854 lines
42 KiB
Python
854 lines
42 KiB
Python
# Copyright 2023 The InstructPix2Pix Authors and 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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import inspect
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from typing import Callable, Dict, List, 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 transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
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from ...image_processor import PipelineImageInput, VaeImageProcessor
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from ...loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
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from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
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from ...schedulers import KarrasDiffusionSchedulers
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from ...utils import PIL_INTERPOLATION, 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 . import StableDiffusionPipelineOutput
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from .safety_checker import StableDiffusionSafetyChecker
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
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def preprocess(image):
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deprecation_message = "The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead"
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deprecate("preprocess", "1.0.0", deprecation_message, standard_warn=False)
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if isinstance(image, torch.Tensor):
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return image
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elif isinstance(image, PIL.Image.Image):
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image = [image]
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if isinstance(image[0], PIL.Image.Image):
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w, h = image[0].size
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w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
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image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
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image = np.concatenate(image, axis=0)
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image = np.array(image).astype(np.float32) / 255.0
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image = image.transpose(0, 3, 1, 2)
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image = 2.0 * image - 1.0
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image = torch.from_numpy(image)
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elif isinstance(image[0], torch.Tensor):
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image = torch.cat(image, dim=0)
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return image
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
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def retrieve_latents(
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encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
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):
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if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
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return encoder_output.latent_dist.sample(generator)
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elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
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return encoder_output.latent_dist.mode()
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elif hasattr(encoder_output, "latents"):
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return encoder_output.latents
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else:
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raise AttributeError("Could not access latents of provided encoder_output")
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class StableDiffusionInstructPix2PixPipeline(
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DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin
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):
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r"""
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Pipeline for pixel-level image editing by following text instructions (based on Stable Diffusion).
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
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implemented for all pipelines (downloading, saving, running on a particular device, etc.).
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The pipeline also inherits the following loading methods:
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- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
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- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
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- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
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- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
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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 ([`StableDiffusionSafetyChecker`]):
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Classification module that estimates whether generated images could be considered offensive or harmful.
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Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for 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", "image_encoder"]
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_exclude_from_cpu_offload = ["safety_checker"]
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_callback_tensor_inputs = ["latents", "prompt_embeds", "image_latents"]
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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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image_encoder: Optional[CLIPVisionModelWithProjection] = None,
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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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image_encoder=image_encoder,
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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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@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: PipelineImageInput = None,
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num_inference_steps: int = 100,
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guidance_scale: float = 7.5,
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image_guidance_scale: float = 1.5,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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eta: float = 0.0,
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generator: Optional[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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ip_adapter_image: Optional[PipelineImageInput] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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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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**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 (`torch.FloatTensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
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`Image` or tensor representing an image batch to be repainted according to `prompt`. Can also accept
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image latents as `image`, but if passing latents directly it is not encoded again.
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num_inference_steps (`int`, *optional*, defaults to 100):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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guidance_scale (`float`, *optional*, defaults to 7.5):
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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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image_guidance_scale (`float`, *optional*, defaults to 1.5):
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Push the generated image towards the inital `image`. Image guidance scale is enabled by setting
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`image_guidance_scale > 1`. Higher image guidance scale encourages generated images that are closely
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linked to the source `image`, usually at the expense of lower image quality. This pipeline requires a
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value of at least `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`, *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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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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ip_adapter_image: (`PipelineImageInput`, *optional*):
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Optional image input to work with IP Adapters.
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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_on_step_end (`Callable`, *optional*):
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A function that calls at the end of each denoising steps during the inference. The function is called
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with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
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callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
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`callback_on_step_end_tensor_inputs`.
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callback_on_step_end_tensor_inputs (`List`, *optional*):
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The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
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will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
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`._callback_tensor_inputs` attribute of your pipeline class.
