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convert_sd3.py

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+import os
+import torch
+import numpy as np
+import gguf  # This needs to be the llama.cpp one specifically!
+import argparse
+from tqdm import tqdm
+
+from safetensors.torch import load_file
+
+QUANTIZATION_THRESHOLD = 1024
+REARRANGE_THRESHOLD = 512
+MAX_TENSOR_NAME_LENGTH = 127
+
+
+class QuantError(Exception):
+    pass
+
+class quants:
+    @staticmethod
+    def quantize(data, data_qtype):
+        # Implement quantization logic here
+        if data_qtype == GGMLQuantizationType.F16:
+            return data.astype(np.float16)
+        elif data_qtype == GGMLQuantizationType.BF16:
+            return data.astype(np.float32)  # BF16 is not supported by NumPy, so use float32 instead
+        else:
+            raise QuantError("Unsupported quantization type")
+
+class ModelTemplate:
+    arch = "invalid"  # string describing architecture
+    shape_fix = False  # whether to reshape tensors
+    keys_detect = []  # list of lists to match in state dict
+    keys_banned = []  # list of keys that should mark model as invalid for conversion
+
+class ModelFlux(ModelTemplate):
+    arch = "fluxz"
+    keys_detect = [
+        ("transformer_blocks.0.attn.norm_added_k.weight",),
+        ("double_blocks.0.img_attn.proj.weight",),
+    ]
+    keys_banned = ["transformer_blocks.0.attn.norm_added_k.weight",]
+
+class ModelSD3(ModelTemplate):
+    arch = "sd3"
+    keys_detect = [
+        ("transformer_blocks.0.attn.add_q_proj.weight",),
+        ("joint_blocks.0.x_block.attn.qkv.weight",),
+    ]
+    keys_banned = ["transformer_blocks.0.attn.add_q_proj.weight",]
+
+class ModelSDXL(ModelTemplate):
+    arch = "sdxl"
+    shape_fix = True
+    keys_detect = [
+        ("down_blocks.0.downsamplers.0.conv.weight", "add_embedding.linear_1.weight",),
+        (
+            "input_blocks.3.0.op.weight", "input_blocks.6.0.op.weight",
+            "output_blocks.2.2.conv.weight", "output_blocks.5.2.conv.weight",
+        ),  # Non-diffusers
+        ("label_emb.0.0.weight",),
+    ]
+
+class ModelSD1(ModelTemplate):
+    arch = "sd1"
+    shape_fix = False
+    keys_detect = [
+        ("down_blocks.0.downsamplers.0.conv.weight",),
+        (
+            "input_blocks.3.0.op.weight", "input_blocks.6.0.op.weight", "input_blocks.9.0.op.weight",
+            "output_blocks.2.1.conv.weight", "output_blocks.5.2.conv.weight", "output_blocks.8.2.conv.weight"
+        ),  # Non-diffusers
+    ]
+
+# Prioritize ModelSD3 over ModelFlux
+arch_list = [ModelSD3, ModelFlux, ModelSDXL, ModelSD1]
+
+def is_model_arch(model, state_dict):
+    # check if model is correct
+    matched = False
+    invalid = False
+    for match_list in model.keys_detect:
+        if all(key in state_dict for key in match_list):
+            matched = True
+            invalid = any(key in state_dict for key in model.keys_banned)
+            break
+    assert not invalid, "Model architecture not allowed for conversion! (i.e. reference VS diffusers format)"
+    return matched
+
+def detect_arch(state_dict):
+    model_arch = None
+    for arch in arch_list:
+        if is_model_arch(arch, state_dict):
+            model_arch = arch
+            break
+    assert model_arch is not None, "Unknown model architecture!"
+    return model_arch
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Generate F16 GGUF files from single UNET")
+    parser.add_argument("--src", required=True, help="Source model ckpt file.")
+    parser.add_argument("--dst", help="Output unet gguf file.")
+    args = parser.parse_args()
+
+    if not os.path.isfile(args.src):
+        parser.error("No input provided!")
