719 lines
38 KiB
Python
719 lines
38 KiB
Python
import inspect
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from itertools import repeat
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from typing import Callable, List, Optional, Union
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import torch
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from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
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from ...image_processor import VaeImageProcessor
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from ...models import AutoencoderKL, UNet2DConditionModel
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from ...pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
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from ...schedulers import KarrasDiffusionSchedulers
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from ...utils import deprecate, logging
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from ...utils.torch_utils import randn_tensor
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from ..pipeline_utils import DiffusionPipeline
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from .pipeline_output import SemanticStableDiffusionPipelineOutput
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class SemanticStableDiffusionPipeline(DiffusionPipeline):
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r"""
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Pipeline for text-to-image generation using Stable Diffusion with latent editing.
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This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionPipeline`]. Check the superclass
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documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular
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device, etc.).
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Args:
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
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text_encoder ([`~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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safety_checker ([`Q16SafetyChecker`]):
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Classification module that estimates whether generated images could be considered offensive or harmful.
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Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
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about a model's potential harms.
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feature_extractor ([`~transformers.CLIPImageProcessor`]):
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A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
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"""
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model_cpu_offload_seq = "text_encoder->unet->vae"
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_optional_components = ["safety_checker", "feature_extractor"]
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def __init__(
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self,
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vae: 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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safety_checker: StableDiffusionSafetyChecker,
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feature_extractor: CLIPImageProcessor,
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requires_safety_checker: bool = True,
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):
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super().__init__()
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if safety_checker is None and requires_safety_checker:
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logger.warning(
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f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
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" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
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" results in services or applications open to the public. Both the diffusers team and Hugging Face"
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" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
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" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
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" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
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)
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if safety_checker is not None and feature_extractor is None:
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raise ValueError(
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"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
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" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
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)
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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safety_checker=safety_checker,
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feature_extractor=feature_extractor,
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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self.register_to_config(requires_safety_checker=requires_safety_checker)
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
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def run_safety_checker(self, image, device, dtype):
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if self.safety_checker is None:
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has_nsfw_concept = None
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else:
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if torch.is_tensor(image):
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feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
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else:
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feature_extractor_input = self.image_processor.numpy_to_pil(image)
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safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
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image, has_nsfw_concept = self.safety_checker(
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images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
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)
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return image, has_nsfw_concept
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
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def decode_latents(self, latents):
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deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
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deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
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latents = 1 / self.vae.config.scaling_factor * latents
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image = self.vae.decode(latents, return_dict=False)[0]
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image = (image / 2 + 0.5).clamp(0, 1)
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# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
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image = image.cpu().permute(0, 2, 3, 1).float().numpy()
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return image
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
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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(inspect.signature(self.scheduler.step).parameters.keys())
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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(inspect.signature(self.scheduler.step).parameters.keys())
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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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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
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def check_inputs(
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self,
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prompt,
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height,
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width,
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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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callback_on_step_end_tensor_inputs=None,
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):
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
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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 callback_on_step_end_tensor_inputs is not None and not all(
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k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
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):
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raise ValueError(
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f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
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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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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
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def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
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shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
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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(shape, generator=generator, device=device, dtype=dtype)
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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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@torch.no_grad()
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def __call__(
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self,
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prompt: Union[str, List[str]],
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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: 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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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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editing_prompt: Optional[Union[str, List[str]]] = None,
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editing_prompt_embeddings: Optional[torch.Tensor] = None,
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reverse_editing_direction: Optional[Union[bool, List[bool]]] = False,
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edit_guidance_scale: Optional[Union[float, List[float]]] = 5,
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edit_warmup_steps: Optional[Union[int, List[int]]] = 10,
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edit_cooldown_steps: Optional[Union[int, List[int]]] = None,
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edit_threshold: Optional[Union[float, List[float]]] = 0.9,
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edit_momentum_scale: Optional[float] = 0.1,
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edit_mom_beta: Optional[float] = 0.4,
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edit_weights: Optional[List[float]] = None,
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sem_guidance: Optional[List[torch.Tensor]] = None,
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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]`):
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The prompt or prompts to guide image generation.
