Delete nodes.py

nodes.py

@@ -1,465 +0,0 @@
-# (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,
-}