├── requirements.txt ├── README.md ├── float_nf4.py ├── __init__.py └── LICENSE.txt /requirements.txt: -------------------------------------------------------------------------------- 1 | bitsandbytes>=0.43.0 2 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Now on the [manager](https://github.com/ltdrdata/ComfyUI-Manager) for easy installation. Make sure to select Channel:dev in the ComfyUI manager menu or install via git url. 2 | 3 | A quickly written custom node that uses code from [Forge](https://github.com/lllyasviel/stable-diffusion-webui-forge) to support the [nf4 flux dev checkpoint](https://huggingface.co/lllyasviel/flux1-dev-bnb-nf4/blob/main/flux1-dev-bnb-nf4.safetensors) and [nf4 flux schnell checkpoint](https://huggingface.co/silveroxides/flux1-nf4-weights/blob/main/flux1-schnell-bnb-nf4.safetensors). 4 | 5 | Requires installing bitsandbytes. 6 | 7 | Make sure your ComfyUI is updated. 8 | 9 | The node is: CheckpointLoaderNF4, just plug it in your flux workflow instead of the regular one. 10 | -------------------------------------------------------------------------------- /float_nf4.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | def calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=None): 4 | mantissa_scaled = torch.where( 5 | normal_mask, 6 | (abs_x / (2.0 ** (exponent - EXPONENT_BIAS)) - 1.0) * (2**MANTISSA_BITS), 7 | (abs_x / (2.0 ** (-EXPONENT_BIAS + 1 - MANTISSA_BITS))) 8 | ) 9 | 10 | mantissa_scaled += torch.rand(mantissa_scaled.size(), dtype=mantissa_scaled.dtype, layout=mantissa_scaled.layout, device=mantissa_scaled.device, generator=generator) 11 | return mantissa_scaled.floor() / (2**MANTISSA_BITS) 12 | 13 | def manual_stochastic_round_to_nf4(x, rounding_format, generator=None): 14 | ''' 15 | if dtype == torch.fp4_e2m1: 16 | EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 2, 1, 3 17 | elif dtype == torch.fp4_e3m0: 18 | EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 3, 0, 7 19 | ''' 20 | 21 | # bias = 2**(EXPONENT_BITS-1) - 1 22 | 23 | rounding_format_arr = rounding_format.split(',') 24 | if len(rounding_format_arr) != 3: 25 | raise Exception(f'Incorrect argument rounding_format={rounding_format}') 26 | 27 | EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = tuple(int(x) for x in rounding_format_arr) 28 | 29 | x = x.half() 30 | sign = torch.sign(x) 31 | abs_x = x.abs() 32 | sign = torch.where(abs_x == 0, 0, sign) 33 | 34 | # Combine exponent calculation and clamping 35 | exponent = torch.clamp( 36 | torch.floor(torch.log2(abs_x)) + EXPONENT_BIAS, 37 | 0, 2**EXPONENT_BITS - 1 38 | ) 39 | 40 | # Combine mantissa calculation and rounding 41 | normal_mask = ~(exponent == 0) 42 | 43 | abs_x[:] = calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=generator) 44 | 45 | sign *= torch.where( 46 | normal_mask, 47 | (2.0 ** (exponent - EXPONENT_BIAS)) * (1.0 + abs_x), 48 | (2.0 ** (-EXPONENT_BIAS + 1)) * abs_x 49 | ) 50 | 51 | return sign 52 | 53 | def stochastic_rounding_nf4(value, rounding_format, seed=0): 54 | generator = torch.Generator(device=value.device) 55 | generator.manual_seed(seed) 56 | output = torch.empty_like(value, dtype=torch.float8_e4m3fn) 57 | num_slices = max(1, (value.numel() / (4096 * 4096))) 58 | slice_size = max(1, round(value.shape[0] / num_slices)) 59 | for i in range(0, value.shape[0], slice_size): 60 | output[i:i+slice_size].copy_(manual_stochastic_round_to_nf4(value[i:i+slice_size], rounding_format, generator=generator)) 61 | return output 62 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | #shamelessly taken from forge 2 | 3 | import logging 4 | import comfy.model_patcher 5 | import folder_paths 6 | 7 | import torch 8 | import collections 9 | import bitsandbytes as bnb 10 | import comfy 11 | import copy 12 | 13 | from bitsandbytes.nn.modules import Params4bit, QuantState 14 | from bitsandbytes.functional import dequantize_4bit 15 | from comfy.cli_args import args as cli_args 16 | from comfy.model_patcher import ModelPatcher, string_to_seed 17 | 18 | from .float_nf4 import stochastic_rounding_nf4 19 | 20 | rounding_format_default = '2,1,7' 21 | dtype_from_str = {"default": None, "float8_e4m3fn": torch.float8_e4m3fn, "float8_e5m2": torch.float8_e5m2} 22 | 23 | def functional_linear_4bits(x, weight, bias): 24 | out = bnb.matmul_4bit(x, weight.t(), bias=bias, quant_state=weight.quant_state) 25 | out = out.to(x) 26 | return out 27 | 28 | def functional_dequantize_4bit(weight): 29 | if not weight.bnb_quantized: 30 | return weight 31 | 32 | weight_original_device = weight.device 33 | 34 | if weight_original_device.type != 'cuda': 35 | weight = weight.cuda() 36 | 37 | weight = dequantize_4bit(weight, quant_state=weight.quant_state, blocksize=weight.blocksize, quant_type=weight.quant_type) 38 | 39 | if weight_original_device.type != 'cuda': 40 | weight = weight.to(device=weight_original_device) 41 | 42 | return weight 43 | 44 | def copy_quant_state(state: QuantState, device: torch.device = None) -> QuantState: 45 | if state is None: 46 | return None 47 | 48 | device = device or state.absmax.device 49 | 50 | state2 = ( 51 | QuantState( 52 | absmax=state.state2.absmax.to(device), 53 | shape=state.state2.shape, 54 | code=state.state2.code.to(device), 55 | blocksize=state.state2.blocksize, 56 | quant_type=state.state2.quant_type, 57 | dtype=state.state2.dtype, 58 | ) 59 | if state.nested 60 | else None 61 | ) 62 | 63 | return QuantState( 64 | absmax=state.absmax.to(device), 65 | shape=state.shape, 66 | code=state.code.to(device), 67 | blocksize=state.blocksize, 68 | quant_type=state.quant_type, 69 | dtype=state.dtype, 70 | offset=state.offset.to(device) if state.nested else None, 71 | state2=state2, 72 | ) 73 | 74 | class ForgeParams4bit(Params4bit): 75 | _torch_fn_depth=0 76 | 77 | @classmethod 78 | def __torch_function__(cls, func, types, args=(), kwargs=None): 79 | if cls._torch_fn_depth > 0 or func != torch._C.TensorBase.detach: 80 | return super().