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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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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://arxiv.org/abs/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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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 is generated by sampling using the supplied random `generator`.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the 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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editing_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to use for semantic guidance. Semantic guidance is disabled by setting
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`editing_prompt = None`. Guidance direction of prompt should be specified via
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`reverse_editing_direction`.
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editing_prompt_embeddings (`torch.Tensor`, *optional*):
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Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be
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specified via `reverse_editing_direction`.
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reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`):
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Whether the corresponding prompt in `editing_prompt` should be increased or decreased.
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edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5):
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Guidance scale for semantic guidance. If provided as a list, values should correspond to
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`editing_prompt`.
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edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10):
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Number of diffusion steps (for each prompt) for which semantic guidance is not applied. Momentum is
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calculated for those steps and applied once all warmup periods are over.
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edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`):
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Number of diffusion steps (for each prompt) after which semantic guidance is longer applied.
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edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9):
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Threshold of semantic guidance.
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edit_momentum_scale (`float`, *optional*, defaults to 0.1):
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Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0,
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momentum is disabled. Momentum is already built up during warmup (for diffusion steps smaller than
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`sld_warmup_steps`). Momentum is only added to latent guidance once all warmup periods are finished.
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edit_mom_beta (`float`, *optional*, defaults to 0.4):
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Defines how semantic guidance momentum builds up. `edit_mom_beta` indicates how much of the previous
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momentum is kept. Momentum is already built up during warmup (for diffusion steps smaller than
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`edit_warmup_steps`).
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edit_weights (`List[float]`, *optional*, defaults to `None`):
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Indicates how much each individual concept should influence the overall guidance. If no weights are
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provided all concepts are applied equally.
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sem_guidance (`List[torch.Tensor]`, *optional*):
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List of pre-generated guidance vectors to be applied at generation. Length of the list has to
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correspond to `num_inference_steps`.
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Examples:
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```py
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>>> import torch
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>>> from diffusers import SemanticStableDiffusionPipeline
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>>> pipe = SemanticStableDiffusionPipeline.from_pretrained(
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... "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
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... )
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>>> pipe = pipe.to("cuda")
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>>> out = pipe(
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... prompt="a photo of the face of a woman",
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... num_images_per_prompt=1,
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... guidance_scale=7,
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... editing_prompt=[
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... "smiling, smile", # Concepts to apply
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... "glasses, wearing glasses",
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... "curls, wavy hair, curly hair",
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... "beard, full beard, mustache",
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... ],
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... reverse_editing_direction=[
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... False,
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... False,
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... False,
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... False,
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... ], # Direction of guidance i.e. increase all concepts
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... edit_warmup_steps=[10, 10, 10, 10], # Warmup period for each concept
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... edit_guidance_scale=[4, 5, 5, 5.4], # Guidance scale for each concept
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... edit_threshold=[
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... 0.99,
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... 0.975,
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... 0.925,
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... 0.96,
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... ], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions
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... edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance
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... edit_mom_beta=0.6, # Momentum beta
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... edit_weights=[1, 1, 1, 1, 1], # Weights of the individual concepts against each other
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... )
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>>> image = out.images[0]
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```
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Returns:
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[`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] or `tuple`:
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If `return_dict` is `True`,
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[`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] is returned, otherwise a
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`tuple` is returned where the first element is a list with the generated images and the second element
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is a list of `bool`s indicating whether the corresponding generated image contains "not-safe-for-work"
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(nsfw) content.
