183 lines
7.0 KiB
Python
183 lines
7.0 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 sd1_clip
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from . import sd2_clip
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from . import sdxl_clip
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from . import supported_models_base
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from . import latent_formats
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from . import diffusers_convert
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class SD15(supported_models_base.BASE):
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unet_config = {
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"context_dim": 768,
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"model_channels": 320,
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"use_linear_in_transformer": False,
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"adm_in_channels": None,
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}
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unet_extra_config = {
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"num_heads": 8,
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"num_head_channels": -1,
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}
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latent_format = latent_formats.SD15
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def process_clip_state_dict(self, state_dict):
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k = list(state_dict.keys())
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for x in k:
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if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."):
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y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.")
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state_dict[y] = state_dict.pop(x)
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if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict:
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ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids']
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if ids.dtype == torch.float32:
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state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
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return state_dict
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def clip_target(self):
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return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel)
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class SD20(supported_models_base.BASE):
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unet_config = {
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"context_dim": 1024,
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"model_channels": 320,
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"use_linear_in_transformer": True,
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"adm_in_channels": None,
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}
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latent_format = latent_formats.SD15
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def v_prediction(self, state_dict, prefix=""):
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if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction
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k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix)
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out = state_dict[k]
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if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
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return True
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return False
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def process_clip_state_dict(self, state_dict):
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state_dict = utils.transformers_convert(state_dict, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24)
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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 = {}
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replace_prefix[""] = "cond_stage_model.model."
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state_dict = supported_models_base.state_dict_prefix_replace(state_dict, replace_prefix)
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state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)
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return state_dict
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def clip_target(self):
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return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel)
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class SD21UnclipL(SD20):
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unet_config = {
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"context_dim": 1024,
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"model_channels": 320,
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"use_linear_in_transformer": True,
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"adm_in_channels": 1536,
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}
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clip_vision_prefix = "embedder.model.visual."
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noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768}
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class SD21UnclipH(SD20):
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unet_config = {
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"context_dim": 1024,
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"model_channels": 320,
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"use_linear_in_transformer": True,
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"adm_in_channels": 2048,
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}
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clip_vision_prefix = "embedder.model.visual."
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noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024}
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class SDXLRefiner(supported_models_base.BASE):
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unet_config = {
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"model_channels": 384,
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"use_linear_in_transformer": True,
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"context_dim": 1280,
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"adm_in_channels": 2560,
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"transformer_depth": [0, 4, 4, 0],
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}
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latent_format = latent_formats.SDXL
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def get_model(self, state_dict, prefix=""):
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return model_base.SDXLRefiner(self)
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def process_clip_state_dict(self, state_dict):
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keys_to_replace = {}
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replace_prefix = {}
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state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.0.model.", "cond_stage_model.clip_g.transformer.text_model.", 32)
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keys_to_replace["conditioner.embedders.0.model.text_projection"] = "cond_stage_model.clip_g.text_projection"
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keys_to_replace["conditioner.embedders.0.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale"
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state_dict = supported_models_base.state_dict_key_replace(state_dict, keys_to_replace)
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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 = {}
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state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
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state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids")
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replace_prefix["clip_g"] = "conditioner.embedders.0.model"
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state_dict_g = supported_models_base.state_dict_prefix_replace(state_dict_g, replace_prefix)
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return state_dict_g
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def clip_target(self):
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return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel)
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class SDXL(supported_models_base.BASE):
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unet_config = {
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"model_channels": 320,
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"use_linear_in_transformer": True,
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"transformer_depth": [0, 2, 10],
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"context_dim": 2048,
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"adm_in_channels": 2816
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}
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latent_format = latent_formats.SDXL
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def get_model(self, state_dict, prefix=""):
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return model_base.SDXL(self)
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def process_clip_state_dict(self, state_dict):
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keys_to_replace = {}
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replace_prefix = {}
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replace_prefix["conditioner.embedders.0.transformer.text_model"] = "cond_stage_model.clip_l.transformer.text_model"
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state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.1.model.", "cond_stage_model.clip_g.transformer.text_model.", 32)
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keys_to_replace["conditioner.embedders.1.model.text_projection"] = "cond_stage_model.clip_g.text_projection"
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keys_to_replace["conditioner.embedders.1.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale"
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state_dict = supported_models_base.state_dict_prefix_replace(state_dict, replace_prefix)
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state_dict = supported_models_base.state_dict_key_replace(state_dict, keys_to_replace)
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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 = {}
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keys_to_replace = {}
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state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
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state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids")
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for k in state_dict:
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if k.startswith("clip_l"):
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state_dict_g[k] = state_dict[k]
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replace_prefix["clip_g"] = "conditioner.embedders.1.model"
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replace_prefix["clip_l"] = "conditioner.embedders.0"
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state_dict_g = supported_models_base.state_dict_prefix_replace(state_dict_g, replace_prefix)
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return state_dict_g
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def clip_target(self):
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return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel)
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models = [SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL]
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