ComfyUI/comfy/sd.py

from __future__ import annotations
import json
import torch
from enum import Enum
import logging

from comfy import model_management
from comfy.utils import ProgressBar
from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine
from .ldm.cascade.stage_a import StageA
from .ldm.cascade.stage_c_coder import StageC_coder
from .ldm.audio.autoencoder import AudioOobleckVAE
import comfy.ldm.genmo.vae.model
import comfy.ldm.lightricks.vae.causal_video_autoencoder
import comfy.ldm.cosmos.vae
import comfy.ldm.wan.vae
import comfy.ldm.hunyuan3d.vae
import yaml
import math

import comfy.utils

from . import clip_vision
from . import gligen
from . import diffusers_convert
from . import model_detection

from . import sd1_clip
from . import sdxl_clip
import comfy.text_encoders.sd2_clip
import comfy.text_encoders.sd3_clip
import comfy.text_encoders.sa_t5
import comfy.text_encoders.aura_t5
import comfy.text_encoders.pixart_t5
import comfy.text_encoders.hydit
import comfy.text_encoders.flux
import comfy.text_encoders.long_clipl
import comfy.text_encoders.genmo
import comfy.text_encoders.lt
import comfy.text_encoders.hunyuan_video
import comfy.text_encoders.cosmos
import comfy.text_encoders.lumina2
import comfy.text_encoders.wan

import comfy.model_patcher
import comfy.lora
import comfy.lora_convert
import comfy.hooks
import comfy.t2i_adapter.adapter
import comfy.taesd.taesd

import comfy.ldm.flux.redux

def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
    key_map = {}
    if model is not None:
        key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
    if clip is not None:
        key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)

    lora = comfy.lora_convert.convert_lora(lora)
    loaded = comfy.lora.load_lora(lora, key_map)
    if model is not None:
        new_modelpatcher = model.clone()
        k = new_modelpatcher.add_patches(loaded, strength_model)
    else:
        k = ()
        new_modelpatcher = None

    if clip is not None:
        new_clip = clip.clone()
        k1 = new_clip.add_patches(loaded, strength_clip)
    else:
        k1 = ()
        new_clip = None
    k = set(k)
    k1 = set(k1)
    for x in loaded:
        if (x not in k) and (x not in k1):
            logging.warning("NOT LOADED {}".format(x))

    return (new_modelpatcher, new_clip)


class CLIP:
    def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, model_options={}):
        if no_init:
            return
        params = target.params.copy()
        clip = target.clip
        tokenizer = target.tokenizer

        load_device = model_options.get("load_device", model_management.text_encoder_device())
        offload_device = model_options.get("offload_device", model_management.text_encoder_offload_device())
        dtype = model_options.get("dtype", None)
        if dtype is None:
            dtype = model_management.text_encoder_dtype(load_device)

        params['dtype'] = dtype
        params['device'] = model_options.get("initial_device", model_management.text_encoder_initial_device(load_device, offload_device, parameters * model_management.dtype_size(dtype)))
        params['model_options'] = model_options

        self.cond_stage_model = clip(**(params))

        for dt in self.cond_stage_model.dtypes:
            if not model_management.supports_cast(load_device, dt):
                load_device = offload_device
                if params['device'] != offload_device:
                    self.cond_stage_model.to(offload_device)
                    logging.warning("Had to shift TE back.")

        self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
        self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
        self.patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram
        self.patcher.is_clip = True
        self.apply_hooks_to_conds = None
        if params['device'] == load_device:
            model_management.load_models_gpu([self.patcher], force_full_load=True)
        self.layer_idx = None
        self.use_clip_schedule = False
        logging.info("CLIP/text encoder model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype))

    def clone(self):
        n = CLIP(no_init=True)
        n.patcher = self.patcher.clone()
        n.cond_stage_model = self.cond_stage_model
        n.tokenizer = self.tokenizer
        n.layer_idx = self.layer_idx
        n.use_clip_schedule = self.use_clip_schedule
        n.apply_hooks_to_conds = self.apply_hooks_to_conds
        return n

    def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
        return self.patcher.add_patches(patches, strength_patch, strength_model)

    def clip_layer(self, layer_idx):
        self.layer_idx = layer_idx

    def tokenize(self, text, return_word_ids=False, **kwargs):
        return self.tokenizer.tokenize_with_weights(text, return_word_ids, **kwargs)

    def add_hooks_to_dict(self, pooled_dict: dict[str]):
        if self.apply_hooks_to_conds:
            pooled_dict["hooks"] = self.apply_hooks_to_conds
        return pooled_dict

