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456 lines
22 KiB
Python
456 lines
22 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass
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from typing import Any, Callable, Optional, Union
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import numpy as np
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import PIL.Image
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import torch
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from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
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from diffusers.pipelines.controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline
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from diffusers.utils import BaseOutput, logging
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from torch.nn import functional as F
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from utils.light_controlnet import ControlNetModel
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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@dataclass
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class LightControlNetPipelineOutput(BaseOutput):
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"""
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Output class for Stable Diffusion 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)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
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nsfw_content_detected (`List[bool]`)
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List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
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(nsfw) content, or `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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nsfw_content_detected: Optional[list[bool]]
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class LightControlNetPipeline(StableDiffusionControlNetPipeline):
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_optional_components = ["safety_checker", "feature_extractor"]
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def check_inputs(
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self,
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prompt,
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image,
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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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controlnet_conditioning_scale=1.0,
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):
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if (callback_steps is None) or (
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callback_steps is not None 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 (not isinstance(prompt, str) and not isinstance(prompt, list)):
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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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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# `prompt` needs more sophisticated handling when there are multiple
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# conditionings.
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if isinstance(self.controlnet, MultiControlNetModel):
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if isinstance(prompt, list):
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logger.warning(
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f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
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" prompts. The conditionings will be fixed across the prompts."
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)
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# Check `image`
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is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
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self.controlnet, torch._dynamo.eval_frame.OptimizedModule
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)
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if (
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isinstance(self.controlnet, ControlNetModel)
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or is_compiled
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and isinstance(self.controlnet._orig_mod, ControlNetModel)
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):
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self.check_image(image, prompt, prompt_embeds)
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elif (
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isinstance(self.controlnet, MultiControlNetModel)
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or is_compiled
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and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
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):
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if not isinstance(image, list):
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raise TypeError("For multiple controlnets: `image` must be type `list`")
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# When `image` is a nested list:
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# (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
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elif any(isinstance(i, list) for i in image):
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raise ValueError("A single batch of multiple conditionings are supported at the moment.")
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elif len(image) != len(self.controlnet.nets):
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raise ValueError(
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"For multiple controlnets: `image` must have the same length as the number of controlnets."
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)
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for image_ in image:
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self.check_image(image_, prompt, prompt_embeds)
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else:
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assert False
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# Check `controlnet_conditioning_scale`
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if (
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isinstance(self.controlnet, ControlNetModel)
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or is_compiled
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and isinstance(self.controlnet._orig_mod, ControlNetModel)
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):
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if not isinstance(controlnet_conditioning_scale, float):
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raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
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elif (
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isinstance(self.controlnet, MultiControlNetModel)
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or is_compiled
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and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
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):
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if isinstance(controlnet_conditioning_scale, list):
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if any(isinstance(i, list) for i in controlnet_conditioning_scale):
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raise ValueError("A single batch of multiple conditionings are supported at the moment.")
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elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
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self.controlnet.nets
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):
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raise ValueError(
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"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
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" the same length as the number of controlnets"
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)
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else:
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assert False
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@torch.no_grad()
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def __call__(
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self,
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prompt: Union[str, list[str]] = None,
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image: Union[
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torch.FloatTensor,
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PIL.Image.Image,
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np.ndarray,
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list[torch.FloatTensor],
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list[PIL.Image.Image],
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list[np.ndarray],
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] = None,
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height: Optional[int] = None,
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width: Optional[int] = None,
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num_inference_steps: int = 50,
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guidance_scale: float = 7.5,
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negative_prompt: Optional[Union[str, list[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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eta: float = 0.0,
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generator: Optional[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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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
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callback_steps: int = 1,
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cross_attention_kwargs: Optional[dict[str, Any]] = None,
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controlnet_conditioning_scale: Union[float, list[float]] = 1.0,
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guess_mode: bool = False,
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):
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r"""
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Function invoked when calling the pipeline for generation.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
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instead.
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image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
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`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
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The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
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the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
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also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
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height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
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specified in init, images must be passed as a list such that each element of the list can be correctly
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batched for input to a single controlnet.
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height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
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The height in pixels of the generated image.
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width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
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The width in pixels of the generated image.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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guidance_scale (`float`, *optional*, defaults to 7.5):
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
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`guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale >
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1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
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usually at the expense of lower image quality.
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. 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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num_images_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
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eta (`float`, *optional*, defaults to 0.0):
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Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to
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[`schedulers.DDIMScheduler`], will be ignored for others.
