import math
from dataclasses import dataclass
from math import ceil
from typing import Callable, Optional, Union
import numpy as np
import torch
from ldm.models.diffusion.cross_attention_control import Arguments, \
remove_cross_attention_control, setup_cross_attention_control, Context, get_cross_attention_modules, \
CrossAttentionType
from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
@dataclass(frozen=True)
class ThresholdSettings:
threshold: float
warmup: float
class InvokeAIDiffuserComponent:
'''
The aim of this component is to provide a single place for code that can be applied identically to
all InvokeAI diffusion procedures.
At the moment it includes the following features:
* Cross attention control ("prompt2prompt")
* Hybrid conditioning (used for inpainting)
'''
debug_thresholding = False
class ExtraConditioningInfo:
def __init__(self, tokens_count_including_eos_bos:int, cross_attention_control_args: Optional[Arguments]):
self.tokens_count_including_eos_bos = tokens_count_including_eos_bos
self.cross_attention_control_args = cross_attention_control_args
@property
def wants_cross_attention_control(self):
return self.cross_attention_control_args is not None
def __init__(self, model, model_forward_callback:
Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]
):
"""
:param model: the unet model to pass through to cross attention control
: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)
"""
self.conditioning = None
self.model = model
self.model_forward_callback = model_forward_callback
self.cross_attention_control_context = None
def setup_cross_attention_control(self, conditioning: ExtraConditioningInfo, step_count: int):
self.conditioning = conditioning
self.cross_attention_control_context = Context(
arguments=self.conditioning.cross_attention_control_args,
step_count=step_count
)
setup_cross_attention_control(self.model, self.cross_attention_control_context)
def remove_cross_attention_control(self):
self.conditioning = None
self.cross_attention_control_context = None
remove_cross_attention_control(self.model)
def setup_attention_map_saving(self, saver: AttentionMapSaver):
def callback(slice, dim, offset, slice_size, key):
if dim is not None:
# sliced tokens attention map saving is not implemented
return
saver.add_attention_maps(slice, key)
tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
for identifier, module in tokens_cross_attention_modules:
key = ('down' if identifier.startswith('down') else
'up' if identifier.startswith('up') else
'mid')
module.set_attention_slice_calculated_callback(
lambda slice, dim, offset, slice_size, key=key: callback(slice, dim, offset, slice_size, key))
def remove_attention_map_saving(self):
tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
for _, module in tokens_cross_attention_modules:
module.set_attention_slice_calculated_callback(None)
def do_diffusion_step(self, x: torch.Tensor, sigma: torch.Tensor,
unconditioning: Union[torch.Tensor,dict],
conditioning: Union[torch.Tensor,dict],
unconditional_guidance_scale: float,
step_index: Optional[int]=None,
total_step_count: Optional[int]=None,
threshold: Optional[ThresholdSettings]=None,
):
"""
:param x: current latents
:param sigma: aka t, passed to the internal model to control how much denoising will occur
: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]
: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]
:param unconditional_guidance_scale: aka CFG scale, controls how much effect the conditioning tensor has
: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 .
:param threshold: threshold to apply after each step
:return: the new latents after applying the model to x using unscaled unconditioning and CFG-scaled conditioning.
"""
cross_attention_control_types_to_do = []
context: Context = self.cross_attention_control_context
if self.cross_attention_control_context is not None:
if step_index is not None and total_step_count is not None:
# 🧨diffusers codepath
percent_through = step_index / total_step_count # will never reach 1.0 - this is deliberate
else:
# legacy compvis codepath
# TODO remove when compvis codepath support is dropped
if step_index is None and sigma is None:
raise ValueError(f"Either step_index or sigma is required when doing cross attention control, but both are None.")
