#!/usr/bin/env python3 """Apply Studio DeLight to three adopted BFS images and the book-restored supporting character. Model card: https://huggingface.co/prithivMLmods/QIE-2511-Studio-DeLight One input per character; no Lightning or BFS adapter in this pass. """ from __future__ import annotations import argparse import json import re import time from pathlib import Path import hashlib import importlib.metadata import platform import sys from PIL import Image ASSETS = Path(__file__).resolve().parent.parent PROJECT_ROOT = ASSETS.parents[3] CACHE_DIR = PROJECT_ROOT / ".tmp" / "download" / "huggingface" / "hub" MODEL_ID = "Qwen/Qwen-Image-Edit-2511" def sha256(path: Path) -> str: digest = hashlib.sha256() with path.open("rb") as source: for block in iter(lambda: source.read(1024 * 1024), b""): digest.update(block) return digest.hexdigest() def runtime_record() -> dict[str, object]: packages = {} for package in ("diffusers", "torch", "transformers", "accelerate"): try: packages[package] = importlib.metadata.version(package) except importlib.metadata.PackageNotFoundError: packages[package] = "not-installed" return { "python": sys.version.split()[0], "platform": platform.platform(), "packages": packages, } def square_canvas(path: Path, size: int) -> Image.Image: """Return an RGB, white-backed square canvas without distorting the input.""" with Image.open(path) as source: source = source.convert("RGBA") source.thumbnail((size, size), Image.Resampling.LANCZOS) canvas = Image.new("RGBA", (size, size), "white") offset = ((size - source.width) // 2, (size - source.height) // 2) canvas.alpha_composite(source, offset) return canvas.convert("RGB") SOURCES = { 'a-mira': ASSETS / 'sec-05/p7-5-5-qwen-2511-bfs-reviewed-a-mira-zero-view-native-headcrop-weight10-zero-view-v3-size-1280x1280-seed-62294-steps-10.png', 'b-mira': ASSETS / 'sec-05/p7-5-5-qwen-2511-bfs-reviewed-b-mira-low45-native-headcrop-weight10-low45-v4-size-1280x1280-seed-62294-steps-10.png', 'c-mira': ASSETS / 'sec-05/p7-5-5-qwen-2511-bfs-reviewed-c-mira-level-native1280-headcrop-weight10-v1-size-1280x1280-seed-62294-steps-10.png', 'c-supporting': ASSETS / 'sec-05/p7-5-5-qwen-2511-identity-c-supporting-stage2-book-v1-size-1280x1280-seed-62294-steps-20.png', } PROMPT = 'Neutral uniform lighting Preserve identity and composition' LORA = ASSETS.parents[3] / '.tmp/download/artifacts/weight-prithivmlmods-qie-2511-studio-delight/QIE-2511-Studio-DeLight-5000.safetensors' def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('--targets', nargs='+', choices=tuple(SOURCES), default=list(SOURCES)) parser.add_argument('--delight-scale', type=float, default=1.0) parser.add_argument('--prompt', default=PROMPT) parser.add_argument('--steps', type=int, default=10) parser.add_argument('--seed', type=int, default=62294) parser.add_argument('--run-label', default='v1') parser.add_argument('--output-dir', type=Path, default=ASSETS / "sec-05") parser.add_argument('--dry-run', action='store_true') args = parser.parse_args() if args.steps < 1 or not args.prompt.strip(): parser.error('Positive steps and a nonempty prompt are required.') if not re.fullmatch(r'[A-Za-z0-9][A-Za-z0-9_-]*', args.run_label): parser.error('Use letters, digits, underscores and hyphens for --run-label.') if args.delight_scale <= 0: parser.error('DeLight scale must be positive.') plans = [] for target in dict.fromkeys(args.targets): source = SOURCES[target].resolve() if not source.is_file(): raise FileNotFoundError(source) stem = (f'p7-5-5-qwen-2511-studio-delight-reviewed-{target}-{args.run_label}' f'-size-1280x1280-seed-{args.seed}-steps-{args.steps}') output = args.output_dir.resolve() / f'{stem}.png' result = output.with_name(f'{stem}-result.json') for path in (output, result): if path.exists(): raise FileExistsError(f'Refusing to overwrite: {path}') plans.append(dict(scene=target, inputs=[dict(role='Picture 1: reviewed character', path=str(source), sha256=sha256(source))], output=str(output), result=str(result))) settings = dict(model=MODEL_ID, prompt=args.prompt.strip(), steps=args.steps, seed=args.seed, size=[1280, 1280], true_cfg_scale=4.0, guidance_scale=1.0, generator_device='cpu', dtype='bfloat16', mask=None, negative_prompt=' ', lora=dict(repository='prithivMLmods/QIE-2511-Studio-DeLight', path=str(LORA), sha256=sha256(LORA), scale=args.delight_scale)) if args.dry_run: print(json.dumps(dict(status='planned', settings=settings, plans=plans), indent=2)) return import torch from diffusers import QwenImageEditPlusPipeline if not torch.cuda.is_available(): raise RuntimeError('A local CUDA GPU is required.') pipeline = QwenImageEditPlusPipeline.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16, cache_dir=CACHE_DIR, local_files_only=True, ) pipeline.load_lora_weights(LORA.parent, weight_name=LORA.name, adapter_name='studio_delight', local_files_only=True) pipeline.set_adapters(['studio_delight'], adapter_weights=[args.delight_scale]) pipeline.enable_attention_slicing('max') pipeline.enable_sequential_cpu_offload() args.output_dir.mkdir(parents=True, exist_ok=True) provenance = dict(source_code_sha256=sha256(Path(__file__)), runtime=runtime_record(), cuda_device=torch.cuda.get_device_name(0), offload='sequential CPU offload', preprocessing='aspect-preserving resize on white 1280x1280 canvases') for plan in plans: for item in plan['inputs']: if sha256(Path(item['path'])) != item['sha256']: raise ValueError(f"Input changed after preflight: {item['path']}") print(f"Starting scene {plan['scene']}", flush=True) started = time.monotonic() image = pipeline( image=[square_canvas(Path(item['path']), 1280) for item in plan['inputs']], prompt=settings['prompt'], height=1280, width=1280, generator=torch.Generator(device='cpu').manual_seed(args.seed), true_cfg_scale=4.0, negative_prompt=' ', num_inference_steps=args.steps, guidance_scale=1.0, num_images_per_prompt=1, ).images[0] with Path(plan['output']).open('xb') as stream: image.save(stream, format='PNG') record = dict(plan, **settings, **provenance, status='generated', stage='studio_delight_after_bfs_or_supporting_book', output_sha256=sha256(Path(plan['output'])), elapsed_seconds=round(time.monotonic()-started, 2)) with Path(plan['result']).open('x', encoding='utf-8') as stream: json.dump(record, stream, ensure_ascii=False, indent=2) stream.write('\n') print(json.dumps({'scene': plan['scene'], 'output': plan['output'], 'result': plan['result']}), flush=True) if __name__ == '__main__': main()