+
+    return args
+
+def load_state_dict(path):
+    if any(path.endswith(x) for x in [".ckpt", ".pt", ".bin", ".pth"]):
+        state_dict = torch.load(path, map_location="cpu", weights_only=True)
+        state_dict = state_dict.get("model", state_dict)
+    else:
+        state_dict = load_file(path)
+
+    # only keep unet with no prefix!
+    sd = {}
+    has_prefix = any(["model.diffusion_model." in x for x in state_dict.keys()])
+    for k, v in state_dict.items():
+        if has_prefix and "model.diffusion_model." not in k:
+            continue
+        if has_prefix:
+            k = k.replace("model.diffusion_model.", "")
+        sd[k] = v
+
+    return sd
+
+def load_model(path):
+    state_dict = load_state_dict(path)
+    model_arch = detect_arch(state_dict)
+    print(f"* Architecture detected from input: {model_arch.arch}")
+    writer = gguf.GGUFWriter(path=None, arch=model_arch.arch)
+    return (writer, state_dict, model_arch)
+
+def handle_tensors(args, writer, state_dict, model_arch):
+    name_lengths = tuple(sorted(
+        ((key, len(key)) for key in state_dict.keys()),
+        key=lambda item: item[1],
+        reverse=True,
+    ))
+    if not name_lengths:
+        return
+    max_name_len = name_lengths[0][1]
+    if max_name_len > MAX_TENSOR_NAME_LENGTH:
+        bad_list = ", ".join(f"{key!r} ({namelen})" for key, namelen in name_lengths if namelen > MAX_TENSOR_NAME_LENGTH)
+        raise ValueError(f"Can only handle tensor names up to {MAX_TENSOR_NAME_LENGTH} characters. Tensors exceeding the limit: {bad_list}")
+    for key, data in tqdm(state_dict.items()):
+        old_dtype = data.dtype
+
+        if data.dtype == torch.bfloat16:
+            data = data.to(torch.float32).numpy()
+        # this is so we don't break torch 2.0.X
+        elif data.dtype in [getattr(torch, "float8_e4m3fn", "_invalid"), getattr(torch, "float8_e5m2", "_invalid")]:
+            data = data.to(torch.float16).numpy()
+        else:
+            data = data.numpy()
+
+        n_dims = len(data.shape)
+        data_shape = data.shape
+        data_qtype = getattr(
+            gguf.GGMLQuantizationType,
+            "BF16" if old_dtype == torch.bfloat16 else "F16"
+        )
+
+        # get number of parameters (AKA elements) in this tensor
+        n_params = 1
+        for dim_size in data_shape:
+            n_params *= dim_size
+
+        # keys to keep as max precision
+        blacklist = {
+            "time_embedding.",
+            "add_embedding.",
+            "time_in.",
+            "txt_in.",
+            "vector_in.",
+            "img_in.",
+            "guidance_in.",
+            "final_layer.",
+        }
+
+        if old_dtype in (torch.float32, torch.bfloat16):
+            if n_dims == 1:
+                # one-dimensional tensors should be kept in F32
+                # also speeds up inference due to not dequantizing
+                data_qtype = gguf.GGMLQuantizationType.F32
+
+            elif n_params <= QUANTIZATION_THRESHOLD:
+                # very small tensors
+                data_qtype = gguf.GGMLQuantizationType.F32
+
+            elif ".weight" in key and any(x in key for x in blacklist):
+                data_qtype = gguf.GGMLQuantizationType.F32
+
+        if (model_arch.shape_fix                        # NEVER reshape for models such as flux
+            and n_dims > 1                              # Skip one-dimensional tensors
+            and n_params >= REARRANGE_THRESHOLD         # Only rearrange tensors meeting the size requirement
+            and (n_params / 256).is_integer()           # Rearranging only makes sense if total elements is divisible by 256
+            and not (data.shape[-1] / 256).is_integer() # Only need to rearrange if the last dimension is not divisible by 256
+        ):
+            orig_shape = data.shape
+            data = data.reshape(n_params // 256, 256)
+            writer.add_array(f"comfy.gguf.orig_shape.{key}", tuple(int(dim) for dim in orig_shape))
+
+        new_name = key # do we need to rename?
