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1ac9744 | 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 | from __future__ import annotations
import math
import os
from dataclasses import asdict, dataclass, field
from typing import Any
from adam.model_profiles import ModelProfile
from adam.models import SystemSnapshot
@dataclass(slots=True)
class SettingsRecommendation:
profile_id: str
epochs: int
settings: dict[str, Any] = field(default_factory=dict)
reasons: list[str] = field(default_factory=list)
warnings: list[str] = field(default_factory=list)
summary: str = ""
estimated_vram_gb: float | None = None
risk_level: str = "normal"
confidence: str = "conservative"
def to_dict(self) -> dict[str, Any]:
return asdict(self)
def _field_default(profile: ModelProfile, key: str, fallback: Any) -> Any:
return profile.training.get(key, {}).get("default", fallback)
def _clamp_to_schema(profile: ModelProfile, key: str, value: Any) -> Any:
spec = profile.training.get(key, {})
kind = str(spec.get("type", "text"))
try:
if kind in {"int", "slider"}:
numeric = int(value)
return max(int(spec.get("min", numeric)), min(numeric, int(spec.get("max", numeric))))
if kind == "float":
numeric = float(value)
return max(float(spec.get("min", numeric)), min(numeric, float(spec.get("max", numeric))))
except (TypeError, ValueError):
return spec.get("default", value)
if kind == "choice":
options = list(spec.get("options", []))
return value if value in options else (options[0] if options else value)
return value
def estimate_vram_gb(profile: ModelProfile, resolution: int | str, batch_size: int, base_model_gb: float = 0.0) -> float:
"""Broad VRAM estimate used only for warnings and conservative defaults."""
architecture = profile.architecture.casefold()
pixels = (max(64, resolution) / 512) ** 2
if profile.id == "lora" or "lora" in architecture:
base = max(6.0, base_model_gb * 1.8)
return base + pixels * max(1, batch_size) * 1.2
if profile.id == "oasis" or "action_conditioned" in architecture:
width, height = (resolution, resolution)
if isinstance(resolution, str) and "x" in resolution:
try:
width, height = (int(part) for part in resolution.lower().split("x", 1))
except ValueError:
width, height = (256, 144)
pixels = (max(width, height) / 512) ** 2
return 4.0 + pixels * max(1, batch_size) * 2.4
if "flow" in architecture:
return 2.8 + pixels * max(1, batch_size) * 1.0
if "diffusion" in architecture:
return 2.2 + pixels * max(1, batch_size) * 0.9
return 3.0 + pixels * max(1, batch_size) * 0.8
def recommend_for_profile(
profile: ModelProfile,
*,
dataset_items: int,
resolution: int | str | None = None,
snapshot: SystemSnapshot | None = None,
base_model_gb: float = 0.0,
) -> SettingsRecommendation:
images = max(10, int(dataset_items or 10))
raw_resolution = resolution or _field_default(profile, "resolution", 256) or 256
if isinstance(raw_resolution, str) and "x" in raw_resolution:
resolution = int(raw_resolution.lower().split("x", 1)[0])
else:
resolution = int(raw_resolution)
reasons: list[str] = []
vram_total = snapshot.vram_total_gb if snapshot and snapshot.vram_total_gb else None
available_vram = (
max(0.0, snapshot.vram_total_gb - snapshot.vram_used_gb)
if snapshot and snapshot.vram_total_gb
else vram_total
)
architecture = profile.architecture.casefold()
target_exposures = 80_000 if profile.id == "lora" else 180_000 if "diffusion" in architecture else 120_000
max_epochs = 220 if profile.id == "lora" else 600 if "diffusion" in architecture else 300
epochs = max(10 if profile.id == "lora" else 25, min(max_epochs, round(target_exposures / images)))
reasons.append(
f"Epochs target roughly {target_exposures:,} image exposures, then clamp to the profile's safe range."
