added the official nodes.py file from city96 as he updated it to work with sd3
Browse files
nodes.py
ADDED
@@ -0,0 +1,402 @@
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1 |
+
# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
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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
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12 |
+
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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()])
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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:
|
32 |
+
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 |
+
|
35 |
+
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", "sd3", "t5", "t5encoder"}:
|
66 |
+
raise ValueError(f"Unexpected architecture type in GGUF file, expected one of flux, sd1, sdxl, t5encoder 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)))
|
84 |
+
# 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.",
|
107 |
+
"token_embd": "shared",
|
108 |
+
"output_norm": "final_layer_norm",
|
109 |
+
"attn_q": "layer.0.SelfAttention.q",
|
110 |
+
"attn_k": "layer.0.SelfAttention.k",
|
111 |
+
"attn_v": "layer.0.SelfAttention.v",
|
112 |
+
"attn_o": "layer.0.SelfAttention.o",
|
113 |
+
"attn_norm": "layer.0.layer_norm",
|
114 |
+
"attn_rel_b": "layer.0.SelfAttention.relative_attention_bias",
|
115 |
+
"ffn_up": "layer.1.DenseReluDense.wi_1",
|
116 |
+
"ffn_down": "layer.1.DenseReluDense.wo",
|
117 |
+
"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 |
+
NODE_CLASS_MAPPINGS = {
|
397 |
+
"UnetLoaderGGUF": UnetLoaderGGUF,
|
398 |
+
"CLIPLoaderGGUF": CLIPLoaderGGUF,
|
399 |
+
"DualCLIPLoaderGGUF": DualCLIPLoaderGGUF,
|
400 |
+
"TripleCLIPLoaderGGUF": TripleCLIPLoaderGGUF,
|
401 |
+
"UnetLoaderGGUFAdvanced": UnetLoaderGGUFAdvanced,
|
402 |
+
}
|