import enum
import math
from typing import Optional, Callable
import psutil
import torch
import diffusers
from torch import nn
from diffusers.models.unet_2d_condition import UNet2DConditionModel
from ldm.invoke.devices import torch_dtype
# adapted from bloc97's CrossAttentionControl colab
# https://github.com/bloc97/CrossAttentionControl
class Arguments:
def __init__(self, edited_conditioning: torch.Tensor, edit_opcodes: list[tuple], edit_options: dict):
"""
:param edited_conditioning: if doing cross-attention control, the edited conditioning [1 x 77 x 768]
:param edit_opcodes: if doing cross-attention control, a list of difflib.SequenceMatcher-like opcodes describing how to map original conditioning tokens to edited conditioning tokens (only the 'equal' opcode is required)
:param edit_options: if doing cross-attention control, per-edit options. there should be 1 item in edit_options for each item in edit_opcodes.
"""
# todo: rewrite this to take embedding fragments rather than a single edited_conditioning vector
self.edited_conditioning = edited_conditioning
self.edit_opcodes = edit_opcodes
if edited_conditioning is not None:
assert len(edit_opcodes) == len(edit_options), \
"there must be 1 edit_options dict for each edit_opcodes tuple"
non_none_edit_options = [x for x in edit_options if x is not None]
assert len(non_none_edit_options)>0, "missing edit_options"
if len(non_none_edit_options)>1:
print('warning: cross-attention control options are not working properly for >1 edit')
self.edit_options = non_none_edit_options[0]
class CrossAttentionType(enum.Enum):
SELF = 1
TOKENS = 2
class Context:
cross_attention_mask: Optional[torch.Tensor]
cross_attention_index_map: Optional[torch.Tensor]
class Action(enum.Enum):
NONE = 0
SAVE = 1,
APPLY = 2
def __init__(self, arguments: Arguments, step_count: int):
"""
:param arguments: Arguments for the cross-attention control process
:param step_count: The absolute total number of steps of diffusion (for img2img this is likely larger than the number of steps that will actually run)
"""
self.cross_attention_mask = None
self.cross_attention_index_map = None
self.self_cross_attention_action = Context.Action.NONE
self.tokens_cross_attention_action = Context.Action.NONE
self.arguments = arguments
self.step_count = step_count
self.self_cross_attention_module_identifiers = []
self.tokens_cross_attention_module_identifiers = []
self.saved_cross_attention_maps = {}
self.clear_requests(cleanup=True)
def register_cross_attention_modules(self, model):
for name,module in get_cross_attention_modules(model, CrossAttentionType.SELF):
if name in self.self_cross_attention_module_identifiers:
assert False, f"name {name} cannot appear more than once"
self.self_cross_attention_module_identifiers.append(name)
for name,module in get_cross_attention_modules(model, CrossAttentionType.TOKENS):
if name in self.tokens_cross_attention_module_identifiers:
assert False, f"name {name} cannot appear more than once"
self.tokens_cross_attention_module_identifiers.append(name)
def request_save_attention_maps(self, cross_attention_type: CrossAttentionType):
if cross_attention_type == CrossAttentionType.SELF:
self.self_cross_attention_action = Context.Action.SAVE
else:
self.tokens_cross_attention_action = Context.Action.SAVE
def request_apply_saved_attention_maps(self, cross_attention_type: CrossAttentionType):
if cross_attention_type == CrossAttentionType.SELF:
self.self_cross_attention_action = Context.Action.APPLY
else:
self.tokens_cross_attention_action = Context.Action.APPLY
def is_tokens_cross_attention(self, module_identifier) -> bool:
return module_identifier in self.tokens_cross_attention_module_identifiers
def get_should_save_maps(self, module_identifier: str) -> bool:
if module_identifier in self.self_cross_attention_module_identifiers:
return self.self_cross_attention_action == Context.Action.SAVE
elif module_identifier in self.tokens_cross_attention_module_identifiers:
return self.tokens_cross_attention_action == Context.Action.SAVE
return False
def get_should_apply_saved_maps(self, module_identifier: str) -> bool:
if module_identifier in self.self_cross_attention_module_identifiers:
return self.self_cross_attention_action == Context.Action.APPLY
elif module_identifier in self.tokens_cross_attention_module_identifiers:
return self.tokens_cross_attention_action == Context.Action.APPLY
return False
def get_active_cross_attention_control_types_for_step(self, percent_through:float=None)\
-> list[CrossAttentionType]:
"""
Should cross-attention control be applied on the given step?
:param percent_through: How far through the step sequence are we (0.0=pure noise, 1.0=completely denoised image). Expected range 0.0..<1.0.
:return: A list of attention types that cross-attention control should be performed for on the given step. May be [].
