969 lines
40 KiB
Python
969 lines
40 KiB
Python
import inspect
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from dataclasses import dataclass
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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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import torch.nn.functional as F
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from diffusers.configuration_utils import register_to_config
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from diffusers.image_processor import VaeImageProcessor
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from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
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from diffusers.models import AutoencoderKL, UNet2DConditionModel
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (
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rescale_noise_cfg,
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)
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from diffusers.schedulers import KarrasDiffusionSchedulers
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from diffusers.utils import CONFIG_NAME, BaseOutput, deprecate, logging, randn_tensor
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from packaging import version
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from transformers import CLIPTextModel, CLIPTokenizer
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logger = logging.get_logger(__name__)
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class VaeImageProcrssorAOV(VaeImageProcessor):
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"""
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Image processor for VAE AOV.
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Args:
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do_resize (`bool`, *optional*, defaults to `True`):
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Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
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vae_scale_factor (`int`, *optional*, defaults to `8`):
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VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
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resample (`str`, *optional*, defaults to `lanczos`):
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Resampling filter to use when resizing the image.
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do_normalize (`bool`, *optional*, defaults to `True`):
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Whether to normalize the image to [-1,1].
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"""
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config_name = CONFIG_NAME
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@register_to_config
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def __init__(
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self,
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do_resize: bool = True,
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vae_scale_factor: int = 8,
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resample: str = "lanczos",
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do_normalize: bool = True,
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):
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super().__init__()
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def postprocess(
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self,
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image: torch.FloatTensor,
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output_type: str = "pil",
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do_denormalize: Optional[List[bool]] = None,
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do_gamma_correction: bool = True,
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):
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if not isinstance(image, torch.Tensor):
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raise ValueError(
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f"Input for postprocessing is in incorrect format: {type(image)}. We only support pytorch tensor"
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)
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if output_type not in ["latent", "pt", "np", "pil"]:
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deprecation_message = (
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f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
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"`pil`, `np`, `pt`, `latent`"
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)
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deprecate(
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"Unsupported output_type",
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"1.0.0",
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deprecation_message,
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standard_warn=False,
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)
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output_type = "np"
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if output_type == "latent":
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return image
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if do_denormalize is None:
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do_denormalize = [self.config.do_normalize] * image.shape[0]
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image = torch.stack(
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[
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self.denormalize(image[i]) if do_denormalize[i] else image[i]
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for i in range(image.shape[0])
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]
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)
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# Gamma correction
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if do_gamma_correction:
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image = torch.pow(image, 1.0 / 2.2)
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if output_type == "pt":
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return image
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image = self.pt_to_numpy(image)
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if output_type == "np":
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return image
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if output_type == "pil":
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return self.numpy_to_pil(image)
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def preprocess_normal(
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self,
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image: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray],
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height: Optional[int] = None,
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width: Optional[int] = None,
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) -> torch.Tensor:
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image = torch.stack([image], axis=0)
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return image
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@dataclass
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class StableDiffusionAOVPipelineOutput(BaseOutput):
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"""
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Output class for Stable Diffusion AOV pipelines.
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Args:
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images (`List[PIL.Image.Image]` or `np.ndarray`)
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List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
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num_channels)`.
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nsfw_content_detected (`List[bool]`)
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List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or
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`None` if safety checking could not be performed.
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"""
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images: Union[List[PIL.Image.Image], np.ndarray]
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predicted_x0_images: Optional[Union[List[PIL.Image.Image], np.ndarray]] = None
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class StableDiffusionAOVDropoutPipeline(
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DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin
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):
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r"""
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Pipeline for AOVs.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
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implemented for all pipelines (downloading, saving, running on a particular device, etc.).
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The pipeline also inherits the following loading methods:
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- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
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- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
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- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
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Args:
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vae ([`AutoencoderKL`]):
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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 ([`~transformers.CLIPTextModel`]):
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Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
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tokenizer ([`~transformers.CLIPTokenizer`]):
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A `CLIPTokenizer` to tokenize text.
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unet ([`UNet2DConditionModel`]):
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A `UNet2DConditionModel` to denoise the encoded image latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
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"""
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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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):
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super().__init__()
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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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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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self.image_processor = VaeImageProcrssorAOV(
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vae_scale_factor=self.vae_scale_factor
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)
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self.register_to_config()
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def _encode_prompt(
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self,
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prompt,
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device,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_prompt=None,
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prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
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device: (`torch.device`):
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torch device
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num_images_per_prompt (`int`):
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number of images that should be generated per prompt
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do_classifier_free_guidance (`bool`):
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whether to use classifier free guidance or not
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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` is
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less than `1`).