__torch_function__(func, types, args, kwargs or {}) 81 | cls._torch_fn_depth += 1 82 | try: 83 | slf = args[0] 84 | n = cls( 85 | torch.nn.Parameter.detach(slf), 86 | requires_grad=slf.requires_grad, 87 | quant_state=copy_quant_state(slf.quant_state, slf.device), 88 | blocksize=slf.blocksize, 89 | compress_statistics=slf.compress_statistics, 90 | quant_type=slf.quant_type, 91 | quant_storage=slf.quant_storage, 92 | bnb_quantized=slf.bnb_quantized, 93 | module=slf.module 94 | ) 95 | return n 96 | finally: 97 | cls._torch_fn_depth -= 1 98 | 99 | def to(self, *args, copy=False, **kwargs): 100 | if copy: 101 | return self.clone().to(*args, **kwargs) 102 | device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs) 103 | if device is not None and device.type == "cuda" and not self.bnb_quantized: 104 | if self.data.dtype != torch.bfloat16 and self.data.dtype != torch.float16: 105 | self.data = self.data.to(torch.device('cuda')).to(torch.bfloat16) 106 | 107 | # after called, model converted in to nf4 and enabled loras bad work! 108 | # when lora exist in WF, 'patch_weight_to_device' backup/restore fp8 weight and loras work good 109 | 110 | # import traceback 111 | # traceback.print_stack() 112 | return self._quantize(device) 113 | 114 | weight = ForgeParams4bit( 115 | self, #self.data, #.to(torch.bfloat16, copy=True), 116 | requires_grad=False, 117 | compress_statistics=self.compress_statistics, 118 | blocksize=self.blocksize, 119 | quant_type='nf4', 120 | quant_storage=torch.uint8, 121 | quant_state=copy_quant_state(self.quant_state, device), 122 | bnb_quantized=self.bnb_quantized, 123 | module=self.module 124 | ) 125 | 126 | weight = weight._quantize(device) 127 | return weight 128 | else: 129 | n = self.__class__( 130 | torch.nn.Parameter.to(self, device=device, dtype=dtype, non_blocking=non_blocking), 131 | requires_grad=self.requires_grad, 132 | quant_state=copy_quant_state(self.quant_state, device), 133 | blocksize=self.blocksize, 134 | compress_statistics=self.compress_statistics, 135 | quant_type=self.quant_type, 136 | quant_storage=self.quant_storage, 137 | bnb_quantized=self.bnb_quantized, 138 | module=self.module 139 | ) 140 | # self.module.quant_state = n.quant_state 141 | self.data = n.data 142 | self.quant_state = n.quant_state 143 | return n 144 | 145 | 146 | class ForgeLoader4Bit(torch.nn.Module): 147 | def __init__(self, *, device, dtype, quant_type, **kwargs): 148 | super().__init__() 149 | self.dummy = torch.nn.Parameter(torch.empty(1, device=device, dtype=dtype)) 150 | self.weight = None 151 | self.quant_state = None 152 | self.bias = None 153 | self.quant_type = quant_type 154 | 155 | def _save_to_state_dict(self, destination, prefix, keep_vars): 156 | super()._save_to_state_dict(destination, prefix, keep_vars) 157 | quant_state = getattr(self.weight, "quant_state", None) 158 | if quant_state is not None: 159 | for k, v in quant_state.as_dict(packed=True).items(): 160 | destination[prefix + "weight." + k] = v if keep_vars else v.detach() 161 | return 162 | 163 | def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): 164 | quant_state_keys = {k[len(prefix + "weight."):] for k in state_dict.keys() if k.startswith(prefix + "weight.")} 165 | 166 | if any('bitsandbytes' in k for k in quant_state_keys): 167 | quant_state_dict = {k: state_dict[prefix + "weight." + k] for k in quant_state_keys} 168 | 169 | self.weight = ForgeParams4bit.from_prequantized( 170 | data=state_dict[prefix + 'weight'], 171 | quantized_stats=quant_state_dict, 172 | requires_grad=False, 173 | device=self.dummy.device, 174 | module=self 175 | ) 176 | self.quant_state = self.weight.quant_state 177 | 178 | if prefix + 'bias' in state_dict: 179 | self.bias = torch.nn.Parameter(state_dict[prefix + 'bias'].to(self.dummy)) 180 | 181 | del self.dummy 182 | elif hasattr(self, 'dummy'): 183 | if prefix + 'weight' in state_dict: 184 | self.weight = ForgeParams4bit( 185 | state_dict[prefix + 'weight'].to(self.dummy), 186 | requires_grad=False, 187 | compress_statistics=True, 188 | quant_type=self.quant_type, 189 | quant_storage=torch.uint8, 190 | module=self, 191 | ) 192 | self.quant_state = self.weight.quant_state 193 | 194 | if prefix + 'bias' in state_dict: 195 | self.bias = torch.nn.Parameter(state_dict[prefix + 'bias'].to(self.dummy)) 196 | 197 | del self.dummy 198 | else: 199 | super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) 200 | 201 | 202 | import comfy.ops 203 | 204 | class NF4ModelPatcher(ModelPatcher): 205 | rounding_format = rounding_format_default 206 | 207 | def patch_weight_to_device(self, key, device_to=None, inplace_update=False): 208 | if key not in self.patches: 209 | return 210 | 211 | weight = comfy.utils.get_attr(self.model, key) 212 | 213 | inplace_update = self.weight_inplace_update or inplace_update 214 | 215 | if key not in self.backup: 216 | self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update) 217 | 218 | bnb_layer = hasattr(weight, 'bnb_quantized') 219 | bnb_kwargs = {} 220 | if bnb_layer: 221 | bnb_kwargs = { 222 | 'compress_statistics': weight.compress_statistics, 223 | 'blocksize': weight.blocksize, 224 | 'quant_type': weight.quant_type, 225 | 'quant_storage': torch.uint8, 226 | 'quant_state': copy_quant_state(weight.quant_state, weight.device), 227 | 'bnb_quantized': weight.bnb_quantized, 228 | 'module': weight.module 229 | } 230 | weight = functional_dequantize_4bit(weight) 231 | 232 | # temp_weight = weight.to(torch.device('cuda'), copy=True, non_blocking=False).to(torch.bfloat16) 233 | if device_to is not None: 234 | temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True) 235 | else: 236 | temp_weight = weight.to(torch.float32, copy=True) 237 | 238 | out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key) 239 | 240 | # To-do: Fix image burnout 241 | if self.rounding_format is not None: 242 | out_weight = stochastic_rounding_nf4(out_weight, self.rounding_format, seed=string_to_seed(key)) 243 | else: 244 | out_weight = comfy.float.stochastic_rounding(out_weight, torch.float8_e4m3fn, seed=string_to_seed(key)) 245 | 246 | out_weight = NF4ModelPatcher.reload_weight(out_weight.to(torch.bfloat16), **bnb_kwargs) # .float() 247 | # out_weight.to(torch.device('cpu')) 248 | 249 | if inplace_update: 250 | comfy.utils.copy_to_param(self.model, key, out_weight) 251 | else: 252 | comfy.utils.set_attr_param(self.model, key, out_weight) 253 | # logging.info(f'patched {key}') 254 | 255 | @staticmethod 256 | def reload_weight(weight, **kwargs): 257 | weight_original_device = weight.device 258 | weight = ForgeParams4bit( 259 | weight, 260 | requires_grad=False, 261 | **kwargs 262 | ) 263 | 264 | weight = weight._quantize(weight_original_device) 265 | 266 | # if weight_original_device.type == 'cuda': 267 | # weight = weight.to(weight_original_device) 268 | # else: 269 | # weight = weight.cuda().to(weight_original_device) 270 | 271 | return weight 272 | 273 | # def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False): 274 | 275 | # mem_counter = 0 276 | # patch_counter = 0 277 | # lowvram_counter = 0 278 | # loading = self._load_list() 279 | 280 | # load_completely = [] 281 | # loading.sort(reverse=True) 282 | # for x in loading: 283 | # n = x[1] 284 | # m = x[2] 285 | # params = x[3] 286 | # module_mem = x[0] 287 | 288 | # lowvram_weight = False 289 | 290 | # if not full_load and hasattr(m, "comfy_cast_weights"): 291 | # if mem_counter + module_mem >= lowvram_model_memory: 292 | # lowvram_weight = True 293 | # lowvram_counter += 1 294 | # if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed 295 | # continue 296 | 297 | # weight_key = "{}.weight".format(n) 298 | # bias_key = "{}.bias".format(n) 299 | 300 | # if lowvram_weight: 301 | # if weight_key in self.patches: 302 | # if force_patch_weights: 303 | # self.patch_weight_to_device(weight_key) 304 | # else: 305 | # m.weight_function = comfy.model_patcher.LowVramPatch(weight_key, self.patches) 306 | # patch_counter += 1 307 | # if bias_key in self.patches: 308 | # if force_patch_weights: 309 | # self.patch_weight_to_device(bias_key) 310 | # else: 311 | # m.bias_function = comfy.model_patcher.LowVramPatch(bias_key, self.patches) 312 | # patch_counter += 1 313 | 314 | # m.prev_comfy_cast_weights = m.comfy_cast_weights 315 | # m.comfy_cast_weights = True 316 | # else: 317 | # if hasattr(m, "comfy_cast_weights"): 318 | # if m.comfy_cast_weights: 319 | # comfy.model_patcher.wipe_lowvram_weight(m) 320 | 321 | # if full_load or mem_counter + module_mem < lowvram_model_memory: 322 | # mem_counter += module_mem 323 | # load_completely.append((module_mem, n, m, params)) 324 | 325 | # load_completely.sort(reverse=True) 326 | # for x in load_completely: 327 | # n = x[1] 328 | # m = x[2] 329 | # params = x[3] 330 | # if hasattr(m, "comfy_patched_weights"): 331 | # if m.comfy_patched_weights == True: 332 | # continue 333 | 334 | # for param in params: 335 | # self.patch_weight_to_device("{}.