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Code Issues Packages Projects Releases Wiki Activity

resolve some undefined symbols in model_cache

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This commit is contained in:
Lincoln Stein
2023-05-18 14:31:47 -04:00
parent b1a99d772c 7025c00581
commit d96175d127
223 changed files with 3236 additions and 8781 deletions
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8
invokeai/backend/stable_diffusion/concepts_lib.py
View File

@@ -18,15 +18,15 @@ from huggingface_hub import (
)
import invokeai.backend.util.logging as logger
from invokeai.backend.globals import Globals
from invokeai.app.services.config import get_invokeai_config
class HuggingFaceConceptsLibrary(object):
def __init__(self, root=None):
"""
Initialize the Concepts object. May optionally pass a root directory.
"""
self.root = root or Globals.root
self.config = get_invokeai_config()
self.root = root or self.config.root
self.hf_api = HfApi()
self.local_concepts = dict()
self.concept_list = None
@@ -58,7 +58,7 @@ class HuggingFaceConceptsLibrary(object):
self.concept_list.extend(list(local_concepts_to_add))
return self.concept_list
return self.concept_list
elif Globals.internet_available is True:
elif self.config.internet_available is True:
try:
models = self.hf_api.list_models(
filter=ModelFilter(model_name="sd-concepts-library/")

12
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@@ -33,8 +33,7 @@ from torchvision.transforms.functional import resize as tv_resize
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from typing_extensions import ParamSpec
from invokeai.backend.globals import Globals
from invokeai.app.services.config import get_invokeai_config
from ..util import CPU_DEVICE, normalize_device
from .diffusion import (
AttentionMapSaver,
@@ -44,7 +43,6 @@ from .diffusion import (
from .offloading import FullyLoadedModelGroup, LazilyLoadedModelGroup, ModelGroup
from .textual_inversion_manager import TextualInversionManager
@dataclass
class PipelineIntermediateState:
run_id: str
@@ -348,10 +346,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
"""
if xformers is available, use it, otherwise use sliced attention.
"""
config = get_invokeai_config()
if (
torch.cuda.is_available()
and is_xformers_available()
and not Globals.disable_xformers
and not config.disable_xformers
):
self.enable_xformers_memory_efficient_attention()
else:
@@ -548,8 +547,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
additional_guidance = []
extra_conditioning_info = conditioning_data.extra
with self.invokeai_diffuser.custom_attention_context(
extra_conditioning_info=extra_conditioning_info,
step_count=len(self.scheduler.timesteps),
self.invokeai_diffuser.model,
extra_conditioning_info=extra_conditioning_info,
step_count=len(self.scheduler.timesteps),
):
yield PipelineIntermediateState(
run_id=run_id,

37
invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
View File

@@ -10,6 +10,7 @@ import diffusers
import psutil
import torch
from compel.cross_attention_control import Arguments
from diffusers.models.unet_2d_condition import UNet2DConditionModel
from diffusers.models.attention_processor import AttentionProcessor
from torch import nn
@@ -352,8 +353,7 @@ def restore_default_cross_attention(
else:
remove_attention_function(model)
def override_cross_attention(model, context: Context, is_running_diffusers=False):
def setup_cross_attention_control_attention_processors(unet: UNet2DConditionModel, context: Context):
"""
Inject attention parameters and functions into the passed in model to enable cross attention editing.
@@ -372,37 +372,22 @@ def override_cross_attention(model, context: Context, is_running_diffusers=False
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):
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.cross_attention_mask = mask.to(device)
context.cross_attention_index_map = indices.to(device)
if is_running_diffusers:
unet = model
old_attn_processors = unet.attn_processors
if torch.backends.mps.is_available():
# see note in StableDiffusionGeneratorPipeline.__init__ about borked slicing on MPS
unet.set_attn_processor(SwapCrossAttnProcessor())
else:
# try to re-use an existing slice size
default_slice_size = 4
slice_size = next(
(
p.slice_size
for p in old_attn_processors.values()
if type(p) is SlicedAttnProcessor
),
default_slice_size,
)
unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
return old_attn_processors
old_attn_processors = unet.attn_processors
if torch.backends.mps.is_available():
# see note in StableDiffusionGeneratorPipeline.__init__ about borked slicing on MPS
unet.set_attn_processor(SwapCrossAttnProcessor())
else:
context.register_cross_attention_modules(model)
inject_attention_function(model, context)
return None
# try to re-use an existing slice size
default_slice_size = 4
slice_size = next((p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size)
unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
def get_cross_attention_modules(
model, which: CrossAttentionType