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generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
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One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
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to make generation deterministic.
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latents (`torch.FloatTensor`, *optional*):
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor will ge generated by sampling using the supplied random `generator`.
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prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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negative_prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generate image. Choose between
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
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plain tuple.
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callback (`Callable`, *optional*):
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A function that will be called every `callback_steps` steps during inference. The function will be
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called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
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callback_steps (`int`, *optional*, defaults to 1):
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The frequency at which the `callback` function will be called. If not specified, the callback will be
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called at every step.
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cross_attention_kwargs (`dict`, *optional*):
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A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
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`self.processor` in
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[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
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controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
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The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
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to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
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corresponding scale as a list.
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guess_mode (`bool`, *optional*, defaults to `False`):
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In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
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you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
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Examples:
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Returns:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
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When returning a tuple, the first element is a list with the generated images, and the second element is a
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list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
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(nsfw) content, according to the `safety_checker`.
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"""
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(
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prompt,
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image,
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callback_steps,
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negative_prompt,
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prompt_embeds,
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negative_prompt_embeds,
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controlnet_conditioning_scale,
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)
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# 2. Define call parameters
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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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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = guidance_scale > 1.0
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controlnet = self.controlnet._orig_mod if hasattr(self.controlnet, "_orig_mod") else self.controlnet
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if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
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controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
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# 3. Encode input prompt
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text_encoder_lora_scale = (
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cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
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)
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prompt_embeds = self._encode_prompt(
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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,
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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lora_scale=text_encoder_lora_scale,
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)
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# 4. Prepare image
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if isinstance(controlnet, ControlNetModel):
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image = self.prepare_image(
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image=image,
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width=width,
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height=height,
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batch_size=batch_size * num_images_per_prompt,
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num_images_per_prompt=num_images_per_prompt,
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device=device,
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dtype=controlnet.dtype,
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do_classifier_free_guidance=do_classifier_free_guidance,
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guess_mode=guess_mode,
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)
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height, width = image.shape[-2:]
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elif isinstance(controlnet, MultiControlNetModel):
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images = []
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for image_ in image:
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image_ = self.prepare_image(
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image=image_,
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width=width,
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height=height,
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batch_size=batch_size * num_images_per_prompt,
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num_images_per_prompt=num_images_per_prompt,
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device=device,
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dtype=controlnet.dtype,
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do_classifier_free_guidance=do_classifier_free_guidance,
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guess_mode=guess_mode,
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)
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images.append(image_)
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image = images
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height, width = image[0].shape[-2:]
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else:
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assert False
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# 5. Prepare timesteps
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self.scheduler.set_timesteps(num_inference_steps, device=device)
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timesteps = self.scheduler.timesteps
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# 6. Prepare latent variables
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num_channels_latents = self.unet.config.in_channels
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latents = self.prepare_latents(
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batch_size * num_images_per_prompt,
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num_channels_latents,
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height,
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width,
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prompt_embeds.dtype,
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device,
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generator,
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latents,
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)
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# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
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extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
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# 8. Denoising loop
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num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
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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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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if 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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# controlnet(s) inference
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if guess_mode and do_classifier_free_guidance:
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# Infer ControlNet only for the conditional batch.
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control_model_input = latents
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control_model_input = self.scheduler.scale_model_input(control_model_input, t)
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else:
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control_model_input = latent_model_input
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# Get the guided hint for the UNet (320 dim)
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guided_hint = self.controlnet(
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controlnet_cond=image,
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)
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# Predict the noise residual
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noise_pred = self.unet(
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latent_model_input,
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t,
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guided_hint=guided_hint,
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encoder_hidden_states=prompt_embeds,
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)[0]
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# perform guidance
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if do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
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# call the callback, if provided
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if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
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progress_bar.update()
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if callback is not None and i % callback_steps == 0:
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callback(i, t, latents)
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# If we do sequential model offloading, let's offload unet and controlnet
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# manually for max memory savings
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if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
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self.unet.to("cpu")
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self.controlnet.to("cpu")
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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elif torch.xpu.is_available():
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torch.xpu.empty_cache()
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if not output_type == "latent":
|
|
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
|
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
|
|
else:
|
|
image = latents
|
|
has_nsfw_concept = None
|
|
|
|
if has_nsfw_concept is None:
|
|
do_denormalize = [True] * image.shape[0]
|
|
else:
|
|
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
|
|
|
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
|
|
|
|
# Offload last model to CPU
|
|
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
|
self.final_offload_hook.offload()
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return LightControlNetPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|