{}".format(n, param), device_to=device_to) 336 | 337 | # logging.info("lowvram: loaded module regularly {} {}".format(n, m)) 338 | # m.comfy_patched_weights = True 339 | 340 | # for x in load_completely: 341 | # x[2].to(device_to) 342 | 343 | # if lowvram_counter > 0: 344 | # logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter)) 345 | # self.model.model_lowvram = True 346 | # else: 347 | # logging.info("loaded completely {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load)) 348 | # self.model.model_lowvram = False 349 | # if full_load: 350 | # self.model.to(device_to) 351 | # mem_counter = self.model_size() 352 | 353 | # self.model.lowvram_patch_counter += patch_counter 354 | # self.model.device = device_to 355 | # self.model.model_loaded_weight_memory = mem_counter 356 | # self.model.current_weight_patches_uuid = self.patches_uuid 357 | 358 | # def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False): 359 | # # revert broken changes https://github.com/comfyanonymous/ComfyUI/commit/bc6be6c11e48114889a368e8c3597df8aac64ae3 360 | 361 | # mem_counter = 0 362 | # patch_counter = 0 363 | # lowvram_counter = 0 364 | # loading = self._load_list() 365 | 366 | # load_completely = [] 367 | # loading.sort(reverse=True) 368 | # for x in loading: 369 | # n = x[1] 370 | # m = x[2] 371 | # params = x[3] 372 | # module_mem = x[0] 373 | 374 | # lowvram_weight = False 375 | 376 | # if not full_load and hasattr(m, "comfy_cast_weights"): 377 | # if mem_counter + module_mem >= lowvram_model_memory: 378 | # lowvram_weight = True 379 | # lowvram_counter += 1 380 | # if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed 381 | # continue 382 | 383 | # weight_key = "{}.weight".format(n) 384 | # bias_key = "{}.bias".format(n) 385 | 386 | # if lowvram_weight: 387 | # if weight_key in self.patches: 388 | # if force_patch_weights: 389 | # self.patch_weight_to_device(weight_key) 390 | # else: 391 | # m.weight_function = comfy.model_patcher.LowVramPatch(weight_key, self.patches) 392 | # patch_counter += 1 393 | # if bias_key in self.patches: 394 | # if force_patch_weights: 395 | # self.patch_weight_to_device(bias_key) 396 | # else: 397 | # m.bias_function = comfy.model_patcher.LowVramPatch(bias_key, self.patches) 398 | # patch_counter += 1 399 | 400 | # m.prev_comfy_cast_weights = m.comfy_cast_weights 401 | # m.comfy_cast_weights = True 402 | # else: 403 | # if hasattr(m, "comfy_cast_weights"): 404 | # if m.comfy_cast_weights: 405 | # comfy.model_patcher.wipe_lowvram_weight(m) 406 | 407 | # mem_counter += module_mem 408 | # load_completely.append((module_mem, n, m, params)) 409 | # # if full_load or mem_counter + module_mem < lowvram_model_memory: 410 | # # mem_counter += module_mem 411 | # # load_completely.append((module_mem, n, m, params)) 412 | 413 | # load_completely.sort(reverse=True) 414 | # for x in load_completely: 415 | # n = x[1] 416 | # m = x[2] 417 | # params = x[3] 418 | # if hasattr(m, "comfy_patched_weights"): 419 | # if m.comfy_patched_weights == True: 420 | # continue 421 | 422 | # for param in params: 423 | # self.patch_weight_to_device("{}.{}".format(n, param), device_to=device_to) 424 | 425 | # logging.debug("lowvram: loaded module regularly {} {}".format(n, m)) 426 | # m.comfy_patched_weights = True 427 | 428 | # for x in load_completely: 429 | # x[2].to(device_to) 430 | 431 | # if lowvram_counter > 0: 432 | # logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter)) 433 | # self.model.model_lowvram = True 434 | # else: 435 | # logging.info("loaded completely {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load)) 436 | # self.model.model_lowvram = False 437 | # if full_load: 438 | # self.model.to(device_to) 439 | # mem_counter = self.model_size() 440 | 441 | # self.model.lowvram_patch_counter += patch_counter 442 | # self.model.device = device_to 443 | # self.model.model_loaded_weight_memory = mem_counter 444 | 445 | # def partially_unload(self, device_to, memory_to_free=0): 446 | # for n, m in self.model.named_modules(): 447 | # if not hasattr(m, "comfy_cast_weights"): # and 'make_ops..OPS.Linear' in str(m.__class__): 448 | # # logging.info(f'n: {n}') 449 | # # append attr from comfy.ops.CastWeightBiasOp for partially unloading weights 450 | # # class Linear(loader_class, comfy.ops.CastWeightBiasOp) raise Exception 'quant_state is not None' 451 | # m.comfy_cast_weights = False 452 | 453 | # mod_size = self.model_size() 454 | # result = super().partially_unload(device_to, memory_to_free) 455 | # logging.info(f'[{self.model.__class__.__name__} ({mod_size/1024**3:.1f}gb)] partially_unload: {device_to}, memory_to_free={memory_to_free/1024**3:.1f}gb / result={result/1024**3:.1f}gb') 456 | # return result 457 | 458 | def partially_unload(self, device_to, memory_to_free=0): 459 | memory_freed = 0 #super().partially_unload(device_to, memory_to_free) 460 | 461 | for n, m in self.model.named_modules(): 462 | if memory_to_free < memory_freed: 463 | break 464 | if 'make_ops..OPS.Linear' not in str(m.__class__): 465 | continue 466 | 467 | msize = comfy.model_management.module_size(m) 468 | m.to(device_to) 469 | memory_freed += msize 470 | 471 | self.model.model_loaded_weight_memory -= memory_freed 472 | mod_size = self.model_size() 473 | logging.info(f'[{self.model.__class__.__name__} ({mod_size/1024**3:.1f}gb)] partially_unload: {device_to}, memory_to_free={memory_to_free/1024**3:.1f}gb / result={memory_freed/1024**3:.1f}gb') 474 | return memory_freed 475 | 476 | # after commit "Improved memory management. (#5450) 2 dec, 2024" https://github.com/comfyanonymous/ComfyUI/commit/79d5ceae6efe458302899a28496b0daf59890109 double gpu memory used! 477 | def partially_load(self, device_to, extra_memory=0, force_patch_weights=False): 478 | res = super().partially_load(device_to, extra_memory, force_patch_weights) 479 | 480 | for n, m in self.model.named_modules(): 481 | if 'make_ops..OPS.Linear' in str(m.