    def encode_from_tokens_scheduled(self, tokens, unprojected=False, add_dict: dict[str]={}, show_pbar=True):
        all_cond_pooled: list[tuple[torch.Tensor, dict[str]]] = []
        all_hooks = self.patcher.forced_hooks
        if all_hooks is None or not self.use_clip_schedule:
            # if no hooks or shouldn't use clip schedule, do unscheduled encode_from_tokens and perform add_dict
            return_pooled = "unprojected" if unprojected else True
            pooled_dict = self.encode_from_tokens(tokens, return_pooled=return_pooled, return_dict=True)
            cond = pooled_dict.pop("cond")
            # add/update any keys with the provided add_dict
            pooled_dict.update(add_dict)
            all_cond_pooled.append([cond, pooled_dict])
        else:
            scheduled_keyframes = all_hooks.get_hooks_for_clip_schedule()

            self.cond_stage_model.reset_clip_options()
            if self.layer_idx is not None:
                self.cond_stage_model.set_clip_options({"layer": self.layer_idx})
            if unprojected:
                self.cond_stage_model.set_clip_options({"projected_pooled": False})

            self.load_model()
            all_hooks.reset()
            self.patcher.patch_hooks(None)
            if show_pbar:
                pbar = ProgressBar(len(scheduled_keyframes))

            for scheduled_opts in scheduled_keyframes:
                t_range = scheduled_opts[0]
                # don't bother encoding any conds outside of start_percent and end_percent bounds
                if "start_percent" in add_dict:
                    if t_range[1] < add_dict["start_percent"]:
                        continue
                if "end_percent" in add_dict:
                    if t_range[0] > add_dict["end_percent"]:
                        continue
                hooks_keyframes = scheduled_opts[1]
                for hook, keyframe in hooks_keyframes:
                    hook.hook_keyframe._current_keyframe = keyframe
                # apply appropriate hooks with values that match new hook_keyframe
                self.patcher.patch_hooks(all_hooks)
                # perform encoding as normal
                o = self.cond_stage_model.encode_token_weights(tokens)
                cond, pooled = o[:2]
                pooled_dict = {"pooled_output": pooled}
                # add clip_start_percent and clip_end_percent in pooled
                pooled_dict["clip_start_percent"] = t_range[0]
                pooled_dict["clip_end_percent"] = t_range[1]
                # add/update any keys with the provided add_dict
                pooled_dict.update(add_dict)
                # add hooks stored on clip
                self.add_hooks_to_dict(pooled_dict)
                all_cond_pooled.append([cond, pooled_dict])
                if show_pbar:
                    pbar.update(1)
                model_management.throw_exception_if_processing_interrupted()
            all_hooks.reset()
        return all_cond_pooled

    def encode_from_tokens(self, tokens, return_pooled=False, return_dict=False):
        self.cond_stage_model.reset_clip_options()

        if self.layer_idx is not None:
            self.cond_stage_model.set_clip_options({"layer": self.layer_idx})

        if return_pooled == "unprojected":
            self.cond_stage_model.set_clip_options({"projected_pooled": False})

        self.load_model()
        o = self.cond_stage_model.encode_token_weights(tokens)
        cond, pooled = o[:2]
        if return_dict:
            out = {"cond": cond, "pooled_output": pooled}
            if len(o) > 2:
                for k in o[2]:
                    out[k] = o[2][k]
            self.add_hooks_to_dict(out)
            return out

        if return_pooled:
            return cond, pooled
        return cond

    def encode(self, text):
        tokens = self.tokenize(text)
        return self.encode_from_tokens(tokens)

    def load_sd(self, sd, full_model=False):
        if full_model:
            return self.cond_stage_model.load_state_dict(sd, strict=False)
        else:
            return self.cond_stage_model.load_sd(sd)

    def get_sd(self):
        sd_clip = self.cond_stage_model.state_dict()
        sd_tokenizer = self.tokenizer.state_dict()
        for k in sd_tokenizer:
            sd_clip[k] = sd_tokenizer[k]
        return sd_clip

    def load_model(self):
        model_management.load_model_gpu(self.patcher)
        return self.patcher

    def get_key_patches(self):
        return self.patcher.get_key_patches()

class VAE:
    def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None):
        if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
            sd = diffusers_convert.convert_vae_state_dict(sd)

        self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower)
        self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype)
        self.downscale_ratio = 8
        self.upscale_ratio = 8
        self.latent_channels = 4
        self.latent_dim = 2
        self.output_channels = 3
        self.process_input = lambda image: image * 2.0 - 1.0
        self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
        self.working_dtypes = [torch.bfloat16, torch.float32]

