1438 lines
67 KiB
Python
1438 lines
67 KiB
Python
import inspect
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from dataclasses import dataclass
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from typing import Callable, List, Optional, Union
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import numpy as np
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import PIL.Image
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import torch
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from transformers import (
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CLIPImageProcessor,
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CLIPTextModel,
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CLIPTokenizer,
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CLIPVisionModelWithProjection,
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GPT2Tokenizer,
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)
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from ...image_processor import VaeImageProcessor
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from ...loaders import StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
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from ...models import AutoencoderKL
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from ...models.lora import adjust_lora_scale_text_encoder
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from ...schedulers import KarrasDiffusionSchedulers
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from ...utils import (
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USE_PEFT_BACKEND,
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deprecate,
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is_torch_xla_available,
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logging,
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scale_lora_layers,
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unscale_lora_layers,
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)
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from ...utils.outputs import BaseOutput
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from ...utils.torch_utils import randn_tensor
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from ..pipeline_utils import DeprecatedPipelineMixin, DiffusionPipeline
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from .modeling_text_decoder import UniDiffuserTextDecoder
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from .modeling_uvit import UniDiffuserModel
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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm
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XLA_AVAILABLE = True
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else:
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XLA_AVAILABLE = False
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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# New BaseOutput child class for joint image-text output
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@dataclass
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class ImageTextPipelineOutput(BaseOutput):
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"""
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Output class for joint image-text pipelines.
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Args:
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images (`List[PIL.Image.Image]` or `np.ndarray`)
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List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
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num_channels)`.
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text (`List[str]` or `List[List[str]]`)
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List of generated text strings of length `batch_size` or a list of list of strings whose outer list has
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length `batch_size`.
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"""
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images: Optional[Union[List[PIL.Image.Image], np.ndarray]]
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text: Optional[Union[List[str], List[List[str]]]]
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class UniDiffuserPipeline(DeprecatedPipelineMixin, DiffusionPipeline):
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r"""
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Pipeline for a bimodal image-text model which supports unconditional text and image generation, text-conditioned
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image generation, image-conditioned text generation, and joint image-text generation.
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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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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. This
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is part of the UniDiffuser image representation along with the CLIP vision encoding.
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text_encoder ([`CLIPTextModel`]):
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Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
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image_encoder ([`CLIPVisionModel`]):
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A [`~transformers.CLIPVisionModel`] to encode images as part of its image representation along with the VAE
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latent representation.
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image_processor ([`CLIPImageProcessor`]):
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[`~transformers.CLIPImageProcessor`] to preprocess an image before CLIP encoding it with `image_encoder`.
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clip_tokenizer ([`CLIPTokenizer`]):
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A [`~transformers.CLIPTokenizer`] to tokenize the prompt before encoding it with `text_encoder`.
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text_decoder ([`UniDiffuserTextDecoder`]):
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Frozen text decoder. This is a GPT-style model which is used to generate text from the UniDiffuser
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embedding.
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text_tokenizer ([`GPT2Tokenizer`]):
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A [`~transformers.GPT2Tokenizer`] to decode text for text generation; used along with the `text_decoder`.
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unet ([`UniDiffuserModel`]):
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A [U-ViT](https://github.com/baofff/U-ViT) model with UNNet-style skip connections between transformer
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layers 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 and/or text latents. The
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original UniDiffuser paper uses the [`DPMSolverMultistepScheduler`] scheduler.
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"""
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_last_supported_version = "0.33.1"
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# TODO: support for moving submodules for components with enable_model_cpu_offload
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model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae->text_decoder"
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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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image_encoder: CLIPVisionModelWithProjection,
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clip_image_processor: CLIPImageProcessor,
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clip_tokenizer: CLIPTokenizer,
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text_decoder: UniDiffuserTextDecoder,
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text_tokenizer: GPT2Tokenizer,
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unet: UniDiffuserModel,
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scheduler: KarrasDiffusionSchedulers,
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):
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super().__init__()
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if text_encoder.config.hidden_size != text_decoder.prefix_inner_dim:
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raise ValueError(
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f"The text encoder hidden size and text decoder prefix inner dim must be the same, but"
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f" `text_encoder.config.hidden_size`: {text_encoder.config.hidden_size} and `text_decoder.prefix_inner_dim`: {text_decoder.prefix_inner_dim}"
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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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image_encoder=image_encoder,
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clip_image_processor=clip_image_processor,
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clip_tokenizer=clip_tokenizer,
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text_decoder=text_decoder,
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text_tokenizer=text_tokenizer,
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unet=unet,
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scheduler=scheduler,
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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self.num_channels_latents = vae.config.latent_channels
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self.text_encoder_seq_len = text_encoder.config.max_position_embeddings
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self.text_encoder_hidden_size = text_encoder.config.hidden_size
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self.image_encoder_projection_dim = image_encoder.config.projection_dim
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self.unet_resolution = unet.config.sample_size
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self.text_intermediate_dim = self.text_encoder_hidden_size
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if self.text_decoder.prefix_hidden_dim is not None:
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self.text_intermediate_dim = self.text_decoder.prefix_hidden_dim
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self.mode = None
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# TODO: handle safety checking?
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self.safety_checker = None
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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://huggingface.co/papers/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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def _infer_mode(self, prompt, prompt_embeds, image, latents, prompt_latents, vae_latents, clip_latents):
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r"""
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Infer the generation task ('mode') from the inputs to `__call__`. If the mode has been manually set, the set
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mode will be used.