__class__): 482 | if m.weight.device != device_to: 483 | m.to(device_to) 484 | 485 | logging.info(f'[{self.model.__class__.__name__}] partially_load: {device_to}') 486 | 487 | return res 488 | 489 | def clone(self, *args, **kwargs): 490 | n = NF4ModelPatcher(self.model, self.load_device, self.offload_device, self.size, weight_inplace_update=self.weight_inplace_update) 491 | n.patches = {} 492 | for k in self.patches: 493 | n.patches[k] = self.patches[k][:] 494 | n.patches_uuid = self.patches_uuid 495 | 496 | n.object_patches = self.object_patches.copy() 497 | n.model_options = copy.deepcopy(self.model_options) 498 | n.backup = self.backup 499 | n.object_patches_backup = self.object_patches_backup 500 | n.rounding_format = getattr(self, "rounding_format", rounding_format_default) 501 | 502 | n.parent = self 503 | # if hasattr(self.model, 'current_weight_patches_uuid'): 504 | # n.model.current_weight_patches_uuid = self.model.current_weight_patches_uuid 505 | # logging.info('set current_weight_patches_uuid') 506 | 507 | # attachments 508 | n.attachments = {} 509 | for k in self.attachments: 510 | if hasattr(self.attachments[k], "on_model_patcher_clone"): 511 | n.attachments[k] = self.attachments[k].on_model_patcher_clone() 512 | else: 513 | n.attachments[k] = self.attachments[k] 514 | # additional models 515 | for k, c in self.additional_models.items(): 516 | n.additional_models[k] = [x.clone() for x in c] 517 | # callbacks 518 | for k, c in self.callbacks.items(): 519 | n.callbacks[k] = {} 520 | for k1, c1 in c.items(): 521 | n.callbacks[k][k1] = c1.copy() 522 | # sample wrappers 523 | for k, w in self.wrappers.items(): 524 | n.wrappers[k] = {} 525 | for k1, w1 in w.items(): 526 | n.wrappers[k][k1] = w1.copy() 527 | # injection 528 | n.is_injected = self.is_injected 529 | n.skip_injection = self.skip_injection 530 | for k, i in self.injections.items(): 531 | n.injections[k] = i.copy() 532 | # hooks 533 | n.hook_patches = comfy.model_patcher.create_hook_patches_clone(self.hook_patches) 534 | n.hook_patches_backup = comfy.model_patcher.create_hook_patches_clone(self.hook_patches_backup) if self.hook_patches_backup else self.hook_patches_backup 535 | for group in self.cached_hook_patches: 536 | n.cached_hook_patches[group] = {} 537 | for k in self.cached_hook_patches[group]: 538 | n.cached_hook_patches[group][k] = self.cached_hook_patches[group][k] 539 | n.hook_backup = self.hook_backup 540 | n.current_hooks = self.current_hooks.clone() if self.current_hooks else self.current_hooks 541 | n.forced_hooks = self.forced_hooks.clone() if self.forced_hooks else self.forced_hooks 542 | n.is_clip = self.is_clip 543 | n.hook_mode = self.hook_mode 544 | 545 | for callback in self.get_all_callbacks(comfy.model_patcher.CallbacksMP.ON_CLONE): 546 | callback(self, n) 547 | 548 | return n 549 | 550 | 551 | def make_ops(loader_class, current_device = None, current_dtype = None, current_manual_cast_enabled = False, current_bnb_dtype = None): 552 | 553 | class OPS(comfy.ops.manual_cast): 554 | class Linear(loader_class): 555 | # class Linear(loader_class, comfy.ops.CastWeightBiasOp): 556 | def __init__(self, *args, device=None, dtype=None, **kwargs): 557 | super().__init__(device=device, dtype=dtype, quant_type=current_bnb_dtype) 558 | self.parameters_manual_cast = current_manual_cast_enabled 559 | 560 | def forward(self, x): 561 | self.weight.quant_state = self.quant_state 562 | 563 | if self.bias is not None and self.bias.dtype != x.dtype: 564 | # Maybe this can also be set to all non-bnb ops since the cost is very low. 565 | # And it only invokes one time, and most linear does not have bias 566 | self.bias.data = self.bias.data.to(x.dtype) 567 | 568 | if not self.parameters_manual_cast: 569 | return functional_linear_4bits(x, self.weight, self.bias) 570 | elif not self.weight.bnb_quantized: 571 | assert x.device.type == 'cuda', 'BNB Must Use CUDA as Computation Device!' 572 | layer_original_device = self.weight.device 573 | self.weight = self.weight._quantize(x.device) 574 | bias = self.bias.to(x.device) if self.bias is not None else None 575 | out = functional_linear_4bits(x, self.weight, bias) 576 | self.weight = self.weight.to(layer_original_device) 577 | return out 578 | else: 579 | raise RuntimeError("Unexpected state in forward") 580 | 581 | return OPS 582 | 583 | class SP_CheckpointLoaderBNB: 584 | @classmethod 585 | def INPUT_TYPES(s): 586 | return {"required": { 587 | "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), 588 | "load_dtype": (("default", "float8_e4m3fn", "float8_e5m2"), {"default": "float8_e4m3fn"}), 589 | }} 590 | 591 | RETURN_TYPES = ("MODEL", "CLIP", "VAE") 592 | FUNCTION = "load_checkpoint" 593 | 594 | CATEGORY = "loaders" 595 | 596 | def load_checkpoint(self, ckpt_name, load_clip='True', load_vae='True', load_dtype='default', bnb_dtype='nf4', rounding_format='default', custom_rounding_format=rounding_format_default): 597 | if bnb_dtype == "default": 598 | bnb_dtype = None 599 | ops = make_ops(ForgeLoader4Bit, current_bnb_dtype = bnb_dtype) 600 | ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) 601 | model, clip, vae, _ = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=load_vae=="True", output_clip=load_clip=="True", embedding_directory=folder_paths.get_folder_paths("embeddings"), model_options={"custom_operations": ops, "dtype": dtype_from_str[load_dtype]}) 602 | 603 | model = NF4ModelPatcher.clone(model) 604 | 605 | if rounding_format=='default': 606 | rounding_format = None 607 | elif rounding_format=='custom': 608 | rounding_format = custom_rounding_format 609 | model.rounding_format = rounding_format 610 | 611 | return model, clip, vae 612 | 613 | class SP_CheckpointLoaderBNB_Advanced(SP_CheckpointLoaderBNB): 614 | @classmethod 615 | def INPUT_TYPES(s): 616 | return {"required": { 617 | "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), 618 | "load_clip": (["True", "False"], ), 619 | "load_vae": (["True", "False"], ), 620 | "load_dtype": (("default", "float8_e4m3fn", "float8_e5m2"), {"default": "float8_e4m3fn"}), 621 | "bnb_dtype": (("default", "nf4", "fp4"), {"default": "nf4"}), 622 | "rounding_format": (("default", "2,1,7", "custom"), ), 623 | "custom_rounding_format": ("STRING", {"default": rounding_format_default}), 624 | }} 625 | 626 | class SP_UnetLoaderBNB: 627 | @classmethod 628 | def INPUT_TYPES(s): 629 | return {"required": { 630 | "unet_name": (folder_paths.get_filename_list("unet"), ), 631 | "load_dtype": (("default", "float8_e4m3fn", "float8_e5m2"), {"default": "float8_e4m3fn"}), 632 | }} 633 | 634 | RETURN_TYPES = ("MODEL",) 635 | FUNCTION = "load_checkpoint" 636 | 637 | CATEGORY = "loaders" 638 | 639 | def load_checkpoint(self, unet_name, load_dtype='default', bnb_dtype='nf4', rounding_format='default', custom_rounding_format=rounding_format_default): 640 | if bnb_dtype == "default": 641 | bnb_dtype = None 642 | ops = make_ops(ForgeLoader4Bit, current_bnb_dtype = bnb_dtype) 643 | unet_path = folder_paths.get_full_path("unet", unet_name) 644 | model = comfy.sd.load_diffusion_model(unet_path, model_options={"custom_operations": ops, "dtype": dtype_from_str[load_dtype]}) 645 | 646 | model = NF4ModelPatcher.clone(model) 647 | 648 | if rounding_format=='default': 649 | rounding_format = None 650 | elif rounding_format=='custom': 651 | rounding_format = custom_rounding_format 652 | model.rounding_format = rounding_format 653 | 654 | return model, 655 | 656 | class SP_UnetLoaderBNB_Advanced(SP_UnetLoaderBNB): 657 | @classmethod 658 | def INPUT_TYPES(s): 659 | return {"required": { 660 | "unet_name": (folder_paths.get_filename_list("unet"), ), 661 | "load_dtype": (("default", "float8_e4m3fn", "float8_e5m2"), {"default": "float8_e4m3fn"}), 662 | "bnb_dtype": (("default", "nf4", "fp4"), {"default": "nf4"}), 663 | "rounding_format": (("default", "2,1,7", "custom"), ), 664 | "custom_rounding_format": ("STRING", {"default": rounding_format_default}), 665 | }} 666 | 667 | NODE_CLASS_MAPPINGS = { 668 | "SP_UnetLoaderBNB": SP_UnetLoaderBNB, 669 | "SP_CheckpointLoaderBNB": SP_CheckpointLoaderBNB, 670 | "SP_UnetLoaderBNB_Advanced": SP_UnetLoaderBNB_Advanced, 671 | "SP_CheckpointLoaderBNB_Advanced": SP_CheckpointLoaderBNB_Advanced, 672 | } 673 | 674 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | Version 3, 19 November 2007 2 | 3 | Copyright (c) 2023 AUTOMATIC1111 4 | 5 | Copyright (C) 2007 Free 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Patents. 461 | 462 | A "contributor" is a copyright holder who authorizes use under this 463 | License of the Program or a work on which the Program is based. The 464 | work thus licensed is called the contributor's "contributor version". 465 | 466 | A contributor's "essential patent claims" are all patent claims 467 | owned or controlled by the contributor, whether already acquired or 468 | hereafter acquired, that would be infringed by some manner, permitted 469 | by this License, of making, using, or selling its contributor version, 470 | but do not include claims that would be infringed only as a 471 | consequence of further modification of the contributor version. For 472 | purposes of this definition, "control" includes the right to grant 473 | patent sublicenses in a manner consistent with the requirements of 474 | this License. 475 | 476 | Each contributor grants you a non-exclusive, worldwide, royalty-free 477 | patent license under the contributor's essential patent claims, to 478 | make, use, sell, offer for sale, import and otherwise run, modify and 479 | propagate the contents of its contributor version. 480 | 481 | In the following three paragraphs, a "patent license" is any express 482 | agreement or commitment, however denominated, not to enforce a patent 483 | (such as an express permission to practice a patent or covenant not to 484 | sue for patent infringement). To "grant" such a patent license to a 485 | party means to make such an agreement or commitment not to enforce a 486 | patent against the party. 