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b6acc0a | 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 | """CRS-Diff modular loading utilities for custom diffusers pipeline."""
import importlib
import json
import sys
from pathlib import Path
from typing import Dict, Optional, Union
import torch
from diffusers import DDIMScheduler
_PIPELINE_DIR = Path(__file__).resolve().parent
if str(_PIPELINE_DIR) not in sys.path:
sys.path.insert(0, str(_PIPELINE_DIR))
_COMPONENT_NAMES = (
"unet",
"vae",
"text_encoder",
"local_adapter",
"global_content_adapter",
"global_text_adapter",
"metadata_encoder",
)
_TARGET_MAP = {
"crs_core.local_adapter.LocalControlUNetModel": "crs_core.local_adapter.LocalControlUNetModel",
"crs_core.autoencoder.AutoencoderKL": "crs_core.autoencoder.AutoencoderKL",
"crs_core.text_encoder.FrozenCLIPEmbedder": "crs_core.text_encoder.FrozenCLIPEmbedder",
"crs_core.local_adapter.LocalAdapter": "crs_core.local_adapter.LocalAdapter",
"crs_core.global_adapter.GlobalContentAdapter": "crs_core.global_adapter.GlobalContentAdapter",
"crs_core.global_adapter.GlobalTextAdapter": "crs_core.global_adapter.GlobalTextAdapter",
"crs_core.metadata_embedding.metadata_embeddings": "crs_core.metadata_embedding.metadata_embeddings",
}
def ensure_model_path(pretrained_model_name_or_path: Union[str, Path]) -> Path:
"""Resolve local path or download HF repo snapshot."""
path = Path(pretrained_model_name_or_path)
if not path.exists():
from huggingface_hub import snapshot_download
path = Path(snapshot_download(str(pretrained_model_name_or_path)))
path = path.resolve()
if str(path) not in sys.path:
sys.path.insert(0, str(path))
return path
def resolve_model_root(candidate: Optional[Union[str, Path]]) -> Optional[Path]:
"""Resolve to folder containing model_index.json."""
if not candidate:
return None
path = ensure_model_path(candidate)
if (path / "model_index.json").exists():
return path
cur = path
for _ in range(5):
parent = cur.parent
if parent == cur:
break
if (parent / "model_index.json").exists():
return parent
cur = parent
return None
def _get_class(target: str):
module_path, cls_name = target.rsplit(".", 1)
mod = importlib.import_module(module_path)
return getattr(mod, cls_name)
def load_component(model_root: Path, name: str):
"""Load single split component from <repo>/<name>/."""
root = Path(model_root)
comp_path = root / name
with (comp_path / "config.json").open("r", encoding="utf-8") as f:
cfg = json.load(f)
target = cfg.pop("_target", None)
if not target:
raise ValueError(f"No _target in {comp_path / 'config.json'}")
target = _TARGET_MAP.get(target, target)
cls_ref = _get_class(target)
params = {k: v for k, v in cfg.items() if not k.startswith("_")}
module = cls_ref(**params)
weight_file = comp_path / "diffusion_pytorch_model.safetensors"
if weight_file.exists():
from safetensors.torch import load_file
state = load_file(str(weight_file))
module.load_state_dict(state, strict=True)
module.eval()
return module
class CRSModelWrapper(torch.nn.Module):
"""Wrap split components to mimic CRSControlNet APIs used by pipeline."""
def __init__(
self,
unet,
vae,
text_encoder,
local_adapter,
global_content_adapter,
global_text_adapter,
metadata_encoder,
channels: int = 4,
):
super().__init__()
self.model = torch.nn.Module()
self.model.add_module("diffusion_model", unet)
self.first_stage_model = vae
self.cond_stage_model = text_encoder
self.local_adapter = local_adapter
self.global_content_adapter = global_content_adapter
self.global_text_adapter = global_text_adapter
self.metadata_emb = metadata_encoder
self.local_control_scales = [1.0] * 13
self.channels = channels
@torch.no_grad()
def get_learned_conditioning(self, prompts):
if hasattr(self.cond_stage_model, "device"):
self.cond_stage_model.device = str(next(self.parameters()).device)
return self.cond_stage_model.encode(prompts)
def apply_model(self, x_noisy, t, cond, metadata=None, global_strength=1.0, **kwargs):
del kwargs
if metadata is None:
metadata = cond["metadata"]
cond_txt = torch.cat(cond["c_crossattn"], 1)
if cond.get("global_control") is not None and cond["global_control"][0] is not None:
metadata = self.metadata_emb(metadata)
content_t, _ = cond["global_control"][0].chunk(2, dim=1)
global_control = self.global_content_adapter(content_t)
cond_txt = self.global_text_adapter(cond_txt)
cond_txt = torch.cat([cond_txt, global_strength * global_control], dim=1)
local_control = None
if cond.get("local_control") is not None and cond["local_control"][0] is not None:
local_control = torch.cat(cond["local_control"], 1)
local_control = self.local_adapter(
x=x_noisy, timesteps=t, context=cond_txt, local_conditions=local_control
)
local_control = [c * s for c, s in zip(local_control, self.local_control_scales)]
return self.model.diffusion_model(
x=x_noisy,
timesteps=t,
metadata=metadata,
context=cond_txt,
local_control=local_control,
meta=True,
)
def decode_first_stage(self, z):
return self.first_stage_model.decode(z)
def load_components(model_root: Union[str, Path]) -> Dict[str, object]:
"""Load pipeline components from split directories."""
root = ensure_model_path(model_root)
scheduler = DDIMScheduler.from_pretrained(root, subfolder="scheduler")
scale_factor = 0.18215
channels = 4
if (root / "model_index.json").exists():
with (root / "model_index.json").open("r", encoding="utf-8") as f:
idx = json.load(f)
scale_factor = float(idx.get("scale_factor", scale_factor))
channels = int(idx.get("channels", channels))
has_split_components = all((root / name / "config.json").exists() for name in _COMPONENT_NAMES)
if not has_split_components:
missing = [name for name in _COMPONENT_NAMES if not (root / name / "config.json").exists()]
raise FileNotFoundError(
f"CRS-Diff split component export incomplete. Missing: {missing}. "
"Expected split folders with config.json and weights."
)
loaded = {name: load_component(root, name) for name in _COMPONENT_NAMES}
crs_model = CRSModelWrapper(
unet=loaded["unet"],
vae=loaded["vae"],
text_encoder=loaded["text_encoder"],
local_adapter=loaded["local_adapter"],
global_content_adapter=loaded["global_content_adapter"],
global_text_adapter=loaded["global_text_adapter"],
metadata_encoder=loaded["metadata_encoder"],
channels=channels,
)
return {"crs_model": crs_model, "scheduler": scheduler, "scale_factor": scale_factor}
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