        self.downscale_index_formula = None
        self.upscale_index_formula = None

        if config is None:
            if "decoder.mid.block_1.mix_factor" in sd:
                encoder_config = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
                decoder_config = encoder_config.copy()
                decoder_config["video_kernel_size"] = [3, 1, 1]
                decoder_config["alpha"] = 0.0
                self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
                                                            encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config},
                                                            decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config})
            elif "taesd_decoder.1.weight" in sd:
                self.latent_channels = sd["taesd_decoder.1.weight"].shape[1]
                self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels)
            elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade
                self.first_stage_model = StageA()
                self.downscale_ratio = 4
                self.upscale_ratio = 4
                #TODO
                #self.memory_used_encode
                #self.memory_used_decode
                self.process_input = lambda image: image
                self.process_output = lambda image: image
            elif "backbone.1.0.block.0.1.num_batches_tracked" in sd: #effnet: encoder for stage c latent of stable cascade
                self.first_stage_model = StageC_coder()
                self.downscale_ratio = 32
                self.latent_channels = 16
                new_sd = {}
                for k in sd:
                    new_sd["encoder.{}".format(k)] = sd[k]
                sd = new_sd
            elif "blocks.11.num_batches_tracked" in sd: #previewer: decoder for stage c latent of stable cascade
                self.first_stage_model = StageC_coder()
                self.latent_channels = 16
                new_sd = {}
                for k in sd:
                    new_sd["previewer.{}".format(k)] = sd[k]
                sd = new_sd
            elif "encoder.backbone.1.0.block.0.1.num_batches_tracked" in sd: #combined effnet and previewer for stable cascade
                self.first_stage_model = StageC_coder()
                self.downscale_ratio = 32
                self.latent_channels = 16
            elif "decoder.conv_in.weight" in sd:
                #default SD1.x/SD2.x VAE parameters
                ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}

                if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE
                    ddconfig['ch_mult'] = [1, 2, 4]
                    self.downscale_ratio = 4
                    self.upscale_ratio = 4