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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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"""
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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if prompt_embeds is None:
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# textual inversion: procecss multi-vector tokens if necessary
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if isinstance(self, TextualInversionLoaderMixin):
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prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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untruncated_ids = self.tokenizer(
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prompt, padding="longest", return_tensors="pt"
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).input_ids
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if untruncated_ids.shape[-1] >= text_input_ids.shape[
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-1
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] and not torch.equal(text_input_ids, untruncated_ids):
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removed_text = self.tokenizer.batch_decode(
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untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
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)
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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if (
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hasattr(self.text_encoder.config, "use_attention_mask")
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and self.text_encoder.config.use_attention_mask
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):
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attention_mask = text_inputs.attention_mask.to(device)
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else:
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attention_mask = None
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prompt_embeds = self.text_encoder(
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text_input_ids.to(device),
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attention_mask=attention_mask,
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)
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prompt_embeds = prompt_embeds[0]
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prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
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bs_embed, seq_len, _ = prompt_embeds.shape
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(
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bs_embed * num_images_per_prompt, seq_len, -1
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)
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance and negative_prompt_embeds is None:
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uncond_tokens: List[str]
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if negative_prompt is None:
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uncond_tokens = [""] * batch_size
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elif type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif isinstance(negative_prompt, str):
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uncond_tokens = [negative_prompt]
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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else:
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uncond_tokens = negative_prompt
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# textual inversion: procecss multi-vector tokens if necessary
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if isinstance(self, TextualInversionLoaderMixin):
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uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
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max_length = prompt_embeds.shape[1]
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uncond_input = self.tokenizer(
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uncond_tokens,
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padding="max_length",
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max_length=max_length,
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truncation=True,
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return_tensors="pt",
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)
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if (
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hasattr(self.text_encoder.config, "use_attention_mask")
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and self.text_encoder.config.use_attention_mask
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):
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attention_mask = uncond_input.attention_mask.to(device)
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else:
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attention_mask = None
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negative_prompt_embeds = self.text_encoder(
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uncond_input.input_ids.to(device),
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attention_mask=attention_mask,
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)
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negative_prompt_embeds = negative_prompt_embeds[0]
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if do_classifier_free_guidance:
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# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
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seq_len = negative_prompt_embeds.shape[1]
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negative_prompt_embeds = negative_prompt_embeds.to(
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dtype=self.text_encoder.dtype, device=device
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)
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negative_prompt_embeds = negative_prompt_embeds.repeat(
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1, num_images_per_prompt, 1
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)
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negative_prompt_embeds = negative_prompt_embeds.view(
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batch_size * num_images_per_prompt, seq_len, -1
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)
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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# pix2pix has two negative embeddings, and unlike in other pipelines latents are ordered [prompt_embeds, negative_prompt_embeds, negative_prompt_embeds]
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prompt_embeds = torch.cat(
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[prompt_embeds, negative_prompt_embeds, negative_prompt_embeds]
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)
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return prompt_embeds
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def prepare_extra_step_kwargs(self, generator, eta):
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(
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inspect.signature(self.scheduler.step).parameters.keys()
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)
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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# check if the scheduler accepts generator
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accepts_generator = "generator" in set(
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inspect.signature(self.scheduler.step).parameters.keys()
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)
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if accepts_generator:
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extra_step_kwargs["generator"] = generator
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return extra_step_kwargs
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def check_inputs(
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self,
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prompt,
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callback_steps,
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negative_prompt=None,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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):
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if (callback_steps is None) or (
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callback_steps is not None
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and (not isinstance(callback_steps, int) or callback_steps <= 0)
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):
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raise ValueError(
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f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
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f" {type(callback_steps)}."
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)
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if prompt is not None and prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
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" only forward one of the two."
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)
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elif prompt is None and prompt_embeds is None:
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raise ValueError(
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"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
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)
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elif prompt is not None and (
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not isinstance(prompt, str) and not isinstance(prompt, list)
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):
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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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if negative_prompt is not None and negative_prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
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f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
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)
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if prompt_embeds is not None and negative_prompt_embeds is not None:
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if prompt_embeds.shape != negative_prompt_embeds.shape:
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raise ValueError(
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"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
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f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
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f" {negative_prompt_embeds.shape}."
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)
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def prepare_latents(
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self,
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batch_size,
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num_channels_latents,
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height,
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width,
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dtype,
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device,
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generator,
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latents=None,
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):
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shape = (
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batch_size,
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num_channels_latents,
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height // self.vae_scale_factor,
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width // self.vae_scale_factor,
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)
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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if latents is None:
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latents = randn_tensor(
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shape, generator=generator, device=device, dtype=dtype
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)
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else:
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latents = latents.to(device)
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# scale the initial noise by the standard deviation required by the scheduler
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latents = latents * self.scheduler.init_noise_sigma
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return latents
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def prepare_image_latents(
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self,
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image,
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batch_size,
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num_images_per_prompt,
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dtype,
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device,
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do_classifier_free_guidance,
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generator=None,
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):
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if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
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raise ValueError(
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f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
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)
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image = image.to(device=device, dtype=dtype)
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batch_size = batch_size * num_images_per_prompt
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if image.shape[1] == 4:
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image_latents = image
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else:
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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if isinstance(generator, list):
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image_latents = [
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self.vae.encode(image[i : i + 1]).latent_dist.mode()
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for i in range(batch_size)
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]
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image_latents = torch.cat(image_latents, dim=0)
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else:
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image_latents = self.vae.encode(image).latent_dist.mode()
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if (
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batch_size > image_latents.shape[0]
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and batch_size % image_latents.shape[0] == 0
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):
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# expand image_latents for batch_size
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deprecation_message = (
|
|
f"You have passed {batch_size} text prompts (`prompt`), but only {image_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_image_per_prompt = batch_size // image_latents.shape[0]
|
|
image_latents = torch.cat(
|
|
[image_latents] * additional_image_per_prompt, dim=0
|
|
)
|
|
elif (
|
|
batch_size > image_latents.shape[0]
|
|
and batch_size % image_latents.shape[0] != 0
|
|
):
|
|
raise ValueError(
|
|
f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
|
|
)
|
|
else:
|
|
image_latents = torch.cat([image_latents], dim=0)
|
|
|
|
if do_classifier_free_guidance:
|
|
uncond_image_latents = torch.zeros_like(image_latents)
|
|
image_latents = torch.cat(
|
|
[image_latents, image_latents, uncond_image_latents], dim=0
|
|
)
|
|
|
|
return image_latents
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
height: int,
|
|
width: int,
|
|
prompt: Union[str, List[str]] = None,
|
|
albedo: Optional[
|
|
Union[
|
|
torch.FloatTensor,
|
|
PIL.Image.Image,
|
|
np.ndarray,
|
|
List[torch.FloatTensor],
|
|
List[PIL.Image.Image],
|
|
List[np.ndarray],
|
|
]
|
|
] = None,
|
|
normal: Optional[
|
|
Union[
|
|
torch.FloatTensor,
|
|
PIL.Image.Image,
|
|
np.ndarray,
|
|
List[torch.FloatTensor],
|
|
List[PIL.Image.Image],
|
|
List[np.ndarray],
|
|
]
|
|
] = None,
|
|
roughness: Optional[
|
|
Union[
|
|
torch.FloatTensor,
|
|
PIL.Image.Image,
|
|
np.ndarray,
|
|
List[torch.FloatTensor],
|
|
List[PIL.Image.Image],
|
|
List[np.ndarray],
|
|
]
|
|
] = None,
|
|
metallic: Optional[
|
|
Union[
|
|
torch.FloatTensor,
|
|
PIL.Image.Image,
|
|
np.ndarray,
|
|
List[torch.FloatTensor],
|
|
List[PIL.Image.Image],
|
|
List[np.ndarray],
|
|
]
|
|
] = None,
|
|
irradiance: Optional[
|
|
Union[
|
|
torch.FloatTensor,
|
|
PIL.Image.Image,
|
|
np.ndarray,
|
|
List[torch.FloatTensor],
|
|
List[PIL.Image.Image],
|
|
List[np.ndarray],
|
|
]
|
|
] = None,
|
|
guidance_scale: float = 0.0,
|
|
image_guidance_scale: float = 0.0,
|
|
guidance_rescale: float = 0.0,
|
|
num_inference_steps: int = 100,
|
|
required_aovs: List[str] = ["albedo"],
|
|
return_predicted_x0s: bool = False,
|
|
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,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
|
image (`torch.FloatTensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
|
|
`Image` or tensor representing an image batch to be repainted according to `prompt`. Can also accept
|
|
image latents as `image`, but if passing latents directly it is not encoded again.
|
|
num_inference_steps (`int`, *optional*, defaults to 100):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference.
|
|
guidance_scale (`float`, *optional*, defaults to 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`.
