| |
| """Re-queue a Gram capture set (the API prompts embedded in output/moss/gram_capture/<src>_*.png) under a new tag. |
| |
| python3 workflows/moss/gram_runner.py <src> <tag> [--image-centered] [--sigma-max 0.75] [--front] [--lora file:strength] |
| [--solve mu,rho,layers,strength,flat_tag,num_tag,gen_tag] [--url ...] |
| The last job saves the Gram to output/moss/grams/<tag>.safetensors. --solve/--lora capture on SFT + that repair + that LoRA. |
| """ |
| import argparse, glob, json, os, urllib.request |
| from PIL import Image |
|
|
| HERE = os.path.dirname(os.path.abspath(__file__)) |
| CAP = os.path.join(HERE, "..", "..", "output", "moss", "gram_capture") |
|
|
| ap = argparse.ArgumentParser() |
| ap.add_argument("src") |
| ap.add_argument("tag") |
| ap.add_argument("--image-centered", action="store_true") |
| ap.add_argument("--sigma-max", type=float, default=1.0) |
| ap.add_argument("--front", action="store_true") |
| ap.add_argument("--lora") |
| ap.add_argument("--solve") |
| ap.add_argument("--url", default="http://localhost:8188") |
| a = ap.parse_args() |
|
|
| files = sorted(glob.glob(os.path.join(CAP, f"{a.src}_[0-9][0-9][0-9]_*_00001_.png"))) |
| for n, f in enumerate(files): |
| p = json.loads(Image.open(f).info["prompt"]) |
| p["90"]["inputs"].update(tag=a.tag, reset=n == 0, image_centered=a.image_centered, sigma_max=a.sigma_max) |
| p["91"]["inputs"].update(tag=a.tag, save=n == len(files) - 1) |
| if a.solve: |
| v = a.solve.split(",") |
| p["190"] = {"class_type": "MOSSGramSolve", "inputs": { |
| "model": ["3", 0], "sft_file": "moss_image_sft_bf16.safetensors", "base_file": "moss_image_base_bf16.safetensors", |
| "flat_tag": v[4] or "flat", "gen_tag": v[6] or "gen", "layers": v[2], "parts": "qkv,o,ffn_up_gate,ffn_down", |
| "mu": float(v[0]), "rho": float(v[1]), "strength": float(v[3]), "device": "cuda:0", |
| "num_tag": v[5].lstrip("!"), "num_complement": v[5].startswith("!")}} |
| p["90"]["inputs"]["model"] = ["190", 0] |
| if a.lora: |
| f_, s_ = a.lora.rsplit(":", 1) |
| p["14"]["inputs"].update(lora_name=f_, strength_model=float(s_)) |
| p["26"]["inputs"]["filename_prefix"] = p["26"]["inputs"]["filename_prefix"].replace(f"/{a.src}_", f"/{a.tag}_") |
| r = json.load(urllib.request.urlopen(urllib.request.Request(a.url + "/prompt", json.dumps({"prompt": p, "front": a.front}).encode(), {"Content-Type": "application/json"}))) |
| assert not r.get("node_errors"), r |
| print(f"{a.tag}: queued {len(files)} from {a.src}") |
|
|