+
+        shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}"
+        tqdm.write(f"{f'%-{max_name_len + 4}s' % f'{new_name}'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
+
+        writer.add_tensor(new_name, data, raw_dtype=data_qtype)
+
+
+
+def load_model(path):
+    state_dict = load_state_dict(path)
+    model_arch = detect_arch(state_dict)
+    print(f"* Architecture detected from input: {model_arch.arch}")
+    return state_dict, model_arch
+
+...
+
+if __name__ == "__main__":
+    args = parse_args()
+    path = args.src
+    state_dict, model_arch = load_model(path)
+
+    if next(iter(state_dict.values())).dtype == torch.bfloat16:
+        out_path = f"{os.path.splitext(path)[0]}-BF16.gguf"
+    else:
+        out_path = f"{os.path.splitext(path)[0]}-F16.gguf"
+
+    out_path = args.dst or out_path
+    if os.path.isfile(out_path):
+        input("Output exists enter to continue or ctrl+c to abort!")
+
+    writer = gguf.GGUFWriter(path=out_path, arch=model_arch.arch)
+    writer.add_quantization_version(1)
+
+    handle_tensors(args, writer, state_dict, model_arch)
+    writer.write_header_to_file()
+    writer.write_kv_data_to_file()
+    writer.write_tensors_to_file()
+    writer.close()

nodes.py

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+# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+import torch
+import gguf
+import copy
+import logging
+
+import comfy.sd
+import comfy.utils
+import comfy.model_management
+import comfy.model_patcher
+import folder_paths
+
+from .ops import GGMLTensor, GGMLOps, move_patch_to_device
+from .dequant import is_quantized, is_torch_compatible
+
+# Add a custom keys for files ending in .gguf
+if "unet_gguf" not in folder_paths.folder_names_and_paths:
+    orig = folder_paths.folder_names_and_paths.get("diffusion_models", folder_paths.folder_names_and_paths.get("unet", [[], set()]))
+    folder_paths.folder_names_and_paths["unet_gguf"] = (orig[0], {".gguf"})
+
+if "clip_gguf" not in folder_paths.folder_names_and_paths:
+    orig = folder_paths.folder_names_and_paths.get("clip", [[], set()])
+    folder_paths.folder_names_and_paths["clip_gguf"] = (orig[0], {".gguf"})
+
+def gguf_sd_loader_get_orig_shape(reader, tensor_name):
+    field_key = f"comfy.gguf.orig_shape.{tensor_name}"
+    field = reader.get_field(field_key)
+    if field is None:
+        return None
+    # Has original shape metadata, so we try to decode it.
+    if len(field.types) != 2 or field.types[0] != gguf.GGUFValueType.ARRAY or field.types[1] != gguf.GGUFValueType.INT32:
+        raise TypeError(f"Bad original shape metadata for {field_key}: Expected ARRAY of INT32, got {field.types}")
+    return torch.Size(tuple(int(field.parts[part_idx][0]) for part_idx in field.data))
+
+def gguf_sd_loader(path, handle_prefix="model.diffusion_model."):
+    """
+    Read state dict as fake tensors
+    """
+    reader = gguf.GGUFReader(path)
+
+    # filter and strip prefix
+    has_prefix = False
+    if handle_prefix is not None:
+        prefix_len = len(handle_prefix)
+        tensor_names = set(tensor.name for tensor in reader.tensors)
+        has_prefix = any(s.startswith(handle_prefix) for s in tensor_names)
+
+    tensors = []
+    for tensor in reader.tensors:
