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ccbbabe | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 | """Hub custom pipeline: JiTPipeline for FD-Loss post-trained JiT checkpoints.
Uses FD-Loss sampling (legacy time convention, velocity Euler/Heun, t: 1→0).
See libs/FD-Loss-diffusers and scripts/evaluate_released_ckpt.sh (JiT_B preset).
"""
from __future__ import annotations
import json
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union
import torch
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.utils.torch_utils import randn_tensor
from scheduling_flow_match_fd import FDLossFlowMatchScheduler
RECOMMENDED_NOISE_BY_SIZE = {
256: 1.0,
512: 2.0,
}
RECOMMENDED_CFG_BY_VARIANT = {
"JiT-B": 3.0,
"JiT-L": 2.4,
"JiT-H": 2.2,
}
class JiTPipeline(DiffusionPipeline):
r"""
Pipeline for FD-Loss post-trained JiT (flow matching, legacy time convention).
Parameters:
transformer ([`JiTTransformer2DModel`]):
Class-conditioned JiT backbone.
scheduler ([`FDLossFlowMatchScheduler`]):
Flow timesteps from 1 (noise) to 0 (data).
legacy_time_convention (`bool`, *optional*, defaults to `True`):
Flip flow time when passing to the backbone (`t_bb = 1 - t`), as in FD-Loss training.
id2label (`dict[int, str]`, *optional*):
ImageNet class id to English label mapping.
"""
model_cpu_offload_seq = "transformer"
def __init__(
self,
transformer,
scheduler=None,
id2label: Optional[Dict[Union[int, str], str]] = None,
legacy_time_convention: bool = True,
):
super().__init__()
if scheduler is None:
scheduler = FDLossFlowMatchScheduler()
self.register_modules(transformer=transformer, scheduler=scheduler)
self.legacy_time_convention = legacy_time_convention
self._id2label = self._normalize_id2label(id2label)
self.labels = self._build_label2id(self._id2label)
self._labels_loaded_from_model_index = bool(self._id2label)
def _backbone_t(self, t: torch.Tensor) -> torch.Tensor:
if self.legacy_time_convention:
return 1.0 - t
return t
def _normalize_class_labels(
self,
class_labels: Union[int, str, List[Union[int, str]]],
) -> List[int]:
if isinstance(class_labels, int):
return [class_labels]
if isinstance(class_labels, str):
return self.get_label_ids(class_labels)
if class_labels and isinstance(class_labels[0], str):
return self.get_label_ids(class_labels)
return list(class_labels)
def _forward_velocity(
self,
z: torch.Tensor,
t: torch.Tensor,
labels: torch.Tensor,
guidance_scale: float,
cfg_interval: Optional[Tuple[float, float]],
t_eps: float,
) -> torch.Tensor:
t_view = t.reshape(-1, *([1] * (z.ndim - 1)))
t_bb = self._backbone_t(t).flatten().expand(z.shape[0])
x_cond = self.transformer(
z,
timestep=t_bb,
class_labels=labels,
interpolate_pos_encoding=interpolate_pos_encoding,
).sample
v_cond = (z - x_cond) / t_view.clamp_min(t_eps)
if guidance_scale == 1.0:
return v_cond
null_class = int(
getattr(self.transformer.config, "num_classes", getattr(self.transformer.config, "num_class_embeds", 1000))
)
class_null = torch.full_like(labels, null_class)
x_uncond = self.transformer(
z,
timestep=t_bb,
class_labels=class_null,
interpolate_pos_encoding=interpolate_pos_encoding,
).sample
v_uncond = (z - x_uncond) / t_view.clamp_min(t_eps)
if cfg_interval is None:
return v_uncond + guidance_scale * (v_cond - v_uncond)
low, high = cfg_interval
mask = (t < high) & ((low == 0) | (t > low))
scale = torch.where(
mask,
torch.tensor(guidance_scale, device=z.device, dtype=z.dtype),
torch.tensor(1.0, device=z.device, dtype=z.dtype),
)
while scale.ndim < v_cond.ndim:
scale = scale.unsqueeze(-1)
return v_uncond + scale * (v_cond - v_uncond)
@staticmethod
def _normalize_id2label(id2label: Optional[Dict[Union[int, str], str]]) -> Dict[int, str]:
if not id2label:
return {}
return {int(key): value for key, value in id2label.items()}
@staticmethod
def _read_id2label_from_model_index(variant_path: Optional[str]) -> Dict[int, str]:
if not variant_path:
return {}
variant_dir = Path(variant_path).resolve()
model_index_path = variant_dir / "model_index.json"
if not model_index_path.exists():
return {}
raw = json.loads(model_index_path.read_text(encoding="utf-8"))
id2label = raw.get("id2label")
if not isinstance(id2label, dict):
return {}
return {int(key): value for key, value in id2label.items()}
@staticmethod
def _build_label2id(id2label: Dict[int, str]) -> Dict[str, int]:
label2id: Dict[str, int] = {}
for class_id, value in id2label.items():
for synonym in value.split(","):
synonym = synonym.strip()
if synonym:
label2id[synonym] = int(class_id)
return dict(sorted(label2id.items()))
def _ensure_labels_loaded(self) -> None:
if self._labels_loaded_from_model_index:
return
loaded = self._read_id2label_from_model_index(getattr(self.config, "_name_or_path", None))
if loaded:
self._id2label = loaded
self.labels = self._build_label2id(self._id2label)
self._labels_loaded_from_model_index = True
@property
def id2label(self) -> Dict[int, str]:
self._ensure_labels_loaded()
return self._id2label
def get_label_ids(self, label: Union[str, List[str]]) -> List[int]:
self._ensure_labels_loaded()
label2id = self.labels
if not label2id:
raise ValueError("No English labels loaded. Ensure `id2label` exists in model_index.json.")
