mirror of
https://github.com/invoke-ai/InvokeAI
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123 lines
4.5 KiB
Python
123 lines
4.5 KiB
Python
import math
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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 invokeai.backend.ip_adapter.ip_adapter import IPAdapter
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@dataclass
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class BasicConditioningInfo:
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"""SD 1/2 text conditioning information produced by Compel."""
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embeds: torch.Tensor
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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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"""SDXL text conditioning information produced by Compel."""
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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 IPAdapterData:
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ip_adapter_model: IPAdapter
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ip_adapter_conditioning: IPAdapterConditioningInfo
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mask: torch.Tensor
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# Either a single weight applied to all steps, or a list of weights for each step.
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weight: Union[float, List[float]] = 1.0
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begin_step_percent: float = 0.0
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end_step_percent: float = 1.0
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def scale_for_step(self, step_index: int, total_steps: int) -> float:
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first_adapter_step = math.floor(self.begin_step_percent * total_steps)
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last_adapter_step = math.ceil(self.end_step_percent * total_steps)
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weight = self.weight[step_index] if isinstance(self.weight, List) else self.weight
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if step_index >= first_adapter_step and step_index <= last_adapter_step:
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# Only apply this IP-Adapter if the current step is within the IP-Adapter's begin/end step range.
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return weight
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# Otherwise, set the IP-Adapter's scale to 0, so it has no effect.
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return 0.0
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@dataclass
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class Range:
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start: int
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end: int
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class TextConditioningRegions:
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def __init__(
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self,
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masks: torch.Tensor,
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ranges: list[Range],
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):
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# A binary mask indicating the regions of the image that the prompt should be applied to.
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# Shape: (1, num_prompts, height, width)
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# Dtype: torch.bool
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self.masks = masks
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# A list of ranges indicating the start and end indices of the embeddings that corresponding mask applies to.
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# ranges[i] contains the embedding range for the i'th prompt / mask.
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self.ranges = ranges
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assert self.masks.shape[1] == len(self.ranges)
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class TextConditioningData:
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def __init__(
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self,
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uncond_text: Union[BasicConditioningInfo, SDXLConditioningInfo],
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cond_text: Union[BasicConditioningInfo, SDXLConditioningInfo],
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uncond_regions: Optional[TextConditioningRegions],
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cond_regions: Optional[TextConditioningRegions],
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guidance_scale: Union[float, List[float]],
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guidance_rescale_multiplier: float = 0,
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):
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self.uncond_text = uncond_text
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self.cond_text = cond_text
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self.uncond_regions = uncond_regions
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self.cond_regions = cond_regions
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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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self.guidance_scale = guidance_scale
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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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# See [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
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self.guidance_rescale_multiplier = guidance_rescale_multiplier
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def is_sdxl(self):
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assert isinstance(self.uncond_text, SDXLConditioningInfo) == isinstance(self.cond_text, SDXLConditioningInfo)
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return isinstance(self.cond_text, SDXLConditioningInfo)
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