44
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@@ -5,11 +5,12 @@ from typing import Any, Callable, Dict, Optional, Union
import numpy as np
import torch
from diffusers import UNet2DConditionModel
from diffusers.models.attention_processor import AttentionProcessor
from typing_extensions import TypeAlias
import invokeai.backend.util.logging as logger
from invokeai.backend.globals import Globals
from invokeai.app.services.config import get_invokeai_config
from .cross_attention_control import (
Arguments,
@@ -17,8 +18,8 @@ from .cross_attention_control import (
CrossAttentionType,
SwapCrossAttnContext,
get_cross_attention_modules,
override_cross_attention,
restore_default_cross_attention,
setup_cross_attention_control_attention_processors,
)
from .cross_attention_map_saving import AttentionMapSaver
@@ -31,7 +32,6 @@ ModelForwardCallback: TypeAlias = Union[
Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor],
]
@dataclass(frozen=True)
class PostprocessingSettings:
threshold: float
@@ -72,31 +72,43 @@ class InvokeAIDiffuserComponent:
: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)
"""
config = get_invokeai_config()
self.conditioning = None
self.model = model
self.is_running_diffusers = is_running_diffusers
self.model_forward_callback = model_forward_callback
self.cross_attention_control_context = None
self.sequential_guidance = Globals.sequential_guidance
self.sequential_guidance = config.sequential_guidance
@classmethod
@contextmanager
def custom_attention_context(
self, extra_conditioning_info: Optional[ExtraConditioningInfo], step_count: int
cls,
unet: UNet2DConditionModel, # note: also may futz with the text encoder depending on requested LoRAs
extra_conditioning_info: Optional[ExtraConditioningInfo],
step_count: int
):
do_swap = (
extra_conditioning_info is not None
and extra_conditioning_info.wants_cross_attention_control
)
old_attn_processor = None
if do_swap:
old_attn_processor = self.override_cross_attention(
extra_conditioning_info, step_count=step_count
)
old_attn_processors = None
if extra_conditioning_info and (
extra_conditioning_info.wants_cross_attention_control
):
old_attn_processors = unet.attn_processors
# Load lora conditions into the model
if extra_conditioning_info.wants_cross_attention_control:
cross_attention_control_context = Context(
arguments=extra_conditioning_info.cross_attention_control_args,
step_count=step_count,
)
setup_cross_attention_control_attention_processors(
unet,
cross_attention_control_context,
)
try:
yield None
finally:
if old_attn_processor is not None:
self.restore_default_cross_attention(old_attn_processor)
if old_attn_processors is not None:
unet.set_attn_processor(old_attn_processors)
# TODO resuscitate attention map saving
# self.remove_attention_map_saving()

3
invokeai/backend/stable_diffusion/schedulers/schedulers.py
View File

@@ -9,7 +9,8 @@ SCHEDULER_MAP = dict(
deis=(DEISMultistepScheduler, dict()),
lms=(LMSDiscreteScheduler, dict()),
pndm=(PNDMScheduler, dict()),
heun=(HeunDiscreteScheduler, dict()),
heun=(HeunDiscreteScheduler, dict(use_karras_sigmas=False)),
heun_k=(HeunDiscreteScheduler, dict(use_karras_sigmas=True)),
euler=(EulerDiscreteScheduler, dict(use_karras_sigmas=False)),
euler_k=(EulerDiscreteScheduler, dict(use_karras_sigmas=True)),
euler_a=(EulerAncestralDiscreteScheduler, dict()),