percent_through = self.estimate_percent_through(step_index, sigma)
cross_attention_control_types_to_do = context.get_active_cross_attention_control_types_for_step(percent_through)
wants_cross_attention_control = (len(cross_attention_control_types_to_do) > 0)
wants_hybrid_conditioning = isinstance(conditioning, dict)
if wants_hybrid_conditioning:
unconditioned_next_x, conditioned_next_x = self.apply_hybrid_conditioning(x, sigma, unconditioning, conditioning)
elif wants_cross_attention_control:
unconditioned_next_x, conditioned_next_x = self.apply_cross_attention_controlled_conditioning(x, sigma, unconditioning, conditioning, cross_attention_control_types_to_do)
else:
unconditioned_next_x, conditioned_next_x = self.apply_standard_conditioning(x, sigma, unconditioning, conditioning)
combined_next_x = self._combine(unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale)
if threshold:
combined_next_x = self._threshold(threshold.threshold, threshold.warmup, combined_next_x, sigma)
return combined_next_x
# methods below are called from do_diffusion_step and should be considered private to this class.
def apply_standard_conditioning(self, x, sigma, unconditioning, conditioning):
# fast batched path
x_twice = torch.cat([x] * 2)
sigma_twice = torch.cat([sigma] * 2)
both_conditionings = torch.cat([unconditioning, conditioning])
both_results = self.model_forward_callback(x_twice, sigma_twice, both_conditionings)
unconditioned_next_x, conditioned_next_x = both_results.chunk(2)
if conditioned_next_x.device.type == 'mps':
# prevent a result filled with zeros. seems to be a torch bug.
conditioned_next_x = conditioned_next_x.clone()
return unconditioned_next_x, conditioned_next_x
def apply_hybrid_conditioning(self, x, sigma, unconditioning, conditioning):
assert isinstance(conditioning, dict)
assert isinstance(unconditioning, dict)
x_twice = torch.cat([x] * 2)
sigma_twice = torch.cat([sigma] * 2)
both_conditionings = dict()
for k in conditioning:
if isinstance(conditioning[k], list):
both_conditionings[k] = [
torch.cat([unconditioning[k][i], conditioning[k][i]])
for i in range(len(conditioning[k]))
]
else:
both_conditionings[k] = torch.cat([unconditioning[k], conditioning[k]])
unconditioned_next_x, conditioned_next_x = self.model_forward_callback(x_twice, sigma_twice, both_conditionings).chunk(2)
return unconditioned_next_x, conditioned_next_x
def apply_cross_attention_controlled_conditioning(self, x:torch.Tensor, sigma, unconditioning, conditioning, cross_attention_control_types_to_do):
# print('pct', percent_through, ': doing cross attention control on', cross_attention_control_types_to_do)
# slower non-batched path (20% slower on mac MPS)
# We are only interested in using attention maps for conditioned_next_x, but batching them with generation of
# unconditioned_next_x causes attention maps to *also* be saved for the unconditioned_next_x.
# This messes app their application later, due to mismatched shape of dim 0 (seems to be 16 for batched vs. 8)
# (For the batched invocation the `wrangler` function gets attention tensor with shape[0]=16,
# representing batched uncond + cond, but then when it comes to applying the saved attention, the
# wrangler gets an attention tensor which only has shape[0]=8, representing just self.edited_conditionings.)
# todo: give CrossAttentionControl's `wrangler` function more info so it can work with a batched call as well.
context:Context = self.cross_attention_control_context
try:
unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning)
# process x using the original prompt, saving the attention maps
#print("saving attention maps for", cross_attention_control_types_to_do)
for ca_type in cross_attention_control_types_to_do:
context.request_save_attention_maps(ca_type)
_ = self.model_forward_callback(x, sigma, conditioning)
context.clear_requests(cleanup=False)
# process x again, using the saved attention maps to control where self.edited_conditioning will be applied
#print("applying saved attention maps for", cross_attention_control_types_to_do)
for ca_type in cross_attention_control_types_to_do:
context.request_apply_saved_attention_maps(ca_type)
edited_conditioning = self.conditioning.cross_attention_control_args.edited_conditioning
conditioned_next_x = self.model_forward_callback(x, sigma, edited_conditioning)
context.clear_requests(cleanup=True)
except:
context.clear_requests(cleanup=True)
raise
return unconditioned_next_x, conditioned_next_x
