File size: 8,552 Bytes
b45ac7a 0e95c75 b45ac7a 0e95c75 b45ac7a 0e95c75 b45ac7a 0e95c75 b45ac7a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 |
import torch
from pathlib import Path
from utils import get_download_file
from stkey import read_safetensors_key
try:
from diffusers import BitsAndBytesConfig
is_nf4 = True
except Exception:
is_nf4 = False
DTYPE_DEFAULT = "default"
DTYPE_DICT = {
"fp16": torch.float16,
"bf16": torch.bfloat16,
"fp32": torch.float32,
"fp8": torch.float8_e4m3fn,
}
#QTYPES = ["NF4"] if is_nf4 else []
QTYPES = []
def get_dtypes():
return list(DTYPE_DICT.keys()) + [DTYPE_DEFAULT] + QTYPES
def get_dtype(dtype: str):
if dtype in set(QTYPES): return torch.bfloat16
return DTYPE_DICT.get(dtype, torch.float16)
from diffusers import (
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
KDPM2DiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
HeunDiscreteScheduler,
LMSDiscreteScheduler,
DDIMScheduler,
DEISMultistepScheduler,
UniPCMultistepScheduler,
LCMScheduler,
PNDMScheduler,
KDPM2AncestralDiscreteScheduler,
DPMSolverSDEScheduler,
EDMDPMSolverMultistepScheduler,
DDPMScheduler,
EDMEulerScheduler,
TCDScheduler,
)
SCHEDULER_CONFIG_MAP = {
"DPM++ 2M": (DPMSolverMultistepScheduler, {"algorithm_type": "dpmsolver++", "use_karras_sigmas": False}),
"DPM++ 2M Karras": (DPMSolverMultistepScheduler, {"algorithm_type": "dpmsolver++", "use_karras_sigmas": True}),
"DPM++ 2M SDE": (DPMSolverMultistepScheduler, {"use_karras_sigmas": False, "algorithm_type": "sde-dpmsolver++"}),
"DPM++ 2M SDE Karras": (DPMSolverMultistepScheduler, {"use_karras_sigmas": True, "algorithm_type": "sde-dpmsolver++"}),
"DPM++ 2S": (DPMSolverSinglestepScheduler, {"algorithm_type": "dpmsolver++", "use_karras_sigmas": False}),
"DPM++ 2S Karras": (DPMSolverSinglestepScheduler, {"algorithm_type": "dpmsolver++", "use_karras_sigmas": True}),
"DPM++ 1S": (DPMSolverMultistepScheduler, {"algorithm_type": "dpmsolver++", "solver_order": 1}),
"DPM++ 1S Karras": (DPMSolverMultistepScheduler, {"algorithm_type": "dpmsolver++", "solver_order": 1, "use_karras_sigmas": True}),
"DPM++ 3M": (DPMSolverMultistepScheduler, {"algorithm_type": "dpmsolver++", "solver_order": 3}),
"DPM++ 3M Karras": (DPMSolverMultistepScheduler, {"algorithm_type": "dpmsolver++", "solver_order": 3, "use_karras_sigmas": True}),
"DPM 3M": (DPMSolverMultistepScheduler, {"algorithm_type": "dpmsolver", "final_sigmas_type": "sigma_min", "solver_order": 3}),
"DPM++ SDE": (DPMSolverSDEScheduler, {"use_karras_sigmas": False}),
"DPM++ SDE Karras": (DPMSolverSDEScheduler, {"use_karras_sigmas": True}),
"DPM2": (KDPM2DiscreteScheduler, {}),
"DPM2 Karras": (KDPM2DiscreteScheduler, {"use_karras_sigmas": True}),
"DPM2 a": (KDPM2AncestralDiscreteScheduler, {}),
"DPM2 a Karras": (KDPM2AncestralDiscreteScheduler, {"use_karras_sigmas": True}),
"Euler": (EulerDiscreteScheduler, {}),
"Euler a": (EulerAncestralDiscreteScheduler, {}),
"Euler trailing": (EulerDiscreteScheduler, {"timestep_spacing": "trailing", "prediction_type": "sample"}),
"Euler a trailing": (EulerAncestralDiscreteScheduler, {"timestep_spacing": "trailing"}),
"Heun": (HeunDiscreteScheduler, {}),
"Heun Karras": (HeunDiscreteScheduler, {"use_karras_sigmas": True}),
"LMS": (LMSDiscreteScheduler, {}),
"LMS Karras": (LMSDiscreteScheduler, {"use_karras_sigmas": True}),
"DDIM": (DDIMScheduler, {}),
"DDIM trailing": (DDIMScheduler, {"timestep_spacing": "trailing"}),
"DEIS": (DEISMultistepScheduler, {}),
"UniPC": (UniPCMultistepScheduler, {}),
"UniPC Karras": (UniPCMultistepScheduler, {"use_karras_sigmas": True}),
"PNDM": (PNDMScheduler, {}),
"Euler EDM": (EDMEulerScheduler, {}),
"Euler EDM Karras": (EDMEulerScheduler, {"use_karras_sigmas": True}),
"DPM++ 2M EDM": (EDMDPMSolverMultistepScheduler, {"solver_order": 2, "solver_type": "midpoint", "final_sigmas_type": "zero", "algorithm_type": "dpmsolver++"}),
