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c8bb1ff53e
* Use HF Papers * Apply style fixes --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
683 lines
31 KiB
Python
683 lines
31 KiB
Python
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import numpy as np
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import torch
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from PIL import Image, ImageFilter
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from diffusers.image_processor import PipelineImageInput
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from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
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from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img import (
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StableDiffusionXLImg2ImgPipeline,
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rescale_noise_cfg,
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retrieve_latents,
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retrieve_timesteps,
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)
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from diffusers.utils import (
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deprecate,
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is_torch_xla_available,
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logging,
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)
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from diffusers.utils.torch_utils import randn_tensor
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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm
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XLA_AVAILABLE = True
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else:
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XLA_AVAILABLE = False
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class MaskedStableDiffusionXLImg2ImgPipeline(StableDiffusionXLImg2ImgPipeline):
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debug_save = 0
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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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prompt_2: Optional[Union[str, List[str]]] = None,
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image: PipelineImageInput = None,
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original_image: PipelineImageInput = None,
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strength: float = 0.3,
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num_inference_steps: Optional[int] = 50,
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timesteps: List[int] = None,
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denoising_start: Optional[float] = None,
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denoising_end: Optional[float] = None,
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guidance_scale: Optional[float] = 5.0,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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negative_prompt_2: Optional[Union[str, List[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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eta: Optional[float] = 0.0,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.FloatTensor] = 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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pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
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ip_adapter_image: Optional[PipelineImageInput] = None,
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ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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cross_attention_kwargs: Optional[Dict[str, Any]] = None,
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guidance_rescale: float = 0.0,
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original_size: Tuple[int, int] = None,
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crops_coords_top_left: Tuple[int, int] = (0, 0),
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target_size: Tuple[int, int] = None,
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negative_original_size: Optional[Tuple[int, int]] = None,
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negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
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negative_target_size: Optional[Tuple[int, int]] = None,
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aesthetic_score: float = 6.0,
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negative_aesthetic_score: float = 2.5,
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clip_skip: Optional[int] = None,
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callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
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callback_on_step_end_tensor_inputs: List[str] = ["latents"],
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mask: Union[
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torch.FloatTensor,
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Image.Image,
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np.ndarray,
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List[torch.FloatTensor],
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List[Image.Image],
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List[np.ndarray],
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] = None,
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blur=24,
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blur_compose=4,
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sample_mode="sample",
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**kwargs,
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):
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r"""
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The call function to 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 image generation. If not defined, you need to pass `prompt_embeds`.
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image (`PipelineImageInput`):
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`Image` or tensor representing an image batch to be used as the starting point. This image might have mask painted on it.
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original_image (`PipelineImageInput`, *optional*):
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`Image` or tensor representing an image batch to be used for blending with the result.
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strength (`float`, *optional*, defaults to 0.8):
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Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
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starting point and more noise is added the higher the `strength`. The number of denoising steps depends
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on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
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process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
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essentially ignores `image`.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference. This parameter is modulated by `strength`.
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guidance_scale (`float`, *optional*, defaults to 7.5):
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A higher guidance scale value encourages the model to generate images closely linked to the text
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,`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide what to not include in image generation. If not defined, you need to
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pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 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) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only applies
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to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
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generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
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A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
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generation deterministic.
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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 (prompt weighting). If not
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provided, text embeddings are generated from the `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 (prompt weighting). If
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not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generated image. Choose between `PIL.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 calls every `callback_steps` steps during inference. The function is called with the
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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 is called. If not specified, the callback is called at
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every step.
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cross_attention_kwargs (`dict`, *optional*):
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A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
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[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
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blur (`int`, *optional*):
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blur to apply to mask
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blur_compose (`int`, *optional*):
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blur to apply for composition of original a
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mask (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`, *optional*):
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A mask with non-zero elements for the area to be inpainted. If not specified, no mask is applied.
