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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_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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