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5cfafaa | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | """Assembly of the independently adapted high- and low-noise fast branches."""
from __future__ import annotations
import copy
import gc
import json
from pathlib import Path
import torch
from diffusers.models import AutoencoderKLWan
from transformers import AutoTokenizer, UMT5EncoderModel
from .diffusers import (
WorldCrafterPipeline,
WorldCrafterScheduler,
WorldCrafterTransformer3DModel,
)
from .fast.compact_ucpe import compact_ucpe
from .fast.attention import FastUcpeSelfAttention
from .fast.contract import load_dmd_inference_contract
from .fast.resident import ResidentBranches
from .kernels import (
replace_rmsnorm_with_fp32,
replace_all_norms_with_flash_norms,
replace_rope_with_flash_rope,
)
from .repencoder import (
RepEncoder,
RepEncoderInferenceMemoryProvider,
RepEncoderInferenceProviderConfig,
)
from .ucpe.bridge import (
enable_ucpe_inference_sdpa_attention,
patch_worldcrafter_transformer_ucpe,
load_ucpe_camera_adapter_weights,
)
def load_fast(
cls,
model_path,
*,
device,
height,
width,
seed,
memory_fov_h_deg,
memory_fov_v_deg,
memory_fov_samples_per_axis,
attention_backend,
enable_compile,
):
from .inference import configure_attention, load_model_adapter, sha256
if (height, width) != (384, 640):
raise ValueError("Fast weights require height=384 and width=640")
device = torch.device(device)
if device.type != "cuda" or not torch.cuda.is_available():
raise RuntimeError("Fast inference requires CUDA")
torch.cuda.set_device(device)
root = Path(model_path).expanduser().resolve()
config = json.loads((root / "inference_config.json").read_text())
manifest = json.loads((root / "manifest.json").read_text())
expected_config = dict(
steps_per_stage=[2, 2, 2],
guidance_scale=1.0,
ucpe_pixel_center=True,
repencoder_target_microbatch=1,
representation="resident_byte_compact_ucpe",
compile=False,
)
if any(config.get(key) != value for key, value in expected_config.items()):
raise ValueError(
"Fast inference configuration differs from the validated release contract"
)
if config["routing"] != [["equal", "equal"], ["equal", "equal"], ["equal", "old"]]:
raise ValueError("Fast I2V requires 5+1 routing")
shared = (root / config["shared_components"]).resolve()
for row in manifest["files"]:
path = root / row["path"]
if not path.is_file() or path.stat().st_size != row["bytes"]:
raise ValueError(f"Incomplete fast checkpoint: {path}")
if path.stat().st_size < 1024 * 1024 and sha256(path) != row["sha256"]:
raise ValueError(f"Fast checkpoint metadata mismatch: {path}")
adapters = [root / "adapter_high_noise", root / "adapter_low_noise"]
contracts = [
load_dmd_inference_contract(p, expected_latent_shape=(16, 9, 48, 80))
for p in adapters
]
contract = contracts[0]
if contract.fingerprint != contracts[
1
].fingerprint or contract.rollout_steps_per_stage != (2, 2, 2):
raise ValueError(
"Fast branches must have identical native 2/2/2 timestep contracts"
)
for adapter in adapters:
frozen = json.loads((adapter / "repencoder_frozen.json").read_text())
if (
frozen["repencoder"]["model_sha256"]
!= manifest["repencoder"]["reference_file_sha256"]
):
raise ValueError("Fast adapter references an unexpected RepEncoder")
enable_ucpe_inference_sdpa_attention()
repencoder = RepEncoder.from_pretrained(
shared / "repencoder", device=device, compute_dtype="bf16", target_microbatch=1
)
if (
repencoder.report["model_sha256"]
!= manifest["repencoder"]["shared_file_sha256"]
):
raise ValueError(
"Shared RepEncoder differs from the verified renamed checkpoint"
)
provider = RepEncoderInferenceMemoryProvider(
repencoder,
RepEncoderInferenceProviderConfig(
seed=seed,
