diff --git a/.gitignore b/.gitignore
index 82f9275..167139a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -160,3 +160,5 @@ cython_debug/
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
+copytoww.bat
+exclude.txt
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index f288702..0000000
--- a/LICENSE
+++ /dev/null
@@ -1,674 +0,0 @@
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- 17. Interpretation of Sections 15 and 16.
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-
- END OF TERMS AND CONDITIONS
-
- How to Apply These Terms to Your New Programs
-
- If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
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-state the exclusion of warranty; and each file should have at least
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- the Free Software Foundation, either version 3 of the License, or
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-.
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-.
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..cd627f6
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,51 @@
+import inspect
+import json
+import os
+import traceback
+import folder_paths
+import importlib
+from . import mz_flux_quantize
+import comfy
+
+AUTHOR_NAME = "MinusZone"
+CATEGORY_NAME = f"{AUTHOR_NAME} - FluxQuantize"
+
+
+class AlwaysEqualProxy(str):
+ def __eq__(self, _):
+ return True
+
+ def __ne__(self, _):
+ return False
+
+
+NODE_CLASS_MAPPINGS = {
+}
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+}
+
+
+class MZ_FluxQuantizeUNETLoader():
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {
+ "unet_name": (folder_paths.get_filename_list("unet"), ),
+ }}
+
+ RETURN_TYPES = ("MODEL",)
+
+ RETURN_NAMES = ("model",)
+
+ FUNCTION = "load_unet"
+
+ CATEGORY = CATEGORY_NAME
+
+ def load_unet(self, **kwargs):
+ importlib.reload(mz_flux_quantize)
+ return mz_flux_quantize.MZ_FluxQuantizeUNETLoader_call(kwargs)
+
+
+NODE_CLASS_MAPPINGS["MZ_FluxQuantizeUNETLoader"] = MZ_FluxQuantizeUNETLoader
+NODE_DISPLAY_NAME_MAPPINGS["MZ_FluxQuantizeUNETLoader"] = f"{AUTHOR_NAME} - FluxQuantizeUNETLoader"
diff --git a/mz_flux_quantize.py b/mz_flux_quantize.py
new file mode 100644
index 0000000..796b708
--- /dev/null
+++ b/mz_flux_quantize.py
@@ -0,0 +1,84 @@
+
+
+import torch
+import comfy.supported_models
+import comfy.model_base
+import comfy.ldm.flux.model
+import comfy.model_patcher
+
+
+def MZ_EmptyModel_call(kwargs):
+ import comfy.supported_models
+ import comfy.model_base
+ import comfy.ldm.flux.model
+ import comfy.model_patcher
+
+ model_type = kwargs["model_type"]
+
+ if model_type == "Flux":
+ model_config = comfy.supported_models.Flux(unet_config={
+ "image_model": "flux",
+ "in_channels": 16,
+ "vec_in_dim": 768,
+ "context_in_dim": 4096,
+ "hidden_size": 3072,
+ "mlp_ratio": 4.0,
+ "num_heads": 24,
+ "depth": 19,
+ "depth_single_blocks": 38,
+ "axes_dim": [16, 56, 56],
+ "theta": 10000,
+ "qkv_bias": True,
+ "guidance_embed": True
+ })
+ model_config.manual_cast_dtype = torch.float32
+
+ model = comfy.model_base.Flux(
+ model_config=model_config,
+ device=None,
+ )
+
+ return (model,)
+
+
+def MZ_ModelLoadStateDict_call(kwargs):
+ model = kwargs["model"]
+ safetensors_file = kwargs["safetensors_file"]
+ import comfy.model_patcher
+
+ import safetensors.torch
+ from comfy import model_management
+
+ print(f"Load state_dict from {safetensors_file}")
+
+ state_dict = safetensors.torch.load_file(safetensors_file)
+
+ from . import q_awq_marlin_loader
+ model.diffusion_model = q_awq_marlin_loader.quantize_loader(
+ model=model.diffusion_model,
+ state_dict=state_dict,
+ bits=4,
+ )
+
+ load_device = model_management.get_torch_device()
+ offload_device = model_management.unet_offload_device()
+ model_patcher = comfy.model_patcher.ModelPatcher(
+ model, load_device=load_device, offload_device=offload_device)
+
+ return (model_patcher,)
+
+
+def MZ_FluxQuantizeUNETLoader_call(kwargs):
+ unet_name = kwargs["unet_name"]
+ import folder_paths
+ unet_path = folder_paths.get_full_path("unet", unet_name)
+
+
+ model = MZ_EmptyModel_call({"model_type": "Flux"})[0]
+
+ model_patcher = MZ_ModelLoadStateDict_call({
+ "model": model,
+ "safetensors_file": unet_path,
+ })[0]
+
+ return (model_patcher,)
\ No newline at end of file
diff --git a/q_awq_marlin_loader.py b/q_awq_marlin_loader.py
new file mode 100644
index 0000000..eb1f62c
--- /dev/null
+++ b/q_awq_marlin_loader.py
@@ -0,0 +1,336 @@
+# This file is used to load the quantized model with the AWQ-GEMV quantization method.