"""
if percent_through is None:
return [CrossAttentionType.SELF, CrossAttentionType.TOKENS]
opts = self.arguments.edit_options
to_control = []
if opts['s_start'] <= percent_through < opts['s_end']:
to_control.append(CrossAttentionType.SELF)
if opts['t_start'] <= percent_through < opts['t_end']:
to_control.append(CrossAttentionType.TOKENS)
return to_control
def save_slice(self, identifier: str, slice: torch.Tensor, dim: Optional[int], offset: int,
slice_size: Optional[int]):
if identifier not in self.saved_cross_attention_maps:
self.saved_cross_attention_maps[identifier] = {
'dim': dim,
'slice_size': slice_size,
'slices': {offset or 0: slice}
}
else:
self.saved_cross_attention_maps[identifier]['slices'][offset or 0] = slice
def get_slice(self, identifier: str, requested_dim: Optional[int], requested_offset: int, slice_size: int):
saved_attention_dict = self.saved_cross_attention_maps[identifier]
if requested_dim is None:
if saved_attention_dict['dim'] is not None:
raise RuntimeError(f"dim mismatch: expected dim=None, have {saved_attention_dict['dim']}")
return saved_attention_dict['slices'][0]
if saved_attention_dict['dim'] == requested_dim:
if slice_size != saved_attention_dict['slice_size']:
raise RuntimeError(
f"slice_size mismatch: expected slice_size={slice_size}, have {saved_attention_dict['slice_size']}")
return saved_attention_dict['slices'][requested_offset]
if saved_attention_dict['dim'] is None:
whole_saved_attention = saved_attention_dict['slices'][0]
if requested_dim == 0:
return whole_saved_attention[requested_offset:requested_offset + slice_size]
elif requested_dim == 1:
return whole_saved_attention[:, requested_offset:requested_offset + slice_size]
raise RuntimeError(f"Cannot convert dim {saved_attention_dict['dim']} to requested dim {requested_dim}")
def get_slicing_strategy(self, identifier: str) -> tuple[Optional[int], Optional[int]]:
saved_attention = self.saved_cross_attention_maps.get(identifier, None)
if saved_attention is None:
return None, None
return saved_attention['dim'], saved_attention['slice_size']
def clear_requests(self, cleanup=True):
self.tokens_cross_attention_action = Context.Action.NONE
self.self_cross_attention_action = Context.Action.NONE
if cleanup:
self.saved_cross_attention_maps = {}
def offload_saved_attention_slices_to_cpu(self):
for key, map_dict in self.saved_cross_attention_maps.items():
for offset, slice in map_dict['slices'].items():
map_dict[offset] = slice.to('cpu')
class InvokeAICrossAttentionMixin:
"""
Enable InvokeAI-flavoured CrossAttention calculation, which does aggressive low-memory slicing and calls
through both to an attention_slice_wrangler and a slicing_strategy_getter for custom attention map wrangling
and dymamic slicing strategy selection.
"""
def __init__(self):
self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
self.attention_slice_wrangler = None
self.slicing_strategy_getter = None
self.attention_slice_calculated_callback = None
def set_attention_slice_wrangler(self, wrangler: Optional[Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]]):
'''
Set custom attention calculator to be called when attention is calculated
:param wrangler: Callback, with args (module, suggested_attention_slice, dim, offset, slice_size),
which returns either the suggested_attention_slice or an adjusted equivalent.
`module` is the current CrossAttention module for which the callback is being invoked.
`suggested_attention_slice` is the default-calculated attention slice
`dim` is -1 if the attenion map has not been sliced, or 0 or 1 for dimension-0 or dimension-1 slicing.
If `dim` is >= 0, `offset` and `slice_size` specify the slice start and length.
Pass None to use the default attention calculation.
