mirror of
https://github.com/invoke-ai/InvokeAI
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093174942b
`generator` now asks `InvokeAIDiffuserComponent` to do postprocessing work on latents after every step. Thresholding - now implemented as replacing latents outside of the threshold with random noise - is called at this point. This postprocessing step is also where we can hook up symmetry and other image latent manipulations in the future. Note: code at this layer doesn't need to worry about MPS as relevant torch functions are wrapped and made MPS-safe by `generator.py`.
407 lines
21 KiB
Python
407 lines
21 KiB
Python
import math
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from contextlib import contextmanager
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from dataclasses import dataclass
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from math import ceil
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from typing import Callable, Optional, Union, Any, Dict
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import numpy as np
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import torch
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from diffusers.models.cross_attention import AttnProcessor
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from ldm.models.diffusion.cross_attention_control import Arguments, \
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restore_default_cross_attention, override_cross_attention, Context, get_cross_attention_modules, \
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CrossAttentionType, SwapCrossAttnContext
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from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
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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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class InvokeAIDiffuserComponent:
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'''
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The aim of this component is to provide a single place for code that can be applied identically to
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all InvokeAI diffusion procedures.
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At the moment it includes the following features:
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* Cross attention control ("prompt2prompt")
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* Hybrid conditioning (used for inpainting)
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'''
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debug_thresholding = False
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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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def __init__(self, model, model_forward_callback:
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Callable[[torch.Tensor, torch.Tensor, torch.Tensor, Optional[dict[str,Any]]], torch.Tensor],
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is_running_diffusers: bool=False,
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):
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"""
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:param model: the unet model to pass through to cross attention control
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:param model_forward_callback: a lambda with arguments (x, sigma, conditioning_to_apply). will be called repeatedly. most likely, this should simply call model.forward(x, sigma, conditioning)
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"""
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self.conditioning = None
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self.model = model
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self.is_running_diffusers = is_running_diffusers
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self.model_forward_callback = model_forward_callback
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self.cross_attention_control_context = None
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@contextmanager
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def custom_attention_context(self,
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extra_conditioning_info: Optional[ExtraConditioningInfo],
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step_count: int):
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do_swap = extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control
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old_attn_processor = None
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if do_swap:
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old_attn_processor = self.override_cross_attention(extra_conditioning_info,
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step_count=step_count)
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try:
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yield None
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finally:
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if old_attn_processor is not None:
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self.restore_default_cross_attention(old_attn_processor)
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# TODO resuscitate attention map saving
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#self.remove_attention_map_saving()
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def override_cross_attention(self, conditioning: ExtraConditioningInfo, step_count: int) -> Dict[str, AttnProcessor]:
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"""
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setup cross attention .swap control. for diffusers this replaces the attention processor, so
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the previous attention processor is returned so that the caller can restore it later.
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"""
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self.conditioning = conditioning
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self.cross_attention_control_context = Context(
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arguments=self.conditioning.cross_attention_control_args,
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step_count=step_count
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)
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return override_cross_attention(self.model,
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self.cross_attention_control_context,
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is_running_diffusers=self.is_running_diffusers)
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def restore_default_cross_attention(self, restore_attention_processor: Optional['AttnProcessor']=None):
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self.conditioning = None
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self.cross_attention_control_context = None
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restore_default_cross_attention(self.model,
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is_running_diffusers=self.is_running_diffusers,
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restore_attention_processor=restore_attention_processor)
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def setup_attention_map_saving(self, saver: AttentionMapSaver):
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def callback(slice, dim, offset, slice_size, key):
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if dim is not None:
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# sliced tokens attention map saving is not implemented
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return
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saver.add_attention_maps(slice, key)
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tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
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for identifier, module in tokens_cross_attention_modules:
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key = ('down' if identifier.startswith('down') else
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'up' if identifier.startswith('up') else
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'mid')
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module.set_attention_slice_calculated_callback(
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lambda slice, dim, offset, slice_size, key=key: callback(slice, dim, offset, slice_size, key))
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def remove_attention_map_saving(self):
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tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
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for _, module in tokens_cross_attention_modules:
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module.set_attention_slice_calculated_callback(None)
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def do_diffusion_step(self, x: torch.Tensor, sigma: torch.Tensor,
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unconditioning: Union[torch.Tensor,dict],
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conditioning: Union[torch.Tensor,dict],
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unconditional_guidance_scale: float,
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step_index: Optional[int]=None,
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total_step_count: Optional[int]=None,
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):
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"""
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:param x: current latents
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:param sigma: aka t, passed to the internal model to control how much denoising will occur
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:param unconditioning: embeddings for unconditioned output. for hybrid conditioning this is a dict of tensors [B x 77 x 768], otherwise a single tensor [B x 77 x 768]
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:param conditioning: embeddings for conditioned output. for hybrid conditioning this is a dict of tensors [B x 77 x 768], otherwise a single tensor [B x 77 x 768]
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:param unconditional_guidance_scale: aka CFG scale, controls how much effect the conditioning tensor has
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:param step_index: counts upwards from 0 to (step_count-1) (as passed to setup_cross_attention_control, if using). May be called multiple times for a single step, therefore do not assume that its value will monotically increase. If None, will be estimated by comparing sigma against self.model.sigmas .