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sample_mode (`str`, *optional*):
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control latents initialisation for the inpaint area, can be one of sample, argmax, random
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Examples:
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Returns:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
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If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
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otherwise a `tuple` is returned where the first element is a list with the generated images and the
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second element is a list of `bool`s indicating whether the corresponding generated image contains
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"not-safe-for-work" (nsfw) content.
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"""
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# code adapted from parent class StableDiffusionXLImg2ImgPipeline
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callback = kwargs.pop("callback", None)
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callback_steps = kwargs.pop("callback_steps", None)
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if callback is not None:
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deprecate(
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"callback",
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"1.0.0",
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"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
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)
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if callback_steps is not None:
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deprecate(
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"callback_steps",
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"1.0.0",
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"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
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)
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# 0. Check inputs. Raise error if not correct
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self.check_inputs(
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prompt,
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prompt_2,
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strength,
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num_inference_steps,
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callback_steps,
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negative_prompt,
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negative_prompt_2,
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prompt_embeds,
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negative_prompt_embeds,
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ip_adapter_image,
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ip_adapter_image_embeds,
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callback_on_step_end_tensor_inputs,
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)
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self._guidance_scale = guidance_scale
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self._guidance_rescale = guidance_rescale
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self._clip_skip = clip_skip
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self._cross_attention_kwargs = cross_attention_kwargs
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self._denoising_end = denoising_end
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self._denoising_start = denoising_start
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self._interrupt = False
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# 1. Define call parameters
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# mask is computed from difference between image and original_image
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if image is not None:
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neq = np.any(np.array(original_image) != np.array(image), axis=-1)
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mask = neq.astype(np.uint8) * 255
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else:
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assert mask is not None
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if not isinstance(mask, Image.Image):
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pil_mask = Image.fromarray(mask)
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if pil_mask.mode != "L":
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pil_mask = pil_mask.convert("L")
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mask_blur = self.blur_mask(pil_mask, blur)
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mask_compose = self.blur_mask(pil_mask, blur_compose)
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if original_image is None:
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original_image = image
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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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device = self._execution_device
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# 2. Encode input prompt
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text_encoder_lora_scale = (
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self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
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)
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(
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prompt_embeds,
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negative_prompt_embeds,
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pooled_prompt_embeds,
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negative_pooled_prompt_embeds,
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) = self.encode_prompt(
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prompt=prompt,
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prompt_2=prompt_2,
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device=device,
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num_images_per_prompt=num_images_per_prompt,
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do_classifier_free_guidance=self.do_classifier_free_guidance,
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negative_prompt=negative_prompt,
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negative_prompt_2=negative_prompt_2,
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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pooled_prompt_embeds=pooled_prompt_embeds,
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negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
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lora_scale=text_encoder_lora_scale,
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clip_skip=self.clip_skip,
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)
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# 3. Preprocess image
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input_image = image if image is not None else original_image
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image = self.image_processor.preprocess(input_image)
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original_image = self.image_processor.preprocess(original_image)
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# 4. set timesteps
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def denoising_value_valid(dnv):
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return isinstance(dnv, float) and 0 < dnv < 1
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timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
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timesteps, num_inference_steps = self.get_timesteps(
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num_inference_steps,
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strength,
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device,
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denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,
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)
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latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
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add_noise = True if self.denoising_start is None else False
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# 5. Prepare latent variables
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# It is sampled from the latent distribution of the VAE
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# that's what we repaint
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latents = self.prepare_latents(
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image,
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latent_timestep,
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batch_size,
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num_images_per_prompt,
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prompt_embeds.dtype,
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device,
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generator,
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add_noise,
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sample_mode=sample_mode,
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)
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# mean of the latent distribution
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# it is multiplied by self.vae.config.scaling_factor
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non_paint_latents = self.prepare_latents(
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original_image,
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latent_timestep,
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batch_size,
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num_images_per_prompt,
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prompt_embeds.dtype,
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device,
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generator,
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add_noise=False,
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sample_mode="argmax",
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)
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if self.debug_save:
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init_img_from_latents = self.latents_to_img(non_paint_latents)
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init_img_from_latents[0].save("non_paint_latents.png")
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# 6. create latent mask
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latent_mask = self._make_latent_mask(latents, mask)
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# 7. Prepare extra step kwargs.