                self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
                if 'post_quant_conv.weight' in sd:
                    self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
                else:
                    self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
                                                                encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
                                                                decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
            elif "decoder.layers.1.layers.0.beta" in sd:
                self.first_stage_model = AudioOobleckVAE()
                self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype)
                self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype)
                self.latent_channels = 64
                self.output_channels = 2
                self.upscale_ratio = 2048
                self.downscale_ratio =  2048
                self.latent_dim = 1
                self.process_output = lambda audio: audio
                self.process_input = lambda audio: audio
                self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
            elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd or "layers.4.layers.1.attn_block.attn.qkv.weight" in sd or "encoder.layers.4.layers.1.attn_block.attn.qkv.weight" in sd: #genmo mochi vae
                if "blocks.2.blocks.3.stack.5.weight" in sd:
                    sd = comfy.utils.state_dict_prefix_replace(sd, {"": "decoder."})
                if "layers.4.layers.1.attn_block.attn.qkv.weight" in sd:
                    sd = comfy.utils.state_dict_prefix_replace(sd, {"": "encoder."})
                self.first_stage_model = comfy.ldm.genmo.vae.model.VideoVAE()
                self.latent_channels = 12
                self.latent_dim = 3
                self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
                self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
                self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8)
                self.upscale_index_formula = (6, 8, 8)
                self.downscale_ratio = (lambda a: max(0, math.floor((a + 5) / 6)), 8, 8)
                self.downscale_index_formula = (6, 8, 8)
                self.working_dtypes = [torch.float16, torch.float32]
            elif "decoder.up_blocks.0.res_blocks.0.conv1.conv.weight" in sd: #lightricks ltxv
                tensor_conv1 = sd["decoder.up_blocks.0.res_blocks.0.conv1.conv.weight"]
                version = 0
                if tensor_conv1.shape[0] == 512:
                    version = 0
                elif tensor_conv1.shape[0] == 1024:
                    version = 1
                    if "encoder.down_blocks.1.conv.conv.bias" in sd:
                        version = 2
                vae_config = None
                if metadata is not None and "config" in metadata:
                    vae_config = json.loads(metadata["config"]).get("vae", None)
                self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version, config=vae_config)
                self.latent_channels = 128
                self.latent_dim = 3
                self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
                self.memory_used_encode = lambda shape, dtype: (70 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 32, 32)
                self.upscale_index_formula = (8, 32, 32)
                self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32)
                self.downscale_index_formula = (8, 32, 32)
                self.working_dtypes = [torch.bfloat16, torch.float32]
            elif "decoder.conv_in.conv.weight" in sd:
                ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
                ddconfig["conv3d"] = True
                ddconfig["time_compress"] = 4
                self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
                self.upscale_index_formula = (4, 8, 8)
                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
                self.downscale_index_formula = (4, 8, 8)
                self.latent_dim = 3
                self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1]
                self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
                self.memory_used_decode = lambda shape, dtype: (1500 * shape[2] * shape[3] * shape[4] * (4 * 8 * 8)) * model_management.dtype_size(dtype)
                self.memory_used_encode = lambda shape, dtype: (900 * max(shape[2], 2) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
            elif "decoder.unpatcher3d.wavelets" in sd:
                self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 8, 8)
                self.upscale_index_formula = (8, 8, 8)
                self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 8, 8)
                self.downscale_index_formula = (8, 8, 8)
                self.latent_dim = 3
                self.latent_channels = 16
                ddconfig = {'z_channels': 16, 'latent_channels': self.latent_channels, 'z_factor': 1, 'resolution': 1024, 'in_channels': 3, 'out_channels': 3, 'channels': 128, 'channels_mult': [2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [32], 'dropout': 0.0, 'patch_size': 4, 'num_groups': 1, 'temporal_compression': 8, 'spacial_compression': 8}
                self.first_stage_model = comfy.ldm.cosmos.vae.CausalContinuousVideoTokenizer(**ddconfig)
                #TODO: these values are a bit off because this is not a standard VAE
                self.memory_used_decode = lambda shape, dtype: (50 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
                self.memory_used_encode = lambda shape, dtype: (50 * (round((shape[2] + 7) / 8) * 8) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                self.working_dtypes = [torch.bfloat16, torch.float32]
            elif "decoder.middle.0.residual.0.gamma" in sd:
                self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
                self.upscale_index_formula = (4, 8, 8)
                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
                self.downscale_index_formula = (4, 8, 8)
                self.latent_dim = 3
                self.latent_channels = 16
                ddconfig = {"dim": 96, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
                self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig)
                self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
                self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype)
                self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype)
            elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd:
                self.latent_dim = 1
                ln_post = "geo_decoder.ln_post.weight" in sd
                inner_size = sd["geo_decoder.output_proj.weight"].shape[1]
                downsample_ratio = sd["post_kl.weight"].shape[0] // inner_size
                mlp_expand = sd["geo_decoder.cross_attn_decoder.mlp.c_fc.weight"].shape[0] // inner_size
                self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype)  # TODO
                self.memory_used_decode = lambda shape, dtype: (1024 * 1024 * 1024 * 2.0) * model_management.dtype_size(dtype)  # TODO
                ddconfig = {"embed_dim": 64, "num_freqs": 8, "include_pi": False, "heads": 16, "width": 1024, "num_decoder_layers": 16, "qkv_bias": False, "qk_norm": True, "geo_decoder_mlp_expand_ratio": mlp_expand, "geo_decoder_downsample_ratio": downsample_ratio, "geo_decoder_ln_post": ln_post}
                self.first_stage_model = comfy.ldm.hunyuan3d.vae.ShapeVAE(**ddconfig)
                self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
            else:
                logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                self.first_stage_model = None
                return
        else:
            self.first_stage_model = AutoencoderKL(**(config['params']))
        self.first_stage_model = self.first_stage_model.eval()

        m, u = self.first_stage_model.load_state_dict(sd, strict=False)
        if len(m) > 0:
            logging.warning("Missing VAE keys {}".format(m))

        if len(u) > 0:
            logging.debug("Leftover VAE keys {}".format(u))

        if device is None:
            device = model_management.vae_device()
        self.device = device
        offload_device = model_management.vae_offload_device()
        if dtype is None:
            dtype = model_management.vae_dtype(self.device, self.working_dtypes)
        self.vae_dtype = dtype
        self.first_stage_model.to(self.vae_dtype)
        self.output_device = model_management.intermediate_device()

        self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
        logging.info("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))

    def throw_exception_if_invalid(self):
        if self.first_stage_model is None:
            raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.")

    def vae_encode_crop_pixels(self, pixels):
        downscale_ratio = self.spacial_compression_encode()

        dims = pixels.shape[1:-1]
        for d in range(len(dims)):
            x = (dims[d] // downscale_ratio) * downscale_ratio
            x_offset = (dims[d] % downscale_ratio) // 2
            if x != dims[d]:
                pixels = pixels.narrow(d + 1, x_offset, x)
        return pixels

    def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
        steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
        steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
        steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
        pbar = comfy.utils.ProgressBar(steps)

        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
        output = self.process_output(
            (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
            comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
             comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar))
            / 3.0)
        return output

    def decode_tiled_1d(self, samples, tile_x=128, overlap=32):
        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
        return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device))

    def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)):
        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
        return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device))

    def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
        steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
        steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
        steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
        pbar = comfy.utils.ProgressBar(steps)