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Examples:
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```py
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>>> import PIL
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>>> import requests
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>>> import torch
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>>> from io import BytesIO
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>>> from diffusers import StableDiffusionInstructPix2PixPipeline
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>>> def download_image(url):
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... response = requests.get(url)
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... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
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>>> img_url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
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>>> image = download_image(img_url).resize((512, 512))
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>>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
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... "timbrooks/instruct-pix2pix", torch_dtype=torch.float16
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... )
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>>> pipe = pipe.to("cuda")
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>>> prompt = "make the mountains snowy"
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>>> image = pipe(prompt=prompt, image=image).images[0]
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```
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Returns:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
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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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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
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self.check_inputs(
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prompt,
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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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callback_on_step_end_tensor_inputs,
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)
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self._guidance_scale = guidance_scale
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self._image_guidance_scale = image_guidance_scale
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device = self._execution_device
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if ip_adapter_image is not None:
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output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
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image_embeds, negative_image_embeds = self.encode_image(
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ip_adapter_image, device, num_images_per_prompt, output_hidden_state
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)
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if self.do_classifier_free_guidance:
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image_embeds = torch.cat([image_embeds, negative_image_embeds, negative_image_embeds])
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if image is None:
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raise ValueError("`image` input cannot be undefined.")
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# 1. Define call parameters
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if 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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# check if scheduler is in sigmas space
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scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas")
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# 2. Encode input prompt
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prompt_embeds = self._encode_prompt(
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prompt,
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device,
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num_images_per_prompt,
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self.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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)
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# 3. Preprocess image
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image = self.image_processor.preprocess(image)
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# 4. set 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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# 5. Prepare Image latents
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image_latents = self.prepare_image_latents(
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image,
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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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self.do_classifier_free_guidance,
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)
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height, width = image_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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# 6. Prepare latent variables
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num_channels_latents = self.vae.config.latent_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. Check that shapes of latents and image match the UNet channels
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num_channels_image = image_latents.shape[1]
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if num_channels_latents + num_channels_image != self.unet.config.in_channels:
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raise ValueError(
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f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
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f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
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f" `num_channels_image`: {num_channels_image} "
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f" = {num_channels_latents+num_channels_image}. Please verify the config of"
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" `pipeline.unet` or your `image` input."
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)
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# 8. 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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# 8.1 Add image embeds for IP-Adapter
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added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
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# 9. Denoising loop
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num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
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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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# Expand the latents if we are doing classifier free guidance.
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# The latents are expanded 3 times because for pix2pix the guidance\
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# is applied for both the text and the input image.
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latent_model_input = torch.cat([latents] * 3) if self.do_classifier_free_guidance else latents
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# concat latents, image_latents in the channel dimension
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scaled_latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
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scaled_latent_model_input = torch.cat([scaled_latent_model_input, image_latents], dim=1)
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# predict the noise residual
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noise_pred = self.unet(
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scaled_latent_model_input,
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t,
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encoder_hidden_states=prompt_embeds,
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added_cond_kwargs=added_cond_kwargs,
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return_dict=False,
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)[0]
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# Hack:
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# For karras style schedulers the model does classifer free guidance using the
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# predicted_original_sample instead of the noise_pred. So we need to compute the
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# predicted_original_sample here if we are using a karras style scheduler.
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if scheduler_is_in_sigma_space:
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step_index = (self.scheduler.timesteps == t).nonzero()[0].item()
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sigma = self.scheduler.sigmas[step_index]
|
|
noise_pred = latent_model_input - sigma * noise_pred
|
|
|
|
# perform guidance
|
|
if self.do_classifier_free_guidance:
|
|
noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3)
|
|
noise_pred = (
|
|
noise_pred_uncond
|
|
+ self.guidance_scale * (noise_pred_text - noise_pred_image)
|
|
+ self.image_guidance_scale * (noise_pred_image - noise_pred_uncond)
|
|
)
|
|
|
|
# Hack:
|
|
# For karras style schedulers the model does classifer free guidance using the
|
|
# predicted_original_sample instead of the noise_pred. But the scheduler.step function
|
|
# expects the noise_pred and computes the predicted_original_sample internally. So we
|
|
# need to overwrite the noise_pred here such that the value of the computed
|
|
# predicted_original_sample is correct.
|
|
if scheduler_is_in_sigma_space:
|
|
noise_pred = (noise_pred - latents) / (-sigma)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
|
|
|
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)
|
|
image_latents = callback_outputs.pop("image_latents", image_latents)
|
|
|
|
# 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 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 all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
|
|
|
def _encode_prompt(
|
|
self,
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt=None,
|
|
prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
):
|
|
r"""
|
|
Encodes the prompt into text encoder hidden states.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
prompt to be encoded
|
|
device: (`torch.device`):
|
|
torch device
|
|
num_images_per_prompt (`int`):
|
|
number of images that should be generated per prompt
|
|
do_classifier_free_guidance (`bool`):
|
|
whether to use classifier free guidance or not
|
|
negative_ prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
|
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
|
less than `1`).