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extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
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height, width = latents.shape[-2:]
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height = height * self.vae_scale_factor
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width = width * self.vae_scale_factor
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original_size = original_size or (height, width)
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target_size = target_size or (height, width)
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# 8. Prepare added time ids & embeddings
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if negative_original_size is None:
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negative_original_size = original_size
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if negative_target_size is None:
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negative_target_size = target_size
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add_text_embeds = pooled_prompt_embeds
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if self.text_encoder_2 is None:
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text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
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else:
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text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
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add_time_ids, add_neg_time_ids = self._get_add_time_ids(
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original_size,
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crops_coords_top_left,
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target_size,
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aesthetic_score,
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negative_aesthetic_score,
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negative_original_size,
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negative_crops_coords_top_left,
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negative_target_size,
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dtype=prompt_embeds.dtype,
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text_encoder_projection_dim=text_encoder_projection_dim,
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)
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add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)
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if self.do_classifier_free_guidance:
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prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
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add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
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add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)
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add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)
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prompt_embeds = prompt_embeds.to(device)
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add_text_embeds = add_text_embeds.to(device)
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add_time_ids = add_time_ids.to(device)
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if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
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image_embeds = self.prepare_ip_adapter_image_embeds(
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ip_adapter_image,
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ip_adapter_image_embeds,
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device,
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batch_size * num_images_per_prompt,
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self.do_classifier_free_guidance,
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)
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# 10. Denoising loop
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num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
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# 10.1 Apply denoising_end
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if (
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self.denoising_end is not None
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and self.denoising_start is not None
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and denoising_value_valid(self.denoising_end)
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and denoising_value_valid(self.denoising_start)
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and self.denoising_start >= self.denoising_end
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):
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raise ValueError(
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f"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: "
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+ f" {self.denoising_end} when using type float."
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)
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elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):
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discrete_timestep_cutoff = int(
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round(
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self.scheduler.config.num_train_timesteps
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- (self.denoising_end * self.scheduler.config.num_train_timesteps)
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)