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:return: the new latents after applying the model to x using unscaled unconditioning and CFG-scaled conditioning.
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"""
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cross_attention_control_types_to_do = []
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context: Context = self.cross_attention_control_context
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if self.cross_attention_control_context is not None:
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percent_through = self.calculate_percent_through(sigma, step_index, total_step_count)
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cross_attention_control_types_to_do = context.get_active_cross_attention_control_types_for_step(percent_through)
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wants_cross_attention_control = (len(cross_attention_control_types_to_do) > 0)
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wants_hybrid_conditioning = isinstance(conditioning, dict)
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if wants_hybrid_conditioning:
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unconditioned_next_x, conditioned_next_x = self.apply_hybrid_conditioning(x, sigma, unconditioning, conditioning)
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elif wants_cross_attention_control:
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unconditioned_next_x, conditioned_next_x = self.apply_cross_attention_controlled_conditioning(x, sigma, unconditioning, conditioning, cross_attention_control_types_to_do)
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else:
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unconditioned_next_x, conditioned_next_x = self.apply_standard_conditioning(x, sigma, unconditioning, conditioning)
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combined_next_x = self._combine(unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale)
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return combined_next_x
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def do_latent_postprocessing(
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self,
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postprocessing_settings: PostprocessingSettings,
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latents: torch.Tensor,
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sigma,
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step_index,
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total_step_count
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) -> torch.Tensor:
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if postprocessing_settings is not None:
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percent_through = self.calculate_percent_through(sigma, step_index, total_step_count)
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latents = self.apply_threshold(postprocessing_settings, latents, percent_through)
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return latents
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def calculate_percent_through(self, sigma, step_index, total_step_count):
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if step_index is not None and total_step_count is not None:
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# 🧨diffusers codepath
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percent_through = step_index / total_step_count # will never reach 1.0 - this is deliberate
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else:
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# legacy compvis codepath
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# TODO remove when compvis codepath support is dropped
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if step_index is None and sigma is None:
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raise ValueError(
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f"Either step_index or sigma is required when doing cross attention control, but both are None.")
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percent_through = self.estimate_percent_through(step_index, sigma)
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return percent_through
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# methods below are called from do_diffusion_step and should be considered private to this class.
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def apply_standard_conditioning(self, x, sigma, unconditioning, conditioning):
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# fast batched path
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x_twice = torch.cat([x] * 2)
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sigma_twice = torch.cat([sigma] * 2)
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both_conditionings = torch.cat([unconditioning, conditioning])
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both_results = self.model_forward_callback(x_twice, sigma_twice, both_conditionings)
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unconditioned_next_x, conditioned_next_x = both_results.chunk(2)
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if conditioned_next_x.device.type == 'mps':
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# prevent a result filled with zeros. seems to be a torch bug.