487 | 488 | If you convey a covered work, knowingly relying on a patent license, 489 | and the Corresponding Source of the work is not available for anyone 490 | to copy, free of charge and under the terms of this License, through a 491 | publicly available network server or other readily accessible means, 492 | then you must either (1) cause the Corresponding Source to be so 493 | available, or (2) arrange to deprive yourself of the benefit of the 494 | patent license for this particular work, or (3) arrange, in a manner 495 | consistent with the requirements of this License, to extend the patent 496 | license to downstream recipients. "Knowingly relying" means you have 497 | actual knowledge that, but for the patent license, your conveying the 498 | covered work in a country, or your recipient's use of the covered work 499 | in a country, would infringe one or more identifiable patents in that 500 | country that you have reason to believe are valid. 501 | 502 | If, pursuant to or in connection with a single transaction or 503 | arrangement, you convey, or propagate by procuring conveyance of, a 504 | covered work, and grant a patent license to some of the parties 505 | receiving the covered work authorizing them to use, propagate, modify 506 | or convey a specific copy of the covered work, then the patent license 507 | you grant is automatically extended to all recipients of the covered 508 | work and works based on it. 509 | 510 | A patent license is "discriminatory" if it does not include within 511 | the scope of its coverage, prohibits the exercise of, or is 512 | conditioned on the non-exercise of one or more of the rights that are 513 | specifically granted under this License. You may not convey a covered 514 | work if you are a party to an arrangement with a third party that is 515 | in the business of distributing software, under which you make payment 516 | to the third party based on the extent of your activity of conveying 517 | the work, and under which the third party grants, to any of the 518 | parties who would receive the covered work from you, a discriminatory 519 | patent license (a) in connection with copies of the covered work 520 | conveyed by you (or copies made from those copies), or (b) primarily 521 | for and in connection with specific products or compilations that 522 | contain the covered work, unless you entered into that arrangement, 523 | or that patent license was granted, prior to 28 March 2007. 524 | 525 | Nothing in this License shall be construed as excluding or limiting 526 | any implied license or other defenses to infringement that may 527 | otherwise be available to you under applicable patent law. 528 | 529 | 12. No Surrender of Others' Freedom. 530 | 531 | If conditions are imposed on you (whether by court order, agreement or 532 | otherwise) that contradict the conditions of this License, they do not 533 | excuse you from the conditions of this License. If you cannot convey a 534 | covered work so as to satisfy simultaneously your obligations under this 535 | License and any other pertinent obligations, then as a consequence you may 536 | not convey it at all. For example, if you agree to terms that obligate you 537 | to collect a royalty for further conveying from those to whom you convey 538 | the Program, the only way you could satisfy both those terms and this 539 | License would be to refrain entirely from conveying the Program. 540 | 541 | 13. Remote Network Interaction; Use with the GNU General Public License. 542 | 543 | Notwithstanding any other provision of this License, if you modify the 544 | Program, your modified version must prominently offer all users 545 | interacting with it remotely through a computer network (if your version 546 | supports such interaction) an opportunity to receive the Corresponding 547 | Source of your version by providing access to the Corresponding Source 548 | from a network server at no charge, through some standard or customary 549 | means of facilitating copying of software. This Corresponding Source 550 | shall include the Corresponding Source for any work covered by version 3 551 | of the GNU General Public License that is incorporated pursuant to the 552 | following paragraph. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the work with which it is combined will remain governed by version 560 | 3 of the GNU General Public License. 561 | 562 | 14. Revised Versions of this License. 563 | 564 | The Free Software Foundation may publish revised and/or new versions of 565 | the GNU Affero General Public License from time to time. Such new versions 566 | will be similar in spirit to the present version, but may differ in detail to 567 | address new problems or concerns. 568 | 569 | Each version is given a distinguishing version number. If the 570 | Program specifies that a certain numbered version of the GNU Affero General 571 | Public License "or any later version" applies to it, you have the 572 | option of following the terms and conditions either of that numbered 573 | version or of any later version published by the Free Software 574 | Foundation. If the Program does not specify a version number of the 575 | GNU Affero General Public License, you may choose any version ever published 576 | by the Free Software Foundation. 577 | 578 | If the Program specifies that a proxy can decide which future 579 | versions of the GNU Affero General Public License can be used, that proxy's 580 | public statement of acceptance of a version permanently authorizes you 581 | to choose that version for the Program. 