)
batch_defaults = {
64: 16,
128: 12,
256: 4,
384: 2,
512: 1,
768: 1,
1024: 1,
}
if profile.id == "flow":
batch_defaults.update({64: 12, 128: 8, 256: 4})
if profile.id == "oasis":
batch_defaults.update({128: 4, 256: 2, 384: 1, 512: 1})
if profile.id == "lora":
batch_defaults.update({512: 2, 768: 1, 1024: 1})
nearest = min(batch_defaults, key=lambda size: abs(size - resolution))
batch_size = batch_defaults[nearest]
reasons.append(f"Batch starts from the closest resolution preset ({nearest}px).")
if available_vram is not None and available_vram < 8:
batch_size = max(1, batch_size // 2)
reasons.append("Available VRAM is below 8 GB, so batch size is reduced conservatively.")
settings: dict[str, Any] = {}
for key in ("resolution", "batch_size"):
if key in profile.training:
value = batch_size
if key == "resolution":
value = raw_resolution if profile.id == "oasis" else resolution
settings[key] = _clamp_to_schema(
profile,
key,
value,
)
if "learning_rate" in profile.training:
settings["learning_rate"] = _clamp_to_schema(
profile,
"learning_rate",
0.00002 if profile.id == "oasis" else 0.0001 if profile.id in {"ddpm", "lora"} else 0.0002,
)
workers = max(1, min(8, (os.cpu_count() or 4) // 2))
for key in ("dataloader_num_workers", "workers"):
if key in profile.training:
settings[key] = _clamp_to_schema(profile, key, workers)
for key, value in {
"gradient_accumulation_steps": 1,
"gradient_accumulation": 1,
"mixed_precision": "fp32" if profile.id == "oasis" else "fp16",
"save_every": max(5, min(25, max(1, epochs // 10))),
"preview_every": max(5, min(50, max(1, epochs // 10))),
"training_intensity": 100,
"gradient_checkpointing": resolution >= 384 or (available_vram is not None and available_vram < 8),
"rank": 16,
"alpha": 16,
"frame_gap": 3,
"sequence_context": 1,
"preview_steps": 1 if profile.id == "oasis" else 50 if profile.id == "ddpm" else 10,
}.items():
if key in profile.training:
settings[key] = _clamp_to_schema(profile, key, value)
estimated = estimate_vram_gb(profile, resolution, int(settings.get("batch_size", batch_size)), base_model_gb)
warnings: list[str] = []
if available_vram is not None and estimated > available_vram * 0.9:
warnings.append(
f"Estimated VRAM need is about {estimated:.1f} GB, above the conservative {available_vram * 0.9:.1f} GB working limit."
)
if "batch_size" in settings and int(settings["batch_size"]) > 1:
settings["batch_size"] = max(1, int(settings["batch_size"]) // 2)
estimated = estimate_vram_gb(profile, resolution, int(settings["batch_size"]), base_model_gb)
warnings.append(f"Batch size was reduced to {settings['batch_size']} for a safer first run.")
reasons.append("The initial VRAM estimate was high, so ADAM reduced the batch before applying the recipe.")
if images < 20:
warnings.append("Dataset is very small; expect overfitting unless this is just a smoke test.")
reasons.append("Very small datasets get a warning because quality usually depends more on data cleanup than long training.")
risk_level = "risky" if warnings else "normal"
memory_note = (
f" using about {available_vram:.1f} GB available VRAM" if available_vram is not None else " without detected VRAM"
)
summary = (
f"Recommended {epochs:,} epochs for {images:,} item(s), "
f"batch {settings.get('batch_size', batch_size)} at {resolution}px{memory_note}. "
"Treat this as a starting recipe, not a guarantee."
)
return SettingsRecommendation(
profile_id=profile.id,
epochs=epochs,
settings=settings,
reasons=reasons,
warnings=warnings,
summary=summary,
estimated_vram_gb=estimated,
risk_level=risk_level,
)
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