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