nodes.py

# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
import copy
import logging
from typing import Final

import gguf
import torch

import comfy.model_management
import comfy.model_patcher
import comfy.sd
import comfy.utils
from comfy.cmd.folder_paths import add_model_folder_path, get_folder_paths
from comfy.model_downloader import KnownDownloadables, get_filename_list_with_downloadable, add_known_models, \
    get_or_download
from comfy.model_downloader_types import HuggingFile
from comfy.nodes.package_typing import CustomNode
from .dequant import is_quantized, is_torch_compatible
from .ops import GGMLTensor, GGMLOps, move_patch_to_device

add_model_folder_path("unet_gguf", extensions={".gguf"})
add_model_folder_path("clip_gguf", extensions={".gguf"})

KNOWN_UNET_GGUF: Final[KnownDownloadables] = KnownDownloadables([
    HuggingFile("city96/FLUX.1-dev-gguf", "flux1-dev-F16.gguf"),
    HuggingFile("city96/FLUX.1-dev-gguf", "flux1-dev-Q2_K.gguf"),
    HuggingFile("city96/FLUX.1-dev-gguf", "flux1-dev-Q3_K_S.gguf"),
    HuggingFile("city96/FLUX.1-dev-gguf", "flux1-dev-Q4_0.gguf"),
    HuggingFile("city96/FLUX.1-dev-gguf", "flux1-dev-Q4_1.gguf"),
    HuggingFile("city96/FLUX.1-dev-gguf", "flux1-dev-Q4_K_S.gguf"),
    HuggingFile("city96/FLUX.1-dev-gguf", "flux1-dev-Q5_0.gguf"),
    HuggingFile("city96/FLUX.1-dev-gguf", "flux1-dev-Q5_1.gguf"),
    HuggingFile("city96/FLUX.1-dev-gguf", "flux1-dev-Q5_K_S.gguf"),
    HuggingFile("city96/FLUX.1-dev-gguf", "flux1-dev-Q6_K.gguf"),
    HuggingFile("city96/FLUX.1-dev-gguf", "flux1-dev-Q8_0.gguf"),

    HuggingFile("city96/FLUX.1-schnell-gguf", "flux1-schnell-F16.gguf"),
    HuggingFile("city96/FLUX.1-schnell-gguf", "flux1-schnell-Q2_K.gguf"),
    HuggingFile("city96/FLUX.1-schnell-gguf", "flux1-schnell-Q3_K_S.gguf"),
    HuggingFile("city96/FLUX.1-schnell-gguf", "flux1-schnell-Q4_0.gguf"),
    HuggingFile("city96/FLUX.1-schnell-gguf", "flux1-schnell-Q4_1.gguf"),
    HuggingFile("city96/FLUX.1-schnell-gguf", "flux1-schnell-Q4_K_S.gguf"),
    HuggingFile("city96/FLUX.1-schnell-gguf", "flux1-schnell-Q5_0.gguf"),
    HuggingFile("city96/FLUX.1-schnell-gguf", "flux1-schnell-Q5_1.gguf"),
    HuggingFile("city96/FLUX.1-schnell-gguf", "flux1-schnell-Q5_K_S.gguf"),
    HuggingFile("city96/FLUX.1-schnell-gguf", "flux1-schnell-Q6_K.gguf"),
    HuggingFile("city96/FLUX.1-schnell-gguf", "flux1-schnell-Q8_0.gguf"),
], "unet_gguf")

KNOWN_CLIP_GGUF: Final[KnownDownloadables] = KnownDownloadables([
    HuggingFile("city96/t5-v1_1-xxl-encoder-gguf", "t5-v1_1-xxl-encoder-Q3_K_L.gguf"),
    HuggingFile("city96/t5-v1_1-xxl-encoder-gguf", "t5-v1_1-xxl-encoder-Q3_K_M.gguf"),
    HuggingFile("city96/t5-v1_1-xxl-encoder-gguf", "t5-v1_1-xxl-encoder-Q3_K_S.gguf"),
    HuggingFile("city96/t5-v1_1-xxl-encoder-gguf", "t5-v1_1-xxl-encoder-Q4_K_M.gguf"),
    HuggingFile("city96/t5-v1_1-xxl-encoder-gguf", "t5-v1_1-xxl-encoder-Q4_K_S.gguf"),
    HuggingFile("city96/t5-v1_1-xxl-encoder-gguf", "t5-v1_1-xxl-encoder-Q5_K_M.gguf"),
    HuggingFile("city96/t5-v1_1-xxl-encoder-gguf", "t5-v1_1-xxl-encoder-Q5_K_S.gguf"),
    HuggingFile("city96/t5-v1_1-xxl-encoder-gguf", "t5-v1_1-xxl-encoder-Q6_K.gguf"),
    HuggingFile("city96/t5-v1_1-xxl-encoder-gguf", "t5-v1_1-xxl-encoder-Q8_0.gguf"),
    HuggingFile("city96/t5-v1_1-xxl-encoder-gguf", "t5-v1_1-xxl-encoder-f16.gguf"),
    HuggingFile("city96/t5-v1_1-xxl-encoder-gguf", "t5-v1_1-xxl-encoder-f32.gguf"),
], "clip_gguf")

add_known_models("unet_gguf", KNOWN_UNET_GGUF)
add_known_models("unet_gguf", KNOWN_CLIP_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"}:
            raise ValueError(f"Unexpected architecture type in GGUF file, expected one of flux, sd1, sdxl, t5encoder 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))
        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(CustomNode):
    @classmethod
    def INPUT_TYPES(s):
        unet_names = [x for x in get_filename_list_with_downloadable("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 = get_or_download("unet_gguf", 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 get_filename_list_with_downloadable("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 += get_filename_list_with_downloadable("clip")
        files += get_filename_list_with_downloadable("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=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 = get_or_download("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])),)


def get_gguf_clips(*clip_names: str) -> list[str]:
    ret = []
    for clip_name in clip_names:
        for folder_name in ("clip_gguf", "clip"):
            candidate = get_or_download(folder_name=folder_name, filename=clip_name)
            if candidate is not None:
                ret.append(candidate)
                break
    return ret


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_paths = get_gguf_clips(clip_name1, clip_name2)
        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_paths = get_gguf_clips(clip_name1, clip_name2, clip_name3)
        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)),)


NODE_CLASS_MAPPINGS = {
    "UnetLoaderGGUF": UnetLoaderGGUF,
    "CLIPLoaderGGUF": CLIPLoaderGGUF,
    "DualCLIPLoaderGGUF": DualCLIPLoaderGGUF,
    "TripleCLIPLoaderGGUF": TripleCLIPLoaderGGUF,
    "UnetLoaderGGUFAdvanced": UnetLoaderGGUFAdvanced,
}