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"""
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prompt_available = (prompt is not None) or (prompt_embeds is not None)
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image_available = image is not None
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input_available = prompt_available or image_available
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prompt_latents_available = prompt_latents is not None
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vae_latents_available = vae_latents is not None
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clip_latents_available = clip_latents is not None
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full_latents_available = latents is not None
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image_latents_available = vae_latents_available and clip_latents_available
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all_indv_latents_available = prompt_latents_available and image_latents_available
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if self.mode is not None:
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# Preferentially use the mode set by the user
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mode = self.mode
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elif prompt_available:
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mode = "text2img"
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elif image_available:
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mode = "img2text"
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else:
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# Neither prompt nor image supplied, infer based on availability of latents
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if full_latents_available or all_indv_latents_available:
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mode = "joint"
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elif prompt_latents_available:
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mode = "text"
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elif image_latents_available:
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mode = "img"
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else:
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# No inputs or latents available
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mode = "joint"
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# Give warnings for ambiguous cases
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if self.mode is None and prompt_available and image_available:
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logger.warning(
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f"You have supplied both a text prompt and image to the pipeline and mode has not been set manually,"
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f" defaulting to mode '{mode}'."
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)
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if self.mode is None and not input_available:
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if vae_latents_available != clip_latents_available:
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# Exactly one of vae_latents and clip_latents is supplied
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logger.warning(
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f"You have supplied exactly one of `vae_latents` and `clip_latents`, whereas either both or none"
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f" are expected to be supplied. Defaulting to mode '{mode}'."
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)
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elif not prompt_latents_available and not vae_latents_available and not clip_latents_available:
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# No inputs or latents supplied
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logger.warning(
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f"No inputs or latents have been supplied, and mode has not been manually set,"
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f" defaulting to mode '{mode}'."
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)
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return mode
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# Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.enable_vae_slicing
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def enable_vae_slicing(self):
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r"""
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Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
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compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
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"""
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self.vae.enable_slicing()
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# Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.disable_vae_slicing
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def disable_vae_slicing(self):
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r"""
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Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_slicing()
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# Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.enable_vae_tiling
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def enable_vae_tiling(self):
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r"""
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Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
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compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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processing larger images.
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"""
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self.vae.enable_tiling()
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# Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.disable_vae_tiling
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def disable_vae_tiling(self):
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r"""
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Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_tiling()
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# Functions to manually set the mode
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def set_text_mode(self):
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r"""Manually set the generation mode to unconditional ("marginal") text generation."""
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self.mode = "text"
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def set_image_mode(self):
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r"""Manually set the generation mode to unconditional ("marginal") image generation."""
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self.mode = "img"
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def set_text_to_image_mode(self):
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r"""Manually set the generation mode to text-conditioned image generation."""
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self.mode = "text2img"
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def set_image_to_text_mode(self):
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r"""Manually set the generation mode to image-conditioned text generation."""
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self.mode = "img2text"
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def set_joint_mode(self):
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r"""Manually set the generation mode to unconditional joint image-text generation."""
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self.mode = "joint"
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def reset_mode(self):
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r"""Removes a manually set mode; after calling this, the pipeline will infer the mode from inputs."""
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self.mode = None
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def _infer_batch_size(
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self,
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mode,
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prompt,
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prompt_embeds,
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image,
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num_images_per_prompt,
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num_prompts_per_image,
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latents,
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prompt_latents,
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vae_latents,
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clip_latents,
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):
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r"""Infers the batch size and multiplier depending on mode and supplied arguments to `__call__`."""
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if num_images_per_prompt is None:
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num_images_per_prompt = 1
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if num_prompts_per_image is None:
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num_prompts_per_image = 1
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assert num_images_per_prompt > 0, "num_images_per_prompt must be a positive integer"
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assert num_prompts_per_image > 0, "num_prompts_per_image must be a positive integer"
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if mode in ["text2img"]:
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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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# Either prompt or prompt_embeds must be present for text2img.
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batch_size = prompt_embeds.shape[0]
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multiplier = num_images_per_prompt
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elif mode in ["img2text"]:
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if isinstance(image, PIL.Image.Image):
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batch_size = 1
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else:
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# Image must be available and type either PIL.Image.Image or torch.Tensor.
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# Not currently supporting something like image_embeds.
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batch_size = image.shape[0]
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multiplier = num_prompts_per_image
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elif mode in ["img"]:
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if vae_latents is not None:
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batch_size = vae_latents.shape[0]
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elif clip_latents is not None:
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batch_size = clip_latents.shape[0]
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else:
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batch_size = 1
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multiplier = num_images_per_prompt
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elif mode in ["text"]:
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if prompt_latents is not None:
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batch_size = prompt_latents.shape[0]
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else:
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batch_size = 1
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multiplier = num_prompts_per_image
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elif mode in ["joint"]:
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if latents is not None:
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batch_size = latents.shape[0]
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elif prompt_latents is not None:
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batch_size = prompt_latents.shape[0]
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elif vae_latents is not None:
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batch_size = vae_latents.shape[0]
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elif clip_latents is not None:
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batch_size = clip_latents.shape[0]
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else:
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batch_size = 1
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if num_images_per_prompt == num_prompts_per_image:
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multiplier = num_images_per_prompt
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else:
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multiplier = min(num_images_per_prompt, num_prompts_per_image)
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logger.warning(
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f"You are using mode `{mode}` and `num_images_per_prompt`: {num_images_per_prompt} and"
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f" num_prompts_per_image: {num_prompts_per_image} are not equal. Using batch size equal to"
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f" `min(num_images_per_prompt, num_prompts_per_image) = {batch_size}."