        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
        samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
        samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
        samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
        samples /= 3.0
        return samples

    def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048):
        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
        return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device)

    def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512, overlap=(1, 64, 64)):
        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
        return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device)

    def decode(self, samples_in, vae_options={}):
        self.throw_exception_if_invalid()
        pixel_samples = None
        try:
            memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
            model_management.load_models_gpu([self.patcher], memory_required=memory_used)
            free_memory = model_management.get_free_memory(self.device)
            batch_number = int(free_memory / memory_used)
            batch_number = max(1, batch_number)

            for x in range(0, samples_in.shape[0], batch_number):
                samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
                out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).float())
                if pixel_samples is None:
                    pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
                pixel_samples[x:x+batch_number] = out
        except model_management.OOM_EXCEPTION:
            logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
            dims = samples_in.ndim - 2
            if dims == 1:
                pixel_samples = self.decode_tiled_1d(samples_in)
            elif dims == 2:
                pixel_samples = self.decode_tiled_(samples_in)
            elif dims == 3:
                tile = 256 // self.spacial_compression_decode()
                overlap = tile // 4
                pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))

        pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
        return pixel_samples

    def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
        self.throw_exception_if_invalid()
        memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile
        model_management.load_models_gpu([self.patcher], memory_required=memory_used)
        dims = samples.ndim - 2
        args = {}
        if tile_x is not None:
            args["tile_x"] = tile_x
        if tile_y is not None:
            args["tile_y"] = tile_y
        if overlap is not None:
            args["overlap"] = overlap

        if dims == 1:
            args.pop("tile_y")
            output = self.decode_tiled_1d(samples, **args)
        elif dims == 2:
            output = self.decode_tiled_(samples, **args)
        elif dims == 3:
            if overlap_t is None:
                args["overlap"] = (1, overlap, overlap)
            else:
                args["overlap"] = (max(1, overlap_t), overlap, overlap)
            if tile_t is not None:
                args["tile_t"] = max(2, tile_t)

            output = self.decode_tiled_3d(samples, **args)
        return output.movedim(1, -1)

    def encode(self, pixel_samples):
        self.throw_exception_if_invalid()
        pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
        pixel_samples = pixel_samples.movedim(-1, 1)
        if self.latent_dim == 3 and pixel_samples.ndim < 5:
            pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
        try:
            memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
            model_management.load_models_gpu([self.patcher], memory_required=memory_used)
            free_memory = model_management.get_free_memory(self.device)
            batch_number = int(free_memory / max(1, memory_used))
            batch_number = max(1, batch_number)
            samples = None
            for x in range(0, pixel_samples.shape[0], batch_number):
                pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device)
                out = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
                if samples is None:
                    samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
                samples[x:x + batch_number] = out

        except model_management.OOM_EXCEPTION:
            logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
            if self.latent_dim == 3:
                tile = 256
                overlap = tile // 4
                samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
            elif self.latent_dim == 1:
                samples = self.encode_tiled_1d(pixel_samples)
            else:
                samples = self.encode_tiled_(pixel_samples)

        return samples

    def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
        self.throw_exception_if_invalid()
        pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
        dims = self.latent_dim
        pixel_samples = pixel_samples.movedim(-1, 1)
        if dims == 3:
            pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)

        memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)  # TODO: calculate mem required for tile
        model_management.load_models_gpu([self.patcher], memory_required=memory_used)

        args = {}
        if tile_x is not None:
            args["tile_x"] = tile_x
        if tile_y is not None:
            args["tile_y"] = tile_y
        if overlap is not None:
            args["overlap"] = overlap

        if dims == 1:
            args.pop("tile_y")
            samples = self.encode_tiled_1d(pixel_samples, **args)
        elif dims == 2:
            samples = self.encode_tiled_(pixel_samples, **args)
        elif dims == 3:
            if tile_t is not None:
                tile_t_latent = max(2, self.downscale_ratio[0](tile_t))
            else:
                tile_t_latent = 9999
            args["tile_t"] = self.upscale_ratio[0](tile_t_latent)

            if overlap_t is None:
                args["overlap"] = (1, overlap, overlap)
            else:
                args["overlap"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), overlap, overlap)
            maximum = pixel_samples.shape[2]
            maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum))

            samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args)

        return samples

    def get_sd(self):
        return self.first_stage_model.state_dict()

    def spacial_compression_decode(self):
        try:
            return self.upscale_ratio[-1]
        except:
            return self.upscale_ratio

    def spacial_compression_encode(self):
        try:
            return self.downscale_ratio[-1]
        except:
            return self.downscale_ratio

    def temporal_compression_decode(self):
        try:
            return round(self.upscale_ratio[0](8192) / 8192)
        except:
            return None