|
|
prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
|
provided, text embeddings will be generated from `prompt` input argument.
|
|
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
|
argument.
|
|
"""
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
if prompt_embeds is None:
|
|
# textual inversion: procecss multi-vector tokens if necessary
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
|
|
|
|
text_inputs = self.tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=self.tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids
|
|
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
|
|
|
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
|
|
text_input_ids, untruncated_ids
|
|
):
|
|
removed_text = self.tokenizer.batch_decode(
|
|
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
|
|
)
|
|
logger.warning(
|
|
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
|
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
|
)
|
|
|
|
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
|
attention_mask = text_inputs.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
prompt_embeds = self.text_encoder(
|
|
text_input_ids.to(device),
|
|
attention_mask=attention_mask,
|
|
)
|
|
prompt_embeds = prompt_embeds[0]
|
|
|
|
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
|
|
|
|
bs_embed, seq_len, _ = prompt_embeds.shape
|
|
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
|
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
|
|
|
# get unconditional embeddings for classifier free guidance
|
|
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
|
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
|
|
|
|
# textual inversion: procecss multi-vector tokens if necessary
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
|
|
|
|
max_length = prompt_embeds.shape[1]
|
|
uncond_input = self.tokenizer(
|
|
uncond_tokens,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
|
attention_mask = uncond_input.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
negative_prompt_embeds = self.text_encoder(
|
|
uncond_input.input_ids.to(device),
|
|
attention_mask=attention_mask,
|
|
)
|
|
negative_prompt_embeds = negative_prompt_embeds[0]
|
|
|
|
if do_classifier_free_guidance:
|
|
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
|
seq_len = negative_prompt_embeds.shape[1]
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
negative_prompt_embeds = negative_prompt_embeds.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
|
|
# pix2pix has two negative embeddings, and unlike in other pipelines latents are ordered [prompt_embeds, negative_prompt_embeds, negative_prompt_embeds]
|
|
prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds, negative_prompt_embeds])
|
|
|
|
return prompt_embeds
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
|
|
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
|
|
dtype = next(self.image_encoder.parameters()).dtype
|
|
|
|
if not isinstance(image, torch.Tensor):
|
|
image = self.feature_extractor(image, return_tensors="pt").pixel_values
|
|
|
|
image = image.to(device=device, dtype=dtype)
|
|
if output_hidden_states:
|
|
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
|
|
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
|
|
uncond_image_enc_hidden_states = self.image_encoder(
|
|
torch.zeros_like(image), output_hidden_states=True
|
|
).hidden_states[-2]
|
|
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
|
|
num_images_per_prompt, dim=0
|
|
)
|
|
return image_enc_hidden_states, uncond_image_enc_hidden_states
|
|
else:
|
|
image_embeds = self.image_encoder(image).image_embeds
|
|
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
|
|
uncond_image_embeds = torch.zeros_like(image_embeds)
|
|
|
|
return image_embeds, uncond_image_embeds
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
|
|
def run_safety_checker(self, image, device, dtype):
|
|
if self.safety_checker is None:
|
|
has_nsfw_concept = None
|
|
else:
|
|
if torch.is_tensor(image):
|
|
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
|
|
else:
|
|
feature_extractor_input = self.image_processor.numpy_to_pil(image)
|
|
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
|
|
image, has_nsfw_concept = self.safety_checker(
|
|
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
|
|
)
|
|
return image, has_nsfw_concept
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
|
def prepare_extra_step_kwargs(self, generator, eta):
|
|
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
|
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
|
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
|
# and should be between [0, 1]
|
|
|
|
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
extra_step_kwargs = {}
|
|
if accepts_eta:
|
|
extra_step_kwargs["eta"] = eta
|
|
|
|
# check if the scheduler accepts generator
|
|
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
if accepts_generator:
|
|
extra_step_kwargs["generator"] = generator
|
|
return extra_step_kwargs
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
|
|
def decode_latents(self, latents):
|
|
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
|
|
deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
|
|
|
|
latents = 1 / self.vae.config.scaling_factor * latents
|
|
image = self.vae.decode(latents, return_dict=False)[0]
|
|
image = (image / 2 + 0.5).clamp(0, 1)
|
|
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
|
|
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
|
|
return image
|
|
|
|
def check_inputs(
|
|
self,
|
|
prompt,
|
|
callback_steps,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
callback_on_step_end_tensor_inputs=None,
|
|
):
|
|
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
|
|
raise ValueError(
|
|
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
|
f" {type(callback_steps)}."
