mirror of
https://github.com/invoke-ai/InvokeAI
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72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
from dataclasses import dataclass
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from typing import 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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extra_conditioning: Optional[ExtraConditioningInfo]
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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
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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 TextConditioningData:
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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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