if isinstance(label, str):
label = [label]
missing = [item for item in label if item not in label2id]
if missing:
preview = ", ".join(list(label2id.keys())[:8])
raise ValueError(f"Unknown English label(s): {missing}. Example valid labels: {preview}, ...")
return [label2id[item] for item in label]
@torch.inference_mode()
def __call__(
self,
class_labels: Union[int, str, List[Union[int, str]]],
num_inference_steps: int = 1,
guidance_scale: float = 3.0,
guidance_interval_min: float = 0.1,
guidance_interval_max: float = 1.0,
sampling_method: str = "euler",
noise_scale: Optional[float] = None,
t_eps: float = 5e-2,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
interpolate_pos_encoding: bool = True,
output_type: Optional[str] = "pil",
return_dict: bool = True,
) -> Union[ImagePipelineOutput, Tuple]:
if num_inference_steps < 1:
raise ValueError("num_inference_steps must be >= 1.")
if output_type not in {"pil", "np", "pt"}:
raise ValueError("output_type must be one of: 'pil', 'np', 'pt'.")
if sampling_method not in {"euler", "heun"}:
raise ValueError("sampling_method must be 'euler' or 'heun'.")
class_label_ids = self._normalize_class_labels(class_labels)
batch_size = len(class_label_ids)
image_size = int(self.transformer.config.sample_size)
patch_size = int(self.transformer.config.patch_size)
height = int(height or image_size)
width = int(width or image_size)
if height % patch_size != 0 or width % patch_size != 0:
raise ValueError(
f"height and width must be divisible by patch_size={patch_size}. Got {(height, width)}."
)
channels = int(self.transformer.config.in_channels)
if noise_scale is None:
noise_scale = RECOMMENDED_NOISE_BY_SIZE.get(max(height, width), 1.0)
z = randn_tensor(
shape=(batch_size, channels, height, width),
generator=generator,
device=self._execution_device,
dtype=self.transformer.dtype,
) * noise_scale
labels = torch.tensor(class_label_ids, device=self._execution_device, dtype=torch.long).reshape(-1)
null_class_val = int(
getattr(self.transformer.config, "num_classes", getattr(self.transformer.config, "num_class_embeds", 1000))
)
labels = labels.clamp(0, null_class_val - 1)
cfg_interval = [
float(self._backbone_t(torch.tensor(guidance_interval_min, device=z.device))),
float(self._backbone_t(torch.tensor(guidance_interval_max, device=z.device))),
]
cfg_interval = (min(cfg_interval), max(cfg_interval))
timesteps = self.scheduler.set_timesteps(num_inference_steps, device=self._execution_device)
ts = timesteps.view(-1, *([1] * z.ndim)).expand(-1, batch_size, -1, -1, -1)
for i in self.progress_bar(range(num_inference_steps - 1)):
t_cur = ts[i]
t_next = ts[i + 1]
if sampling_method == "heun":
dt = t_next - t_cur
v1 = self._forward_velocity(z, t_cur, labels, guidance_scale, cfg_interval, t_eps)
z_mid = z + dt * v1
v2 = self._forward_velocity(z_mid, t_next, labels, guidance_scale, cfg_interval, t_eps)
z = z + dt * 0.5 * (v1 + v2)
else:
v = self._forward_velocity(z, t_cur, labels, guidance_scale, cfg_interval, t_eps)
z = z + (t_next - t_cur) * v
if num_inference_steps >= 1:
t_cur = ts[-2]
t_next = ts[-1]
v = self._forward_velocity(z, t_cur, labels, guidance_scale, cfg_interval, t_eps)
z = z + (t_next - t_cur) * v
images_pt = ((z.float().clamp(-1, 1) + 1.0) / 2.0).cpu()
if output_type == "pt":
images = images_pt
elif output_type == "np":
images = images_pt.permute(0, 2, 3, 1).numpy()
else:
images = self.numpy_to_pil(images_pt.permute(0, 2, 3, 1).numpy())
self.maybe_free_model_hooks()
if not return_dict:
return (images,)
return ImagePipelineOutput(images=images)
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