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Running
on
Zero
Running
on
Zero
| 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, | |
| } | |
| required_keys = {} | |
| clip_prefix = [] | |
| clip_vision_prefix = None | |
| noise_aug_config = None | |
| sampling_settings = {} | |
| latent_format = latent_formats.LatentFormat | |
| vae_key_prefix = ["first_stage_model."] | |
| text_encoder_key_prefix = ["cond_stage_model."] | |
| supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] | |
| memory_usage_factor = 2.0 | |
| manual_cast_dtype = None | |
| def matches(s, unet_config, state_dict=None): | |
| for k in s.unet_config: | |
| if k not in unet_config or s.unet_config[k] != unet_config[k]: | |
| return False | |
| if state_dict is not None: | |
| for k in s.required_keys: | |
| if k not in state_dict: | |
| 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.copy() | |
| self.sampling_settings = self.sampling_settings.copy() | |
| 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): | |
| state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) | |
| 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 = {"": self.text_encoder_key_prefix[0]} | |
| return utils.state_dict_prefix_replace(state_dict, replace_prefix) | |
| def process_clip_vision_state_dict_for_saving(self, state_dict): | |
| replace_prefix = {} | |
| if self.clip_vision_prefix is not None: | |
| replace_prefix[""] = self.clip_vision_prefix | |
| 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 = {"": self.vae_key_prefix[0]} | |
| return utils.state_dict_prefix_replace(state_dict, replace_prefix) | |
| def set_inference_dtype(self, dtype, manual_cast_dtype): | |
| self.unet_config['dtype'] = dtype | |
| self.manual_cast_dtype = manual_cast_dtype | |