| |
| """50-step gate for the orbit-alpha LoRA (run on a GPU job). |
| |
| Checks, in order: |
| 1. lora_B audit : every lora_B tensor in the saved adapter must be non-zero |
| (min |mean| > 1e-8). Guards against the all-zero-lora_B bug. |
| 2. diffusers load : QwenImage21Pipeline.load_lora_weights must accept the |
| ai-toolkit (ComfyUI-keyed) file without conversion. |
| 3. visible change : the adapter must change the output on TRAINING pairs |
| (LPIPS between base and adapter output > 0.02) and must |
| not break the eval pairs (alpha IoU stays sane). |
| |
| Outputs contact sheet + gate_results.json to the dataset repo eval/gate/<ckpt>/. |
| Prints a final 'GATE PASS' or 'GATE FAIL: <reason>' line. |
| |
| Fix note: metrics_v() unpacks its third argument as a tuple |
| (lp, dino_model, dino_proc); pass METRICS (the tuple from make_metrics()) |
| directly — building a dict here made lp bind to the string 'lp'. |
| """ |
| import argparse, json, os, sys |
| import numpy as np |
| import torch |
| from PIL import Image |
| from huggingface_hub import hf_hub_download, HfApi |
|
|
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) |
| from eval_edit import (load_pipe, make_metrics, metrics_v, composite, |
| get_file, pair_seed, SIZE, STEPS) |
|
|
| CKPT_REPO = 'ysharma/orbit-alpha-lora' |
| DS_REPO = 'ysharma/gso-orbit-rgba' |
|
|
|
|
| def changed_lpips(lpf, img_a, img_b): |
| ca, cb = composite(img_a), composite(img_b) |
| ta = torch.from_numpy(ca).permute(2, 0, 1)[None].float() * 2 - 1 |
| tb = torch.from_numpy(cb).permute(2, 0, 1)[None].float() * 2 - 1 |
| with torch.no_grad(): |
| return float(lpf(ta, tb).item()) |
|
|
|
|
| def gen_rows(pipe, METRICS, pairs, out_dir, tag): |
| rows, imgs = [], {} |
| for p in pairs: |
| pid = p['pair_id'] |
| src = Image.open(get_file(p['source_file'])).convert('RGBA') |
| gt = Image.open(get_file(p['target_file'])).convert('RGBA') |
| gen = torch.Generator('cuda').manual_seed(pair_seed(pid)) |
| t0 = __import__('time').time() |
| out = pipe(prompt=p['instruction'], image=src, width=SIZE, height=SIZE, |
| num_inference_steps=STEPS, generator=gen, |
| use_kv_cache=True).images[0] |
| dt = __import__('time').time() - t0 |
| mv = metrics_v(out, gt, METRICS) |
| imgs[pid] = out |
| rows.append({'pair_id': pid, 'tag': tag, 's_edit': round(dt, 2), **mv}) |
| print(json.dumps(rows[-1]), flush=True) |
| out.convert('RGBA').save(os.path.join(out_dir, f'{tag}_{pid}.png')) |
| return rows, imgs |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument('--ckpt', default='steps50') |
| ap.add_argument('--out', default='/out/gate') |
| args = ap.parse_args() |
| os.makedirs(args.out, exist_ok=True) |
|
|
| |
| fname = f'checkpoints/{args.ckpt}/orbit_alpha_lora/orbit_alpha_lora.safetensors' |
| ckpt = hf_hub_download(CKPT_REPO, fname, repo_type='model') |
| print(f'ckpt: {fname} ({os.path.getsize(ckpt)/1e6:.1f} MB)', flush=True) |
| from safetensors.torch import load_file |
| sd = load_file(ckpt) |
| b_keys = [k for k in sd if 'lora_B' in k] |
| a_keys = [k for k in sd if 'lora_A' in k] |
| norms = [float(sd[k].abs().float().mean()) for k in b_keys] |
