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Delete nodes.py

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- # (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
2
- import torch
3
- import gguf
4
- import copy
5
- import logging
6
-
7
- import comfy.sd
8
- import comfy.utils
9
- import comfy.model_management
10
- import comfy.model_patcher
11
- import folder_paths
12
-
13
- from .ops import GGMLTensor, GGMLOps, move_patch_to_device
14
- from .dequant import is_quantized, is_torch_compatible
15
-
16
- # Add a custom keys for files ending in .gguf
17
- if "unet_gguf" not in folder_paths.folder_names_and_paths:
18
- orig = folder_paths.folder_names_and_paths.get("diffusion_models", folder_paths.folder_names_and_paths.get("unet", [[], set()]))
19
- folder_paths.folder_names_and_paths["unet_gguf"] = (orig[0], {".gguf"})
20
-
21
- if "clip_gguf" not in folder_paths.folder_names_and_paths:
22
- orig = folder_paths.folder_names_and_paths.get("clip", [[], set()])
23
- folder_paths.folder_names_and_paths["clip_gguf"] = (orig[0], {".gguf"})
24
-
25
- def gguf_sd_loader_get_orig_shape(reader, tensor_name):
26
- field_key = f"comfy.gguf.orig_shape.{tensor_name}"
27
- field = reader.get_field(field_key)
28
- if field is None:
29
- return None
30
- # Has original shape metadata, so we try to decode it.
31
- if len(field.types) != 2 or field.types[0] != gguf.GGUFValueType.ARRAY or field.types[1] != gguf.GGUFValueType.INT32:
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- raise TypeError(f"Bad original shape metadata for {field_key}: Expected ARRAY of INT32, got {field.types}")
33
- return torch.Size(tuple(int(field.parts[part_idx][0]) for part_idx in field.data))
34
-
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- def gguf_sd_loader(path, handle_prefix="model.diffusion_model."):
36
- """
37
- Read state dict as fake tensors
38
- """
39
- reader = gguf.GGUFReader(path)
40
-
41
- # filter and strip prefix
42
- has_prefix = False
43
- if handle_prefix is not None:
44
- prefix_len = len(handle_prefix)
45
- tensor_names = set(tensor.name for tensor in reader.tensors)
46
- has_prefix = any(s.startswith(handle_prefix) for s in tensor_names)
47
-
48
- tensors = []
49
- for tensor in reader.tensors:
50
- sd_key = tensor_name = tensor.name
51
- if has_prefix:
52
- if not tensor_name.startswith(handle_prefix):
53
- continue
54
- sd_key = tensor_name[prefix_len:]
55
- tensors.append((sd_key, tensor))
56
-
57
- # detect and verify architecture
58
- compat = None
59
- arch_str = None
60
- arch_field = reader.get_field("general.architecture")
61
- if arch_field is not None:
62
- if len(arch_field.types) != 1 or arch_field.types[0] != gguf.GGUFValueType.STRING:
63
- raise TypeError(f"Bad type for GGUF general.architecture key: expected string, got {arch_field.types!r}")
64
- arch_str = str(arch_field.parts[arch_field.data[-1]], encoding="utf-8")
65
- if arch_str not in {"flux", "sd1", "sdxl", "t5", "t5encoder", "sd3"}:
66
- raise ValueError(f"Unexpected architecture type in GGUF file, expected one of flux, sd1, sdxl, t5encoder, sd3 but got {arch_str!r}")
67
- else: # stable-diffusion.cpp
68
- # import here to avoid changes to convert.py breaking regular models
69
- from .tools.convert import detect_arch
70
- arch_str = detect_arch(set(val[0] for val in tensors)).arch
71
- compat = "sd.cpp"
72
-
73
- # main loading loop
74
- state_dict = {}
75
- qtype_dict = {}
76
- for sd_key, tensor in tensors:
77
- tensor_name = tensor.name
78
- tensor_type_str = str(tensor.tensor_type)
79
- torch_tensor = torch.from_numpy(tensor.data) # mmap
80
-
81
- shape = gguf_sd_loader_get_orig_shape(reader, tensor_name)
82
- if shape is None:
83
- shape = torch.Size(tuple(int(v) for v in reversed(tensor.shape)))
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- # Workaround for stable-diffusion.cpp SDXL detection.
