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Update Backend

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Update Backend
This commit is contained in:
lllyasviel
2023-10-25 09:40:13 -07:00
parent bb965067e0
commit 38e70cebcc
11 changed files with 288 additions and 252 deletions
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backend/headless/fcbh/model_base.py
+14 -7
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@@ -4,6 +4,7 @@ from fcbh.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmen
from fcbh.ldm.modules.diffusionmodules.util import make_beta_schedule
from fcbh.ldm.modules.diffusionmodules.openaimodel import Timestep
import fcbh.model_management
import fcbh.conds
import numpy as np
from enum import Enum
from . import utils
@@ -49,7 +50,7 @@ class BaseModel(torch.nn.Module):
self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
def apply_model(self, x, t, c_concat=None, c_crossattn=None, c_adm=None, control=None, transformer_options={}):
def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
if c_concat is not None:
xc = torch.cat([x] + [c_concat], dim=1)
else:
@@ -59,9 +60,10 @@ class BaseModel(torch.nn.Module):
xc = xc.to(dtype)
t = t.to(dtype)
context = context.to(dtype)
if c_adm is not None:
c_adm = c_adm.to(dtype)
return self.diffusion_model(xc, t, context=context, y=c_adm, control=control, transformer_options=transformer_options).float()
extra_conds = {}
for o in kwargs:
extra_conds[o] = kwargs[o].to(dtype)
return self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
def get_dtype(self):
return self.diffusion_model.dtype
@@ -72,7 +74,8 @@ class BaseModel(torch.nn.Module):
def encode_adm(self, **kwargs):
return None
def cond_concat(self, **kwargs):
def extra_conds(self, **kwargs):
out = {}
if self.inpaint_model:
concat_keys = ("mask", "masked_image")
cond_concat = []
@@ -101,8 +104,12 @@ class BaseModel(torch.nn.Module):
cond_concat.append(torch.ones_like(noise)[:,:1])
elif ck == "masked_image":
cond_concat.append(blank_inpaint_image_like(noise))
return cond_concat
return None
data = torch.cat(cond_concat, dim=1)
out['c_concat'] = fcbh.conds.CONDNoiseShape(data)
adm = self.encode_adm(**kwargs)
if adm is not None:
out['y'] = fcbh.conds.CONDRegular(adm)
return out
def load_model_weights(self, sd, unet_prefix=""):
to_load = {}