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)
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return batch_size, multiplier
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
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def _encode_prompt(
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self,
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prompt,
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device,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_prompt=None,
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prompt_embeds: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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lora_scale: Optional[float] = None,
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**kwargs,
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):
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deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
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deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
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prompt_embeds_tuple = self.encode_prompt(
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prompt=prompt,
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device=device,
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num_images_per_prompt=num_images_per_prompt,
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do_classifier_free_guidance=do_classifier_free_guidance,
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negative_prompt=negative_prompt,
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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lora_scale=lora_scale,
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**kwargs,
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)
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# concatenate for backwards comp
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prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
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return prompt_embeds
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with self.tokenizer->self.clip_tokenizer
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def encode_prompt(
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self,
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prompt,
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device,
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num_images_per_prompt,
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do_classifier_free_guidance,
|
|
negative_prompt=None,
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prompt_embeds: Optional[torch.Tensor] = None,
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|
negative_prompt_embeds: Optional[torch.Tensor] = None,
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lora_scale: Optional[float] = None,
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|
clip_skip: Optional[int] = None,
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|
):
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r"""
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|
Encodes the prompt into text encoder hidden states.
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|
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|
Args:
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prompt (`str` or `List[str]`, *optional*):
|
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prompt to be encoded
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|
device: (`torch.device`):
|
|
torch device
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|
num_images_per_prompt (`int`):
|
|
number of images that should be generated per prompt
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|
do_classifier_free_guidance (`bool`):
|
|
whether to use classifier free guidance or not
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|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. If not defined, one has to pass
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`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
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less than `1`).
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prompt_embeds (`torch.Tensor`, *optional*):
|
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
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provided, text embeddings will be generated from `prompt` input argument.
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negative_prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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lora_scale (`float`, *optional*):
|
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A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
|
|
clip_skip (`int`, *optional*):
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Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
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the output of the pre-final layer will be used for computing the prompt embeddings.
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|
"""
|
|
# set lora scale so that monkey patched LoRA
|
|
# function of text encoder can correctly access it
|
|
if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
|
|
self._lora_scale = lora_scale
|
|
|
|
# dynamically adjust the LoRA scale
|
|
if not USE_PEFT_BACKEND:
|
|
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
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|
else:
|
|
scale_lora_layers(self.text_encoder, lora_scale)
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|
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
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|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
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|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
if prompt_embeds is None:
|
|
# textual inversion: process multi-vector tokens if necessary
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
prompt = self.maybe_convert_prompt(prompt, self.clip_tokenizer)
|
|
|
|
text_inputs = self.clip_tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=self.clip_tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids
|
|
untruncated_ids = self.clip_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.clip_tokenizer.batch_decode(
|
|
untruncated_ids[:, self.clip_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.clip_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
|
|
|
|
if clip_skip is None:
|
|
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
|
|
prompt_embeds = prompt_embeds[0]
|
|
else:
|
|
prompt_embeds = self.text_encoder(
|
|
text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
|
|
)
|
|
# Access the `hidden_states` first, that contains a tuple of
|
|
# all the hidden states from the encoder layers. Then index into
|
|
# the tuple to access the hidden states from the desired layer.
|
|
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
|
|
# We also need to apply the final LayerNorm here to not mess with the
|
|
# representations. The `last_hidden_states` that we typically use for
|
|
# obtaining the final prompt representations passes through the LayerNorm
|
|
# layer.
|
|
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
|
|
|
|
if self.text_encoder is not None:
|
|
prompt_embeds_dtype = self.text_encoder.dtype
|
|
elif self.unet is not None:
|
|
prompt_embeds_dtype = self.unet.dtype
|
|
else:
|
|
prompt_embeds_dtype = prompt_embeds.dtype
|
|
|
|
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_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 prompt is not None and 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: process multi-vector tokens if necessary
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.clip_tokenizer)
|
|
|
|
max_length = prompt_embeds.shape[1]
|
|
uncond_input = self.clip_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=prompt_embeds_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)
|
|
|
|
if self.text_encoder is not None:
|
|
if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
|
|
# Retrieve the original scale by scaling back the LoRA layers
|
|
unscale_lora_layers(self.text_encoder, lora_scale)
|
|
|
|
return prompt_embeds, negative_prompt_embeds
|
|
|
|
# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_instruct_pix2pix.StableDiffusionInstructPix2PixPipeline.prepare_image_latents
|
|
# Add num_prompts_per_image argument, sample from autoencoder moment distribution
|
|
def encode_image_vae_latents(
|
|
self,
|
|
image,
|
|
batch_size,
|
|
num_prompts_per_image,
|
|
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_prompts_per_image
|
|
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 isinstance(generator, list):
|
|
image_latents = [
|
|
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
|
|
* self.vae.config.scaling_factor
|
|
for i in range(batch_size)
|
|
]
|
|
image_latents = torch.cat(image_latents, dim=0)
|
|
else:
|
|
image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
|
|
# Scale image_latents by the VAE's scaling factor
|
|
image_latents = image_latents * self.vae.config.scaling_factor
|
|
|
|
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
|
|
|
|
def encode_image_clip_latents(
|
|
self,
|
|
image,
|
|
batch_size,
|
|
num_prompts_per_image,
|
|
dtype,
|
|
device,
|
|
generator=None,
|
|
):
|
|
# Map image to CLIP embedding.
|
|
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)}"
|
|
)
|
|
|
|
preprocessed_image = self.clip_image_processor.preprocess(
|
|
image,
|
|
return_tensors="pt",
|
|
)
|
|
preprocessed_image = preprocessed_image.to(device=device, dtype=dtype)
|
|
|
|
batch_size = batch_size * num_prompts_per_image
|
|
if isinstance(generator, list):
|
|
image_latents = [
|
|
self.image_encoder(**preprocessed_image[i : i + 1]).image_embeds for i in range(batch_size)
|
|
]
|
|
image_latents = torch.cat(image_latents, dim=0)
|
|
else:
|
|
image_latents = self.image_encoder(**preprocessed_image).image_embeds
|
|
|
|
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 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."