85
- if compat == "sd.cpp" and arch_str == "sdxl":
86
- if any([tensor_name.endswith(x) for x in (".proj_in.weight", ".proj_out.weight")]):
87
- while len(shape) > 2 and shape[-1] == 1:
88
- shape = shape[:-1]
89
-
90
- # add to state dict
91
- if tensor.tensor_type in {gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}:
92
- torch_tensor = torch_tensor.view(*shape)
93
- state_dict[sd_key] = GGMLTensor(torch_tensor, tensor_type=tensor.tensor_type, tensor_shape=shape)
94
- qtype_dict[tensor_type_str] = qtype_dict.get(tensor_type_str, 0) + 1
95
-
96
- # sanity check debug print
97
- print("\nggml_sd_loader:")
98
- for k,v in qtype_dict.items():
99
- print(f" {k:30}{v:3}")
100
-
101
- return state_dict
102
-
103
- # for remapping llama.cpp -> original key names
104
- clip_sd_map = {
105
- "enc.": "encoder.",
106
- ".blk.": ".block.",
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- "token_embd": "shared",
108
- "output_norm": "final_layer_norm",
109
- "attn_q": "layer.0.SelfAttention.q",
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- "attn_k": "layer.0.SelfAttention.k",
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- "attn_v": "layer.0.SelfAttention.v",
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- "attn_o": "layer.0.SelfAttention.o",
113
- "attn_norm": "layer.0.layer_norm",
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- "attn_rel_b": "layer.0.SelfAttention.relative_attention_bias",
115
- "ffn_up": "layer.1.DenseReluDense.wi_1",
116
- "ffn_down": "layer.1.DenseReluDense.wo",
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- "ffn_gate": "layer.1.DenseReluDense.wi_0",
118
- "ffn_norm": "layer.1.layer_norm",
119
- }
120
-
121
- def gguf_clip_loader(path):
122
- raw_sd = gguf_sd_loader(path)
123
- assert "enc.blk.23.ffn_up.weight" in raw_sd, "Invalid Text Encoder!"
124
- sd = {}
125
- for k,v in raw_sd.items():
126
- for s,d in clip_sd_map.items():
127
- k = k.replace(s,d)
128
- sd[k] = v
129
- return sd
130
-
131
- # TODO: Temporary fix for now
132
- import collections
133
- class GGUFModelPatcher(comfy.model_patcher.ModelPatcher):
134
- patch_on_device = False
135
-
136
- def patch_weight_to_device(self, key, device_to=None, inplace_update=False):
137
- if key not in self.patches:
138
- return
139
- weight = comfy.utils.get_attr(self.model, key)
140
-
141
- try:
142
- from comfy.lora import calculate_weight
143
- except Exception:
144
- calculate_weight = self.calculate_weight
145
-
146
- patches = self.patches[key]
147
- if is_quantized(weight):
148
- out_weight = weight.to(device_to)
149
- patches = move_patch_to_device(patches, self.load_device if self.patch_on_device else self.offload_device)
150
- # TODO: do we ever have legitimate duplicate patches? (i.e. patch on top of patched weight)
151
- out_weight.patches = [(calculate_weight, patches, key)]
152
- else:
153
- inplace_update = self.weight_inplace_update or inplace_update
154
- if key not in self.backup:
155
- self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(
156
- weight.to(device=self.offload_device, copy=inplace_update), inplace_update
157
- )
158
-
159
- if device_to is not None:
160
- temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
161
- else:
162
- temp_weight = weight.to(torch.float32, copy=True)
163
-
164
- out_weight = calculate_weight(patches, temp_weight, key)
165
- out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype)
166
-
167
- if inplace_update:
168
- comfy.utils.copy_to_param(self.model, key, out_weight)
169
- else:
170
- comfy.utils.set_attr_param(self.model, key, out_weight)