+import numpy as np
+import torch
+import torch.nn as nn
+import gc
+
+
+def quantize_loader(model, state_dict, bits=4, device='cuda', includes=[]):
+ qkey = []
+ for key in state_dict.keys():
+ if key.endswith(".qweight"):
+ if len(includes) > 0:
+ is_skip = True
+ for include in includes:
+ if include.find("&") != -1:
+ include_ands = include.split("&")
+ is_skip = False
+ for include_and in include_ands:
+ if include_and not in key:
+ is_skip = True
+ break
+ else:
+ if include in key:
+ is_skip = False
+ break
+ if is_skip:
+ continue
+
+ qkey.append(key.replace(".qweight", ""))
+
+ for name, module in model.named_modules():
+ if name in qkey:
+ print(f"Quantizing {name}")
+
+ module = module.to(dtype=torch.float16)
+
+ q_linear = WQLinear_Marlin.from_linear(
+ linear=module,
+ w_bit=bits,
+ group_size=128,
+ init_only=True,
+ )
+ q_linear.post_init()
+
+ module.cpu()
+ q_linear = q_linear.to(device)
+ set_op_by_name(model, name, q_linear)
+ clear_memory(module)
+
+ model.load_state_dict(state_dict, strict=False)
+ return model
+
+
+def clear_memory(weight=None):
+ if weight is not None:
+ del weight
+ gc.collect()
+ torch.cuda.empty_cache()
+
+
+def set_op_by_name(layer, name, new_module):
+ levels = name.split(".")
+ if len(levels) > 1:
+ mod_ = layer
+ for l_idx in range(len(levels) - 1):
+ if levels[l_idx].isdigit():
+ mod_ = mod_[int(levels[l_idx])]
+ else:
+ mod_ = getattr(mod_, levels[l_idx])
+ setattr(mod_, levels[-1], new_module)
+ else:
+ setattr(layer, name, new_module)
+
+
+MARLIN_INSTALLED = False
+
+
+try:
+ import marlin_cuda # with CUDA kernels (AutoAWQ_kernels)
+
+ MARLIN_INSTALLED = True
+except:
+ MARLIN_INSTALLED = False
+# Adapted from https://github.com/compressa-ai/AutoAWQ/tree/dev
+
+
+from torch.autograd import Function
+
+
+def get_best_device():
+ if torch.backends.mps.is_available():
+ return "mps"
+ elif torch.cuda.is_available():
+ return "cuda:0"
+ else:
+ return "cpu"
+
+
+def _get_perms():
+ perm = []
+ for i in range(32):
+ perm1 = []
+ col = i // 4
+ for block in [0, 1]:
+ for row in [
+ 2 * (i % 4),
+ 2 * (i % 4) + 1,
+ 2 * (i % 4 + 4),
+ 2 * (i % 4 + 4) + 1,
+ ]:
+ perm1.append(16 * row + col + 8 * block)
+
+ for j in range(4):
+ perm.extend([p + 256 * j for p in perm1])
+
+ perm = np.array(perm)
+ interleave = np.array([0, 2, 4, 6, 1, 3, 5, 7])
+ perm = perm.reshape((-1, 8))[:, interleave].ravel()
+ perm = torch.from_numpy(perm)
+ scale_perm = []
+ for i in range(8):
+ scale_perm.extend([i + 8 * j for j in range(8)])
+ scale_perm_single = []
+ for i in range(4):
+ scale_perm_single.extend(
+ [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]])
+ return perm, scale_perm, scale_perm_single
+
+
+_perm, _scale_perm, _scale_perm_single = _get_perms()
+
+
+class WQLinear_Marlin(nn.Module):
+ def __init__(self, w_bit, group_size, in_features, out_features, bias, dev):
+ super().__init__()
+
+ if w_bit not in [4]:
+ raise NotImplementedError("Only 4-bit are supported for now.")