|
|
)
|
|
|
|
return image_latents
|
|
|
|
def prepare_text_latents(
|
|
self, batch_size, num_images_per_prompt, seq_len, hidden_size, dtype, device, generator, latents=None
|
|
):
|
|
# Prepare latents for the CLIP embedded prompt.
|
|
shape = (batch_size * num_images_per_prompt, seq_len, hidden_size)
|
|
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 is assumed to have shace (B, L, D)
|
|
latents = latents.repeat(num_images_per_prompt, 1, 1)
|
|
latents = latents.to(device=device, dtype=dtype)
|
|
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
|
latents = latents * self.scheduler.init_noise_sigma
|
|
return latents
|
|
|
|
# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
|
|
# Rename prepare_latents -> prepare_image_vae_latents and add num_prompts_per_image argument.
|
|
def prepare_image_vae_latents(
|
|
self,
|
|
batch_size,
|
|
num_prompts_per_image,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
latents=None,
|
|
):
|
|
shape = (
|
|
batch_size * num_prompts_per_image,
|
|
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 is assumed to have shape (B, C, H, W)
|
|
latents = latents.repeat(num_prompts_per_image, 1, 1, 1)
|
|
latents = latents.to(device=device, dtype=dtype)
|
|
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
|
latents = latents * self.scheduler.init_noise_sigma
|
|
return latents
|
|
|
|
def prepare_image_clip_latents(
|
|
self, batch_size, num_prompts_per_image, clip_img_dim, dtype, device, generator, latents=None
|
|
):
|
|
# Prepare latents for the CLIP embedded image.
|
|
shape = (batch_size * num_prompts_per_image, 1, clip_img_dim)
|
|
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 is assumed to have shape (B, L, D)
|
|
latents = latents.repeat(num_prompts_per_image, 1, 1)
|
|
latents = latents.to(device=device, dtype=dtype)
|
|
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
|
latents = latents * self.scheduler.init_noise_sigma
|
|
return latents
|
|
|
|
def decode_text_latents(self, text_latents, device):
|
|
output_token_list, seq_lengths = self.text_decoder.generate_captions(
|
|
text_latents, self.text_tokenizer.eos_token_id, device=device
|
|
)
|
|
output_list = output_token_list.cpu().numpy()
|
|
generated_text = [
|
|
self.text_tokenizer.decode(output[: int(length)], skip_special_tokens=True)
|
|
for output, length in zip(output_list, seq_lengths)
|
|
]
|
|
return generated_text
|
|
|
|
def _split(self, x, height, width):
|
|
r"""
|
|
Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim) into two tensors of shape (B, C, H, W)
|
|
and (B, 1, clip_img_dim)
|
|
"""
|
|
batch_size = x.shape[0]
|
|
latent_height = height // self.vae_scale_factor
|
|
latent_width = width // self.vae_scale_factor
|
|
img_vae_dim = self.num_channels_latents * latent_height * latent_width
|
|
|
|
img_vae, img_clip = x.split([img_vae_dim, self.image_encoder_projection_dim], dim=1)
|
|
|
|
img_vae = torch.reshape(img_vae, (batch_size, self.num_channels_latents, latent_height, latent_width))
|
|
img_clip = torch.reshape(img_clip, (batch_size, 1, self.image_encoder_projection_dim))
|
|
return img_vae, img_clip
|
|
|
|
def _combine(self, img_vae, img_clip):
|
|
r"""
|
|
Combines a latent image img_vae of shape (B, C, H, W) and a CLIP-embedded image img_clip of shape (B, 1,
|
|
clip_img_dim) into a single tensor of shape (B, C * H * W + clip_img_dim).
|
|
"""
|
|
img_vae = torch.reshape(img_vae, (img_vae.shape[0], -1))
|
|
img_clip = torch.reshape(img_clip, (img_clip.shape[0], -1))
|
|
return torch.concat([img_vae, img_clip], dim=-1)
|
|
|
|
def _split_joint(self, x, height, width):
|
|
r"""
|
|
Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim + text_seq_len * text_dim] into (img_vae,
|
|
img_clip, text) where img_vae is of shape (B, C, H, W), img_clip is of shape (B, 1, clip_img_dim), and text is
|
|
of shape (B, text_seq_len, text_dim).
|
|
"""
|
|
batch_size = x.shape[0]
|
|
latent_height = height // self.vae_scale_factor
|
|
latent_width = width // self.vae_scale_factor
|
|
img_vae_dim = self.num_channels_latents * latent_height * latent_width
|
|
text_dim = self.text_encoder_seq_len * self.text_intermediate_dim
|
|
|
|
img_vae, img_clip, text = x.split([img_vae_dim, self.image_encoder_projection_dim, text_dim], dim=1)
|
|
|
|
img_vae = torch.reshape(img_vae, (batch_size, self.num_channels_latents, latent_height, latent_width))
|
|
img_clip = torch.reshape(img_clip, (batch_size, 1, self.image_encoder_projection_dim))
|
|
text = torch.reshape(text, (batch_size, self.text_encoder_seq_len, self.text_intermediate_dim))
|
|
return img_vae, img_clip, text
|
|
|
|
def _combine_joint(self, img_vae, img_clip, text):
|
|
r"""
|
|
Combines a latent image img_vae of shape (B, C, H, W), a CLIP-embedded image img_clip of shape (B, L_img,
|
|
clip_img_dim), and a text embedding text of shape (B, L_text, text_dim) into a single embedding x of shape (B,
|
|
C * H * W + L_img * clip_img_dim + L_text * text_dim).