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conditioned_next_x = conditioned_next_x.clone()
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return unconditioned_next_x, conditioned_next_x
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def apply_hybrid_conditioning(self, x, sigma, unconditioning, conditioning):
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assert isinstance(conditioning, dict)
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assert isinstance(unconditioning, dict)
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x_twice = torch.cat([x] * 2)
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sigma_twice = torch.cat([sigma] * 2)
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both_conditionings = dict()
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for k in conditioning:
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if isinstance(conditioning[k], list):
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both_conditionings[k] = [
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torch.cat([unconditioning[k][i], conditioning[k][i]])
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for i in range(len(conditioning[k]))
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]
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else:
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both_conditionings[k] = torch.cat([unconditioning[k], conditioning[k]])
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unconditioned_next_x, conditioned_next_x = self.model_forward_callback(x_twice, sigma_twice, both_conditionings).chunk(2)
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return unconditioned_next_x, conditioned_next_x
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def apply_cross_attention_controlled_conditioning(self,
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x: torch.Tensor,
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sigma,
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unconditioning,
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conditioning,
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cross_attention_control_types_to_do):
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if self.is_running_diffusers:
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return self.apply_cross_attention_controlled_conditioning__diffusers(x, sigma, unconditioning, conditioning, cross_attention_control_types_to_do)
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else:
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return self.apply_cross_attention_controlled_conditioning__compvis(x, sigma, unconditioning, conditioning, cross_attention_control_types_to_do)
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def apply_cross_attention_controlled_conditioning__diffusers(self,
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x: torch.Tensor,
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sigma,
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unconditioning,
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conditioning,
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cross_attention_control_types_to_do):
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context: Context = self.cross_attention_control_context
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cross_attn_processor_context = SwapCrossAttnContext(modified_text_embeddings=context.arguments.edited_conditioning,
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index_map=context.cross_attention_index_map,
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mask=context.cross_attention_mask,
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cross_attention_types_to_do=[])
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# no cross attention for unconditioning (negative prompt)
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unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning,
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{"swap_cross_attn_context": cross_attn_processor_context})
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# do requested cross attention types for conditioning (positive prompt)
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cross_attn_processor_context.cross_attention_types_to_do = cross_attention_control_types_to_do
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conditioned_next_x = self.model_forward_callback(x, sigma, conditioning,
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{"swap_cross_attn_context": cross_attn_processor_context})
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return unconditioned_next_x, conditioned_next_x
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def apply_cross_attention_controlled_conditioning__compvis(self, x:torch.Tensor, sigma, unconditioning, conditioning, cross_attention_control_types_to_do):
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# print('pct', percent_through, ': doing cross attention control on', cross_attention_control_types_to_do)
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# slower non-batched path (20% slower on mac MPS)
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# We are only interested in using attention maps for conditioned_next_x, but batching them with generation of
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# unconditioned_next_x causes attention maps to *also* be saved for the unconditioned_next_x.
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# This messes app their application later, due to mismatched shape of dim 0 (seems to be 16 for batched vs. 8)
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# (For the batched invocation the `wrangler` function gets attention tensor with shape[0]=16,
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# representing batched uncond + cond, but then when it comes to applying the saved attention, the
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# wrangler gets an attention tensor which only has shape[0]=8, representing just self.edited_conditionings.)
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# todo: give CrossAttentionControl's `wrangler` function more info so it can work with a batched call as well.
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context:Context = self.cross_attention_control_context
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try:
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unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning)
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# process x using the original prompt, saving the attention maps
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#print("saving attention maps for", cross_attention_control_types_to_do)
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for ca_type in cross_attention_control_types_to_do:
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context.request_save_attention_maps(ca_type)
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_ = self.model_forward_callback(x, sigma, conditioning)
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context.clear_requests(cleanup=False)
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# process x again, using the saved attention maps to control where self.edited_conditioning will be applied
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#print("applying saved attention maps for", cross_attention_control_types_to_do)
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for ca_type in cross_attention_control_types_to_do:
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context.request_apply_saved_attention_maps(ca_type)
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edited_conditioning = self.conditioning.cross_attention_control_args.edited_conditioning
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conditioned_next_x = self.model_forward_callback(x, sigma, edited_conditioning)
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context.clear_requests(cleanup=True)
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except:
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context.clear_requests(cleanup=True)
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raise
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return unconditioned_next_x, conditioned_next_x
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def _combine(self, unconditioned_next_x, conditioned_next_x, guidance_scale):
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# to scale how much effect conditioning has, calculate the changes it does and then scale that
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scaled_delta = (conditioned_next_x - unconditioned_next_x) * guidance_scale
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combined_next_x = unconditioned_next_x + scaled_delta
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return combined_next_x
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def apply_threshold(
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self,
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postprocessing_settings: PostprocessingSettings,
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latents: torch.Tensor,
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percent_through
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) -> torch.Tensor:
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threshold = postprocessing_settings.threshold
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warmup = postprocessing_settings.warmup
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if percent_through < warmup:
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current_threshold = threshold + threshold * 5 * (1 - (percent_through / warmup))
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else:
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current_threshold = threshold
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if current_threshold <= 0:
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return latents
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maxval = latents.max().item()
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minval = latents.min().item()
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scale = 0.7 # default value from #395
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if self.debug_thresholding:
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std, mean = [i.item() for i in torch.std_mean(latents)]
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outside = torch.count_nonzero((latents < -current_threshold) | (latents > current_threshold))
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print(f"\nThreshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})\n"
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f" | min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}\n"
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f" | {outside / latents.numel() * 100:.2f}% values outside threshold")
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if maxval < current_threshold and minval > -current_threshold:
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return latents
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num_altered = 0
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# MPS torch.rand_like is fine because torch.rand_like is wrapped in generate.py!