171
-
172
- def unpatch_model(self, device_to=None, unpatch_weights=True):
173
- if unpatch_weights:
174
- for p in self.model.parameters():
175
- if is_torch_compatible(p):
176
- continue
177
- patches = getattr(p, "patches", [])
178
- if len(patches) > 0:
179
- p.patches = []
180
- # TODO: Find another way to not unload after patches
181
- return super().unpatch_model(device_to=device_to, unpatch_weights=unpatch_weights)
182
-
183
- mmap_released = False
184
- def load(self, *args, force_patch_weights=False, **kwargs):
185
- # always call `patch_weight_to_device` even for lowvram
186
- super().load(*args, force_patch_weights=True, **kwargs)
187
-
188
- # make sure nothing stays linked to mmap after first load
189
- if not self.mmap_released:
190
- linked = []
191
- if kwargs.get("lowvram_model_memory", 0) > 0:
192
- for n, m in self.model.named_modules():
193
- if hasattr(m, "weight"):
194
- device = getattr(m.weight, "device", None)
195
- if device == self.offload_device:
196
- linked.append((n, m))
197
- continue
198
- if hasattr(m, "bias"):
199
- device = getattr(m.bias, "device", None)
200
- if device == self.offload_device:
201
- linked.append((n, m))
202
- continue
203
- if linked:
204
- print(f"Attempting to release mmap ({len(linked)})")
205
- for n, m in linked:
206
- # TODO: possible to OOM, find better way to detach
207
- m.to(self.load_device).to(self.offload_device)
208
- self.mmap_released = True
209
-
210
- def clone(self, *args, **kwargs):
211
- n = GGUFModelPatcher(self.model, self.load_device, self.offload_device, self.size, weight_inplace_update=self.weight_inplace_update)
212
- n.patches = {}
213
- for k in self.patches:
214
- n.patches[k] = self.patches[k][:]
215
- n.patches_uuid = self.patches_uuid
216
-
217
- n.object_patches = self.object_patches.copy()
218
- n.model_options = copy.deepcopy(self.model_options)
219
- n.backup = self.backup
220
- n.object_patches_backup = self.object_patches_backup
221
- n.patch_on_device = getattr(self, "patch_on_device", False)
222
- return n
223
-
224
- class UnetLoaderGGUF:
225
- @classmethod
226
- def INPUT_TYPES(s):
227
- unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
228
- return {
229
- "required": {
230
- "unet_name": (unet_names,),
231
- }
232
- }
233
-
234
- RETURN_TYPES = ("MODEL",)
235
- FUNCTION = "load_unet"
236
- CATEGORY = "bootleg"
237
- TITLE = "Unet Loader (GGUF)"
238
-
239
- def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None):
240
- ops = GGMLOps()
241
-
242
- if dequant_dtype in ("default", None):
243
- ops.Linear.dequant_dtype = None
244
- elif dequant_dtype in ["target"]:
245
- ops.Linear.dequant_dtype = dequant_dtype
246
- else:
247
- ops.Linear.dequant_dtype = getattr(torch, dequant_dtype)
248
-
249
- if patch_dtype in ("default", None):
250
- ops.Linear.patch_dtype = None
251
- elif patch_dtype in ["target"]:
252
- ops.Linear.patch_dtype = patch_dtype
253
- else:
254
- ops.Linear.patch_dtype = getattr(torch, patch_dtype)
255
-
256
- # init model
257
- unet_path = folder_paths.get_full_path("unet", unet_name)
258
- sd = gguf_sd_loader(unet_path)
259
- model = comfy.sd.load_diffusion_model_state_dict(
260
- sd, model_options={"custom_operations": ops}
261
- )
262
- if model is None:
263
- logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
264
- raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
265
- model = GGUFModelPatcher.clone(model)
266
- model.patch_on_device = patch_on_device
267
- return (model,)
268
-
269
- class UnetLoaderGGUFAdvanced(UnetLoaderGGUF):
270
- @classmethod