|
|
"""
|
|
img_vae = torch.reshape(img_vae, (img_vae.shape[0], -1))
|
|
img_clip = torch.reshape(img_clip, (img_clip.shape[0], -1))
|
|
text = torch.reshape(text, (text.shape[0], -1))
|
|
return torch.concat([img_vae, img_clip, text], dim=-1)
|
|
|
|
def _get_noise_pred(
|
|
self,
|
|
mode,
|
|
latents,
|
|
t,
|
|
prompt_embeds,
|
|
img_vae,
|
|
img_clip,
|
|
max_timestep,
|
|
data_type,
|
|
guidance_scale,
|
|
generator,
|
|
device,
|
|
height,
|
|
width,
|
|
):
|
|
r"""
|
|
Gets the noise prediction using the `unet` and performs classifier-free guidance, if necessary.
|
|
"""
|
|
if mode == "joint":
|
|
# Joint text-image generation
|
|
img_vae_latents, img_clip_latents, text_latents = self._split_joint(latents, height, width)
|
|
|
|
img_vae_out, img_clip_out, text_out = self.unet(
|
|
img_vae_latents, img_clip_latents, text_latents, timestep_img=t, timestep_text=t, data_type=data_type
|
|
)
|
|
|
|
x_out = self._combine_joint(img_vae_out, img_clip_out, text_out)
|
|
|
|
if guidance_scale <= 1.0:
|
|
return x_out
|
|
|
|
# Classifier-free guidance
|
|
img_vae_T = randn_tensor(img_vae.shape, generator=generator, device=device, dtype=img_vae.dtype)
|
|
img_clip_T = randn_tensor(img_clip.shape, generator=generator, device=device, dtype=img_clip.dtype)
|
|
text_T = randn_tensor(prompt_embeds.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)
|
|
|
|
_, _, text_out_uncond = self.unet(
|
|
img_vae_T, img_clip_T, text_latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type
|
|
)
|
|
|
|
img_vae_out_uncond, img_clip_out_uncond, _ = self.unet(
|
|
img_vae_latents,
|
|
img_clip_latents,
|
|
text_T,
|
|
timestep_img=t,
|
|
timestep_text=max_timestep,
|
|
data_type=data_type,
|
|
)
|
|
|
|
x_out_uncond = self._combine_joint(img_vae_out_uncond, img_clip_out_uncond, text_out_uncond)
|
|
|
|
return guidance_scale * x_out + (1.0 - guidance_scale) * x_out_uncond
|
|
elif mode == "text2img":
|
|
# Text-conditioned image generation
|
|
img_vae_latents, img_clip_latents = self._split(latents, height, width)
|
|
|
|
img_vae_out, img_clip_out, text_out = self.unet(
|
|
img_vae_latents, img_clip_latents, prompt_embeds, timestep_img=t, timestep_text=0, data_type=data_type
|
|
)
|
|
|
|
img_out = self._combine(img_vae_out, img_clip_out)
|
|
|
|
if guidance_scale <= 1.0:
|
|
return img_out
|
|
|
|
# Classifier-free guidance
|
|
text_T = randn_tensor(prompt_embeds.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)
|
|
|
|
img_vae_out_uncond, img_clip_out_uncond, text_out_uncond = self.unet(
|
|
img_vae_latents,
|
|
img_clip_latents,
|
|
text_T,
|
|
timestep_img=t,
|
|
timestep_text=max_timestep,
|
|
data_type=data_type,
|
|
)
|
|
|
|
img_out_uncond = self._combine(img_vae_out_uncond, img_clip_out_uncond)
|
|
|
|
return guidance_scale * img_out + (1.0 - guidance_scale) * img_out_uncond
|
|
elif mode == "img2text":
|
|
# Image-conditioned text generation
|
|
img_vae_out, img_clip_out, text_out = self.unet(
|
|
img_vae, img_clip, latents, timestep_img=0, timestep_text=t, data_type=data_type
|
|
)
|
|
|
|
if guidance_scale <= 1.0:
|
|
return text_out
|
|
|
|
# Classifier-free guidance
|
|
img_vae_T = randn_tensor(img_vae.shape, generator=generator, device=device, dtype=img_vae.dtype)
|
|
img_clip_T = randn_tensor(img_clip.shape, generator=generator, device=device, dtype=img_clip.dtype)
|
|
|
|
img_vae_out_uncond, img_clip_out_uncond, text_out_uncond = self.unet(
|
|
img_vae_T, img_clip_T, latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type
|
|
)
|
|
|
|
return guidance_scale * text_out + (1.0 - guidance_scale) * text_out_uncond
|
|
elif mode == "text":
|
|
# Unconditional ("marginal") text generation (no CFG)
|
|
img_vae_out, img_clip_out, text_out = self.unet(
|
|
img_vae, img_clip, latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type
|
|
)
|
|
|
|
return text_out
|
|
elif mode == "img":
|
|
# Unconditional ("marginal") image generation (no CFG)
|
|
img_vae_latents, img_clip_latents = self._split(latents, height, width)
|
|
|
|
img_vae_out, img_clip_out, text_out = self.unet(
|
|
img_vae_latents,
|
|
img_clip_latents,
|
|
prompt_embeds,
|
|
timestep_img=t,
|
|
timestep_text=max_timestep,
|
|
data_type=data_type,
|
|
)
|
|
|
|
img_out = self._combine(img_vae_out, img_clip_out)
|
|
return img_out
|
|
|
|
def check_latents_shape(self, latents_name, latents, expected_shape):
|
|
latents_shape = latents.shape
|
|
expected_num_dims = len(expected_shape) + 1 # expected dimensions plus the batch dimension
|
|
expected_shape_str = ", ".join(str(dim) for dim in expected_shape)
|
|
if len(latents_shape) != expected_num_dims:
|
|
raise ValueError(
|
|
f"`{latents_name}` should have shape (batch_size, {expected_shape_str}), but the current shape"
|
|
f" {latents_shape} has {len(latents_shape)} dimensions."