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"""
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# 0. Default height and width to unet
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height = height or self.unet.config.sample_size * self.vae_scale_factor
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width = width or self.unet.config.sample_size * self.vae_scale_factor
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(prompt, height, width, callback_steps)
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# 2. Define call parameters
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batch_size = 1 if isinstance(prompt, str) else len(prompt)
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if editing_prompt:
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enable_edit_guidance = True
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if isinstance(editing_prompt, str):
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editing_prompt = [editing_prompt]
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enabled_editing_prompts = len(editing_prompt)
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elif editing_prompt_embeddings is not None:
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enable_edit_guidance = True
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enabled_editing_prompts = editing_prompt_embeddings.shape[0]
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else:
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enabled_editing_prompts = 0
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enable_edit_guidance = False
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# get prompt text embeddings
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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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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
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removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
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logger.warning(
|
|
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
|
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
|
)
|
|
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
|
|
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
|
|
|
|
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
|
bs_embed, seq_len, _ = text_embeddings.shape
|
|
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
|
|
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
|
|
|
if enable_edit_guidance:
|
|
# get safety text embeddings
|
|
if editing_prompt_embeddings is None:
|
|
edit_concepts_input = self.tokenizer(
|
|
[x for item in editing_prompt for x in repeat(item, batch_size)],
|
|
padding="max_length",
|
|
max_length=self.tokenizer.model_max_length,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
edit_concepts_input_ids = edit_concepts_input.input_ids
|
|
|
|
if edit_concepts_input_ids.shape[-1] > self.tokenizer.model_max_length:
|
|
removed_text = self.tokenizer.batch_decode(
|
|
edit_concepts_input_ids[:, self.tokenizer.model_max_length :]
|
|
)
|
|
logger.warning(
|
|
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
|
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
|
)
|
|
edit_concepts_input_ids = edit_concepts_input_ids[:, : self.tokenizer.model_max_length]
|
|
edit_concepts = self.text_encoder(edit_concepts_input_ids.to(self.device))[0]
|
|
else:
|
|
edit_concepts = editing_prompt_embeddings.to(self.device).repeat(batch_size, 1, 1)
|
|
|
|
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
|
bs_embed_edit, seq_len_edit, _ = edit_concepts.shape
|
|
edit_concepts = edit_concepts.repeat(1, num_images_per_prompt, 1)
|
|
edit_concepts = edit_concepts.view(bs_embed_edit * num_images_per_prompt, seq_len_edit, -1)
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
# get unconditional embeddings for classifier free guidance
|
|
|
|
if do_classifier_free_guidance:
|
|
uncond_tokens: List[str]
|
|
if negative_prompt is None:
|
|
uncond_tokens = [""] * batch_size
|
|
elif type(prompt) is not type(negative_prompt):
|
|
raise TypeError(
|
|
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
|
f" {type(prompt)}."
|
|
)
|
|
elif isinstance(negative_prompt, str):
|
|
uncond_tokens = [negative_prompt]
|
|
elif batch_size != len(negative_prompt):
|
|
raise ValueError(
|
|
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
|
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
|
" the batch size of `prompt`."
|
|
)
|
|
else:
|
|
uncond_tokens = negative_prompt
|
|
|
|
max_length = text_input_ids.shape[-1]
|
|
uncond_input = self.tokenizer(
|
|
uncond_tokens,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
|
|
|
|
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
|
seq_len = uncond_embeddings.shape[1]
|
|
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
|
|
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
|
|
|
|
# For classifier free guidance, we need to do two forward passes.
|
|
# Here we concatenate the unconditional and text embeddings into a single batch
|
|
# to avoid doing two forward passes
|
|
if enable_edit_guidance:
|
|
text_embeddings = torch.cat([uncond_embeddings, text_embeddings, edit_concepts])
|
|
else:
|
|
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
|
|
# get the initial random noise unless the user supplied it
|
|
|
|
# 4. Prepare timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=self.device)
|
|
timesteps = self.scheduler.timesteps
|
|
|
|
# 5. Prepare latent variables
|
|
num_channels_latents = self.unet.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
text_embeddings.dtype,
|
|
self.device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# 6. Prepare extra step kwargs.