class StyleModel:
    def __init__(self, model, device="cpu"):
        self.model = model

    def get_cond(self, input):
        return self.model(input.last_hidden_state)


def load_style_model(ckpt_path):
    model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
    keys = model_data.keys()
    if "style_embedding" in keys:
        model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
    elif "redux_down.weight" in keys:
        model = comfy.ldm.flux.redux.ReduxImageEncoder()
    else:
        raise Exception("invalid style model {}".format(ckpt_path))
    model.load_state_dict(model_data)
    return StyleModel(model)

class CLIPType(Enum):
    STABLE_DIFFUSION = 1
    STABLE_CASCADE = 2
    SD3 = 3
    STABLE_AUDIO = 4
    HUNYUAN_DIT = 5
    FLUX = 6
    MOCHI = 7
    LTXV = 8
    HUNYUAN_VIDEO = 9
    PIXART = 10
    COSMOS = 11
    LUMINA2 = 12
    WAN = 13


def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
    clip_data = []
    for p in ckpt_paths:
        clip_data.append(comfy.utils.load_torch_file(p, safe_load=True))
    return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options)


class TEModel(Enum):
    CLIP_L = 1
    CLIP_H = 2
    CLIP_G = 3
    T5_XXL = 4
    T5_XL = 5
    T5_BASE = 6
    LLAMA3_8 = 7
    T5_XXL_OLD = 8
    GEMMA_2_2B = 9

def detect_te_model(sd):
    if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
        return TEModel.CLIP_G
    if "text_model.encoder.layers.22.mlp.fc1.weight" in sd:
        return TEModel.CLIP_H
    if "text_model.encoder.layers.0.mlp.fc1.weight" in sd:
        return TEModel.CLIP_L
    if "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd:
        weight = sd["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
        if weight.shape[-1] == 4096:
            return TEModel.T5_XXL
        elif weight.shape[-1] == 2048:
            return TEModel.T5_XL
    if 'encoder.block.23.layer.1.DenseReluDense.wi.weight' in sd:
        return TEModel.T5_XXL_OLD
    if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd:
        return TEModel.T5_BASE
    if 'model.layers.0.post_feedforward_layernorm.weight' in sd:
        return TEModel.GEMMA_2_2B
    if "model.layers.0.post_attention_layernorm.weight" in sd:
        return TEModel.LLAMA3_8
    return None


def t5xxl_detect(clip_data):
    weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight"
    weight_name_old = "encoder.block.23.layer.1.DenseReluDense.wi.weight"

    for sd in clip_data:
        if weight_name in sd or weight_name_old in sd:
            return comfy.text_encoders.sd3_clip.t5_xxl_detect(sd)

    return {}

def llama_detect(clip_data):
    weight_name = "model.layers.0.self_attn.k_proj.weight"

    for sd in clip_data:
        if weight_name in sd:
            return comfy.text_encoders.hunyuan_video.llama_detect(sd)

    return {}

def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
    clip_data = state_dicts

    class EmptyClass:
        pass

    for i in range(len(clip_data)):
        if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
            clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "")
        else:
            if "text_projection" in clip_data[i]:
                clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node