def _combine(self, unconditioned_next_x, conditioned_next_x, guidance_scale):
# to scale how much effect conditioning has, calculate the changes it does and then scale that
scaled_delta = (conditioned_next_x - unconditioned_next_x) * guidance_scale
combined_next_x = unconditioned_next_x + scaled_delta
return combined_next_x
def _threshold(self, threshold, warmup, latents: torch.Tensor, sigma) -> torch.Tensor:
warmup_scale = (1 - sigma.item() / 1000) / warmup if warmup else math.inf
if warmup_scale < 1:
# This arithmetic based on https://github.com/invoke-ai/InvokeAI/pull/395
warming_threshold = 1 + (threshold - 1) * warmup_scale
current_threshold = np.clip(warming_threshold, 1, threshold)
else:
current_threshold = threshold
if current_threshold <= 0:
return latents
maxval = latents.max().item()
minval = latents.min().item()
scale = 0.7 # default value from #395
if self.debug_thresholding:
std, mean = [i.item() for i in torch.std_mean(latents)]
outside = torch.count_nonzero((latents < -current_threshold) | (latents > current_threshold))
print(f"\nThreshold: 𝜎={sigma.item()} threshold={current_threshold:.3f} (of {threshold:.3f})\n"
f" | min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}\n"
f" | {outside / latents.numel() * 100:.2f}% values outside threshold")
if maxval < current_threshold and minval > -current_threshold:
return latents
if maxval > current_threshold:
maxval = np.clip(maxval * scale, 1, current_threshold)
if minval < -current_threshold:
minval = np.clip(minval * scale, -current_threshold, -1)
if self.debug_thresholding:
outside = torch.count_nonzero((latents < minval) | (latents > maxval))
print(f" | min, , max = {minval:.3f}, , {maxval:.3f}\t(scaled by {scale})\n"
f" | {outside / latents.numel() * 100:.2f}% values will be clamped")
return latents.clamp(minval, maxval)
def estimate_percent_through(self, step_index, sigma):
if step_index is not None and self.cross_attention_control_context is not None:
# percent_through will never reach 1.0 (but this is intended)
return float(step_index) / float(self.cross_attention_control_context.step_count)
# find the best possible index of the current sigma in the sigma sequence
smaller_sigmas = torch.nonzero(self.model.sigmas <= sigma)
sigma_index = smaller_sigmas[-1].item() if smaller_sigmas.shape[0] > 0 else 0
# flip because sigmas[0] is for the fully denoised image
# percent_through must be <1
return 1.0 - float(sigma_index + 1) / float(self.model.sigmas.shape[0])
# print('estimated percent_through', percent_through, 'from sigma', sigma.item())
# todo: make this work
@classmethod
def apply_conjunction(cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale):
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2) # aka sigmas
deltas = None
uncond_latents = None
weighted_cond_list = c_or_weighted_c_list if type(c_or_weighted_c_list) is list else [(c_or_weighted_c_list, 1)]
# below is fugly omg
num_actual_conditionings = len(c_or_weighted_c_list)
conditionings = [uc] + [c for c,weight in weighted_cond_list]
weights = [1] + [weight for c,weight in weighted_cond_list]
chunk_count = ceil(len(conditionings)/2)
deltas = None
for chunk_index in range(chunk_count):
offset = chunk_index*2
chunk_size = min(2, len(conditionings)-offset)
if chunk_size == 1:
c_in = conditionings[offset]
latents_a = forward_func(x_in[:-1], t_in[:-1], c_in)
latents_b = None
else:
c_in = torch.cat(conditionings[offset:offset+2])
latents_a, latents_b = forward_func(x_in, t_in, c_in).chunk(2)
# first chunk is guaranteed to be 2 entries: uncond_latents + first conditioining
if chunk_index == 0:
uncond_latents = latents_a
deltas = latents_b - uncond_latents
else:
deltas = torch.cat((deltas, latents_a - uncond_latents))
if latents_b is not None:
deltas = torch.cat((deltas, latents_b - uncond_latents))
# merge the weighted deltas together into a single merged delta
per_delta_weights = torch.tensor(weights[1:], dtype=deltas.dtype, device=deltas.device)
normalize = False
if normalize:
per_delta_weights /= torch.sum(per_delta_weights)
reshaped_weights = per_delta_weights.reshape(per_delta_weights.shape + (1, 1, 1))
deltas_merged = torch.sum(deltas * reshaped_weights, dim=0, keepdim=True)
# old_return_value = super().forward(x, sigma, uncond, cond, cond_scale)
# assert(0 == len(torch.nonzero(old_return_value - (uncond_latents + deltas_merged * cond_scale))))
return uncond_latents + deltas_merged * global_guidance_scale