|
|
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
|
|
|
# Initialize edit_momentum to None
|
|
edit_momentum = None
|
|
|
|
self.uncond_estimates = None
|
|
self.text_estimates = None
|
|
self.edit_estimates = None
|
|
self.sem_guidance = None
|
|
|
|
for i, t in enumerate(self.progress_bar(timesteps)):
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = (
|
|
torch.cat([latents] * (2 + enabled_editing_prompts)) if do_classifier_free_guidance else latents
|
|
)
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
|
|
|
|
# perform guidance
|
|
if do_classifier_free_guidance:
|
|
noise_pred_out = noise_pred.chunk(2 + enabled_editing_prompts) # [b,4, 64, 64]
|
|
noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
|
|
noise_pred_edit_concepts = noise_pred_out[2:]
|
|
|
|
# default text guidance
|
|
noise_guidance = guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
# noise_guidance = (noise_pred_text - noise_pred_edit_concepts[0])
|
|
|
|
if self.uncond_estimates is None:
|
|
self.uncond_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_uncond.shape))
|
|
self.uncond_estimates[i] = noise_pred_uncond.detach().cpu()
|
|
|
|
if self.text_estimates is None:
|
|
self.text_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))
|
|
self.text_estimates[i] = noise_pred_text.detach().cpu()
|
|
|
|
if self.edit_estimates is None and enable_edit_guidance:
|
|
self.edit_estimates = torch.zeros(
|
|
(num_inference_steps + 1, len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape)
|
|
)
|
|
|
|
if self.sem_guidance is None:
|
|
self.sem_guidance = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))
|
|
|
|
if edit_momentum is None:
|
|
edit_momentum = torch.zeros_like(noise_guidance)
|
|
|
|
if enable_edit_guidance:
|
|
concept_weights = torch.zeros(
|
|
(len(noise_pred_edit_concepts), noise_guidance.shape[0]),
|
|
device=self.device,
|
|
dtype=noise_guidance.dtype,
|
|
)
|
|
noise_guidance_edit = torch.zeros(
|
|
(len(noise_pred_edit_concepts), *noise_guidance.shape),
|
|
device=self.device,
|
|
dtype=noise_guidance.dtype,
|
|
)
|
|
# noise_guidance_edit = torch.zeros_like(noise_guidance)
|
|
warmup_inds = []
|
|
for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):
|
|
self.edit_estimates[i, c] = noise_pred_edit_concept
|
|
if isinstance(edit_guidance_scale, list):
|
|
edit_guidance_scale_c = edit_guidance_scale[c]
|
|
else:
|
|
edit_guidance_scale_c = edit_guidance_scale
|
|
|
|
if isinstance(edit_threshold, list):
|
|
edit_threshold_c = edit_threshold[c]
|
|
else:
|
|
edit_threshold_c = edit_threshold
|
|
if isinstance(reverse_editing_direction, list):
|
|
reverse_editing_direction_c = reverse_editing_direction[c]
|
|
else:
|
|
reverse_editing_direction_c = reverse_editing_direction
|
|
if edit_weights:
|
|
edit_weight_c = edit_weights[c]
|
|
else:
|
|
edit_weight_c = 1.0
|
|
if isinstance(edit_warmup_steps, list):
|
|
edit_warmup_steps_c = edit_warmup_steps[c]
|
|
else:
|
|
edit_warmup_steps_c = edit_warmup_steps
|
|
|
|
if isinstance(edit_cooldown_steps, list):
|
|
edit_cooldown_steps_c = edit_cooldown_steps[c]
|
|
elif edit_cooldown_steps is None:
|
|
edit_cooldown_steps_c = i + 1
|
|
else:
|
|
edit_cooldown_steps_c = edit_cooldown_steps
|
|
if i >= edit_warmup_steps_c:
|
|
warmup_inds.append(c)
|
|
if i >= edit_cooldown_steps_c:
|
|
noise_guidance_edit[c, :, :, :, :] = torch.zeros_like(noise_pred_edit_concept)
|
|
continue
|
|
|
|
noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond
|
|
# tmp_weights = (noise_pred_text - noise_pred_edit_concept).sum(dim=(1, 2, 3))
|
|
tmp_weights = (noise_guidance - noise_pred_edit_concept).sum(dim=(1, 2, 3))
|
|
|
|