:return:
'''
self.attention_slice_wrangler = wrangler
def set_slicing_strategy_getter(self, getter: Optional[Callable[[nn.Module], tuple[int,int]]]):
self.slicing_strategy_getter = getter
def set_attention_slice_calculated_callback(self, callback: Optional[Callable[[torch.Tensor], None]]):
self.attention_slice_calculated_callback = callback
def einsum_lowest_level(self, query, key, value, dim, offset, slice_size):
# calculate attention scores
#attention_scores = torch.einsum('b i d, b j d -> b i j', q, k)
attention_scores = torch.baddbmm(
torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device),
query,
key.transpose(-1, -2),
beta=0,
alpha=self.scale,
)
# calculate attention slice by taking the best scores for each latent pixel
default_attention_slice = attention_scores.softmax(dim=-1, dtype=attention_scores.dtype)
attention_slice_wrangler = self.attention_slice_wrangler
if attention_slice_wrangler is not None:
attention_slice = attention_slice_wrangler(self, default_attention_slice, dim, offset, slice_size)
else:
attention_slice = default_attention_slice
if self.attention_slice_calculated_callback is not None:
self.attention_slice_calculated_callback(attention_slice, dim, offset, slice_size)
hidden_states = torch.bmm(attention_slice, value)
return hidden_states
def einsum_op_slice_dim0(self, q, k, v, slice_size):
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
for i in range(0, q.shape[0], slice_size):
end = i + slice_size
r[i:end] = self.einsum_lowest_level(q[i:end], k[i:end], v[i:end], dim=0, offset=i, slice_size=slice_size)
return r
def einsum_op_slice_dim1(self, q, k, v, slice_size):
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
r[:, i:end] = self.einsum_lowest_level(q[:, i:end], k, v, dim=1, offset=i, slice_size=slice_size)
return r
def einsum_op_mps_v1(self, q, k, v):
if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
return self.einsum_lowest_level(q, k, v, None, None, None)
else:
slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
return self.einsum_op_slice_dim1(q, k, v, slice_size)
def einsum_op_mps_v2(self, q, k, v):
if self.mem_total_gb > 8 and q.shape[1] <= 4096:
return self.einsum_lowest_level(q, k, v, None, None, None)
else:
return self.einsum_op_slice_dim0(q, k, v, 1)
def einsum_op_tensor_mem(self, q, k, v, max_tensor_mb):
size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
if size_mb <= max_tensor_mb:
return self.einsum_lowest_level(q, k, v, None, None, None)
div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
if div <= q.shape[0]:
return self.einsum_op_slice_dim0(q, k, v, q.shape[0] // div)
return self.einsum_op_slice_dim1(q, k, v, max(q.shape[1] // div, 1))
def einsum_op_cuda(self, q, k, v):
# check if we already have a slicing strategy (this should only happen during cross-attention controlled generation)
slicing_strategy_getter = self.slicing_strategy_getter
if slicing_strategy_getter is not None:
(dim, slice_size) = slicing_strategy_getter(self)
if dim is not None:
# print("using saved slicing strategy with dim", dim, "slice size", slice_size)
if dim == 0:
return self.einsum_op_slice_dim0(q, k, v, slice_size)
elif dim == 1:
return self.einsum_op_slice_dim1(q, k, v, slice_size)
# fallback for when there is no saved strategy, or saved strategy does not slice
mem_free_total = get_mem_free_total(q.device)
# Divide factor of safety as there's copying and fragmentation
return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
def get_invokeai_attention_mem_efficient(self, q, k, v):
if q.device.type == 'cuda':
#print("in get_attention_mem_efficient with q shape", q.shape, ", k shape", k.shape, ", free memory is", get_mem_free_total(q.device))
return self.einsum_op_cuda(q, k, v)
if q.device.type == 'mps' or q.device.type == 'cpu':
if self.mem_total_gb >= 32:
return self.einsum_op_mps_v1(q, k, v)
return self.einsum_op_mps_v2(q, k, v)
# Smaller slices are faster due to L2/L3/SLC caches.
# Tested on i7 with 8MB L3 cache.
return self.einsum_op_tensor_mem(q, k, v, 32)
def remove_cross_attention_control(model):
remove_attention_function(model)
def setup_cross_attention_control(model, context: Context):
"""
Inject attention parameters and functions into the passed in model to enable cross attention editing.
:param model: The unet model to inject into.
:param cross_attention_control_args: Arugments passeed to the CrossAttentionControl implementations
:return: None
"""
# adapted from init_attention_edit
device = context.arguments.edited_conditioning.device
# urgh. should this be hardcoded?
max_length = 77
# mask=1 means use base prompt attention, mask=0 means use edited prompt attention
mask = torch.zeros(max_length)
indices_target = torch.arange(max_length, dtype=torch.long)
indices = torch.arange(max_length, dtype=torch.long)
for name, a0, a1, b0, b1 in context.arguments.edit_opcodes:
if b0 < max_length:
if name == "equal":# or (name == "replace" and a1 - a0 == b1 - b0):
# these tokens have not been edited
indices[b0:b1] = indices_target[a0:a1]
mask[b0:b1] = 1
context.register_cross_attention_modules(model)
context.cross_attention_mask = mask.to(device)
context.cross_attention_index_map = indices.to(device)
inject_attention_function(model, context)
def get_cross_attention_modules(model, which: CrossAttentionType) -> list[tuple[str, InvokeAICrossAttentionMixin]]:
from ldm.modules.attention import CrossAttention # avoid circular import
cross_attention_class: type = InvokeAIDiffusersCrossAttention if isinstance(model,UNet2DConditionModel) else CrossAttention
which_attn = "attn1" if which is CrossAttentionType.SELF else "attn2"
attention_module_tuples = [(name,module) for name, module in model.named_modules() if
isinstance(module, cross_attention_class) and which_attn in name]
cross_attention_modules_in_model_count = len(attention_module_tuples)
expected_count = 16
if cross_attention_modules_in_model_count != expected_count:
# non-fatal error but .swap() won't work.
print(f"Error! CrossAttentionControl found an unexpected number of {cross_attention_class} modules in the model " +
f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching failed " +
f"or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, " +
f"and/or update the {expected_count} above to an appropriate number, and/or find and inform someone who knows " +
f"what it means. This error is non-fatal, but it is likely that .swap() and attention map display will not " +
f"work properly until it is fixed.")