+        sd_key = tensor_name = tensor.name
+        if has_prefix:
+            if not tensor_name.startswith(handle_prefix):
+                continue
+            sd_key = tensor_name[prefix_len:]
+        tensors.append((sd_key, tensor))
+
+    # detect and verify architecture
+    compat = None
+    arch_str = None
+    arch_field = reader.get_field("general.architecture")
+    if arch_field is not None:
+        if len(arch_field.types) != 1 or arch_field.types[0] != gguf.GGUFValueType.STRING:
+            raise TypeError(f"Bad type for GGUF general.architecture key: expected string, got {arch_field.types!r}")
+        arch_str = str(arch_field.parts[arch_field.data[-1]], encoding="utf-8")
+        if arch_str not in {"flux", "sd1", "sdxl", "t5", "t5encoder", "sd3"}:
+            raise ValueError(f"Unexpected architecture type in GGUF file, expected one of flux, sd1, sdxl, t5encoder, sd3 but got {arch_str!r}")
+    else: # stable-diffusion.cpp
+        # import here to avoid changes to convert.py breaking regular models
+        from .tools.convert import detect_arch
+        arch_str = detect_arch(set(val[0] for val in tensors)).arch
+        compat = "sd.cpp"
+
+    # main loading loop
+    state_dict = {}
+    qtype_dict = {}
+    for sd_key, tensor in tensors:
+        tensor_name = tensor.name
+        tensor_type_str = str(tensor.tensor_type)
+        torch_tensor = torch.from_numpy(tensor.data) # mmap
+
+        shape = gguf_sd_loader_get_orig_shape(reader, tensor_name)
+        if shape is None:
+            shape = torch.Size(tuple(int(v) for v in reversed(tensor.shape)))
+            # Workaround for stable-diffusion.cpp SDXL detection.
+            if compat == "sd.cpp" and arch_str == "sdxl":
+                if any([tensor_name.endswith(x) for x in (".proj_in.weight", ".proj_out.weight")]):
+                    while len(shape) > 2 and shape[-1] == 1:
+                        shape = shape[:-1]
+
+        # add to state dict
+        if tensor.tensor_type in {gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}:
+            torch_tensor = torch_tensor.view(*shape)
+        state_dict[sd_key] = GGMLTensor(torch_tensor, tensor_type=tensor.tensor_type, tensor_shape=shape)
+        qtype_dict[tensor_type_str] = qtype_dict.get(tensor_type_str, 0) + 1
+
+    # sanity check debug print
+    print("\nggml_sd_loader:")
+    for k,v in qtype_dict.items():
+        print(f" {k:30}{v:3}")
+
+    return state_dict
+
+# for remapping llama.cpp -> original key names
+clip_sd_map = {
+    "enc.": "encoder.",
+    ".blk.": ".block.",
+    "token_embd": "shared",
+    "output_norm": "final_layer_norm",
+    "attn_q": "layer.0.SelfAttention.q",
+    "attn_k": "layer.0.SelfAttention.k",
+    "attn_v": "layer.0.SelfAttention.v",
+    "attn_o": "layer.0.SelfAttention.o",
+    "attn_norm": "layer.0.layer_norm",
+    "attn_rel_b": "layer.0.SelfAttention.relative_attention_bias",
+    "ffn_up": "layer.1.DenseReluDense.wi_1",
+    "ffn_down": "layer.1.DenseReluDense.wo",
+    "ffn_gate": "layer.1.DenseReluDense.wi_0",
+    "ffn_norm": "layer.1.layer_norm",
+}
+
+def gguf_clip_loader(path):
+    raw_sd = gguf_sd_loader(path)
+    assert "enc.blk.23.ffn_up.weight" in raw_sd, "Invalid Text Encoder!"