271
- def INPUT_TYPES(s):
272
- unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
273
- return {
274
- "required": {
275
- "unet_name": (unet_names,),
276
- "dequant_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
277
- "patch_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
278
- "patch_on_device": ("BOOLEAN", {"default": False}),
279
- }
280
- }
281
- TITLE = "Unet Loader (GGUF/Advanced)"
282
-
283
- clip_name_dict = {
284
- "stable_diffusion": comfy.sd.CLIPType.STABLE_DIFFUSION,
285
- "stable_cascade": comfy.sd.CLIPType.STABLE_CASCADE,
286
- "stable_audio": comfy.sd.CLIPType.STABLE_AUDIO,
287
- "sdxl": comfy.sd.CLIPType.STABLE_DIFFUSION,
288
- "sd3": comfy.sd.CLIPType.SD3,
289
- "flux": comfy.sd.CLIPType.FLUX,
290
- }
291
-
292
- class CLIPLoaderGGUF:
293
- @classmethod
294
- def INPUT_TYPES(s):
295
- return {
296
- "required": {
297
- "clip_name": (s.get_filename_list(),),
298
- "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio"],),
299
- }
300
- }
301
-
302
- RETURN_TYPES = ("CLIP",)
303
- FUNCTION = "load_clip"
304
- CATEGORY = "bootleg"
305
- TITLE = "CLIPLoader (GGUF)"
306
-
307
- @classmethod
308
- def get_filename_list(s):
309
- files = []
310
- files += folder_paths.get_filename_list("clip")
311
- files += folder_paths.get_filename_list("clip_gguf")
312
- return sorted(files)
313
-
314
- def load_data(self, ckpt_paths):
315
- clip_data = []
316
- for p in ckpt_paths:
317
- if p.endswith(".gguf"):
318
- clip_data.append(gguf_clip_loader(p))
319
- else:
320
- sd = comfy.utils.load_torch_file(p, safe_load=True)
321
- clip_data.append(
322
- {k:GGMLTensor(v, tensor_type=gguf.GGMLQuantizationType.F16, tensor_shape=v.shape) for k,v in sd.items()}
323
- )
324
- return clip_data
325
-
326
- def load_patcher(self, clip_paths, clip_type, clip_data):
327
- clip = comfy.sd.load_text_encoder_state_dicts(
328
- clip_type = clip_type,
329
- state_dicts = clip_data,
330
- model_options = {
331
- "custom_operations": GGMLOps,
332
- "initial_device": comfy.model_management.text_encoder_offload_device()
333
- },
334
- embedding_directory = folder_paths.get_folder_paths("embeddings"),
335
- )
336
- clip.patcher = GGUFModelPatcher.clone(clip.patcher)
337
-
338
- # for some reason this is just missing in some SAI checkpoints
339
- if getattr(clip.cond_stage_model, "clip_l", None) is not None:
340
- if getattr(clip.cond_stage_model.clip_l.transformer.text_projection.weight, "tensor_shape", None) is None:
341
- clip.cond_stage_model.clip_l.transformer.text_projection = comfy.ops.manual_cast.Linear(768, 768)
342
- if getattr(clip.cond_stage_model, "clip_g", None) is not None:
343
- if getattr(clip.cond_stage_model.clip_g.transformer.text_projection.weight, "tensor_shape", None) is None:
344
- clip.cond_stage_model.clip_g.transformer.text_projection = comfy.ops.manual_cast.Linear(1280, 1280)
345
-
346
- return clip
347
-
348
- def load_clip(self, clip_name, type="stable_diffusion"):
349
- clip_path = folder_paths.get_full_path("clip", clip_name)
350
- clip_type = clip_name_dict.get(type, comfy.sd.CLIPType.STABLE_DIFFUSION)
351
- return (self.load_patcher([clip_path], clip_type, self.load_data([clip_path])),)
352
-
353
- class DualCLIPLoaderGGUF(CLIPLoaderGGUF):
354
- @classmethod
355
- def INPUT_TYPES(s):
356
- file_options = (s.get_filename_list(), )
357
- return {
358
- "required": {
359
- "clip_name1": file_options,
360
- "clip_name2": file_options,
361
- "type": (("sdxl", "sd3", "flux"), ),
362
- }
363
- }
364
-
365
- TITLE = "DualCLIPLoader (GGUF)"
366
-
367
