guidance\
|
|
|
|
# is applied for both the text and the input image.
|
|
|
|
latent_model_input = (
|
|
|
|
np.concatenate([latents] * 3)
|
|
|
|
if do_classifier_free_guidance
|
|
|
|
else latents
|
|
|
|
)
|
|
|
|
|
|
|
|
scaled_latent_model_input = self.scheduler.scale_model_input(
|
|
|
|
torch.from_numpy(latent_model_input), t
|
|
|
|
)
|
|
|
|
scaled_latent_model_input = scaled_latent_model_input.cpu().numpy()
|
|
|
|
|
|
|
|
scaled_latent_model_input = np.concatenate(
|
|
|
|
[scaled_latent_model_input, image_latents], axis=1
|
|
|
|
)
|
|
|
|
|
|
|
|
# predict the noise residual
|
|
|
|
|
|
|
|
noise_pred = self.unet(
|
|
|
|
sample=scaled_latent_model_input,
|
|
|
|
timestep=np.array([t], dtype=timestep_dtype),
|
|
|
|
encoder_hidden_states=prompt_embeds,
|
|
|
|
)[0]
|
|
|
|
|
|
|
|
# Hack:
|
|
|
|
# For karras style schedulers the model does classifer free guidance using the
|
|
|
|
# predicted_original_sample instead of the noise_pred. So we need to compute the
|
|
|
|
# predicted_original_sample here if we are using a karras style scheduler.
|
|
|
|
if scheduler_is_in_sigma_space:
|
|
|
|
step_index = (self.scheduler.timesteps == t).nonzero().item()
|
|
|
|
sigma = self.scheduler.sigmas[step_index]
|
|
|
|
noise_pred = latent_model_input - sigma.numpy() * noise_pred
|
|
|
|
|
|
|
|
# perform guidance
|
|
|
|
if do_classifier_free_guidance:
|
|
|
|
noise_pred_text, noise_pred_image, noise_pred_uncond = np.split(
|
|
|
|
noise_pred, 3
|
|
|
|
)
|
|
|
|
noise_pred = (
|
|
|
|
noise_pred_uncond
|
|
|
|
+ guidance_scale * (noise_pred_text - noise_pred_image)
|
|
|
|
+ image_guidance_scale * (noise_pred_image - noise_pred_uncond)
|
|
|
|
)
|
|
|
|
|
|
|
|
# Hack:
|
|
|
|
# For karras style schedulers the model does classifer free guidance using the
|
|
|
|
# predicted_original_sample instead of the noise_pred. But the scheduler.step function
|
|
|
|
# expects the noise_pred and computes the predicted_original_sample internally. So we
|
|
|
|
# need to overwrite the noise_pred here such that the value of the computed
|
|
|
|
# predicted_original_sample is correct.
|
|
|
|
if scheduler_is_in_sigma_space:
|
|
|
|
noise_pred = (noise_pred - latents) / (-sigma)
|
|
|
|
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
|
|
scheduler_output = self.scheduler.step(
|
|
|
|
noise_pred, t, torch.from_numpy(latents), **extra_step_kwargs
|
|
|
|
)
|
|
|
|
latents = scheduler_output.prev_sample.numpy()
|
|
|
|
|
|
|
|
# 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:
|
2023-04-13 23:41:22 +00:00
|
|
|
callback(i, t, latents)
|
2023-04-12 00:29:25 +00:00
|
|
|
|
|
|
|
# 10. Post-processing
|
|
|
|
image = self.decode_latents(latents)
|
|
|
|
|
|
|
|
# 11. Run safety checker
|
|
|
|
image, has_nsfw_concept = self.run_safety_checker(image)
|
|
|
|
|
|
|
|
# 12. Convert to PIL
|
|
|
|
if output_type == "pil":
|
|
|
|
image = self.numpy_to_pil(image)
|
|
|
|
|
|
|
|
if not return_dict:
|
|
|
|
return (image, has_nsfw_concept)
|
|
|
|
|
|
|
|
return StableDiffusionPipelineOutput(
|
|
|
|
images=image, nsfw_content_detected=has_nsfw_concept
|
|
|
|
)
|
|
|
|
|
|
|
|
def _encode_prompt(
|
|
|
|
self,
|
|
|
|
prompt,
|
|
|
|
num_images_per_prompt,
|
|
|
|
do_classifier_free_guidance,
|
|
|
|
negative_prompt,
|
|
|
|
):
|
|
|
|
r"""
|
|
|
|
Encodes the prompt into text encoder hidden states.
