File size: 10,509 Bytes
e0265b9 | 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 | """Image-generation bridge for the connected DDPM project."""
from __future__ import annotations
import importlib.util
import json
import random
import re
import sys
from datetime import datetime, timezone
from pathlib import Path
from types import ModuleType
from adam.config import ConfigManager
from adam.executor import ToolContext, ToolExecutionError
from adam.generations import generation_metadata_path, generation_output_folder
from adam.generation_previews import accepts_preview_callback, publish_generation_preview
_backend_module: ModuleType | None = None
_backend_script: Path | None = None
def _load_backend(script: Path) -> ModuleType:
global _backend_module, _backend_script
if _backend_module is not None and _backend_script == script:
return _backend_module
module_name = "_adam_connected_ddpm_generator"
spec = importlib.util.spec_from_file_location(module_name, script)
if spec is None or spec.loader is None:
raise ToolExecutionError("The connected DDPM generator could not be loaded.")
module = importlib.util.module_from_spec(spec)
sys.modules[module_name] = module
try:
spec.loader.exec_module(module)
except Exception as exc:
sys.modules.pop(module_name, None)
raise ToolExecutionError(f"Could not load the DDPM generator: {exc}") from exc
if not callable(getattr(module, "generate_images", None)):
raise ToolExecutionError(
"The connected DDPM app does not expose its generate_images function."
)
_backend_module = module
_backend_script = script
return module
def _safe_label(value: str) -> str:
label = value.strip()[:96] or "DDPM"
label = re.sub(r"[<>:\"/\\|?*\x00-\x1f]+", " ", label)
label = re.sub(r"\s+", " ", label).strip(" .")
return label or "DDPM"
def generate_ddpm_images(
context: ToolContext,
model_name: str,
model_path: str,
prompt: str,
image_count: int,
steps: int,
seed: int,
sampler: str,
aspect_ratio: str,
reference_image: str = "",
reference_strength: int = 65,
width: int = 0,
height: int = 0,
preview_interval: int = 0,
) -> dict[str, object]:
config = ConfigManager(context.root)
trainer_root = Path(
str(config.get("tool_folders", {}).get("ddpm_trainer", ""))
).expanduser().resolve()
script = trainer_root / "appStableDiffusion.py"
if not script.is_file():
raise ToolExecutionError(
"DDPM appStableDiffusion.py was not found. Re-scan the DDPM folder in Settings."
)
model = Path(model_path).expanduser().resolve()
allowed_root = (trainer_root / "output").resolve()
try:
model.relative_to(allowed_root)
except ValueError as exc:
raise ToolExecutionError(
"The DDPM model must be inside the connected DDPM output folder."
) from exc
if not model.is_dir() or not (model / "model_index.json").is_file():
raise ToolExecutionError(
"Choose a completed DDPM pipeline. Resume checkpoint folders cannot generate images."
)
missing_packages = [
package
for package in ("torch", "diffusers", "numpy", "PIL")
if importlib.util.find_spec(package) is None
]
if missing_packages:
raise ToolExecutionError(
"ADAM's Python environment is missing DDPM generation packages: "
+ ", ".join(missing_packages)
+ ". Close ADAM and open Launch ADAM.bat to install the connected DDPM requirements."
)
count = int(image_count)
step_count = int(steps)
if not 1 <= count <= 48:
raise ToolExecutionError("Image count must be between 1 and 48.")
if not 5 <= step_count <= 500:
raise ToolExecutionError("Generation steps must be between 5 and 500.")
sampler = sampler.upper().strip()
if sampler not in {"DDPM", "DDIM"}:
raise ToolExecutionError("DDPM generation supports the DDPM and DDIM samplers.")
allowed_aspects = {
"1:1 (Square)", "16:9 (Widescreen)", "9:16 (Portrait)",
"4:3 (Classic)", "3:4 (Portrait Classic)", "3:2 (Photo)",
"2:3 (Portrait Photo)",
}
if aspect_ratio not in allowed_aspects:
raise ToolExecutionError("Choose one of the supported DDPM aspect ratios.")
if len(prompt) > 500:
raise ToolExecutionError("The generation label must be 500 characters or shorter.")
reference_path = Path(reference_image).expanduser() if reference_image.strip() else None
if reference_path is not None:
reference_path = reference_path.resolve()
if not reference_path.is_file():
raise ToolExecutionError("Choose an existing DDPM reference image.")
if reference_path.suffix.casefold() not in {".png", ".jpg", ".jpeg", ".webp", ".bmp"}:
raise ToolExecutionError("DDPM reference images must be PNG, JPG, WEBP, or BMP files.")