+    sd = {}
+    for k,v in raw_sd.items():
+        for s,d in clip_sd_map.items():
+            k = k.replace(s,d)
+        sd[k] = v
+    return sd
+
+# TODO: Temporary fix for now
+import collections
+class GGUFModelPatcher(comfy.model_patcher.ModelPatcher):
+    patch_on_device = False
+
+    def patch_weight_to_device(self, key, device_to=None, inplace_update=False):
+        if key not in self.patches:
+            return
+        weight = comfy.utils.get_attr(self.model, key)
+
+        try:
+            from comfy.lora import calculate_weight
+        except Exception:
+            calculate_weight = self.calculate_weight
+
+        patches = self.patches[key]
+        if is_quantized(weight):
+            out_weight = weight.to(device_to)
+            patches = move_patch_to_device(patches, self.load_device if self.patch_on_device else self.offload_device)
+            # TODO: do we ever have legitimate duplicate patches? (i.e. patch on top of patched weight)
+            out_weight.patches = [(calculate_weight, patches, key)]
+        else:
+            inplace_update = self.weight_inplace_update or inplace_update
+            if key not in self.backup:
+                self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(
+                    weight.to(device=self.offload_device, copy=inplace_update), inplace_update
+                )
+
+            if device_to is not None:
+                temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
+            else:
+                temp_weight = weight.to(torch.float32, copy=True)
+
+            out_weight = calculate_weight(patches, temp_weight, key)
+            out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype)
+
+        if inplace_update:
+            comfy.utils.copy_to_param(self.model, key, out_weight)
+        else:
+            comfy.utils.set_attr_param(self.model, key, out_weight)
+
+    def unpatch_model(self, device_to=None, unpatch_weights=True):
+        if unpatch_weights:
+            for p in self.model.parameters():
+                if is_torch_compatible(p):
+                    continue
+                patches = getattr(p, "patches", [])
+                if len(patches) > 0:
+                    p.patches = []
+        # TODO: Find another way to not unload after patches
+        return super().unpatch_model(device_to=device_to, unpatch_weights=unpatch_weights)
+
+    mmap_released = False
+    def load(self, *args, force_patch_weights=False, **kwargs):
+        # always call `patch_weight_to_device` even for lowvram
+        super().load(*args, force_patch_weights=True, **kwargs)
+
+        # make sure nothing stays linked to mmap after first load
+        if not self.mmap_released:
+            linked = []
+            if kwargs.get("lowvram_model_memory", 0) > 0:
+                for n, m in self.model.named_modules():
+                    if hasattr(m, "weight"):
+                        device = getattr(m.weight, "device", None)
+                        if device == self.offload_device:
+                            linked.append((n, m))
+                            continue
+                    if hasattr(m, "bias"):
+                        device = getattr(m.bias, "device", None)
+                        if device == self.offload_device:
+                            linked.append((n, m))
+                            continue
+            if linked:
+                print(f"Attempting to release mmap ({len(linked)})")
+                for n, m in linked:
+                    # TODO: possible to OOM, find better way to detach
+                    m.to(self.load_device).to(self.offload_device)
+            self.mmap_released = True
+
+    def clone(self, *args, **kwargs):
+        n = GGUFModelPatcher(self.model, self.load_device, self.offload_device, self.size, weight_inplace_update=self.weight_inplace_update)
+        n.patches = {}
+        for k in self.patches:
+            n.patches[k] = self.patches[k][:]
+        n.patches_uuid = self.patches_uuid
+
+        n.object_patches = self.object_patches.copy()
+        n.model_options = copy.deepcopy(self.model_options)
+        n.backup = self.backup
+        n.object_patches_backup = self.object_patches_backup
+        n.patch_on_device = getattr(self, "patch_on_device", False)
+        return n
+
+class UnetLoaderGGUF:
+    @classmethod
+    def INPUT_TYPES(s):