|
|
|
|
|
|
|
|
Args:
|
|
|
|
prompt (`str` or `List[str]`):
|
|
|
|
prompt to be encoded
|
|
|
|
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]`):
|
|
|
|
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
|
|
|
if `guidance_scale` is less than `1`).
|
|
|
|
"""
|
|
|
|
negative_prompt_embeds = None
|
|
|
|
batch_size = len(prompt) if isinstance(prompt, list) else 1
|
|
|
|
|
|
|
|
# get prompt text embeddings
|
|
|
|
text_inputs = self.tokenizer(
|
|
|
|
prompt,
|
|
|
|
padding="max_length",
|
|
|
|
max_length=self.tokenizer.model_max_length,
|
|
|
|
truncation=True,
|
|
|
|
return_tensors="np",
|
|
|
|
)
|
|
|
|
text_input_ids = text_inputs.input_ids
|
|
|
|
untruncated_ids = self.tokenizer(
|
|
|
|
prompt, padding="max_length", return_tensors="np"
|
|
|
|
).input_ids
|
|
|
|
|
|
|
|
if not np.array_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}"
|
|
|
|
)
|
|
|
|
|
|
|
|
prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
|
|
|
|
prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
|
|
|
|
|
|
|
|
# get unconditional embeddings for classifier free guidance
|
|
|
|
if do_classifier_free_guidance:
|
|
|
|
uncond_tokens: List[str]
|
|
|
|
if negative_prompt is None:
|
|
|
|
uncond_tokens = [""] * batch_size
|
|
|
|
elif 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] * batch_size
|
|
|
|
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
|
|
|
|
|
|
|
|
max_length = text_input_ids.shape[-1]
|
|
|
|
uncond_input = self.tokenizer(
|
|
|
|
uncond_tokens,
|
|
|
|
padding="max_length",
|
|
|
|
max_length=max_length,
|
|
|
|
truncation=True,
|
|
|
|
return_tensors="np",
|
|
|
|
)
|
|
|
|
negative_prompt_embeds = self.text_encoder(
|
|
|
|
input_ids=uncond_input.input_ids.astype(np.int32)
|
|
|
|
)[0]
|
|
|
|
negative_prompt_embeds = np.repeat(
|
|
|
|
negative_prompt_embeds, num_images_per_prompt, axis=0
|
|
|
|
)
|
|
|
|
|
|
|
|
# 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
|
|
|
|
# pix2pix has two negative embeddings, and unlike in other pipelines latents are ordered
|
|
|
|
# [prompt_embeds, negative_prompt_embeds, negative_prompt_embeds]
|
|
|
|
|
|
|
|
prompt_embeds = np.concatenate(
|
|
|
|
(prompt_embeds, negative_prompt_embeds, negative_prompt_embeds)
|
|
|
|
)
|
|
|
|
|
|
|
|
return prompt_embeds
|
|
|
|
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
|
|
|
|
def run_safety_checker(self, image):
|
|
|
|
if self.safety_checker is not None:
|
|
|
|
safety_checker_input = self.feature_extractor(
|
|
|
|
self.numpy_to_pil(image), return_tensors="np"
|
|
|
|
).pixel_values.astype(image.dtype)
|
|
|
|
# safety_checker does not support batched inputs yet
|
|
|
|
images, has_nsfw_concept = [], []
|
|
|
|
for i in range(image.shape[0]):
|
|
|
|
image_i, has_nsfw_concept_i = self.safety_checker(
|
|
|
|
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
|
|
|
|
)
|
|
|
|
images.append(image_i)
|
|
|
|
has_nsfw_concept.append(has_nsfw_concept_i[0])
|
|
|
|
image = np.concatenate(images)
|
|
|
|
else:
|
|
|
|
has_nsfw_concept = None
|
|
|
|
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, torch_gen):
|
|
|
|
# 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"] = torch_gen
|
|
|
|
return extra_step_kwargs
|
|
|
|
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
|
|
|
|
def decode_latents(self, latents):
|
|
|
|
latents = 1 / 0.18215 * latents
|
|
|
|
image = np.concatenate(
|
|
|
|
[
|
|
|
|
self.vae_decoder(latent_sample=latents[i : i + 1])[0]
|
|
|
|
for i in range(latents.shape[0])
|
|
|
|
]
|
|
|
|
)
|
|
|
|
image = np.clip(image / 2 + 0.5, 0, 1)
|
|
|
|
image = image.transpose((0, 2, 3, 1))
|
|
|
|
return image
|
|
|
|
|
|
|
|
def check_inputs(self, prompt, callback_steps):
|
|
|
|
if 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 (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)}."
|
|
|
|
)
|