|
|
)
|
|
for i in range(1, expected_num_dims):
|
|
if latents_shape[i] != expected_shape[i - 1]:
|
|
raise ValueError(
|
|
f"`{latents_name}` should have shape (batch_size, {expected_shape_str}), but the current shape"
|
|
f" {latents_shape} has {latents_shape[i]} != {expected_shape[i - 1]} at dimension {i}."
|
|
)
|
|
|
|
def check_inputs(
|
|
self,
|
|
mode,
|
|
prompt,
|
|
image,
|
|
height,
|
|
width,
|
|
callback_steps,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
latents=None,
|
|
prompt_latents=None,
|
|
vae_latents=None,
|
|
clip_latents=None,
|
|
):
|
|
# Check inputs before running the generative process.
|
|
if height % self.vae_scale_factor != 0 or width % self.vae_scale_factor != 0:
|
|
raise ValueError(
|
|
f"`height` and `width` have to be divisible by {self.vae_scale_factor} but are {height} and {width}."
|
|
)
|
|
|
|
if (callback_steps is None) or (
|
|
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 mode == "text2img":
|
|
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}."
|
|
)
|
|
|
|
if mode == "img2text":
|
|
if image is None:
|
|
raise ValueError("`img2text` mode requires an image to be provided.")
|
|
|
|
# Check provided latents
|
|
latent_height = height // self.vae_scale_factor
|
|
latent_width = width // self.vae_scale_factor
|
|
full_latents_available = latents is not None
|
|
prompt_latents_available = prompt_latents is not None
|
|
vae_latents_available = vae_latents is not None
|
|
clip_latents_available = clip_latents is not None
|
|
|
|
if full_latents_available:
|
|
individual_latents_available = (
|
|
prompt_latents is not None or vae_latents is not None or clip_latents is not None
|
|
)
|
|
if individual_latents_available:
|
|
logger.warning(
|
|
"You have supplied both `latents` and at least one of `prompt_latents`, `vae_latents`, and"
|
|
" `clip_latents`. The value of `latents` will override the value of any individually supplied latents."
|
|
)
|
|
# Check shape of full latents
|
|
img_vae_dim = self.num_channels_latents * latent_height * latent_width
|
|
text_dim = self.text_encoder_seq_len * self.text_encoder_hidden_size
|
|
latents_dim = img_vae_dim + self.image_encoder_projection_dim + text_dim
|
|
latents_expected_shape = (latents_dim,)
|
|
self.check_latents_shape("latents", latents, latents_expected_shape)
|
|
|
|
# Check individual latent shapes, if present
|
|
if prompt_latents_available:
|
|
prompt_latents_expected_shape = (self.text_encoder_seq_len, self.text_encoder_hidden_size)
|
|
self.check_latents_shape("prompt_latents", prompt_latents, prompt_latents_expected_shape)
|
|
|
|
if vae_latents_available:
|
|
vae_latents_expected_shape = (self.num_channels_latents, latent_height, latent_width)
|
|
self.check_latents_shape("vae_latents", vae_latents, vae_latents_expected_shape)
|
|
|
|
if clip_latents_available:
|
|
clip_latents_expected_shape = (1, self.image_encoder_projection_dim)
|
|
self.check_latents_shape("clip_latents", clip_latents, clip_latents_expected_shape)
|
|
|
|
if mode in ["text2img", "img"] and vae_latents_available and clip_latents_available:
|
|
if vae_latents.shape[0] != clip_latents.shape[0]:
|
|
raise ValueError(
|
|
f"Both `vae_latents` and `clip_latents` are supplied, but their batch dimensions are not equal:"
|
|
f" {vae_latents.shape[0]} != {clip_latents.shape[0]}."
|
|
)
|
|
|
|
if mode == "joint" and prompt_latents_available and vae_latents_available and clip_latents_available:
|
|
if prompt_latents.shape[0] != vae_latents.shape[0] or prompt_latents.shape[0] != clip_latents.shape[0]:
|
|
raise ValueError(
|
|
f"All of `prompt_latents`, `vae_latents`, and `clip_latents` are supplied, but their batch"
|
|
f" dimensions are not equal: {prompt_latents.shape[0]} != {vae_latents.shape[0]}"
|
|
f" != {clip_latents.shape[0]}."
|
|
)
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
prompt: Optional[Union[str, List[str]]] = None,
|
|
image: Optional[Union[torch.Tensor, PIL.Image.Image]] = None,
|
|
height: Optional[int] = None,
|
|
width: Optional[int] = None,
|
|
data_type: Optional[int] = 1,
|
|
num_inference_steps: int = 50,
|
|
guidance_scale: float = 8.0,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
num_prompts_per_image: Optional[int] = 1,
|
|
eta: float = 0.0,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.Tensor] = None,
|
|
prompt_latents: Optional[torch.Tensor] = None,
|
|
vae_latents: Optional[torch.Tensor] = None,
|
|
clip_latents: Optional[torch.Tensor] = None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
|
|
callback_steps: int = 1,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
|
Required for text-conditioned image generation (`text2img`) mode.
|
|
image (`torch.Tensor` or `PIL.Image.Image`, *optional*):
|
|
`Image` or tensor representing an image batch. Required for image-conditioned text generation
|
|
(`img2text`) mode.
|
|
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The height in pixels of the generated image.
|
|
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The width in pixels of the generated image.
|
|
data_type (`int`, *optional*, defaults to 1):
|
|
The data type (either 0 or 1). Only used if you are loading a checkpoint which supports a data type
|
|
embedding; this is added for compatibility with the
|
|
[UniDiffuser-v1](https://huggingface.co/thu-ml/unidiffuser-v1) checkpoint.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference.
|
|
guidance_scale (`float`, *optional*, defaults to 8.0):
|
|
A higher guidance scale value encourages the model to generate images closely linked to the text
|
|
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
|
|
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). Used in
|
|
text-conditioned image generation (`text2img`) mode.