try:
strength = int(reference_strength)
except (TypeError, ValueError) as exc:
raise ToolExecutionError("DDPM reference strength must be a whole number from 0 to 100.") from exc
if not 0 <= strength <= 100:
raise ToolExecutionError("DDPM reference strength must be between 0 and 100.")
try:
custom_width, custom_height = int(width or 0), int(height or 0)
except (TypeError, ValueError) as exc:
raise ToolExecutionError("Custom DDPM dimensions must be whole numbers.") from exc
if bool(custom_width) != bool(custom_height):
raise ToolExecutionError("Set both custom DDPM width and height, or leave both blank.")
if custom_width and not (64 <= custom_width <= 2048 and 64 <= custom_height <= 2048):
raise ToolExecutionError("Custom DDPM dimensions must be between 64 and 2048 pixels.")
if not 0 <= int(preview_interval) <= step_count:
raise ToolExecutionError("Preview interval must be between 0 and the total number of steps.")
base_seed = int(seed)
if base_seed <= 0:
base_seed = random.randint(1, 2_147_483_647 - count)
if base_seed + count - 1 > 2_147_483_647:
raise ToolExecutionError("The seed is too large for this image count.")
timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
output = generation_output_folder(context.root, context.tool.id, _safe_label(model_name))
backend = _load_backend(script)
preview_enabled = int(preview_interval) > 0
preview_supported = accepts_preview_callback(backend.generate_images)
if reference_path:
preview_supported = preview_supported and accepts_preview_callback(backend.generate_reference_images)
if reference_path and not callable(getattr(backend, "generate_reference_images", None)):
raise ToolExecutionError(
"The connected DDPM app does not support reference-image generation. Update the DDPM app and restart ADAM."
)
context.log(f"Loading completed DDPM model: {model_name}")
if reference_path:
context.log(f"Reimagining reference image at {strength}% influence.")
context.log("DDPM models generate learned visual samples; the label is saved as metadata, not used as a text prompt.")
if preview_enabled and not preview_supported:
context.log("This connected DDPM generator does not yet expose denoising previews; generation will continue normally.")
image_paths: list[str] = []
for index in range(count):
context.checkpoint()
current_seed = base_seed + index
context.progress(
max(1, round(index * 100 / count)),
f"Generating image {index + 1} of {count}",
)
try:
settings = {
"seed": current_seed,
"num_inference_steps": step_count,
"batch_size": 1,
"sampler": sampler,
"aspect_ratio": aspect_ratio,
}
if preview_enabled and preview_supported:
settings["preview_callback"] = lambda payload, step=0, total_steps=step_count, current=index: publish_generation_preview(
context, output, payload, image_index=current, image_count=count,
step=step, total_steps=total_steps,
)
settings["preview_interval"] = int(preview_interval)
if custom_width:
settings.update({"width": custom_width, "height": custom_height})
if reference_path:
images = backend.generate_reference_images(
str(model), str(reference_path), reference_strength=strength, **settings
)
else:
images = backend.generate_images(str(model), **settings)
image = images[0]
destination = output / f"{timestamp}_{context.job_id}_{sampler}_seed_{current_seed}.png"
image.save(destination, format="PNG")
except Exception as exc:
raise ToolExecutionError(f"DDPM generation failed: {exc}") from exc
image_paths.append(str(destination))
created_at = datetime.now(timezone.utc).isoformat()
metadata = {
"version": 1,
"provider_id": context.tool.id,
"provider_name": context.tool.name,
"model_name": _safe_label(model_name),
"model_path": str(model),
"prompt": prompt.strip(),
"prompt_behavior": "label_only",
"seed": base_seed,
"image_seeds": [base_seed + index for index in range(count)],
"image_count": count,
"steps": step_count,
"sampler": sampler,
"aspect_ratio": aspect_ratio,
"width": custom_width or None,
"height": custom_height or None,
"reference_image": str(reference_path) if reference_path else "",
"reference_strength": strength if reference_path else None,
"preview_interval": int(preview_interval),
"preview_supported": preview_supported,
"images": image_paths,
"created_at": created_at,
}
generation_metadata_path(output, timestamp, context.job_id).write_text(
json.dumps(metadata, indent=2), encoding="utf-8"
)
context.progress(100, f"Generated {count} image(s)")
return {
"output_folder": str(output),
"assets": [
{
"kind": "generation",
"name": f"{_safe_label(model_name)} · {timestamp}",
"path": str(output),
"trainer": "ddpm",
}
],
}
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