+
+ self.w_bit = w_bit
+ self.in_features = in_features
+ self.out_features = out_features
+ self.group_size = group_size if group_size != -1 else in_features
+ self.max_par = 8 # partitioning for large inputs
+
+ # quick sanity check (make sure aligment)
+ assert self.in_features % self.group_size == 0
+ assert out_features % (32 // self.w_bit) == 0
+
+ ######################################################
+ ## These shapes are only specific for Marlin models ##
+ self.register_buffer(
+ "qweight",
+ torch.zeros(
+ (in_features // 16, out_features * 16 // 8),
+ dtype=torch.int32,
+ device=dev,
+ ),
+ )
+ self.register_buffer(
+ "scales",
+ torch.zeros(
+ (in_features // group_size, out_features),
+ dtype=torch.float16,
+ device=dev,
+ ),
+ )
+ ######################################################
+
+ if bias:
+ self.register_buffer(
+ "bias",
+ torch.zeros(
+ (out_features),
+ dtype=torch.float16,
+ device=dev,
+ ),
+ )
+ else:
+ self.bias = None
+
+ @classmethod
+ def from_linear(
+ cls,
+ linear,
+ w_bit,
+ group_size,
+ init_only=False,
+ scales=None,
+ zeros=None,
+ ):
+ awq_linear = cls(
+ w_bit,
+ group_size,
+ linear.in_features,
+ linear.out_features,
+ linear.bias is not None,
+ linear.weight.device,
+ )
+ if init_only: # just prepare for loading sd
+ return awq_linear
+
+ assert zeros is None and scales is not None
+
+ tile = 16
+ maxq = 2**4 - 1
+ s = scales.t()
+ w = linear.weight.data.t()
+ if awq_linear.group_size != awq_linear.in_features:
+ w = w.reshape((-1, awq_linear.group_size, awq_linear.out_features))
+ w = w.permute(1, 0, 2)
+ w = w.reshape((awq_linear.group_size, -1))
+ s = s.reshape((1, -1))
+ w = torch.round(w / s).int()
+ w += (maxq + 1) // 2
+ w = torch.clamp(w, 0, maxq)
+ if awq_linear.group_size != awq_linear.in_features:
+ w = w.reshape((awq_linear.group_size, -1, awq_linear.out_features))
+ w = w.permute(1, 0, 2)
+ w = w.reshape(
+ (awq_linear.in_features, awq_linear.out_features)
+ ).contiguous()
+ s = s.reshape((-1, len(_scale_perm)))[:, _scale_perm]
+ else:
+ s = s.reshape((-1, len(_scale_perm_single)))[:, _scale_perm_single]
+ s = s.reshape((-1, awq_linear.out_features)).contiguous()
+ w = w.reshape(
+ (
+ awq_linear.in_features // tile,
+ tile,
+ awq_linear.out_features // tile,
+ tile,
+ )
+ )
+ w = w.permute((0, 2, 1, 3))
+ w = w.reshape((awq_linear.in_features // tile,
+ awq_linear.out_features * tile))
+ res = w
+ res = res.reshape((-1, _perm.numel()))[:, _perm].reshape(res.shape)
+ q = np.zeros((res.shape[0], res.shape[1] // 8), dtype=np.uint32)
+ res = res.cpu().numpy().astype(np.uint32)
+ for i in range(8):
+ q |= res[:, i::8] << 4 * i
+ q = torch.from_numpy(q.astype(np.int32)).to(w.device)
+ awq_linear.qweight[:] = q.to(awq_linear.qweight.device)
+ awq_linear.scales[:] = s.to(awq_linear.qweight.device)
+
+ if awq_linear.bias is not None:
+ awq_linear.bias[:] = linear.bias.data.to(awq_linear.bias.device)
+
+ return awq_linear
+
+ def post_init(self):
+ self.register_buffer(
+ "workspace",
+ torch.zeros(
+ self.out_features // 128 * self.max_par,
+ dtype=torch.int32,
+ device=self.qweight.device,
+ ),
+ persistent=False,
+ )
+
+ @torch.no_grad()
+ def forward(self, x):
+ assert hasattr(self, "workspace"), (
+ "module.post_init() must be called before module.forward(). "
+ "Use marlin_post_init() on the whole model."
+ )
+ assert MARLIN_INSTALLED, (
+ "Marlin kernels are not installed. "
+ "Please install AWQ compatible Marlin kernels from AutoAWQ_kernels."
+ )
+
+ out_shape = x.shape[:-1] + (self.out_features,)
+
+ input_dtype = x.dtype
+ if input_dtype != torch.float16:
+ x = x.half()
+
+ x = x.view(-1, x.shape[-1])
+
+ out = torch.empty(
+ (x.shape[0], self.out_features),
+ dtype=torch.float16,
+ device=x.device,
+ )
+ marlin_cuda.mul(
+ x,
+ self.qweight,
+ out,
+ self.scales,
+ self.workspace,
+ -1, # thread_k
+ -1, # thread_n
+ -1, # sms
+ self.max_par,
+ )
+
+ if input_dtype != torch.float16:
+ out = out.to(dtype=input_dtype)
+
+ if self.bias is not None:
+ out.add_(self.bias)
+
+ return out.view(out_shape)
+
+ def extra_repr(self) -> str:
+ return (
+ "in_features={}, out_features={}, bias={}, w_bit={}, group_size={}".format(
+ self.in_features,
+ self.out_features,
+ self.bias is not None,
+ self.w_bit,
+ self.group_size,
+ )
+ )
+
+
+def make_divisible(c, divisor):
+ return (c + divisor - 1) // divisor
+
+
+def calculate_zeros_width(in_features, group_size=128, pack_num=8):
+ if group_size >= 128:
+ size_multiplier = 1
+ elif group_size == 64:
+ size_multiplier = 2
+ elif group_size == 32:
+ size_multiplier = 4
+ else:
+ raise NotImplementedError
+ base_width = make_divisible(in_features // group_size, pack_num)
+ base_width = make_divisible(
+ base_width, size_multiplier) * size_multiplier
+ return base_width