+        unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
+        return {
+            "required": {
+                "unet_name": (unet_names,),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_unet"
+    CATEGORY = "bootleg"
+    TITLE = "Unet Loader (GGUF)"
+
+    def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None):
+        ops = GGMLOps()
+
+        if dequant_dtype in ("default", None):
+            ops.Linear.dequant_dtype = None
+        elif dequant_dtype in ["target"]:
+            ops.Linear.dequant_dtype = dequant_dtype
+        else:
+            ops.Linear.dequant_dtype = getattr(torch, dequant_dtype)
+
+        if patch_dtype in ("default", None):
+            ops.Linear.patch_dtype = None
+        elif patch_dtype in ["target"]:
+            ops.Linear.patch_dtype = patch_dtype
+        else:
+            ops.Linear.patch_dtype = getattr(torch, patch_dtype)
+
+        # init model
+        unet_path = folder_paths.get_full_path("unet", unet_name)
+        sd = gguf_sd_loader(unet_path)
+        model = comfy.sd.load_diffusion_model_state_dict(
+            sd, model_options={"custom_operations": ops}
+        )
+        if model is None:
+            logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
+            raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
+        model = GGUFModelPatcher.clone(model)
+        model.patch_on_device = patch_on_device
+        return (model,)
+
+class UnetLoaderGGUFAdvanced(UnetLoaderGGUF):
+    @classmethod
+    def INPUT_TYPES(s):
+        unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
+        return {
+            "required": {
+                "unet_name": (unet_names,),
+                "dequant_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
+                "patch_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
+                "patch_on_device": ("BOOLEAN", {"default": False}),
+            }
+        }
+    TITLE = "Unet Loader (GGUF/Advanced)"
+
+clip_name_dict = {
+    "stable_diffusion": comfy.sd.CLIPType.STABLE_DIFFUSION,
+    "stable_cascade": comfy.sd.CLIPType.STABLE_CASCADE,
+    "stable_audio": comfy.sd.CLIPType.STABLE_AUDIO,
+    "sdxl": comfy.sd.CLIPType.STABLE_DIFFUSION,
+    "sd3": comfy.sd.CLIPType.SD3,
+    "flux": comfy.sd.CLIPType.FLUX,
+}
+
+class CLIPLoaderGGUF:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "clip_name": (s.get_filename_list(),),
+                "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio"],),
+            }
+        }
+
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "load_clip"
+    CATEGORY = "bootleg"
+    TITLE = "CLIPLoader (GGUF)"
+
+    @classmethod
+    def get_filename_list(s):
+        files = []
+        files += folder_paths.get_filename_list("clip")
+        files += folder_paths.get_filename_list("clip_gguf")
+        return sorted(files)
+
+    def load_data(self, ckpt_paths):
+        clip_data = []
+        for p in ckpt_paths:
+            if p.endswith(".gguf"):
+                clip_data.append(gguf_clip_loader(p))
+            else:
+                sd = comfy.utils.load_torch_file(p, safe_load=True)
+                clip_data.append(
+                    {k:GGMLTensor(v, tensor_type=gguf.GGMLQuantizationType.F16, tensor_shape=v.shape) for k,v in sd.items()}
+                )
+        return clip_data
+
+    def load_patcher(self, clip_paths, clip_type, clip_data):
+        clip = comfy.sd.load_text_encoder_state_dicts(
+            clip_type = clip_type,
+            state_dicts = clip_data,
+            model_options = {
+                "custom_operations": GGMLOps,
+                "initial_device": comfy.model_management.text_encoder_offload_device()
+            },
+            embedding_directory = folder_paths.get_folder_paths("embeddings"),
+        )
+        clip.patcher = GGUFModelPatcher.clone(clip.patcher)
+
+        # for some reason this is just missing in some SAI checkpoints
+        if getattr(clip.cond_stage_model, "clip_l", None) is not None:
+            if getattr(clip.cond_stage_model.clip_l.transformer.text_projection.weight, "tensor_shape", None) is None:
+                clip.cond_stage_model.clip_l.transformer.text_projection = comfy.ops.manual_cast.Linear(768, 768)
+        if getattr(clip.cond_stage_model, "clip_g", None) is not None:
+            if getattr(clip.cond_stage_model.clip_g.transformer.text_projection.weight, "tensor_shape", None) is None:
+                clip.cond_stage_model.clip_g.transformer.text_projection = comfy.ops.manual_cast.Linear(1280, 1280)
+
+        return clip
+
+    def load_clip(self, clip_name, type="stable_diffusion"):
+        clip_path = folder_paths.get_full_path("clip", clip_name)
+        clip_type = clip_name_dict.get(type, comfy.sd.CLIPType.STABLE_DIFFUSION)
+        return (self.load_patcher([clip_path], clip_type, self.load_data([clip_path])),)
+
+class DualCLIPLoaderGGUF(CLIPLoaderGGUF):
+    @classmethod
+    def INPUT_TYPES(s):
+        file_options = (s.get_filename_list(), )
+        return {
+            "required": {
+                "clip_name1": file_options,
+                "clip_name2": file_options,
+                "type": (("sdxl", "sd3", "flux"), ),
+            }
+        }
+
+    TITLE = "DualCLIPLoader (GGUF)"
+
+    def load_clip(self, clip_name1, clip_name2, type):
+        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip_paths = (clip_path1, clip_path2)
+        clip_type = clip_name_dict.get(type, comfy.sd.CLIPType.STABLE_DIFFUSION)
+        return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),)
+
+class TripleCLIPLoaderGGUF(CLIPLoaderGGUF):
+    @classmethod
+    def INPUT_TYPES(s):
+        file_options = (s.get_filename_list(), )
+        return {
+            "required": {
+                "clip_name1": file_options,
+                "clip_name2": file_options,
+                "clip_name3": file_options,
+            }
+        }
+
+    TITLE = "TripleCLIPLoader (GGUF)"
+
+    def load_clip(self, clip_name1, clip_name2, clip_name3, type="sd3"):
+        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip_path3 = folder_paths.get_full_path("clip", clip_name3)
+        clip_paths = (clip_path1, clip_path2, clip_path3)
+        clip_type = clip_name_dict.get(type, comfy.sd.CLIPType.STABLE_DIFFUSION)
+        return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),)
+
+class UnetLoaderSD3GGUF(UnetLoaderGGUF):
+    @classmethod
+    def INPUT_TYPES(s):
+        unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
+        return {
+            "required": {
+                "unet_name": (unet_names,),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_unet"
+    CATEGORY = "bootleg"
+    TITLE = "Unet Loader SD3 (GGUF)"
+
+    def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None):
+        ops = GGMLOps()
+
+        if dequant_dtype in ("default", None):
+            ops.Linear.dequant_dtype = None
+        elif dequant_dtype in ["target"]:
+            ops.Linear.dequant_dtype = dequant_dtype
+        else:
+            ops.Linear.dequant_dtype = getattr(torch, dequant_dtype)
+
+        if patch_dtype in ("default", None):
+            ops.Linear.patch_dtype = None
+        elif patch_dtype in ["target"]:
+            ops.Linear.patch_dtype = patch_dtype
+        else:
+            ops.Linear.patch_dtype = getattr(torch, patch_dtype)
+
+        # init model
+        unet_path = folder_paths.get_full_path("unet", unet_name)
+        sd = gguf_sd_loader(unet_path)
+        model = comfy.sd.load_diffusion_model_state_dict(
+            sd, model_options={"custom_operations": ops, "model_type": "sd3"}
+        )
+        if model is None:
+            logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
+            raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
+        model = GGUFModelPatcher.clone(model)
+        model.patch_on_device = patch_on_device
+        return (model,)
+
+class UnetLoaderSD3GGUFAdvanced(UnetLoaderSD3GGUF):
+    @classmethod
+    def INPUT_TYPES(s):
+        unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
+        return {
+            "required": {
+                "unet_name": (unet_names,),
+                "dequant_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
+                "patch_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
+                "patch_on_device": ("BOOLEAN", {"default": False}),
+            }
+        }
+    TITLE = "Unet Loader SD3 (GGUF/Advanced)"
+
+
+
+NODE_CLASS_MAPPINGS = {
+    "UnetLoaderGGUF": UnetLoaderGGUF,
+    "CLIPLoaderGGUF": CLIPLoaderGGUF,
+    "DualCLIPLoaderGGUF": DualCLIPLoaderGGUF,
+    "TripleCLIPLoaderGGUF": TripleCLIPLoaderGGUF,
+    "UnetLoaderGGUFAdvanced": UnetLoaderGGUFAdvanced,
+    "UnetLoaderSD3GGUF": UnetLoaderSD3GGUF,
+    "UnetLoaderSD3GGUFAdvanced": UnetLoaderSD3GGUFAdvanced,
+}