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt. Used in `text2img` (text-conditioned image generation) and
|
|
`img` mode. If the mode is joint and both `num_images_per_prompt` and `num_prompts_per_image` are
|
|
supplied, `min(num_images_per_prompt, num_prompts_per_image)` samples are generated.
|
|
num_prompts_per_image (`int`, *optional*, defaults to 1):
|
|
The number of prompts to generate per image. Used in `img2text` (image-conditioned text generation) and
|
|
`text` mode. If the mode is joint and both `num_images_per_prompt` and `num_prompts_per_image` are
|
|
supplied, `min(num_images_per_prompt, num_prompts_per_image)` samples are generated.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only
|
|
applies to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
|
generation deterministic.
|
|
latents (`torch.Tensor`, *optional*):
|
|
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for joint
|
|
image-text generation. Can be used to tweak the same generation with different prompts. If not
|
|
provided, a latents tensor is generated by sampling using the supplied random `generator`. This assumes
|
|
a full set of VAE, CLIP, and text latents, if supplied, overrides the value of `prompt_latents`,
|
|
`vae_latents`, and `clip_latents`.
|
|
prompt_latents (`torch.Tensor`, *optional*):
|
|
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for text
|
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
|
tensor is generated by sampling using the supplied random `generator`.
|
|
vae_latents (`torch.Tensor`, *optional*):
|
|
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
|
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
|
tensor is generated by sampling using the supplied random `generator`.
|
|
clip_latents (`torch.Tensor`, *optional*):
|
|
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
|
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
|
tensor is generated by sampling using the supplied random `generator`.
|
|
prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
|
|
provided, text embeddings are generated from the `prompt` input argument. Used in text-conditioned
|
|
image generation (`text2img`) mode.
|
|
negative_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
|
not provided, `negative_prompt_embeds` are be generated from the `negative_prompt` input argument. Used
|
|
in text-conditioned image generation (`text2img`) mode.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.ImageTextPipelineOutput`] instead of a plain tuple.
|
|
callback (`Callable`, *optional*):
|
|
A function that calls every `callback_steps` steps during inference. The function is called with the
|
|
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
|
|
callback_steps (`int`, *optional*, defaults to 1):
|
|
The frequency at which the `callback` function is called. If not specified, the callback is called at
|
|
every step.
|
|
|
|
Returns:
|
|
[`~pipelines.unidiffuser.ImageTextPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.unidiffuser.ImageTextPipelineOutput`] is returned, otherwise a
|
|
`tuple` is returned where the first element is a list with the generated images and the second element
|
|
is a list of generated texts.
|
|
"""
|
|
|
|
# 0. Default height and width to unet
|
|
height = height or self.unet_resolution * self.vae_scale_factor
|
|
width = width or self.unet_resolution * self.vae_scale_factor
|
|
|
|
# 1. Check inputs
|
|
# Recalculate mode for each call to the pipeline.
|
|
mode = self._infer_mode(prompt, prompt_embeds, image, latents, prompt_latents, vae_latents, clip_latents)
|
|
self.check_inputs(
|
|
mode,
|
|
prompt,
|
|
image,
|
|
height,
|
|
width,
|
|
callback_steps,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
latents,
|
|
prompt_latents,
|
|
vae_latents,
|
|
clip_latents,
|
|
)
|
|
|
|
# 2. Define call parameters
|
|
batch_size, multiplier = self._infer_batch_size(
|
|
mode,
|
|
prompt,
|
|
prompt_embeds,
|
|
image,
|
|
num_images_per_prompt,
|
|
num_prompts_per_image,
|
|
latents,
|
|
prompt_latents,
|
|
vae_latents,
|
|
clip_latents,
|
|
)
|
|
device = self._execution_device
|
|
reduce_text_emb_dim = self.text_intermediate_dim < self.text_encoder_hidden_size or self.mode != "text2img"