"DPM++ 2M EDM Karras": (EDMDPMSolverMultistepScheduler, {"use_karras_sigmas": True, "solver_order": 2, "solver_type": "midpoint", "final_sigmas_type": "zero", "algorithm_type": "dpmsolver++"}),
"DDPM": (DDPMScheduler, {}),
"DPM++ 2M Lu": (DPMSolverMultistepScheduler, {"algorithm_type": "dpmsolver++", "use_lu_lambdas": True}),
"DPM++ 2M Ef": (DPMSolverMultistepScheduler, {"algorithm_type": "dpmsolver++", "euler_at_final": True}),
"DPM++ 2M SDE Lu": (DPMSolverMultistepScheduler, {"use_lu_lambdas": True, "algorithm_type": "sde-dpmsolver++"}),
"DPM++ 2M SDE Ef": (DPMSolverMultistepScheduler, {"algorithm_type": "sde-dpmsolver++", "euler_at_final": True}),
"LCM": (LCMScheduler, {}),
"TCD": (TCDScheduler, {}),
"LCM trailing": (LCMScheduler, {"timestep_spacing": "trailing"}),
"TCD trailing": (TCDScheduler, {"timestep_spacing": "trailing"}),
"LCM Auto-Loader": (LCMScheduler, {}),
"TCD Auto-Loader": (TCDScheduler, {}),
"EDM": (EDMDPMSolverMultistepScheduler, {}),
"EDM Karras": (EDMDPMSolverMultistepScheduler, {"use_karras_sigmas": True}),
"Euler (V-Prediction)": (EulerDiscreteScheduler, {"prediction_type": "v_prediction", "rescale_betas_zero_snr": True}),
"Euler a (V-Prediction)": (EulerAncestralDiscreteScheduler, {"prediction_type": "v_prediction", "rescale_betas_zero_snr": True}),
"Euler EDM (V-Prediction)": (EDMEulerScheduler, {"prediction_type": "v_prediction"}),
"Euler EDM Karras (V-Prediction)": (EDMEulerScheduler, {"use_karras_sigmas": True, "prediction_type": "v_prediction"}),
"DPM++ 2M EDM (V-Prediction)": (EDMDPMSolverMultistepScheduler, {"solver_order": 2, "solver_type": "midpoint", "final_sigmas_type": "zero", "algorithm_type": "dpmsolver++", "prediction_type": "v_prediction"}),
"DPM++ 2M EDM Karras (V-Prediction)": (EDMDPMSolverMultistepScheduler, {"use_karras_sigmas": True, "solver_order": 2, "solver_type": "midpoint", "final_sigmas_type": "zero", "algorithm_type": "dpmsolver++", "prediction_type": "v_prediction"}),
"EDM (V-Prediction)": (EDMDPMSolverMultistepScheduler, {"prediction_type": "v_prediction"}),
"EDM Karras (V-Prediction)": (EDMDPMSolverMultistepScheduler, {"use_karras_sigmas": True, "prediction_type": "v_prediction"}),
}
def get_scheduler_config(name: str):
if not name in SCHEDULER_CONFIG_MAP.keys(): return SCHEDULER_CONFIG_MAP["Euler a"]
return SCHEDULER_CONFIG_MAP[name]
def fuse_loras(pipe, lora_dict: dict, temp_dir: str, civitai_key: str="", dkwargs: dict={}):
if not lora_dict or not isinstance(lora_dict, dict): return pipe
a_list = []
w_list = []
for k, v in lora_dict.items():
if not k: continue
new_lora_file = get_download_file(temp_dir, k, civitai_key)
if not new_lora_file or not Path(new_lora_file).exists():
print(f"LoRA file not found: {k}")
continue
w_name = Path(new_lora_file).name
a_name = Path(new_lora_file).stem
pipe.load_lora_weights(new_lora_file, weight_name=w_name, adapter_name=a_name, low_cpu_mem_usage=False, **dkwargs)
a_list.append(a_name)
w_list.append(v)
if Path(new_lora_file).exists(): Path(new_lora_file).unlink()
if len(a_list) == 0: return pipe
pipe.set_adapters(a_list, adapter_weights=w_list)
pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0)
pipe.unload_lora_weights()
return pipe
MODEL_TYPE_KEY = {
"model.diffusion_model.output_blocks.1.1.norm.bias": "SDXL",
"model.diffusion_model.input_blocks.11.0.out_layers.3.weight": "SD 1.5",
"double_blocks.0.img_attn.norm.key_norm.scale": "FLUX",
"model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale": "FLUX",
"model.diffusion_model.joint_blocks.9.x_block.attn.ln_k.weight": "SD 3.5",
}
def get_model_type_from_key(path: str):
default = "SDXL"
try:
keys = read_safetensors_key(path)
for k, v in MODEL_TYPE_KEY.items():
if k in set(keys):
print(f"Model type is {v}.")
return v
print("Model type could not be identified.")
except Exception:
return default
return default
def get_process_dtype(dtype: str, model_type: str):
if dtype in set(["fp8"] + QTYPES): return torch.bfloat16 if model_type in ["FLUX", "SD 3.5"] else torch.float16
return DTYPE_DICT.get(dtype, torch.float16)
|