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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nodes/qwen_judge.py
+17 -4
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@@ -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)