supported_models.py

import torch
from . import model_base
from . import utils

from . import sd1_clip
from . import sd2_clip
from . import sdxl_clip

from . import supported_models_base
from . import latent_formats

from . import diffusers_convert

class SD15(supported_models_base.BASE):
    unet_config = {
        "context_dim": 768,
        "model_channels": 320,
        "use_linear_in_transformer": False,
        "adm_in_channels": None,
    }

    unet_extra_config = {
        "num_heads": 8,
        "num_head_channels": -1,
    }

    latent_format = latent_formats.SD15

    def process_clip_state_dict(self, state_dict):
        k = list(state_dict.keys())
        for x in k:
            if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."):
                y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.")
                state_dict[y] = state_dict.pop(x)

        if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict:
            ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids']
            if ids.dtype == torch.float32:
                state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()

        return state_dict

    def clip_target(self):
        return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel)

class SD20(supported_models_base.BASE):
    unet_config = {
        "context_dim": 1024,
        "model_channels": 320,
        "use_linear_in_transformer": True,
        "adm_in_channels": None,
    }

    latent_format = latent_formats.SD15

    def model_type(self, state_dict, prefix=""):
        if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction
            k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix)
            out = state_dict[k]
            if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
                return model_base.ModelType.V_PREDICTION
        return model_base.ModelType.EPS

    def process_clip_state_dict(self, state_dict):
        state_dict = utils.transformers_convert(state_dict, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24)
        return state_dict

    def process_clip_state_dict_for_saving(self, state_dict):
        replace_prefix = {}
        replace_prefix[""] = "cond_stage_model.model."
        state_dict = supported_models_base.state_dict_prefix_replace(state_dict, replace_prefix)
        state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)
        return state_dict

    def clip_target(self):
        return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel)

class SD21UnclipL(SD20):
    unet_config = {
        "context_dim": 1024,
        "model_channels": 320,
        "use_linear_in_transformer": True,
        "adm_in_channels": 1536,
    }

    clip_vision_prefix = "embedder.model.visual."
    noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768}


class SD21UnclipH(SD20):
    unet_config = {
        "context_dim": 1024,
        "model_channels": 320,
        "use_linear_in_transformer": True,
        "adm_in_channels": 2048,
    }

    clip_vision_prefix = "embedder.model.visual."
    noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024}

class SDXLRefiner(supported_models_base.BASE):
    unet_config = {
        "model_channels": 384,
        "use_linear_in_transformer": True,
        "context_dim": 1280,
        "adm_in_channels": 2560,
        "transformer_depth": [0, 4, 4, 0],
    }

    latent_format = latent_formats.SDXL

    def get_model(self, state_dict, prefix=""):
        return model_base.SDXLRefiner(self)

    def process_clip_state_dict(self, state_dict):
        keys_to_replace = {}
        replace_prefix = {}

        state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.0.model.", "cond_stage_model.clip_g.transformer.text_model.", 32)
        keys_to_replace["conditioner.embedders.0.model.text_projection"] = "cond_stage_model.clip_g.text_projection"
        keys_to_replace["conditioner.embedders.0.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale"

        state_dict = supported_models_base.state_dict_key_replace(state_dict, keys_to_replace)
        return state_dict

    def process_clip_state_dict_for_saving(self, state_dict):
        replace_prefix = {}
        state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
        state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids")
        replace_prefix["clip_g"] = "conditioner.embedders.0.model"
        state_dict_g = supported_models_base.state_dict_prefix_replace(state_dict_g, replace_prefix)
        return state_dict_g

    def clip_target(self):
        return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel)

class SDXL(supported_models_base.BASE):
    unet_config = {
        "model_channels": 320,
        "use_linear_in_transformer": True,
        "transformer_depth": [0, 2, 10],
        "context_dim": 2048,
        "adm_in_channels": 2816
    }

    latent_format = latent_formats.SDXL

    def model_type(self, state_dict, prefix=""):
        if "v_pred" in state_dict:
            return model_base.ModelType.V_PREDICTION
        else:
            return model_base.ModelType.EPS

    def get_model(self, state_dict, prefix=""):
        return model_base.SDXL(self, model_type=self.model_type(state_dict, prefix))

    def process_clip_state_dict(self, state_dict):
        keys_to_replace = {}
        replace_prefix = {}

        replace_prefix["conditioner.embedders.0.transformer.text_model"] = "cond_stage_model.clip_l.transformer.text_model"
        state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.1.model.", "cond_stage_model.clip_g.transformer.text_model.", 32)
        keys_to_replace["conditioner.embedders.1.model.text_projection"] = "cond_stage_model.clip_g.text_projection"
        keys_to_replace["conditioner.embedders.1.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale"

        state_dict = supported_models_base.state_dict_prefix_replace(state_dict, replace_prefix)
        state_dict = supported_models_base.state_dict_key_replace(state_dict, keys_to_replace)
        return state_dict

    def process_clip_state_dict_for_saving(self, state_dict):
        replace_prefix = {}
        keys_to_replace = {}
        state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
        state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids")
        for k in state_dict:
            if k.startswith("clip_l"):
                state_dict_g[k] = state_dict[k]

        replace_prefix["clip_g"] = "conditioner.embedders.1.model"
        replace_prefix["clip_l"] = "conditioner.embedders.0"
        state_dict_g = supported_models_base.state_dict_prefix_replace(state_dict_g, replace_prefix)
        return state_dict_g

    def clip_target(self):
        return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel)


models = [SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL]