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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,
}