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 beta_schedule = "linear" 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_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)