582 | 583 | Later license versions may give you additional or different 584 | permissions. However, no additional obligations are imposed on any 585 | author or copyright holder as a result of your choosing to follow a 586 | later version. 587 | 588 | 15. Disclaimer of Warranty. 589 | 590 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 591 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 592 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 593 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 594 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 595 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 596 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 597 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 598 | 599 | 16. Limitation of Liability. 600 | 601 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 602 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 603 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 604 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 605 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 606 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 607 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 608 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 609 | SUCH DAMAGES. 610 | 611 | 17. Interpretation of Sections 15 and 16. 612 | 613 | If the disclaimer of warranty and limitation of liability provided 614 | above cannot be given local legal effect according to their terms, 615 | reviewing courts shall apply local law that most closely approximates 616 | an absolute waiver of all civil liability in connection with the 617 | Program, unless a warranty or assumption of liability accompanies a 618 | copy of the Program in return for a fee. 619 | 620 | END OF TERMS AND CONDITIONS 621 | 622 | How to Apply These Terms to Your New Programs 623 | 624 | If you develop a new program, and you want it to be of the greatest 625 | possible use to the public, the best way to achieve this is to make it 626 | free software which everyone can redistribute and change under these terms. 627 | 628 | To do so, attach the following notices to the program. It is safest 629 | to attach them to the start of each source file to most effectively 630 | state the exclusion of warranty; and each file should have at least 631 | the "copyright" line and a pointer to where the full notice is found. 632 | 633 | 634 | Copyright (C) 635 | 636 | This program is free software: you can redistribute it and/or modify 637 | it under the terms of the GNU Affero General Public License as published by 638 | the Free Software Foundation, either version 3 of the License, or 639 | (at your option) any later version. 640 | 641 | This program is distributed in the hope that it will be useful, 642 | but WITHOUT ANY WARRANTY; without even the implied warranty of 643 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 644 | GNU Affero General Public License for more details. 645 | 646 | You should have received a copy of the GNU Affero General Public License 647 | along with this program. If not, see . 648 | 649 | Also add information on how to contact you by electronic and paper mail. 650 | 651 | If your software can interact with users remotely through a computer 652 | network, you should also make sure that it provides a way for users to 653 | get its source. For example, if your program is a web application, its 654 | interface could display a "Source" link that leads users to an archive 655 | of the code. There are many ways you could offer source, and different 656 | solutions will be better for different programs; see section 13 for the 657 | specific requirements. 658 | 659 | You should also get your employer (if you work as a programmer) or school, 660 | if any, to sign a "copyright disclaimer" for the program, if necessary. 661 | For more information on this, and how to apply and follow the GNU AGPL, see 662 | . 663 | 664 | 665 | ---------------------------------Facebook BNB------------------------------- 666 | 667 | MIT License 668 | 669 | Copyright (c) Facebook, Inc. and its affiliates. 670 | 671 | Permission is hereby granted, free of charge, to any person obtaining a copy 672 | of this software and associated documentation files (the "Software"), to deal 673 | in the Software without restriction, including without limitation the rights 674 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 675 | copies of the Software, and to permit persons to whom the Software is 676 | furnished to do so, subject to the following conditions: 677 | 678 | The above copyright notice and this permission notice shall be included in all 679 | copies or substantial portions of the Software. 680 | 681 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 682 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 683 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 684 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 685 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 686 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 687 | SOFTWARE. 688 | --------------------------------------------------------------------------------