mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
0beb08686c
* add support for CFG rescale * fix typo * move rescale position and tweak docs * move input position * implement suggestions from github and discord * cleanup unused code * add back dropped FieldDescription * fix(ui): revert unrelated UI changes * chore(nodes): bump denoise_latents version 1.4.0 -> 1.5.0 * feat(nodes): add cfg_rescale_multiplier to metadata node * feat(ui): add cfg rescale multiplier to linear UI - add param to state - update graph builders - add UI under advanced - add metadata handling & recall - regen types * chore: black * fix(backend): make `StableDiffusionGeneratorPipeline._rescale_cfg()` staticmethod This doesn't need access to class. * feat(backend): add docstring for `_rescale_cfg()` method * feat(ui): update cfg rescale mult translation string --------- Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
106 lines
3.7 KiB
Python
106 lines
3.7 KiB
Python
import dataclasses
|
|
import inspect
|
|
from dataclasses import dataclass, field
|
|
from typing import Any, List, Optional, Union
|
|
|
|
import torch
|
|
|
|
from .cross_attention_control import Arguments
|
|
|
|
|
|
@dataclass
|
|
class ExtraConditioningInfo:
|
|
tokens_count_including_eos_bos: int
|
|
cross_attention_control_args: Optional[Arguments] = None
|
|
|
|
@property
|
|
def wants_cross_attention_control(self):
|
|
return self.cross_attention_control_args is not None
|
|
|
|
|
|
@dataclass
|
|
class BasicConditioningInfo:
|
|
embeds: torch.Tensor
|
|
# TODO(ryand): Right now we awkwardly copy the extra conditioning info from here up to `ConditioningData`. This
|
|
# should only be stored in one place.
|
|
extra_conditioning: Optional[ExtraConditioningInfo]
|
|
# weight: float
|
|
# mode: ConditioningAlgo
|
|
|
|
def to(self, device, dtype=None):
|
|
self.embeds = self.embeds.to(device=device, dtype=dtype)
|
|
return self
|
|
|
|
|
|
@dataclass
|
|
class SDXLConditioningInfo(BasicConditioningInfo):
|
|
pooled_embeds: torch.Tensor
|
|
add_time_ids: torch.Tensor
|
|
|
|
def to(self, device, dtype=None):
|
|
self.pooled_embeds = self.pooled_embeds.to(device=device, dtype=dtype)
|
|
self.add_time_ids = self.add_time_ids.to(device=device, dtype=dtype)
|
|
return super().to(device=device, dtype=dtype)
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class PostprocessingSettings:
|
|
threshold: float
|
|
warmup: float
|
|
h_symmetry_time_pct: Optional[float]
|
|
v_symmetry_time_pct: Optional[float]
|
|
|
|
|
|
@dataclass
|
|
class IPAdapterConditioningInfo:
|
|
cond_image_prompt_embeds: torch.Tensor
|
|
"""IP-Adapter image encoder conditioning embeddings.
|
|
Shape: (num_images, num_tokens, encoding_dim).
|
|
"""
|
|
uncond_image_prompt_embeds: torch.Tensor
|
|
"""IP-Adapter image encoding embeddings to use for unconditional generation.
|
|
Shape: (num_images, num_tokens, encoding_dim).
|
|
"""
|
|
|
|
|
|
@dataclass
|
|
class ConditioningData:
|
|
unconditioned_embeddings: BasicConditioningInfo
|
|
text_embeddings: BasicConditioningInfo
|
|
"""
|
|
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
|
`guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
|
|
Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
|
|
images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
|
|
"""
|
|
guidance_scale: Union[float, List[float]]
|
|
""" for models trained using zero-terminal SNR ("ztsnr"), it's suggested to use guidance_rescale_multiplier of 0.7 .
|
|
ref [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf)
|
|
"""
|
|
guidance_rescale_multiplier: float = 0
|
|
extra: Optional[ExtraConditioningInfo] = None
|
|
scheduler_args: dict[str, Any] = field(default_factory=dict)
|
|
"""
|
|
Additional arguments to pass to invokeai_diffuser.do_latent_postprocessing().
|
|
"""
|
|
postprocessing_settings: Optional[PostprocessingSettings] = None
|
|
|
|
ip_adapter_conditioning: Optional[list[IPAdapterConditioningInfo]] = None
|
|
|
|
@property
|
|
def dtype(self):
|
|
return self.text_embeddings.dtype
|
|
|
|
def add_scheduler_args_if_applicable(self, scheduler, **kwargs):
|
|
scheduler_args = dict(self.scheduler_args)
|
|
step_method = inspect.signature(scheduler.step)
|
|
for name, value in kwargs.items():
|
|
try:
|
|
step_method.bind_partial(**{name: value})
|
|
except TypeError:
|
|
# FIXME: don't silently discard arguments
|
|
pass # debug("%s does not accept argument named %r", scheduler, name)
|
|
else:
|
|
scheduler_args[name] = value
|
|
return dataclasses.replace(self, scheduler_args=scheduler_args)
|