return attention_module_tuples
def inject_attention_function(unet, context: Context):
# ORIGINAL SOURCE CODE: https://github.com/huggingface/diffusers/blob/91ddd2a25b848df0fa1262d4f1cd98c7ccb87750/src/diffusers/models/attention.py#L276
def attention_slice_wrangler(module, suggested_attention_slice:torch.Tensor, dim, offset, slice_size):
#memory_usage = suggested_attention_slice.element_size() * suggested_attention_slice.nelement()
attention_slice = suggested_attention_slice
if context.get_should_save_maps(module.identifier):
#print(module.identifier, "saving suggested_attention_slice of shape",
# suggested_attention_slice.shape, "dim", dim, "offset", offset)
slice_to_save = attention_slice.to('cpu') if dim is not None else attention_slice
context.save_slice(module.identifier, slice_to_save, dim=dim, offset=offset, slice_size=slice_size)
elif context.get_should_apply_saved_maps(module.identifier):
#print(module.identifier, "applying saved attention slice for dim", dim, "offset", offset)
saved_attention_slice = context.get_slice(module.identifier, dim, offset, slice_size)
# slice may have been offloaded to CPU
saved_attention_slice = saved_attention_slice.to(suggested_attention_slice.device)
if context.is_tokens_cross_attention(module.identifier):
index_map = context.cross_attention_index_map
remapped_saved_attention_slice = torch.index_select(saved_attention_slice, -1, index_map)
this_attention_slice = suggested_attention_slice
mask = context.cross_attention_mask.to(torch_dtype(suggested_attention_slice.device))
saved_mask = mask
this_mask = 1 - mask
attention_slice = remapped_saved_attention_slice * saved_mask + \
this_attention_slice * this_mask
else:
# just use everything
attention_slice = saved_attention_slice
return attention_slice
cross_attention_modules = get_cross_attention_modules(unet, CrossAttentionType.TOKENS) + get_cross_attention_modules(unet, CrossAttentionType.SELF)
for identifier, module in cross_attention_modules:
module.identifier = identifier
try:
module.set_attention_slice_wrangler(attention_slice_wrangler)
module.set_slicing_strategy_getter(
lambda module: context.get_slicing_strategy(identifier)
)
except AttributeError as e:
if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
print(f"TODO: implement set_attention_slice_wrangler for {type(module)}") # TODO
else:
raise
def remove_attention_function(unet):
cross_attention_modules = get_cross_attention_modules(unet, CrossAttentionType.TOKENS) + get_cross_attention_modules(unet, CrossAttentionType.SELF)
for identifier, module in cross_attention_modules:
try:
# clear wrangler callback
module.set_attention_slice_wrangler(None)
module.set_slicing_strategy_getter(None)
except AttributeError as e:
if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")
else:
raise
def is_attribute_error_about(error: AttributeError, attribute: str):
if hasattr(error, 'name'): # Python 3.10
return error.name == attribute
else: # Python 3.9
return attribute in str(error)
def get_mem_free_total(device):
#only on cuda
if not torch.cuda.is_available():
return None
stats = torch.cuda.memory_stats(device)
mem_active = stats['active_bytes.all.current']
mem_reserved = stats['reserved_bytes.all.current']
mem_free_cuda, _ = torch.cuda.mem_get_info(device)
mem_free_torch = mem_reserved - mem_active
mem_free_total = mem_free_cuda + mem_free_torch
return mem_free_total
class InvokeAIDiffusersCrossAttention(diffusers.models.attention.CrossAttention, InvokeAICrossAttentionMixin):
def __init__(self, **kwargs):
super().__init__(**kwargs)
InvokeAICrossAttentionMixin.__init__(self)
def _attention(self, query, key, value, attention_mask=None):
#default_result = super()._attention(query, key, value)
if attention_mask is not None:
print(f"{type(self).__name__} ignoring passed-in attention_mask")
attention_result = self.get_invokeai_attention_mem_efficient(query, key, value)
hidden_states = self.reshape_batch_dim_to_heads(attention_result)
return hidden_states