Ethanfel/ComfyUI-Prompt-Calibrator
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chat mode: json_output toggle to return clean extracted JSON

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For JSON-producing system prompts (e.g. LTX prompt-relay), json_output=true pulls
the JSON object out of the reply (strips reasoning/prose/code-fences via _parse_json,
which handles nested schemas and reasoning-then-JSON) and returns it re-serialized;
falls back to raw text if none parses. agent_bridge gains --json-output.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Ethanfel
2026-07-02 02:09:36 +02:00
parent f7ea559690
commit 8b567cb531
3 changed files with 28 additions and 6 deletions
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README.md
+3
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@@ -58,6 +58,9 @@ node — feed an image (and optionally a second), write your own `system_prompt`
and read the model's text from the `analysis` output. Reuses the same model dropdown, quant,
and auto-download as the judge, so it's a one-node abliterated VLM for captioning, tagging,
Q&A, prompt-from-image, etc. (CLI: `agent_bridge.py --mode chat --user-prompt "..."`).
Set **`json_output=true`** for JSON-producing system prompts — it extracts the JSON object
from the reply (stripping any reasoning, prose, or ```fences) and returns it clean and
re-serialized (falls back to raw text if none parses). Works even with `enable_thinking` on.
## Performance / speed
agent_bridge.py
+8 -2
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@@ -49,7 +49,8 @@ def _http_json(url: str, payload: dict | None = None, timeout: int = 30):
def _inject(graph: dict, prompt: str, negative: str, seed: int, run_tag: str, mode: str,
reference_description: str = "", profile: str = "", model_select: str = "",
model_path: str = "", system_prompt: str = "", user_prompt: str = ""):
model_path: str = "", system_prompt: str = "", user_prompt: str = "",
json_output: bool = False):
"""Set the receptor's prompt/seed and the judge's mode/run_tag in-place.
compare mode needs a receptor (to inject the prompt). describe mode is the first
@@ -79,6 +80,8 @@ def _inject(graph: dict, prompt: str, negative: str, seed: int, run_tag: str, mo
inputs["system_prompt"] = system_prompt
if user_prompt:
inputs["user_prompt"] = user_prompt
if json_output:
inputs["json_output"] = True
if mode == "compare" and not found_receptor:
raise SystemExit(
f"[agent_bridge] no '{RECEPTOR_CLASS}' node in the workflow — add the "
@@ -123,6 +126,8 @@ def main(argv=None):
help="describe = first pass over the reference; chat = general VLM with your prompts")
ap.add_argument("--system-prompt", default="", help="chat mode: system prompt")
ap.add_argument("--user-prompt", default="", help="chat mode: user prompt over the image(s)")
ap.add_argument("--json-output", action="store_true",
help="chat mode: extract & return clean JSON from the reply")
ap.add_argument("--prompt", default="", help="generation prompt (required for compare)")
ap.add_argument("--negative", default="")
ap.add_argument("--seed", type=int, default=0)
@@ -155,7 +160,8 @@ def main(argv=None):
graph = json.load(f)
_inject(graph, args.prompt, args.negative, args.seed, args.run_tag, args.mode, ref_desc,
args.profile, args.model_select, args.model_path, args.system_prompt, args.user_prompt)
args.profile, args.model_select, args.model_path, args.system_prompt, args.user_prompt,
args.json_output)
client_id = uuid.uuid4().hex
try:
nodes/qwen_judge.py
+17 -4
View File
@@ -825,6 +825,9 @@ class QwenVLImageJudge:
# Reasoning models (Qwen3.5/3.6) judge verdicts FAR better with thinking on
# (off -> they rubber-stamp 'match'). Costs more tokens; raise max_new_tokens.
"enable_thinking": ("BOOLEAN", {"default": True}),
# chat mode: extract the JSON object from the reply and return it clean
# (strips reasoning/prose/code-fences). Falls back to raw text if no JSON.
"json_output": ("BOOLEAN", {"default": False}),
"keep_loaded": ("BOOLEAN", {"default": True}),
"auto_download": ("BOOLEAN", {"default": True}),
# Small config values stay as typeable fields.
@@ -847,8 +850,8 @@ class QwenVLImageJudge:
def judge(self, reference_image, mode, model_path, precision,
max_new_tokens, temperature, swap_eval, profile="general",
enable_thinking=True, model_select=MANUAL_CHOICE, generated_image=None,
keep_loaded=True, auto_download=True,
enable_thinking=True, json_output=False, model_select=MANUAL_CHOICE,
generated_image=None, keep_loaded=True, auto_download=True,
report_dir="", run_tag="", axes="", reference_description="",
system_prompt="", user_prompt="Describe this image."):
# `axes` overrides the profile when provided; otherwise use the profile's axis set.
@@ -889,7 +892,7 @@ class QwenVLImageJudge:
gen_pil = _tensor_to_pil(generated_image) if generated_image is not None else None
return self._chat(model, processor, ref_pil, gen_pil, system_prompt, user_prompt,
max_new_tokens, temperature, resolved_path, eff_precision,
keep_loaded, report_dir, run_tag, enable_thinking)
keep_loaded, report_dir, run_tag, enable_thinking, json_output)
if mode == "describe":
return self._describe(model, processor, ref_pil, axis_list, max_new_tokens,
@@ -950,7 +953,7 @@ class QwenVLImageJudge:
def _chat(self, model, processor, ref_pil, gen_pil, system_prompt, user_prompt,
max_new_tokens, temperature, resolved_path, precision, keep_loaded,
report_dir, run_tag, think=True):
report_dir, run_tag, think=True, json_output=False):
"""General-VLM mode: not a judge — just runs your prompt over the image(s)."""
images = [ref_pil] + ([gen_pil] if gen_pil is not None else [])
text = _run_chat(model, processor, images, system_prompt, user_prompt,
@@ -959,6 +962,16 @@ class QwenVLImageJudge:
_MODEL_CACHE.pop((resolved_path, precision), None)
del model
torch.cuda.empty_cache()
# json_output: pull the JSON object out (strips reasoning/prose/```fences) and
# return it clean & re-serialized. Falls back to the raw text if none parses.
if json_output:
obj = _parse_json(text)
if obj is not None:
text = json.dumps(obj, ensure_ascii=False, indent=2)
else:
print("[QwenVLImageJudge] json_output: no JSON found, returning raw text.")
report_path = _write_chat_report(report_dir, run_tag, system_prompt, user_prompt, text)
return (1.0, "{}", text, text, report_path)