trajectory_fov_horizontal_fov_degrees=memory_fov_h_deg,
trajectory_fov_vertical_fov_degrees=memory_fov_v_deg,
trajectory_fov_samples_per_axis=memory_fov_samples_per_axis,
),
)
def transformer(branch):
model = WorldCrafterTransformer3DModel.from_pretrained(
root / f"transformer_{branch}_noise", torch_dtype=torch.bfloat16
)
patch_worldcrafter_transformer_ucpe(
model,
method="relray_absmap",
height=height,
width=width,
attn_compress=8,
adaptation_method="parallel",
attention_cls=FastUcpeSelfAttention,
)
loaded = load_ucpe_camera_adapter_weights(
model, root / f"adapter_{branch}_noise" / "transformer_partial.pth"
)
if loaded["loaded_tensor_keys"] != loaded["expected_tensor_keys"]:
raise ValueError(f"Incomplete {branch} UCPE state")
model = replace_rmsnorm_with_fp32(model)
model = replace_all_norms_with_flash_norms(model)
configure_attention(model, attention_backend)
return model
early = transformer("high")
replace_rope_with_flash_rope()
provenance = contract.student_scheduler
scheduler = WorldCrafterScheduler.from_config(
WorldCrafterScheduler.from_pretrained(shared / "scheduler").config,
num_train_timesteps=provenance.num_train_timesteps,
shift=provenance.shift,
stages=provenance.stages,
stage_range=list(provenance.stage_range),
gamma=provenance.gamma,
scheduler_type="dmd",
use_dynamic_shifting=provenance.use_dynamic_shifting,
time_shift_type=provenance.time_shift_type,
)
pipe = WorldCrafterPipeline(
tokenizer=AutoTokenizer.from_pretrained(shared / "tokenizer"),
text_encoder=UMT5EncoderModel.from_pretrained(
shared / "text_encoder", torch_dtype=torch.bfloat16
),
transformer=early,
vae=AutoencoderKLWan.from_pretrained(shared / "vae", torch_dtype=torch.float32),
scheduler=scheduler,
is_distilled=True,
)
early_lora = load_model_adapter(pipe, adapters[0])
pipe.dmd_timestep_contract = contract
pipe.to(device)
late = transformer("low")
loader = copy.copy(pipe)
loader.register_modules(transformer=late)
late_lora = load_model_adapter(loader, adapters[1])
compact = [compact_ucpe(m) for m in (early, late)]
pipe.resident_branches = ResidentBranches(early, late)
late.to(device)
pipe.stage_transformers = (early, early, late)
pipe.stage_model_trace = []
def record(branch):
def hook(module, args, kwargs, output):
chunk, stage, step, stage_steps = pipe.stage_forward_context
low_noise = (
stage >= 1
if getattr(pipe, "fast_inference_mode", "i2v") == "t2v"
else stage == 2 and step >= stage_steps // 2
)
expected = "old" if low_noise else "equal"
if branch != expected or pipe.resident_branches.active != branch:
raise RuntimeError("Fast transformer/adapter routing mismatch")
pipe.stage_model_trace.append(
dict(
chunk=chunk,
stage=stage,
step=step,
stage_steps=stage_steps,
branch=branch,
)
)
return hook
early.register_forward_hook(record("equal"), with_kwargs=True)
late.register_forward_hook(record("old"), with_kwargs=True)
if enable_compile:
# Keep routing and shared-weight switches outside compiled graphs.
# Compile each branch's blocks without changing parameter storage.
for branch in (early, late):
for block in branch.blocks:
block.compile(mode="default", dynamic=False)
gc.collect()
torch.cuda.empty_cache()
model = cls(
pipeline=pipe,
memory_provider=provider,
model_path=root,
device=device,
attention_backend=attention_backend,
adapter_load={"high": early_lora, "low": late_lora},
height=height,
width=width,
)
model.model_type = "fast"
model.fast_config = config
model.fast_report = dict(
compile_enabled=bool(enable_compile),
compile_scope="transformer_blocks" if enable_compile else None,
contract_fingerprint=contract.fingerprint,
compact_ucpe=compact,
resident=pipe.resident_branches.report,
)
return model
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