|
|
)
|
|
|
|
if callback_on_step_end_tensor_inputs is not None and not all(
|
|
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
|
|
):
|
|
raise ValueError(
|
|
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]}"
|
|
)
|
|
|
|
if prompt is not None and prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
|
" only forward one of the two."
|
|
)
|
|
elif prompt is None and prompt_embeds is None:
|
|
raise ValueError(
|
|
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
|
)
|
|
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
|
|
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
|
|
|
if negative_prompt is not None and negative_prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
|
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
|
)
|
|
|
|
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
|
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
|
raise ValueError(
|
|
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
|
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
|
f" {negative_prompt_embeds.shape}."
|
|
)
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
|
|
def prepare_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 prepare_image_latents(
|
|
self, image, batch_size, num_images_per_prompt, dtype, device, do_classifier_free_guidance, generator=None
|
|
):
|
|
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
|
|
raise ValueError(
|
|
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
|
|
)
|
|
|
|
image = image.to(device=device, dtype=dtype)
|
|
|
|
batch_size = batch_size * num_images_per_prompt
|
|
|
|
if image.shape[1] == 4:
|
|
image_latents = image
|
|
else:
|
|
image_latents = retrieve_latents(self.vae.encode(image), sample_mode="argmax")
|
|
|
|
if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
|
|
# expand image_latents for batch_size
|
|
deprecation_message = (
|
|
f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial"
|
|
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
|
|
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
|
|
" your script to pass as many initial images as text prompts to suppress this warning."
|
|
)
|
|
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
|
|
additional_image_per_prompt = batch_size // image_latents.shape[0]
|
|
image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
|
|
elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
|
|
raise ValueError(
|
|
f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
|
|
)
|
|
else:
|
|
image_latents = torch.cat([image_latents], dim=0)
|
|
|
|
if do_classifier_free_guidance:
|
|
uncond_image_latents = torch.zeros_like(image_latents)
|
|
image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0)
|
|
|
|
return image_latents
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu
|
|
def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
|
|
r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
|
|
|
|
The suffixes after the scaling factors represent the stages where they are being applied.
|
|
|
|
Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
|
|
that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
|
|
|
|
Args:
|
|
s1 (`float`):
|
|
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
|
|
mitigate "oversmoothing effect" in the enhanced denoising process.
|
|
s2 (`float`):
|
|
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
|
|
mitigate "oversmoothing effect" in the enhanced denoising process.
|
|
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
|
|
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
|
|
"""
|
|
if not hasattr(self, "unet"):
|
|
raise ValueError("The pipeline must have `unet` for using FreeU.")
|
|
self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)
|
|
|
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu
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def disable_freeu(self):
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"""Disables the FreeU mechanism if enabled."""
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self.unet.disable_freeu()
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@property
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def guidance_scale(self):
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return self._guidance_scale
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@property
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def image_guidance_scale(self):
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return self._image_guidance_scale
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@property
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def num_timesteps(self):
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return self._num_timesteps
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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@property
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def do_classifier_free_guidance(self):
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return self.guidance_scale > 1.0 and self.image_guidance_scale >= 1.0
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