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if maxval > current_threshold:
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latents = torch.clone(latents)
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maxval = np.clip(maxval * scale, 1, current_threshold)
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num_altered += torch.count_nonzero(latents > maxval)
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latents[latents > maxval] = torch.rand_like(latents[latents > maxval]) * maxval
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if minval < -current_threshold:
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latents = torch.clone(latents)
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minval = np.clip(minval * scale, -current_threshold, -1)
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num_altered += torch.count_nonzero(latents < minval)
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latents[latents < minval] = torch.rand_like(latents[latents < minval]) * minval
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if self.debug_thresholding:
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print(f" | min, , max = {minval:.3f}, , {maxval:.3f}\t(scaled by {scale})\n"
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f" | {num_altered / latents.numel() * 100:.2f}% values altered")
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return latents
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def estimate_percent_through(self, step_index, sigma):
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if step_index is not None and self.cross_attention_control_context is not None:
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# percent_through will never reach 1.0 (but this is intended)
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return float(step_index) / float(self.cross_attention_control_context.step_count)
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# find the best possible index of the current sigma in the sigma sequence
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smaller_sigmas = torch.nonzero(self.model.sigmas <= sigma)
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sigma_index = smaller_sigmas[-1].item() if smaller_sigmas.shape[0] > 0 else 0
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# flip because sigmas[0] is for the fully denoised image
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# percent_through must be <1
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return 1.0 - float(sigma_index + 1) / float(self.model.sigmas.shape[0])
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# print('estimated percent_through', percent_through, 'from sigma', sigma.item())
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# todo: make this work
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@classmethod
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def apply_conjunction(cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale):
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x_in = torch.cat([x] * 2)
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t_in = torch.cat([t] * 2) # aka sigmas
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deltas = None
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uncond_latents = None
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weighted_cond_list = c_or_weighted_c_list if type(c_or_weighted_c_list) is list else [(c_or_weighted_c_list, 1)]
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# below is fugly omg
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num_actual_conditionings = len(c_or_weighted_c_list)
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conditionings = [uc] + [c for c,weight in weighted_cond_list]
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weights = [1] + [weight for c,weight in weighted_cond_list]
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chunk_count = ceil(len(conditionings)/2)
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deltas = None
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for chunk_index in range(chunk_count):
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offset = chunk_index*2
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chunk_size = min(2, len(conditionings)-offset)
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if chunk_size == 1:
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c_in = conditionings[offset]
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latents_a = forward_func(x_in[:-1], t_in[:-1], c_in)
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latents_b = None
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else:
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c_in = torch.cat(conditionings[offset:offset+2])
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latents_a, latents_b = forward_func(x_in, t_in, c_in).chunk(2)
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# first chunk is guaranteed to be 2 entries: uncond_latents + first conditioining
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if chunk_index == 0:
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uncond_latents = latents_a
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deltas = latents_b - uncond_latents
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else:
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deltas = torch.cat((deltas, latents_a - uncond_latents))
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if latents_b is not None:
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deltas = torch.cat((deltas, latents_b - uncond_latents))
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# merge the weighted deltas together into a single merged delta
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per_delta_weights = torch.tensor(weights[1:], dtype=deltas.dtype, device=deltas.device)
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normalize = False
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if normalize:
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per_delta_weights /= torch.sum(per_delta_weights)
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reshaped_weights = per_delta_weights.reshape(per_delta_weights.shape + (1, 1, 1))
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deltas_merged = torch.sum(deltas * reshaped_weights, dim=0, keepdim=True)
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# old_return_value = super().forward(x, sigma, uncond, cond, cond_scale)
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# assert(0 == len(torch.nonzero(old_return_value - (uncond_latents + deltas_merged * cond_scale))))
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return uncond_latents + deltas_merged * global_guidance_scale
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