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import torch
from . import model_base
from . import utils
from . import latent_formats
class ClipTarget:
def __init__(self, tokenizer, clip):
self.clip = clip
self.tokenizer = tokenizer
self.params = {}
class BASE:
unet_config = {}
unet_extra_config = {
"num_heads": -1,
"num_head_channels": 64,
}
clip_prefix = []
clip_vision_prefix = None
noise_aug_config = None
sampling_settings = {}
latent_format = latent_formats.LatentFormat
@classmethod
def matches(s, unet_config):
for k in s.unet_config:
if s.unet_config[k] != unet_config[k]:
return False
return True
def model_type(self, state_dict, prefix=""):
return model_base.ModelType.EPS
def inpaint_model(self):
return self.unet_config["in_channels"] > 4
def __init__(self, unet_config):
self.unet_config = unet_config
self.latent_format = self.latent_format()
for x in self.unet_extra_config:
self.unet_config[x] = self.unet_extra_config[x]
def get_model(self, state_dict, prefix="", device=None):
if self.noise_aug_config is not None:
out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device)
else:
out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device)
if self.inpaint_model():
out.set_inpaint()
return out
def process_clip_state_dict(self, state_dict):
return state_dict
def process_unet_state_dict(self, state_dict):
return state_dict
def process_vae_state_dict(self, state_dict):
return state_dict
def process_clip_state_dict_for_saving(self, state_dict):
replace_prefix = {"": "cond_stage_model."}
return utils.state_dict_prefix_replace(state_dict, replace_prefix)
def process_unet_state_dict_for_saving(self, state_dict):
replace_prefix = {"": "model.diffusion_model."}
return utils.state_dict_prefix_replace(state_dict, replace_prefix)
def process_vae_state_dict_for_saving(self, state_dict):
replace_prefix = {"": "first_stage_model."}
return utils.state_dict_prefix_replace(state_dict, replace_prefix)
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