メモ Froge GGUF SDXL

 メモです。

 

reader = gguf.GGUFReader(path)

 

 オリジナルシェイプ

def 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))
 

 

シェイプはintにしないとエラーになる。

shape = torch.Size(tuple(int(v) for v in reversed(tensor.shape)))

 

F16は、形を整える。

        if tensor.tensor_type in {gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}:
            torch_tensor = torch_tensor.view(*shape)

 

sd-webui-forge-classic-neo\modules_forge\packages\huggingface_guess\detection.py

model_channels GGUFで変形してると合わない。のでモデルが分かりませんエラーになる

 

sd-webui-forge-classic-neo\modules_forge\packages\huggingface_guess\__init__.py

モデルの識別

def guess(state_dict):
    unet_key_prefix = unet_prefix_from_state_dict(state_dict)
    print("unet_key_prefix",unet_key_prefix)
    result = model_config_from_unet(
        state_dict, unet_key_prefix, use_base_if_no_match=False
    )

 

モデルの読み込み 

sd-webui-forge-classic-neo\backend\utils.py 

def load_torch_file(ckpt: str, safe_load=False, device=None, *, return_metadata=False):

    elif ckpt.lower().endswith(".gguf"):
        reader = gguf.GGUFReader(ckpt)
        sd = {}
        for tensor in reader.tensors:
            sd[str(tensor.name)] = ParameterGGUF(tensor)

モデルのロード

sd-webui-forge-classic-neo\backend\loader.py

入れ物を作ってデータを転送する。 

def load_huggingface_component(guess, component_name, lib_name, cls_name, repo_path, state_dict):

入れ物を作る

if cls_name == "UNet2DConditionModel":
                model_loader = lambda c: IntegratedUNet2DConditionModel.from_config(c)

モデルにstate_dictを転送　ここでサイズが合わなくてエラーになる。

load_state_dict(model, state_dict)

 

sd-webui-forge-classic-neo\backend\state_dict.py 

モデルにデータを送る 

def load_state_dict(model, sd, ignore_errors=[], log_name=None, ignore_start=None):
    missing, unexpected = model.load_state_dict(sd, strict=False)
    missing = [x for x in missing if x not in ignore_errors]
    unexpected = [x for x in unexpected if x not in ignore_errors]
 

 

モデルをダウンロードする。

def split_state_dict(sd, additional_state_dicts: list = None):
    sd, metadata = load_torch_file(sd, return_metadata=True)
    sd = preprocess_state_dict(sd)
    guess = huggingface_guess.guess(sd)

 sd-webui-forge-classic-neo\backend\nn\unet.py

 unetモデルの入れ物をつくる

 class IntegratedUNet2DConditionModel(nn.Module, ConfigMixin):

 

inner_dimをintにした方が良い 

class SpatialTransformer(nn.Module):
    def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None, disable_self_attn=False, use_linear=False, use_checkpoint=True):
        super().__init__()
        if exists(context_dim) and not isinstance(context_dim, list):
            context_dim = [context_dim] * depth
        self.in_channels = in_channels
        inner_dim = int(n_heads * d_head)

 

 

sd-webui-forge-classic-neo\backend\operations.py

 

 

sd-webui-forge-classic-neo\backend\operations_gguf.py

延滞読込用　Lazy Loading

class ParameterGGUF(torch.nn.Parameter):

self.real_shape = torch.Size(reversed(list(tensor.shape)))

↑ここをintにした方が良い

self.real_shape = torch.Size(tuple(int(v) for v in reversed(tensor.shape)))