    tokenizer_data = {}
    clip_target = EmptyClass()
    clip_target.params = {}
    if len(clip_data) == 1:
        te_model = detect_te_model(clip_data[0])
        if te_model == TEModel.CLIP_G:
            if clip_type == CLIPType.STABLE_CASCADE:
                clip_target.clip = sdxl_clip.StableCascadeClipModel
                clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer
            elif clip_type == CLIPType.SD3:
                clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False)
                clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
            else:
                clip_target.clip = sdxl_clip.SDXLRefinerClipModel
                clip_target.tokenizer = sdxl_clip.SDXLTokenizer
        elif te_model == TEModel.CLIP_H:
            clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel
            clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer
        elif te_model == TEModel.T5_XXL:
            if clip_type == CLIPType.SD3:
                clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, **t5xxl_detect(clip_data))
                clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
            elif clip_type == CLIPType.LTXV:
                clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data))
                clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer
            elif clip_type == CLIPType.PIXART:
                clip_target.clip = comfy.text_encoders.pixart_t5.pixart_te(**t5xxl_detect(clip_data))
                clip_target.tokenizer = comfy.text_encoders.pixart_t5.PixArtTokenizer
            elif clip_type == CLIPType.WAN:
                clip_target.clip = comfy.text_encoders.wan.te(**t5xxl_detect(clip_data))
                clip_target.tokenizer = comfy.text_encoders.wan.WanT5Tokenizer
                tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
            else: #CLIPType.MOCHI
                clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
                clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer
        elif te_model == TEModel.T5_XXL_OLD:
            clip_target.clip = comfy.text_encoders.cosmos.te(**t5xxl_detect(clip_data))
            clip_target.tokenizer = comfy.text_encoders.cosmos.CosmosT5Tokenizer
        elif te_model == TEModel.T5_XL:
            clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
            clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
        elif te_model == TEModel.T5_BASE:
            clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model
            clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer
        elif te_model == TEModel.GEMMA_2_2B:
            clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data))
            clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer
            tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
        else:
            if clip_type == CLIPType.SD3:
                clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)
                clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
            else:
                clip_target.clip = sd1_clip.SD1ClipModel
                clip_target.tokenizer = sd1_clip.SD1Tokenizer
    elif len(clip_data) == 2:
        if clip_type == CLIPType.SD3:
            te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])]
            clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=TEModel.CLIP_L in te_models, clip_g=TEModel.CLIP_G in te_models, t5=TEModel.T5_XXL in te_models, **t5xxl_detect(clip_data))
            clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
        elif clip_type == CLIPType.HUNYUAN_DIT:
            clip_target.clip = comfy.text_encoders.hydit.HyditModel
            clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
        elif clip_type == CLIPType.FLUX:
            clip_target.clip = comfy.text_encoders.flux.flux_clip(**t5xxl_detect(clip_data))
            clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer
        elif clip_type == CLIPType.HUNYUAN_VIDEO:
            clip_target.clip = comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**llama_detect(clip_data))
            clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer
        else:
            clip_target.clip = sdxl_clip.SDXLClipModel
            clip_target.tokenizer = sdxl_clip.SDXLTokenizer
    elif len(clip_data) == 3:
        clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(**t5xxl_detect(clip_data))
        clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer

    parameters = 0
    for c in clip_data:
        parameters += comfy.utils.calculate_parameters(c)
        tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)

    clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options)
    for c in clip_data:
        m, u = clip.load_sd(c)
        if len(m) > 0:
            logging.warning("clip missing: {}".format(m))

        if len(u) > 0:
            logging.debug("clip unexpected: {}".format(u))
    return clip

def load_gligen(ckpt_path):
    data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
    model = gligen.load_gligen(data)
    if model_management.should_use_fp16():
        model = model.half()
    return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())

def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None):
    logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.")
    model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True)
    #TODO: this function is a mess and should be removed eventually
    if config is None:
        with open(config_path, 'r') as stream:
            config = yaml.safe_load(stream)
    model_config_params = config['model']['params']
    clip_config = model_config_params['cond_stage_config']

    if "parameterization" in model_config_params:
        if model_config_params["parameterization"] == "v":
            m = model.clone()
            class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, comfy.model_sampling.V_PREDICTION):
                pass
            m.add_object_patch("model_sampling", ModelSamplingAdvanced(model.model.model_config))
            model = m

    layer_idx = clip_config.get("params", {}).get("layer_idx", None)
    if layer_idx is not None:
        clip.clip_layer(layer_idx)

    return (model, clip, vae)

def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}):
    sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True)
    out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata)
    if out is None:
        raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
    return out

def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None):
    clip = None
    clipvision = None
    vae = None
    model = None
    model_patcher = None

    diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
    parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix)
    weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix)
    load_device = model_management.get_torch_device()

    model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata)
    if model_config is None:
        logging.warning("Warning, This is not a checkpoint file, trying to load it as a diffusion model only.")
        diffusion_model = load_diffusion_model_state_dict(sd, model_options={})
        if diffusion_model is None:
            return None
        return (diffusion_model, None, VAE(sd={}), None)  # The VAE object is there to throw an exception if it's actually used'


    unet_weight_dtype = list(model_config.supported_inference_dtypes)
    if model_config.scaled_fp8 is not None:
        weight_dtype = None

    model_config.custom_operations = model_options.get("custom_operations", None)
    unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None))

    if unet_dtype is None:
        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype)

    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)

    if model_config.clip_vision_prefix is not None:
        if output_clipvision:
            clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)

    if output_model:
        inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
        model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device)
        model.load_model_weights(sd, diffusion_model_prefix)

    if output_vae:
        vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True)
        vae_sd = model_config.process_vae_state_dict(vae_sd)
        vae = VAE(sd=vae_sd, metadata=metadata)

    if output_clip:
        clip_target = model_config.clip_target(state_dict=sd)
        if clip_target is not None:
            clip_sd = model_config.process_clip_state_dict(sd)
            if len(clip_sd) > 0:
                parameters = comfy.utils.calculate_parameters(clip_sd)
                clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, model_options=te_model_options)
                m, u = clip.load_sd(clip_sd, full_model=True)
                if len(m) > 0:
                    m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
                    if len(m_filter) > 0:
                        logging.warning("clip missing: {}".format(m))
                    else:
                        logging.debug("clip missing: {}".format(m))

                if len(u) > 0:
                    logging.debug("clip unexpected {}:".format(u))
            else:
                logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.")