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
# Note that this differs from the formulation in the unidiffusers paper!
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
|
|
# check if scheduler is in sigmas space
|
|
# scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas")
|
|
|
|
# 3. Encode input prompt, if available; otherwise prepare text latents
|
|
if latents is not None:
|
|
# Overwrite individual latents
|
|
vae_latents, clip_latents, prompt_latents = self._split_joint(latents, height, width)
|
|
|
|
if mode in ["text2img"]:
|
|
# 3.1. Encode input prompt, if available
|
|
assert prompt is not None or prompt_embeds is not None
|
|
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
|
|
prompt=prompt,
|
|
device=device,
|
|
num_images_per_prompt=multiplier,
|
|
do_classifier_free_guidance=do_classifier_free_guidance,
|
|
negative_prompt=negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
)
|
|
|
|
# if do_classifier_free_guidance:
|
|
# prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
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else:
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# 3.2. Prepare text latent variables, if input not available
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|
prompt_embeds = self.prepare_text_latents(
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batch_size=batch_size,
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|
num_images_per_prompt=multiplier,
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|
seq_len=self.text_encoder_seq_len,
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|
hidden_size=self.text_encoder_hidden_size,
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|
dtype=self.text_encoder.dtype, # Should work with both full precision and mixed precision
|
|
device=device,
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|
generator=generator,
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|
latents=prompt_latents,
|
|
)
|
|
|
|
if reduce_text_emb_dim:
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|
prompt_embeds = self.text_decoder.encode(prompt_embeds)
|
|
|
|
# 4. Encode image, if available; otherwise prepare image latents
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|
if mode in ["img2text"]:
|
|
# 4.1. Encode images, if available
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|
assert image is not None, "`img2text` requires a conditioning image"
|
|
# Encode image using VAE
|
|
image_vae = self.image_processor.preprocess(image)
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|
height, width = image_vae.shape[-2:]
|
|
image_vae_latents = self.encode_image_vae_latents(
|
|
image=image_vae,
|
|
batch_size=batch_size,
|
|
num_prompts_per_image=multiplier,
|
|
dtype=prompt_embeds.dtype,
|
|
device=device,
|
|
do_classifier_free_guidance=False, # Copied from InstructPix2Pix, don't use their version of CFG
|
|
generator=generator,
|
|
)
|
|
|
|
# Encode image using CLIP
|
|
image_clip_latents = self.encode_image_clip_latents(
|
|
image=image,
|
|
batch_size=batch_size,
|
|
num_prompts_per_image=multiplier,
|
|
dtype=prompt_embeds.dtype,
|
|
device=device,
|
|
generator=generator,
|
|
)
|
|
# (batch_size, clip_hidden_size) => (batch_size, 1, clip_hidden_size)
|
|
image_clip_latents = image_clip_latents.unsqueeze(1)
|
|
else:
|
|
# 4.2. Prepare image latent variables, if input not available
|
|
# Prepare image VAE latents in latent space
|
|
image_vae_latents = self.prepare_image_vae_latents(
|
|
batch_size=batch_size,
|
|
num_prompts_per_image=multiplier,
|
|
num_channels_latents=self.num_channels_latents,
|
|
height=height,
|
|
width=width,
|
|
dtype=prompt_embeds.dtype,
|
|
device=device,
|
|
generator=generator,
|
|
latents=vae_latents,
|
|
)
|
|
|
|
# Prepare image CLIP latents
|
|
image_clip_latents = self.prepare_image_clip_latents(
|
|
batch_size=batch_size,
|
|
num_prompts_per_image=multiplier,
|
|
clip_img_dim=self.image_encoder_projection_dim,
|
|
dtype=prompt_embeds.dtype,
|
|
device=device,
|
|
generator=generator,
|
|
latents=clip_latents,
|
|
)
|
|
|
|
# 5. Set timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps = self.scheduler.timesteps
|
|
# max_timestep = timesteps[0]
|
|
max_timestep = self.scheduler.config.num_train_timesteps
|
|
|
|
# 6. Prepare latent variables
|
|
if mode == "joint":
|
|
latents = self._combine_joint(image_vae_latents, image_clip_latents, prompt_embeds)
|
|
elif mode in ["text2img", "img"]:
|
|
latents = self._combine(image_vae_latents, image_clip_latents)
|
|
elif mode in ["img2text", "text"]:
|
|
latents = prompt_embeds
|
|
|
|
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
|
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
|
|
|
logger.debug(f"Scheduler extra step kwargs: {extra_step_kwargs}")
|
|
|
|
# 8. Denoising loop
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
# predict the noise residual
|
|
# Also applies classifier-free guidance as described in the UniDiffuser paper
|
|
noise_pred = self._get_noise_pred(
|
|
mode,
|
|
latents,
|
|
t,
|
|
prompt_embeds,
|
|
image_vae_latents,
|
|
image_clip_latents,
|
|
max_timestep,
|
|
data_type,
|
|
guidance_scale,
|
|
generator,
|
|
device,
|
|
height,
|
|
width,
|
|
)
|
|
|
|
# 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 i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
progress_bar.update()
|
|
if callback is not None and i % callback_steps == 0:
|
|
step_idx = i // getattr(self.scheduler, "order", 1)
|
|
callback(step_idx, t, latents)
|
|
|
|
if XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
# 9. Post-processing
|
|
image = None
|
|
text = None
|
|
if mode == "joint":
|
|
image_vae_latents, image_clip_latents, text_latents = self._split_joint(latents, height, width)
|
|
|
|
if not output_type == "latent":
|
|
# Map latent VAE image back to pixel space
|
|
image = self.vae.decode(image_vae_latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
|
else:
|
|
image = image_vae_latents
|
|
|
|
text = self.decode_text_latents(text_latents, device)
|
|
elif mode in ["text2img", "img"]:
|
|
image_vae_latents, image_clip_latents = self._split(latents, height, width)
|
|
|
|
if not output_type == "latent":
|
|
# Map latent VAE image back to pixel space
|
|
image = self.vae.decode(image_vae_latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
|
else:
|
|
image = image_vae_latents
|
|
elif mode in ["img2text", "text"]:
|
|
text_latents = latents
|
|
text = self.decode_text_latents(text_latents, device)
|
|
|
|
self.maybe_free_model_hooks()
|
|
|
|
# 10. Postprocess the image, if necessary
|
|
if image is not None:
|
|
do_denormalize = [True] * image.shape[0]
|
|
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
|
|
|
|
# Offload last model to CPU
|
|
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
|
self.final_offload_hook.offload()
|
|
|
|
if not return_dict:
|
|
return (image, text)
|
|
|
|
return ImageTextPipelineOutput(images=image, text=text)
|