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)
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num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
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timesteps = timesteps[:num_inference_steps]
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# 10.2 Optionally get Guidance Scale Embedding
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timestep_cond = None
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if self.unet.config.time_cond_proj_dim is not None:
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guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
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timestep_cond = self.get_guidance_scale_embedding(
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guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
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).to(device=device, dtype=latents.dtype)
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self._num_timesteps = len(timesteps)
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with self.progress_bar(total=num_inference_steps) as progress_bar:
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for i, t in enumerate(timesteps):
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if self.interrupt:
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continue
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shape = non_paint_latents.shape
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noise = randn_tensor(shape, generator=generator, device=device, dtype=latents.dtype)
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# noisy latent code of input image at current step
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orig_latents_t = non_paint_latents
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orig_latents_t = self.scheduler.add_noise(non_paint_latents, noise, t.unsqueeze(0))
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# orig_latents_t (1 - latent_mask) + latents * latent_mask
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latents = torch.lerp(orig_latents_t, latents, latent_mask)
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if self.debug_save:
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img1 = self.latents_to_img(latents)
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t_str = str(t.int().item())
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for i in range(3 - len(t_str)):
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t_str = "0" + t_str
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img1[0].save(f"step{t_str}.png")
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
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latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
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# predict the noise residual
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added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
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if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
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added_cond_kwargs["image_embeds"] = image_embeds
|
|
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
timestep_cond=timestep_cond,
|
|
cross_attention_kwargs=self.cross_attention_kwargs,
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if self.do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
|
|
if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
|
|
# Based on 3.4. in https://huggingface.co/papers/2305.08891
|
|
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents_dtype = latents.dtype
|
|
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
|
|
|
if latents.dtype != latents_dtype:
|
|
if torch.backends.mps.is_available():
|
|
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
|
|
latents = latents.to(latents_dtype)
|
|
|
|
if callback_on_step_end is not None:
|
|
callback_kwargs = {}
|
|
for k in callback_on_step_end_tensor_inputs:
|
|
callback_kwargs[k] = locals()[k]
|
|
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
|
|
|
|
latents = callback_outputs.pop("latents", latents)
|
|
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
|
|
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
|
|
add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
|
|
negative_pooled_prompt_embeds = callback_outputs.pop(
|
|
"negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
|
|
)
|
|
add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
|
|
add_neg_time_ids = callback_outputs.pop("add_neg_time_ids", add_neg_time_ids)
|
|
|
|
# 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:
|
|
step_idx = i // getattr(self.scheduler, "order", 1)
|
|
callback(step_idx, t, latents)
|
|
|
|
if XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
if not output_type == "latent":
|
|
# make sure the VAE is in float32 mode, as it overflows in float16
|
|
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
|
|
|
|
if needs_upcasting:
|
|
self.upcast_vae()
|
|
elif latents.dtype != self.vae.dtype:
|
|
if torch.backends.mps.is_available():
|
|
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
|
|
self.vae = self.vae.to(latents.dtype)
|
|
|
|
if self.debug_save:
|
|
image_gen = self.latents_to_img(latents)
|
|
image_gen[0].save("from_latent.png")
|
|
|
|
if latent_mask is not None:
|
|
# interpolate with latent mask
|
|
latents = torch.lerp(non_paint_latents, latents, latent_mask)
|
|
|
|
latents = self.denormalize(latents)
|
|
image = self.vae.decode(latents, return_dict=False)[0]
|
|
m = mask_compose.permute(2, 0, 1).unsqueeze(0).to(image)
|
|
img_compose = m * image + (1 - m) * original_image.to(image)