    left_over = sd.keys()
    if len(left_over) > 0:
        logging.debug("left over keys: {}".format(left_over))

    if output_model:
        model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device())
        if inital_load_device != torch.device("cpu"):
            logging.info("loaded diffusion model directly to GPU")
            model_management.load_models_gpu([model_patcher], force_full_load=True)

    return (model_patcher, clip, vae, clipvision)


def load_diffusion_model_state_dict(sd, model_options={}):
    """
    Loads a UNet diffusion model from a state dictionary, supporting both diffusers and regular formats.

    Args:
        sd (dict): State dictionary containing model weights and configuration
        model_options (dict, optional): Additional options for model loading. Supports:
            - dtype: Override model data type
            - custom_operations: Custom model operations
            - fp8_optimizations: Enable FP8 optimizations

    Returns:
        ModelPatcher: A wrapped model instance that handles device management and weight loading.
        Returns None if the model configuration cannot be detected.

    The function:
    1. Detects and handles different model formats (regular, diffusers, mmdit)
    2. Configures model dtype based on parameters and device capabilities
    3. Handles weight conversion and device placement
    4. Manages model optimization settings
    5. Loads weights and returns a device-managed model instance
    """
    dtype = model_options.get("dtype", None)

    #Allow loading unets from checkpoint files
    diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
    temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
    if len(temp_sd) > 0:
        sd = temp_sd

    parameters = comfy.utils.calculate_parameters(sd)
    weight_dtype = comfy.utils.weight_dtype(sd)

    load_device = model_management.get_torch_device()
    model_config = model_detection.model_config_from_unet(sd, "")

    if model_config is not None:
        new_sd = sd
    else:
        new_sd = model_detection.convert_diffusers_mmdit(sd, "")
        if new_sd is not None: #diffusers mmdit
            model_config = model_detection.model_config_from_unet(new_sd, "")
            if model_config is None:
                return None
        else: #diffusers unet
            model_config = model_detection.model_config_from_diffusers_unet(sd)
            if model_config is None:
                return None

            diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config)

            new_sd = {}
            for k in diffusers_keys:
                if k in sd:
                    new_sd[diffusers_keys[k]] = sd.pop(k)
                else:
                    logging.warning("{} {}".format(diffusers_keys[k], k))

    offload_device = model_management.unet_offload_device()
    unet_weight_dtype = list(model_config.supported_inference_dtypes)
    if model_config.scaled_fp8 is not None:
        weight_dtype = None

    if dtype is None:
        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype)
    else:
        unet_dtype = dtype

    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
    model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
    if model_options.get("fp8_optimizations", False):
        model_config.optimizations["fp8"] = True

    model = model_config.get_model(new_sd, "")
    model = model.to(offload_device)
    model.load_model_weights(new_sd, "")
    left_over = sd.keys()
    if len(left_over) > 0:
        logging.info("left over keys in unet: {}".format(left_over))
    return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)


def load_diffusion_model(unet_path, model_options={}):
    sd = comfy.utils.load_torch_file(unet_path)
    model = load_diffusion_model_state_dict(sd, model_options=model_options)
    if model is None:
        logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
        raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
    return model

def load_unet(unet_path, dtype=None):
    logging.warning("The load_unet function has been deprecated and will be removed please switch to: load_diffusion_model")
    return load_diffusion_model(unet_path, model_options={"dtype": dtype})

def load_unet_state_dict(sd, dtype=None):
    logging.warning("The load_unet_state_dict function has been deprecated and will be removed please switch to: load_diffusion_model_state_dict")
    return load_diffusion_model_state_dict(sd, model_options={"dtype": dtype})

def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}):
    clip_sd = None
    load_models = [model]
    if clip is not None:
        load_models.append(clip.load_model())
        clip_sd = clip.get_sd()
    vae_sd = None
    if vae is not None:
        vae_sd = vae.get_sd()

    model_management.load_models_gpu(load_models, force_patch_weights=True)
    clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None
    sd = model.model.state_dict_for_saving(clip_sd, vae_sd, clip_vision_sd)
    for k in extra_keys:
        sd[k] = extra_keys[k]

    for k in sd:
        t = sd[k]
        if not t.is_contiguous():
            sd[k] = t.contiguous()

    comfy.utils.save_torch_file(sd, output_path, metadata=metadata)