| zero_b = sum(1 for n in norms if n < 1e-8) |
| audit = {'n_lora_B': len(b_keys), 'n_lora_A': len(a_keys), |
| 'n_all_zero_B': zero_b, |
| 'min_mean_abs_B': min(norms) if norms else None, |
| 'max_mean_abs_B': max(norms) if norms else None} |
| print('LORA_B AUDIT:', json.dumps(audit), flush=True) |
|
|
| |
| pipe = load_pipe() |
| METRICS = make_metrics() |
| ev = [json.loads(l) for l in open(get_file('pairs_eval.jsonl'))][:4] |
| tr = [json.loads(l) for l in open(get_file('pairs_train.jsonl'))][:2] |
|
|
| base_rows, base_imgs = gen_rows(pipe, METRICS, tr + ev, args.out, 'base') |
| pipe.load_lora_weights(ckpt) |
| print('LOAD_LORA_WEIGHTS OK', flush=True) |
| lora_rows, lora_imgs = gen_rows(pipe, METRICS, tr + ev, args.out, 'lora') |
|
|
| |
| ch = [] |
| train_ids = {p['pair_id'] for p in tr} |
| for r_b, r_l in zip(base_rows, lora_rows): |
| c = changed_lpips(METRICS[0], base_imgs[r_b['pair_id']], |
| lora_imgs[r_b['pair_id']]) |
| ch.append({'pair_id': r_b['pair_id'], 'is_train': r_b['pair_id'] in |
| train_ids, 'changed_lpips': round(c, 4)}) |
| print(json.dumps(ch[-1]), flush=True) |
| tr_ch = [c['changed_lpips'] for c in ch if c['is_train']] |
| ev_ch = [c['changed_lpips'] for c in ch if not c['is_train']] |
|
|
| |
| from PIL import ImageDraw |
| pairs_all = tr + ev |
| cell = 256 |
| sheet = Image.new('RGB', (cell * 4, cell * len(pairs_all)), (30, 30, 30)) |
| d = ImageDraw.Draw(sheet) |
| for i, p in enumerate(pairs_all): |
| pid = p['pair_id'] |
| tiles = [composite(Image.open(get_file(p['source_file'])).convert('RGBA')), |
| composite(Image.open(get_file(p['target_file'])).convert('RGBA')), |
| composite(base_imgs[pid]), composite(lora_imgs[pid])] |
| for j, t in enumerate(tiles): |
| sheet.paste(Image.fromarray(t).resize((cell, cell)), (j * cell, i * cell)) |
| sheet.save(os.path.join(args.out, 'contact_sheet.png')) |
|
|
| results = {'ckpt': args.ckpt, 'audit': audit, |
| 'base_rows': base_rows, 'lora_rows': lora_rows, 'changed': ch, |
| 'mean_changed_train': float(np.mean(tr_ch)) if tr_ch else None, |
| 'mean_changed_eval': float(np.mean(ev_ch)) if ev_ch else None} |
| with open(os.path.join(args.out, 'gate_results.json'), 'w') as f: |
| json.dump(results, f, indent=1) |
|
|
| api = HfApi() |
| for f_ in ('contact_sheet.png', 'gate_results.json'): |
| try: |
| api.upload_file(path_or_fileobj=os.path.join(args.out, f_), |
| path_in_repo=f'eval/gate/{args.ckpt}/{f_}', |
| repo_id=DS_REPO, repo_type='dataset') |
| print(f'[upload] {f_} -> eval/gate/{args.ckpt}/ OK', flush=True) |
| except Exception as e: |
| print(f'[upload] {f_} FAILED: {e}', flush=True) |
|
|
| |
| ok_audit = len(b_keys) > 0 and zero_b == 0 |
| ok_change = bool(tr_ch) and min(tr_ch) > 0.02 |
| ok_iou = np.mean([r['alpha_iou'] for r in lora_rows if r['pair_id'] in |
| train_ids]) > 0.5 |
| if ok_audit and ok_change and ok_iou: |
| print(f'GATE PASS (audit ok, changed_train mean ' |
| f'{np.mean(tr_ch):.3f}, changed_eval mean ' |
| f'{np.mean(ev_ch):.3f})', flush=True) |
| else: |
| print(f'GATE FAIL: audit_ok={ok_audit} change_ok={ok_change} ' |
| f'iou_ok={ok_iou}', flush=True) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
