File size: 10,433 Bytes
7344bef | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 | from __future__ import annotations
import os
from typing import Any, Callable
from postprocessing.flashvsr.sparse_backend_config import (
SPARSE_BACKEND_AUTO,
SPARSE_BACKEND_SPARGE,
SPARSE_BACKEND_TRITON_SPARSE,
normalize_sparse_backend,
)
class FlashVSRBridge:
MODE_OFF = 0
MODE_TINY = 1
MODE_FULL = 2
MODE_TINY_LONG = 3
PERSIST_UNLOAD = 1
PERSIST_RAM = 2
BACKEND_AUTO = SPARSE_BACKEND_AUTO
BACKEND_TRITON_SPARSE = SPARSE_BACKEND_TRITON_SPARSE
BACKEND_SPARGE = SPARSE_BACKEND_SPARGE
TOPK_RATIO_DEFAULT = 0.0
TOPK_RATIO_MAX = 4.0
UPSAMPLING_VALUE_PREFIX = "flashvsr"
UPSAMPLING_TWO_PASS_VALUE_PREFIX = "flashvsr2pass"
UPSAMPLING_RATIOS = (1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0)
TRANSFORMER_FILENAME = "FlashVSR_v1.1_transformer_bf16.safetensors"
LQ_PROJ_FILENAME = "FlashVSR_v1.1_lq_proj_bf16.safetensors"
TCDECODER_FILENAME = "FlashVSR_v1.1_tcdecoder_bf16.safetensors"
POSI_PROMPT_FILENAME = "FlashVSR_v1.1_posi_prompt_bf16.safetensors"
VAE_FILENAME = "Wan2.1_VAE.safetensors"
_VARIANTS = {
MODE_TINY: "tiny",
MODE_FULL: "full",
MODE_TINY_LONG: "tiny-long",
}
def __init__(self, server_config: dict[str, Any], files_locator):
self.server_config = server_config
self.files_locator = files_locator
@classmethod
def normalize_topk_ratio(cls, value: Any) -> float:
try:
value = float(value)
except (TypeError, ValueError):
value = cls.TOPK_RATIO_DEFAULT
return max(0.0, min(cls.TOPK_RATIO_MAX, value))
@classmethod
def normalize_backend(cls, value: Any) -> str:
return normalize_sparse_backend(value)
def normalize_config(self, config: dict[str, Any] | None = None) -> tuple[int, int]:
config = self.server_config if config is None else config
mode = config.get("flashvsr_mode", self.MODE_OFF)
persistence = config.get("flashvsr_persistence", self.PERSIST_UNLOAD)
try:
mode = int(mode)
except (TypeError, ValueError):
mode = self.MODE_OFF
try:
persistence = int(persistence)
except (TypeError, ValueError):
persistence = self.PERSIST_UNLOAD
if mode not in self._VARIANTS and mode != self.MODE_OFF:
mode = self.MODE_OFF
if persistence not in (self.PERSIST_UNLOAD, self.PERSIST_RAM):
persistence = self.PERSIST_UNLOAD
config["flashvsr_mode"] = mode
config["flashvsr_persistence"] = persistence
config["flashvsr_backend"] = self.normalize_backend(config.get("flashvsr_backend", self.BACKEND_AUTO))
config["flashvsr_topk_ratio"] = self.normalize_topk_ratio(config.get("flashvsr_topk_ratio", self.TOPK_RATIO_DEFAULT))
return mode, persistence
def settings(self, config: dict[str, Any] | None = None) -> tuple[bool, str | None, int]:
mode, persistence = self.normalize_config(config)
return mode != self.MODE_OFF, self._VARIANTS.get(mode), persistence
def topk_ratio(self) -> float:
return self.normalize_topk_ratio(self.server_config.get("flashvsr_topk_ratio", self.TOPK_RATIO_DEFAULT))
def backend(self) -> str:
return self.normalize_backend(self.server_config.get("flashvsr_backend", self.BACKEND_AUTO))
def enabled(self) -> bool:
return self.settings()[0]
@classmethod
def format_ratio(cls, scale: float) -> str:
scale = float(scale)
return str(int(scale)) if scale.is_integer() else f"{scale:g}"
@classmethod
def format_ratio_label(cls, scale: float) -> str:
return f"{float(scale):.1f}"
@classmethod
def upsampling_value(cls, scale: float) -> str:
return f"{cls.UPSAMPLING_VALUE_PREFIX}{cls.format_ratio(scale)}"
@classmethod
def upsampling_two_pass_value(cls, scale: float) -> str:
return f"{cls.UPSAMPLING_TWO_PASS_VALUE_PREFIX}{cls.format_ratio(scale)}"
@classmethod
def upsampling_choices(cls, include_name: bool = True, include_two_pass: bool = False) -> list[tuple[str, str]]:
prefix = "FlashVSR " if include_name else ""
choices = [(f"{prefix}x{cls.format_ratio_label(scale)}", cls.upsampling_value(scale)) for scale in cls.UPSAMPLING_RATIOS]
return choices + ([(f"{prefix}Two Pass x{cls.format_ratio_label(scale)}", cls.upsampling_two_pass_value(scale)) for scale in cls.UPSAMPLING_RATIOS] if include_two_pass else [])
@classmethod
def scale_for_upsampling(cls, spatial_upsampling) -> float | None:
text = str(spatial_upsampling or "").strip().lower()
prefix = cls.UPSAMPLING_TWO_PASS_VALUE_PREFIX if text.startswith(cls.UPSAMPLING_TWO_PASS_VALUE_PREFIX) else cls.UPSAMPLING_VALUE_PREFIX
if not text.startswith(prefix):
return None
try:
scale = float(text[len(prefix):])
except ValueError:
return None
return scale if scale in cls.UPSAMPLING_RATIOS else None
@classmethod
def is_two_pass_upsampling(cls, spatial_upsampling) -> bool:
return str(spatial_upsampling or "").strip().lower().startswith(cls.UPSAMPLING_TWO_PASS_VALUE_PREFIX)
@classmethod
def query_edit_mode_def(cls, include_name: bool = True) -> dict[str, Any]:
return {
"name": "FlashVSR",
"spatial_upsampling_choices": cls.upsampling_choices(include_name=include_name, include_two_pass=True),
"default_spatial_upsampling": cls.upsampling_value(2.0),
}
def is_upsampling(self, spatial_upsampling) -> bool:
return self.scale_for_upsampling(spatial_upsampling) is not None
def validate_upsampling(self, spatial_upsampling, image_mode: int) -> str:
if not self.is_upsampling(spatial_upsampling):
return ""
if not self.enabled():
return "FlashVSR Spatial Upsampling is disabled in Configuration > Extensions"
return ""
def query_download_def(self, enabled_only: bool = True) -> dict[str, Any] | None:
if enabled_only and not self.enabled():
return None
return {
"repoId": "DeepBeepMeep/Wan2.1",
"sourceFolderList": ["FlashVSR", ""],
"fileList": [[self.TRANSFORMER_FILENAME, self.LQ_PROJ_FILENAME, self.TCDECODER_FILENAME, self.POSI_PROMPT_FILENAME], [self.VAE_FILENAME]],
}
def _locate_flashvsr_file(self, filename: str) -> str:
return self.files_locator.locate_file(os.path.join("FlashVSR", filename))
def paths(self, variant: str):
from postprocessing.flashvsr.runtime import FlashVSRPaths
return FlashVSRPaths(
transformer=self._locate_flashvsr_file(self.TRANSFORMER_FILENAME),
lq_proj=self._locate_flashvsr_file(self.LQ_PROJ_FILENAME),
posi_prompt=self._locate_flashvsr_file(self.POSI_PROMPT_FILENAME),
tcdecoder=None if variant == "full" else self._locate_flashvsr_file(self.TCDECODER_FILENAME),
vae=self.files_locator.locate_file(self.VAE_FILENAME) if variant == "full" else None,
)
def vae_tile_size(self, vae_config: int, output_height: int | None = None, output_width: int | None = None) -> int:
import torch
from models.wan.modules.vae import WanVAE
device_mem_capacity = torch.cuda.get_device_properties(0).total_memory / 1048576 if torch.cuda.is_available() else 0
mixed_precision = self.server_config.get("vae_precision", "16") == "32"
return WanVAE.get_VAE_tile_size(vae_config, device_mem_capacity, mixed_precision, output_height=output_height, output_width=output_width)
def download(self, process_files: Callable[..., Any], send_cmd=None, status_text: str | None = None) -> bool:
flashvsr_def = self.query_download_def()
if flashvsr_def is None:
return False
_, variant, _ = self.settings()
required = [os.path.join("FlashVSR", self.TRANSFORMER_FILENAME), os.path.join("FlashVSR", self.LQ_PROJ_FILENAME), os.path.join("FlashVSR", self.POSI_PROMPT_FILENAME)]
required.append(self.VAE_FILENAME if variant == "full" else os.path.join("FlashVSR", self.TCDECODER_FILENAME))
if all(self.files_locator.locate_file(path, error_if_none=False) is not None for path in required):
return False
from shared.utils.download import send_download_status
send_download_status(send_cmd, status_text)
process_files(**flashvsr_def)
return True
def upscale(self, sample, spatial_upsampling, *, seed=0, continue_cache=None, return_continue_cache=False, vae_tile_size=None, process_files: Callable[..., Any], vae_config: int, init_pipe: Callable[..., int], profile, still_image=False, abort_callback=None, progress_callback=None):
scale = self.scale_for_upsampling(spatial_upsampling)
if scale is None:
raise ValueError(f"Unknown FlashVSR upsampling mode: {spatial_upsampling}")
enabled, variant, persistence = self.settings()
if not enabled:
raise RuntimeError("FlashVSR spatial upsampling is disabled in Configuration > Extensions.")
self.download(process_files)
from postprocessing.flashvsr.attention_backend import set_sparse_backend
set_sparse_backend(self.backend())
from postprocessing.flashvsr.runtime import upscale_video
output_height = int(sample.shape[-2] * scale)
output_width = int(sample.shape[-1] * scale)
flashvsr_tile_size = self.vae_tile_size(vae_config, output_height, output_width)
return upscale_video(
sample,
scale,
self.paths(variant),
variant=variant,
seed=seed,
continue_cache=continue_cache,
return_continue_cache=return_continue_cache,
persistent_models=persistence == self.PERSIST_RAM,
vae_tile_size=flashvsr_tile_size,
topk_ratio=self.topk_ratio(),
init_pipe=init_pipe,
profile=profile,
still_image=still_image,
two_pass=self.is_two_pass_upsampling(spatial_upsampling),
abort_callback=abort_callback,
progress_callback=progress_callback,
)
def release_vram(self) -> None:
from postprocessing.flashvsr.runtime import release_models
release_models()
|