- def load_clip(self, clip_name1, clip_name2, type):
368
- clip_path1 = folder_paths.get_full_path("clip", clip_name1)
369
- clip_path2 = folder_paths.get_full_path("clip", clip_name2)
370
- clip_paths = (clip_path1, clip_path2)
371
- clip_type = clip_name_dict.get(type, comfy.sd.CLIPType.STABLE_DIFFUSION)
372
- return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),)
373
-
374
- class TripleCLIPLoaderGGUF(CLIPLoaderGGUF):
375
- @classmethod
376
- def INPUT_TYPES(s):
377
- file_options = (s.get_filename_list(), )
378
- return {
379
- "required": {
380
- "clip_name1": file_options,
381
- "clip_name2": file_options,
382
- "clip_name3": file_options,
383
- }
384
- }
385
-
386
- TITLE = "TripleCLIPLoader (GGUF)"
387
-
388
- def load_clip(self, clip_name1, clip_name2, clip_name3, type="sd3"):
389
- clip_path1 = folder_paths.get_full_path("clip", clip_name1)
390
- clip_path2 = folder_paths.get_full_path("clip", clip_name2)
391
- clip_path3 = folder_paths.get_full_path("clip", clip_name3)
392
- clip_paths = (clip_path1, clip_path2, clip_path3)
393
- clip_type = clip_name_dict.get(type, comfy.sd.CLIPType.STABLE_DIFFUSION)
394
- return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),)
395
-
396
- class UnetLoaderSD3GGUF(UnetLoaderGGUF):
397
- @classmethod
398
- def INPUT_TYPES(s):
399
- unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
400
- return {
401
- "required": {
402
- "unet_name": (unet_names,),
403
- }
404
- }
405
-
406
- RETURN_TYPES = ("MODEL",)
407
- FUNCTION = "load_unet"
408
- CATEGORY = "bootleg"
409
- TITLE = "Unet Loader SD3 (GGUF)"
410
-
411
- def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None):
412
- ops = GGMLOps()
413
-
414
- if dequant_dtype in ("default", None):
415
- ops.Linear.dequant_dtype = None
416
- elif dequant_dtype in ["target"]:
417
- ops.Linear.dequant_dtype = dequant_dtype
418
- else:
419
- ops.Linear.dequant_dtype = getattr(torch, dequant_dtype)
420
-
421
- if patch_dtype in ("default", None):
422
- ops.Linear.patch_dtype = None
423
- elif patch_dtype in ["target"]:
424
- ops.Linear.patch_dtype = patch_dtype
425
- else:
426
- ops.Linear.patch_dtype = getattr(torch, patch_dtype)
427
-
428
- # init model
429
- unet_path = folder_paths.get_full_path("unet", unet_name)
430
- sd = gguf_sd_loader(unet_path)
431
- model = comfy.sd.load_diffusion_model_state_dict(
432
- sd, model_options={"custom_operations": ops, "model_type": "sd3"}
433
- )
434
- if model is None:
435
- logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
436
- raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
437
- model = GGUFModelPatcher.clone(model)
438
- model.patch_on_device = patch_on_device
439
- return (model,)
440
-
441
- class UnetLoaderSD3GGUFAdvanced(UnetLoaderSD3GGUF):
442
- @classmethod
443
- def INPUT_TYPES(s):
444
- unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
445
- return {
446
- "required": {
447
- "unet_name": (unet_names,),
448
- "dequant_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
449
- "patch_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
450
- "patch_on_device": ("BOOLEAN", {"default": False}),
451
- }
452
- }
453
- TITLE = "Unet Loader SD3 (GGUF/Advanced)"
454
-
455
-
456
-
457
- NODE_CLASS_MAPPINGS = {
458
- "UnetLoaderGGUF": UnetLoaderGGUF,
459
- "CLIPLoaderGGUF": CLIPLoaderGGUF,
460
- "DualCLIPLoaderGGUF": DualCLIPLoaderGGUF,
461
- "TripleCLIPLoaderGGUF": TripleCLIPLoaderGGUF,
462
- "UnetLoaderGGUFAdvanced": UnetLoaderGGUFAdvanced,
463
- "UnetLoaderSD3GGUF": UnetLoaderSD3GGUF,
464
- "UnetLoaderSD3GGUFAdvanced": UnetLoaderSD3GGUFAdvanced,
465
- }