|
|
image_guidance_scale (`float`, *optional*, defaults to 1.5):
|
|
Push the generated image towards the inital `image`. Image guidance scale is enabled by setting
|
|
`image_guidance_scale > 1`. Higher image guidance scale encourages generated images that are closely
|
|
linked to the source `image`, usually at the expense of lower image quality. This pipeline requires a
|
|
value of at least `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.
|
|
|
|
Examples:
|
|
|
|
```py
|
|
>>> import PIL
|
|
>>> import requests
|
|
>>> import torch
|
|
>>> from io import BytesIO
|
|
|
|
>>> from diffusers import StableDiffusionInstructPix2PixPipeline
|
|
|
|
|
|
>>> def download_image(url):
|
|
... response = requests.get(url)
|
|
... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
|
|
|
|
|
|
>>> img_url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
|
|
|
|
>>> image = download_image(img_url).resize((512, 512))
|
|
|
|
>>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
|
... "timbrooks/instruct-pix2pix", torch_dtype=torch.float16
|
|
... )
|
|
>>> pipe = pipe.to("cuda")
|
|
|
|
>>> prompt = "make the mountains snowy"
|
|
>>> image = pipe(prompt=prompt, image=image).images[0]
|
|
```
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
|
|
otherwise a `tuple` is returned where the first element is a list with the generated images and the
|
|
second element is a list of `bool`s indicating whether the corresponding generated image contains
|
|
"not-safe-for-work" (nsfw) content.
|
|
"""
|
|
# 0. Check inputs
|
|
self.check_inputs(
|
|
prompt,
|
|
callback_steps,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
)
|
|
|
|
# 1. Define call parameters
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
device = self._execution_device
|
|
do_classifier_free_guidance = (
|
|
guidance_scale >= 1.0 and image_guidance_scale >= 1.0
|
|
)
|
|
# check if scheduler is in sigmas space
|
|
scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas")
|
|
|
|
# 2. 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,
|
|
)
|
|
|
|
# 3. Preprocess image
|
|
# For normal, the preprocessing does nothing
|
|
# For others, the preprocessing remap the values to [-1, 1]
|
|
preprocessed_aovs = {}
|
|
for aov_name in required_aovs:
|
|
if aov_name == "albedo":
|
|
if albedo is not None:
|
|
preprocessed_aovs[aov_name] = self.image_processor.preprocess(
|
|
albedo
|
|
)
|
|
else:
|
|
preprocessed_aovs[aov_name] = None
|
|
|
|
if aov_name == "normal":
|
|
if normal is not None:
|
|
preprocessed_aovs[aov_name] = (
|
|
self.image_processor.preprocess_normal(normal)
|
|
)
|
|
else:
|
|
preprocessed_aovs[aov_name] = None
|
|
|
|
if aov_name == "roughness":
|
|
if roughness is not None:
|
|
preprocessed_aovs[aov_name] = self.image_processor.preprocess(
|
|
roughness
|
|
)
|
|
else:
|
|
preprocessed_aovs[aov_name] = None
|
|
if aov_name == "metallic":
|
|
if metallic is not None:
|
|
preprocessed_aovs[aov_name] = self.image_processor.preprocess(
|
|
metallic
|
|
)
|
|
else:
|
|
preprocessed_aovs[aov_name] = None
|
|
if aov_name == "irradiance":
|
|
if irradiance is not None:
|
|
preprocessed_aovs[aov_name] = self.image_processor.preprocess(
|
|
irradiance
|
|
)
|
|
else:
|
|
preprocessed_aovs[aov_name] = None
|
|
|
|
# 4. set timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps = self.scheduler.timesteps
|
|
|
|
# 5. Prepare latent variables
|
|
num_channels_latents = self.vae.config.latent_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
height_latent, width_latent = latents.shape[-2:]
|
|
|
|
# 6. Prepare Image latents
|
|
image_latents = []
|
|
# Magicial scaling factors for each AOV (calculated from the training data)
|
|
scaling_factors = {
|
|
"albedo": 0.17301377137652138,
|
|
"normal": 0.17483895473058078,
|
|
"roughness": 0.1680724853626448,
|
|
"metallic": 0.13135013390855135,
|
|
}
|
|
for aov_name, aov in preprocessed_aovs.items():
|
|
if aov is None:
|
|
image_latent = torch.zeros(
|
|
batch_size,
|
|
num_channels_latents,
|
|
height_latent,
|
|
width_latent,
|
|
dtype=prompt_embeds.dtype,
|