tmp_weights = torch.full_like(tmp_weights, edit_weight_c) # * (1 / enabled_editing_prompts)
|
|
if reverse_editing_direction_c:
|
|
noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1
|
|
concept_weights[c, :] = tmp_weights
|
|
|
|
noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c
|
|
|
|
# torch.quantile function expects float32
|
|
if noise_guidance_edit_tmp.dtype == torch.float32:
|
|
tmp = torch.quantile(
|
|
torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2),
|
|
edit_threshold_c,
|
|
dim=2,
|
|
keepdim=False,
|
|
)
|
|
else:
|
|
tmp = torch.quantile(
|
|
torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2).to(torch.float32),
|
|
edit_threshold_c,
|
|
dim=2,
|
|
keepdim=False,
|
|
).to(noise_guidance_edit_tmp.dtype)
|
|
|
|
noise_guidance_edit_tmp = torch.where(
|
|
torch.abs(noise_guidance_edit_tmp) >= tmp[:, :, None, None],
|
|
noise_guidance_edit_tmp,
|
|
torch.zeros_like(noise_guidance_edit_tmp),
|
|
)
|
|
noise_guidance_edit[c, :, :, :, :] = noise_guidance_edit_tmp
|
|
|
|
# noise_guidance_edit = noise_guidance_edit + noise_guidance_edit_tmp
|
|
|
|
warmup_inds = torch.tensor(warmup_inds).to(self.device)
|
|
if len(noise_pred_edit_concepts) > warmup_inds.shape[0] > 0:
|
|
concept_weights = concept_weights.to("cpu") # Offload to cpu
|
|
noise_guidance_edit = noise_guidance_edit.to("cpu")
|
|
|
|
concept_weights_tmp = torch.index_select(concept_weights.to(self.device), 0, warmup_inds)
|
|
concept_weights_tmp = torch.where(
|
|
concept_weights_tmp < 0, torch.zeros_like(concept_weights_tmp), concept_weights_tmp
|
|
)
|
|
concept_weights_tmp = concept_weights_tmp / concept_weights_tmp.sum(dim=0)
|
|
# concept_weights_tmp = torch.nan_to_num(concept_weights_tmp)
|
|
|
|
noise_guidance_edit_tmp = torch.index_select(
|
|
noise_guidance_edit.to(self.device), 0, warmup_inds
|
|
)
|
|
noise_guidance_edit_tmp = torch.einsum(
|
|
"cb,cbijk->bijk", concept_weights_tmp, noise_guidance_edit_tmp
|
|
)
|
|
noise_guidance_edit_tmp = noise_guidance_edit_tmp
|
|
noise_guidance = noise_guidance + noise_guidance_edit_tmp
|
|
|
|
self.sem_guidance[i] = noise_guidance_edit_tmp.detach().cpu()
|
|
|
|
del noise_guidance_edit_tmp
|
|
del concept_weights_tmp
|
|
concept_weights = concept_weights.to(self.device)
|
|
noise_guidance_edit = noise_guidance_edit.to(self.device)
|
|
|
|
concept_weights = torch.where(
|
|
concept_weights < 0, torch.zeros_like(concept_weights), concept_weights
|
|
)
|
|
|
|
concept_weights = torch.nan_to_num(concept_weights)
|
|
|
|
noise_guidance_edit = torch.einsum("cb,cbijk->bijk", concept_weights, noise_guidance_edit)
|
|
|
|
noise_guidance_edit = noise_guidance_edit + edit_momentum_scale * edit_momentum
|
|
|
|
edit_momentum = edit_mom_beta * edit_momentum + (1 - edit_mom_beta) * noise_guidance_edit
|
|
|
|
if warmup_inds.shape[0] == len(noise_pred_edit_concepts):
|
|
noise_guidance = noise_guidance + noise_guidance_edit
|
|
self.sem_guidance[i] = noise_guidance_edit.detach().cpu()
|
|
|
|
if sem_guidance is not None:
|
|
edit_guidance = sem_guidance[i].to(self.device)
|
|
noise_guidance = noise_guidance + edit_guidance
|
|
|
|
noise_pred = noise_pred_uncond + noise_guidance
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
|
|
|
# call the callback, if provided
|
|
if callback is not None and i % callback_steps == 0:
|
|
step_idx = i // getattr(self.scheduler, "order", 1)
|
|
callback(step_idx, t, latents)
|
|
|
|
# 8. Post-processing
|
|
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, self.device, text_embeddings.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)
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return SemanticStableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|