|
|
image = img_compose
|
|
# cast back to fp16 if needed
|
|
if needs_upcasting:
|
|
self.vae.to(dtype=torch.float16)
|
|
else:
|
|
image = latents
|
|
|
|
# apply watermark if available
|
|
if self.watermark is not None:
|
|
image = self.watermark.apply_watermark(image)
|
|
|
|
image = self.image_processor.postprocess(image, output_type=output_type)
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (image,)
|
|
|
|
return StableDiffusionXLPipelineOutput(images=image)
|
|
|
|
def _make_latent_mask(self, latents, mask):
|
|
if mask is not None:
|
|
latent_mask = []
|
|
if not isinstance(mask, list):
|
|
tmp_mask = [mask]
|
|
else:
|
|
tmp_mask = mask
|
|
_, l_channels, l_height, l_width = latents.shape
|
|
for m in tmp_mask:
|
|
if not isinstance(m, Image.Image):
|
|
if len(m.shape) == 2:
|
|
m = m[..., np.newaxis]
|
|
if m.max() > 1:
|
|
m = m / 255.0
|
|
m = self.image_processor.numpy_to_pil(m)[0]
|
|
if m.mode != "L":
|
|
m = m.convert("L")
|
|
resized = self.image_processor.resize(m, l_height, l_width)
|
|
if self.debug_save:
|
|
resized.save("latent_mask.png")
|
|
latent_mask.append(np.repeat(np.array(resized)[np.newaxis, :, :], l_channels, axis=0))
|
|
latent_mask = torch.as_tensor(np.stack(latent_mask)).to(latents)
|
|
latent_mask = latent_mask / max(latent_mask.max(), 1)
|
|
return latent_mask
|
|
|
|
def prepare_latents(
|
|
self,
|
|
image,
|
|
timestep,
|
|
batch_size,
|
|
num_images_per_prompt,
|
|
dtype,
|
|
device,
|
|
generator=None,
|
|
add_noise=True,
|
|
sample_mode: str = "sample",
|
|
):
|
|
if not isinstance(image, (torch.Tensor, Image.Image, list)):
|
|
raise ValueError(
|
|
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
|
|
)
|
|
|
|
# Offload text encoder if `enable_model_cpu_offload` was enabled
|
|
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
|
self.text_encoder_2.to("cpu")
|
|
torch.cuda.empty_cache()
|
|
|
|
image = image.to(device=device, dtype=dtype)
|
|
|
|
batch_size = batch_size * num_images_per_prompt
|
|
|
|
if image.shape[1] == 4:
|
|
init_latents = image
|
|
elif sample_mode == "random":
|
|
height, width = image.shape[-2:]
|
|
num_channels_latents = self.unet.config.in_channels
|
|
latents = self.random_latents(
|
|
batch_size,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
)
|
|
return self.vae.config.scaling_factor * latents
|
|
else:
|
|
# make sure the VAE is in float32 mode, as it overflows in float16
|
|
if self.vae.config.force_upcast:
|
|
image = image.float()
|
|
self.vae.to(dtype=torch.float32)
|
|
|
|
if isinstance(generator, list) and len(generator) != batch_size:
|
|
raise ValueError(
|
|
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
|
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
|
)
|
|
|
|
elif isinstance(generator, list):
|
|
init_latents = [
|
|
retrieve_latents(
|
|
self.vae.encode(image[i : i + 1]), generator=generator[i], sample_mode=sample_mode
|
|
)
|
|
for i in range(batch_size)
|
|
]
|
|
init_latents = torch.cat(init_latents, dim=0)
|
|
else:
|
|
init_latents = retrieve_latents(self.vae.encode(image), generator=generator, sample_mode=sample_mode)
|
|
|
|
if self.vae.config.force_upcast:
|
|
self.vae.to(dtype)
|
|
|
|
init_latents = init_latents.to(dtype)
|
|
init_latents = self.vae.config.scaling_factor * init_latents
|
|
|
|
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
|
|
# expand init_latents for batch_size
|
|
additional_image_per_prompt = batch_size // init_latents.shape[0]
|
|
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
|
|
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
|
|
raise ValueError(
|
|
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
|
|
)
|
|
else:
|
|
init_latents = torch.cat([init_latents], dim=0)
|
|
|
|
if add_noise:
|
|
shape = init_latents.shape
|
|
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
# get latents
|
|
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
|
|
|
|
latents = init_latents
|
|
|
|
return latents
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
|
|
def random_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
|
|
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
|
|
if isinstance(generator, list) and len(generator) != batch_size:
|
|
raise ValueError(
|
|
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
|
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
|
)
|
|
|
|
if latents is None:
|
|
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
else:
|
|
latents = latents.to(device)
|
|
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
|
latents = latents * self.scheduler.init_noise_sigma
|
|
return latents
|
|
|
|
def denormalize(self, latents):
|
|
# unscale/denormalize the latents
|
|
# denormalize with the mean and std if available and not None
|
|
has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
|
|
has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
|
|
if has_latents_mean and has_latents_std:
|
|
latents_mean = (
|
|
torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
|
|
)
|
|
latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
|
|
latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
|
|
else:
|
|
latents = latents / self.vae.config.scaling_factor
|
|
|
|
return latents
|
|
|
|
def latents_to_img(self, latents):
|
|
l1 = self.denormalize(latents)
|
|
img1 = self.vae.decode(l1, return_dict=False)[0]
|
|
img1 = self.image_processor.postprocess(img1, output_type="pil", do_denormalize=[True])
|
|
return img1
|
|
|
|
def blur_mask(self, pil_mask, blur):
|
|
mask_blur = pil_mask.filter(ImageFilter.GaussianBlur(radius=blur))
|
|
mask_blur = np.array(mask_blur)
|
|
return torch.from_numpy(np.tile(mask_blur / mask_blur.max(), (3, 1, 1)).transpose(1, 2, 0))
|