mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
111 lines
3.8 KiB
Python
111 lines
3.8 KiB
Python
import dataclasses
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import inspect
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from dataclasses import dataclass, field
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from typing import Any, List, Optional, Union
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import torch
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from .cross_attention_control import Arguments
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@dataclass
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class ExtraConditioningInfo:
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tokens_count_including_eos_bos: int
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cross_attention_control_args: Optional[Arguments] = None
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@property
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def wants_cross_attention_control(self):
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return self.cross_attention_control_args is not None
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@dataclass
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class BasicConditioningInfo:
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embeds: torch.Tensor
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# TODO(ryand): Right now we awkwardly copy the extra conditioning info from here up to `ConditioningData`. This
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# should only be stored in one place.
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extra_conditioning: Optional[ExtraConditioningInfo]
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# weight: float
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# mode: ConditioningAlgo
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def to(self, device, dtype=None):
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self.embeds = self.embeds.to(device=device, dtype=dtype)
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return self
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@dataclass
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class ConditioningFieldData:
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conditionings: List[BasicConditioningInfo]
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@dataclass
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class SDXLConditioningInfo(BasicConditioningInfo):
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pooled_embeds: torch.Tensor
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add_time_ids: torch.Tensor
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def to(self, device, dtype=None):
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self.pooled_embeds = self.pooled_embeds.to(device=device, dtype=dtype)
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self.add_time_ids = self.add_time_ids.to(device=device, dtype=dtype)
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return super().to(device=device, dtype=dtype)
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@dataclass(frozen=True)
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class PostprocessingSettings:
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threshold: float
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warmup: float
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h_symmetry_time_pct: Optional[float]
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v_symmetry_time_pct: Optional[float]
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@dataclass
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class IPAdapterConditioningInfo:
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cond_image_prompt_embeds: torch.Tensor
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"""IP-Adapter image encoder conditioning embeddings.
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Shape: (num_images, num_tokens, encoding_dim).
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"""
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uncond_image_prompt_embeds: torch.Tensor
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"""IP-Adapter image encoding embeddings to use for unconditional generation.
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Shape: (num_images, num_tokens, encoding_dim).
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"""
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@dataclass
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class ConditioningData:
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unconditioned_embeddings: BasicConditioningInfo
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text_embeddings: BasicConditioningInfo
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"""
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
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`guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
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Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
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images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
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"""
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guidance_scale: Union[float, List[float]]
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""" for models trained using zero-terminal SNR ("ztsnr"), it's suggested to use guidance_rescale_multiplier of 0.7 .
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ref [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf)
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"""
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guidance_rescale_multiplier: float = 0
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extra: Optional[ExtraConditioningInfo] = None
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scheduler_args: dict[str, Any] = field(default_factory=dict)
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"""
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Additional arguments to pass to invokeai_diffuser.do_latent_postprocessing().
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"""
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postprocessing_settings: Optional[PostprocessingSettings] = None
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ip_adapter_conditioning: Optional[list[IPAdapterConditioningInfo]] = None
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@property
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def dtype(self):
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return self.text_embeddings.dtype
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def add_scheduler_args_if_applicable(self, scheduler, **kwargs):
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scheduler_args = dict(self.scheduler_args)
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step_method = inspect.signature(scheduler.step)
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for name, value in kwargs.items():
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try:
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step_method.bind_partial(**{name: value})
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except TypeError:
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# FIXME: don't silently discard arguments
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pass # debug("%s does not accept argument named %r", scheduler, name)
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else:
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scheduler_args[name] = value
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return dataclasses.replace(self, scheduler_args=scheduler_args)
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