|
device=device,
|
|
)
|
|
if aov_name == "irradiance":
|
|
image_latent = image_latent[:, 0:3]
|
|
if do_classifier_free_guidance:
|
|
image_latents.append(
|
|
torch.cat([image_latent, image_latent, image_latent], dim=0)
|
|
)
|
|
else:
|
|
image_latents.append(image_latent)
|
|
else:
|
|
if aov_name == "irradiance":
|
|
image_latent = F.interpolate(
|
|
aov.to(device=device, dtype=prompt_embeds.dtype),
|
|
size=(height_latent, width_latent),
|
|
mode="bilinear",
|
|
align_corners=False,
|
|
antialias=True,
|
|
)
|
|
if do_classifier_free_guidance:
|
|
uncond_image_latent = torch.zeros_like(image_latent)
|
|
image_latent = torch.cat(
|
|
[image_latent, image_latent, uncond_image_latent], dim=0
|
|
)
|
|
else:
|
|
scaling_factor = scaling_factors[aov_name]
|
|
image_latent = (
|
|
self.prepare_image_latents(
|
|
aov,
|
|
batch_size,
|
|
num_images_per_prompt,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
do_classifier_free_guidance,
|
|
generator,
|
|
)
|
|
* scaling_factor
|
|
)
|
|
image_latents.append(image_latent)
|
|
image_latents = torch.cat(image_latents, dim=1)
|
|
|
|
# 7. Check that shapes of latents and image match the UNet channels
|
|
num_channels_image = image_latents.shape[1]
|
|
if num_channels_latents + num_channels_image != self.unet.config.in_channels:
|
|
raise ValueError(
|
|
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
|
|
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
|
|
f" `num_channels_image`: {num_channels_image} "
|
|
f" = {num_channels_latents+num_channels_image}. Please verify the config of"
|
|
" `pipeline.unet` or your `image` input."
|
|
)
|
|
|
|
# 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)
|
|
|
|
predicted_x0s = []
|
|
|
|
# 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 = (
|
|
torch.cat([latents] * 3) if do_classifier_free_guidance else latents
|
|
)
|
|
|
|
# concat latents, image_latents in the channel dimension
|
|
scaled_latent_model_input = self.scheduler.scale_model_input(
|
|
latent_model_input, t
|
|
)
|
|
scaled_latent_model_input = torch.cat(
|
|
[scaled_latent_model_input, image_latents], dim=1
|
|
)
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(
|
|
scaled_latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if do_classifier_free_guidance:
|
|
(
|
|
noise_pred_text,
|
|
noise_pred_image,
|
|
noise_pred_uncond,
|
|
) = noise_pred.chunk(3)
|
|
noise_pred = (
|
|
noise_pred_uncond
|
|
+ guidance_scale * (noise_pred_text - noise_pred_image)
|
|
+ image_guidance_scale * (noise_pred_image - noise_pred_uncond)
|
|
)
|
|
|
|
if do_classifier_free_guidance and guidance_rescale > 0.0:
|
|
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
|
noise_pred = rescale_noise_cfg(
|
|
noise_pred, noise_pred_text, guidance_rescale=guidance_rescale
|
|
)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
output = self.scheduler.step(
|
|
noise_pred, t, latents, **extra_step_kwargs, return_dict=True
|
|
)
|
|
|
|
latents = output[0]
|
|
|
|
if return_predicted_x0s:
|
|
predicted_x0s.append(output[1])
|
|
|
|
# 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]
|
|
|
|
if return_predicted_x0s:
|
|
predicted_x0_images = [
|
|
self.vae.decode(
|
|
predicted_x0 / self.vae.config.scaling_factor, return_dict=False
|
|
)[0]
|
|
for predicted_x0 in predicted_x0s
|
|
]
|
|
else:
|
|
image = latents
|
|
predicted_x0_images = predicted_x0s
|
|
|
|
do_denormalize = [True] * image.shape[0]
|
|
|
|
image = self.image_processor.postprocess(
|
|
image, output_type=output_type, do_denormalize=do_denormalize
|
|
)
|
|
|
|
if return_predicted_x0s:
|
|
predicted_x0_images = [
|
|
self.image_processor.postprocess(
|
|
predicted_x0_image,
|
|
output_type=output_type,
|
|
do_denormalize=do_denormalize,
|
|
)
|
|
for predicted_x0_image in predicted_x0_images
|
|
]
|
|
|
|
# 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
|
|
|
|
if return_predicted_x0s:
|
|
return StableDiffusionAOVPipelineOutput(
|
|
images=image, predicted_x0_images=predicted_x0_images
|
|
)
|
|
else:
|
|
return StableDiffusionAOVPipelineOutput(images=image)
|