120 lines
4.2 KiB
Python
120 lines
4.2 KiB
Python
"""
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This file is part of ComfyUI.
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Copyright (C) 2024 Comfy
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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"""
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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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required_keys = {}
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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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sampling_settings = {}
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latent_format = latent_formats.LatentFormat
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vae_key_prefix = ["first_stage_model."]
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text_encoder_key_prefix = ["cond_stage_model."]
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supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
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memory_usage_factor = 2.0
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manual_cast_dtype = None
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custom_operations = None
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scaled_fp8 = False
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optimizations = {"fp8": False}
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@classmethod
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def matches(s, unet_config, state_dict=None):
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for k in s.unet_config:
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if k not in unet_config or s.unet_config[k] != unet_config[k]:
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return False
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if state_dict is not None:
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for k in s.required_keys:
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if k not in state_dict:
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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.copy()
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self.sampling_settings = self.sampling_settings.copy()
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self.latent_format = self.latent_format()
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self.optimizations = self.optimizations.copy()
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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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state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
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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 = {"": self.text_encoder_key_prefix[0]}
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return utils.state_dict_prefix_replace(state_dict, replace_prefix)
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def process_clip_vision_state_dict_for_saving(self, state_dict):
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replace_prefix = {}
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if self.clip_vision_prefix is not None:
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replace_prefix[""] = self.clip_vision_prefix
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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 = {"": self.vae_key_prefix[0]}
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return utils.state_dict_prefix_replace(state_dict, replace_prefix)
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def set_inference_dtype(self, dtype, manual_cast_dtype):
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self.unet_config['dtype'] = dtype
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self.manual_cast_dtype = manual_cast_dtype
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