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Running on Zero
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7d03019 | 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 | from __future__ import annotations
import secrets
import json
import threading
import time
from datetime import datetime, timezone
from typing import Callable
import torch
from PIL import Image
from .model_resolver import prepare_models_root
from .realesrgan_upscaler import ResidentHDUpscaler
from .space_config import SpaceConfig
from .space_postprocess import (
apply_rife_seam,
close_rife_model,
prepare_rife_model,
save_frame_bundle,
)
from .wanvideo_loop_runtime import WrapperLoopRuntime
def _diag(event: str, **fields) -> None:
payload = {
"ts": datetime.now(timezone.utc).isoformat(timespec="milliseconds"),
"event": event,
**fields,
}
print(f"[WAN_SERVICE] {json.dumps(payload, sort_keys=True)}", flush=True)
class LoopGeneratorService:
def __init__(self, config: SpaceConfig) -> None:
self.config = config
started = time.perf_counter()
_diag("service.init.start")
models_root, names = prepare_models_root()
self.runtime = WrapperLoopRuntime(
models_root=models_root,
high_model_name=names["high"],
low_model_name=names["low"],
clip_name=names["text"],
int8_clip_name=names["text_int8"],
vae_name=names["vae"],
sampler_name=config.sampler,
scheduler_mode=config.scheduler,
split_step=config.split_step,
riflex_k=0,
loop_shift_skip=0,
loop_start_percent=0.0,
loop_end_percent=1.0,
start_latent_strength=config.start_strength,
end_latent_strength=config.end_strength,
end_temporal_mask_strength=config.end_mask_strength,
decode_end_image_hint=True,
fun_or_fl2v_model=False,
zero_end_latent_conditioning=False,
end_latent_conditioning_strength=1.0,
low_pass_end_conditioning_strength=1.0,
custom_sigmas=(),
attention_mode="sdpa",
text_encoder_quantization=config.text_encoder_quantization,
global_resident_models=config.global_resident_models,
vae_tiling=config.vae_tiling,
)
if config.use_rife:
prepare_rife_model()
self.upscaler = ResidentHDUpscaler()
self._job_lock = threading.Lock()
_diag("service.init.done", elapsed_s=round(time.perf_counter() - started, 3))
def cleanup_job(self) -> None:
self.runtime.cleanup_job()
def close(self) -> None:
"""Destroy process-global resources. Do not call after a normal job."""
self.upscaler.close()
close_rife_model()
self.runtime.shutdown()
def generate_iter(
self,
image: Image.Image,
prompt: str,
progress_callback: Callable[..., None] | None = None,
):
if image is None:
raise ValueError("Upload an image.")
if not (prompt or "").strip():
raise ValueError("Enter a prompt.")
if not self._job_lock.acquire(blocking=False):
raise RuntimeError("The generator is already processing another request.")
image = image.convert("RGB")
frames = None
try:
self.runtime.cleanup_job()
with torch.inference_mode():
for stage_result in self.runtime.generate_segment_iter(
prompt=prompt,
negative_prompt="",
start_image=image,
end_image=image,
width=self.config.width,
height=self.config.height,
num_frames=self.config.frame_count,
steps=self.config.steps,
cfg=self.config.cfg,
shift=self.config.shift,
seed=secrets.randbits(63),
progress_callback=progress_callback,
):
if isinstance(stage_result, dict) and "stage" in stage_result:
yield stage_result
else:
frames = stage_result
if frames is None:
raise RuntimeError("Frame generation ended without decoded frames.")
metrics = dict(self.runtime.last_metrics)
if self.config.use_rife:
if progress_callback is not None:
progress_callback(0.87, desc="Smoothing loop seam…")
yield {"stage": "Smoothing loop seam…"}
if torch.cuda.is_available():
torch.cuda.synchronize()
rife_started = time.perf_counter()
_diag("rife.start", input_frames=int(frames.shape[0]))
frames = apply_rife_seam(frames, self.config.rife_frames)
if torch.cuda.is_available():
torch.cuda.synchronize()
metrics["rife_s"] = time.perf_counter() - rife_started
if torch.cuda.is_available():
metrics["peak_vram_gib"] = torch.cuda.max_memory_allocated() / (1024**3)
_diag(
"rife.done",
elapsed_s=round(metrics["rife_s"], 3),
output_frames=int(frames.shape[0]),
output_device=str(frames.device),
)
if progress_callback is not None:
progress_callback(0.91, desc="Upscaling to HD…")
yield {"stage": "Upscaling to HD…"}
if torch.cuda.is_available():
torch.cuda.synchronize()
upscale_started = time.perf_counter()
_diag(
"upscale.start",
input_width=int(frames.shape[2]),
input_height=int(frames.shape[1]),
input_frames=int(frames.shape[0]),
)
frames = self.upscaler.upscale_to_hd(frames)
if torch.cuda.is_available():
torch.cuda.synchronize()
upscale_s = time.perf_counter() - upscale_started
metrics["upscale_s"] = upscale_s
if torch.cuda.is_available():
metrics["peak_vram_gib"] = torch.cuda.max_memory_allocated() / (1024**3)
_diag(
"upscale.done",
elapsed_s=round(upscale_s, 3),
output_width=int(frames.shape[2]),
output_height=int(frames.shape[1]),
output_frames=int(frames.shape[0]),
output_device=str(frames.device),
)
print(f"[SPACE_METRICS_FINAL] {json.dumps(metrics, sort_keys=True)}", flush=True)
if progress_callback is not None:
progress_callback(0.95, desc="Preparing frames…")
yield {"stage": "Preparing frames…"}
bundle_started = time.perf_counter()
_diag("frame_bundle.start", input_device=str(frames.device))
bundle = save_frame_bundle(frames)
_diag("frame_bundle.done", elapsed_s=round(time.perf_counter() - bundle_started, 3))
yield {"bundle": bundle}
finally:
frames = None
self.runtime.cleanup_job()
self._job_lock.release()
|