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import torch
from packaging import version
from . import devices
from .sd_hijack_utils import CondFunc
from torch.nn.functional import silu
import comfy
from comfy import ldm
import contextlib
class TorchHijackForUnet:
"""
This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
"""
def __getattr__(self, item):
if item == 'cat':
return self.cat
if hasattr(torch, item):
return getattr(torch, item)
raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
def cat(self, tensors, *args, **kwargs):
if len(tensors) == 2:
a, b = tensors
if a.shape[-2:] != b.shape[-2:]:
a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
tensors = (a, b)
return torch.cat(tensors, *args, **kwargs)
th = TorchHijackForUnet()
from . import sd_hijack_optimizations
from comfy.model_base import BaseModel
from functools import wraps
sdp_no_mem = sd_hijack_optimizations.SdOptimizationSdpNoMem()
BaseModel.apply_model_orig = BaseModel.apply_model
# @contextmanager
class ApplyOptimizationsContext:
def __init__(self):
self.nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
self.th = ldm.modules.diffusionmodules.openaimodel.th
ldm.modules.diffusionmodules.model.nonlinearity = silu
ldm.modules.diffusionmodules.openaimodel.th = th
sdp_no_mem.apply()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
ldm.modules.diffusionmodules.model.nonlinearity = self.nonlinearity
ldm.modules.diffusionmodules.openaimodel.th = self.th
sd_hijack_optimizations.undo()
def ApplyOptimizationsContext3(func):
@wraps(func)
def wrapper(*args, **kwargs):
with ApplyOptimizationsContext():
return func(*args, **kwargs)
return wrapper
precision_scope_null = lambda a, dtype=None: contextlib.nullcontext(a)
# def apply_model(orig_func, self, x_noisy, t, c_concat=None, c_crossattn=None, c_adm=None, control=None, transformer_options={}, *args, **kwargs):
def apply_model(orig_func, self, *args, **kwargs):
transformer_options = kwargs['transformer_options'] if 'transformer_options' in kwargs else {}
c_crossattn = kwargs['c_crossattn'] if 'c_crossattn' in kwargs else args[3]
x_noisy = kwargs['x_noisy'] if 'x_noisy' in kwargs else args[0]
if not transformer_options.get('from_smZ', False):
return self.apply_model_orig(*args, **kwargs)
cond=c_crossattn
if isinstance(cond, dict):
for y in cond.keys():
if isinstance(cond[y], list):
cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
else:
cond[y] = cond[y].to(devices.dtype_unet) if isinstance(cond[y], torch.Tensor) else cond[y]
if x_noisy.dtype != torch.float32:
precision_scope = torch.autocast
else:
precision_scope = precision_scope_null
with precision_scope(comfy.model_management.get_autocast_device(x_noisy.device), dtype=x_noisy.dtype): # , torch.float32):
# with devices.autocast():
out = orig_func(self, *args, **kwargs).float()
return out
class GELUHijack(torch.nn.GELU, torch.nn.Module):
def __init__(self, *args, **kwargs):
torch.nn.GELU.__init__(self, *args, **kwargs)
def forward(self, x):
if devices.unet_needs_upcast:
return torch.nn.GELU.forward(self.float(), x.float()).to(devices.dtype_unet)
else:
return torch.nn.GELU.forward(self, x)
ddpm_edit_hijack = None
def hijack_ddpm_edit():
global ddpm_edit_hijack
if not ddpm_edit_hijack:
CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
# CondFunc('comfy.model_base.BaseModel.apply_model', apply_model, unet_needs_upcast)
# CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
# CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
# if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
# CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
# CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
# try:
# CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
# except:
# CondFunc('comfy.t2i_adapter.adapter.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
first_stage_cond = lambda _, self, *args, **kwargs: devices.unet_needs_upcast and self.model.diffusion_model.dtype == torch.float16
first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devices.dtype_vae), **kwargs)
# CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
# CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
# CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
# CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model, unet_needs_upcast)
# CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
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