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Handle reasoning models (Qwen3.5/3.6): no-think + JSON-only + prose fallback

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Qwen3.5/3.6 are reasoning models — they 'think out loud' in markdown and never
reach the JSON, then get cut off at the token limit -> '(no parseable judgement)'.
Fixes: apply_chat_template(enable_thinking=False) + strip <think> blocks; hardened
'output ONLY JSON, do not think out loud' instruction; default max_new_tokens
1024->2048 (max 8192); and a markdown fallback parser (_parse_markdown_verdicts /
_parse_axes) that extracts per-axis {verdict,ref,gen} from the prose the model
reliably emits. describe falls back to using the raw text as the caption.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Ethanfel
2026-06-27 10:25:16 +02:00
parent f5be04a5cb
commit 0e9e99b8b2
2 changed files with 64 additions and 13 deletions
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README.md
+1 -1
View File
@@ -38,7 +38,7 @@ can act on it.
| `precision` | bf16 / fp8 / nf4 | bf16 | **the quant** — applies to the selected model (VRAM table below) |
| `model_path` | STRING | "" (empty) | **manual override** of the dropdown — local dir, HF repo id, or alias (`8b`/`30b-a3b`/`3.5-9b`/`3.6-27b`/`3.6-35b`). Empty = use `model_select` |
| `axes` | STRING | "" (empty) | **override** the profile's axis set with a custom comma/newline list; empty = use `profile` |
| `max_new_tokens` | INT | 1024 | |
| `max_new_tokens` | INT | 2048 | raise it if a reasoning model (Qwen3.5/3.6) gets cut off before finishing |
| `temperature` | FLOAT | 0.0 | 0 = greedy/repeatable |
| `swap_eval` | BOOL | true | run twice with images swapped, average → cuts position bias |
| `keep_loaded` | BOOL | true | cache weights across loop iterations |
nodes/qwen_judge.py
+63 -12
View File
@@ -348,7 +348,9 @@ def _build_system_prompt(axes: list[str], reference_description: str = "") -> st
"to 'partial' — if the values are identical use 'match', if clearly different "
"use 'mismatch'.\n")
tail = (
"Reply with STRICT JSON only, no prose, no markdown fences, exactly:\n"
"Output ONLY the JSON object — no reasoning, no step-by-step analysis, no "
"markdown, no commentary. Do NOT think out loud. Your entire reply must start "
"with '{' and end with '}', exactly:\n"
"{\n"
' "axes": {\n'
f"{axis_lines}\n"
@@ -415,13 +417,25 @@ def _format_chatml_qwenvl(messages):
return "".join(parts)
def _apply_template(processor, messages):
"""apply_chat_template with thinking disabled (Qwen3.5/3.6 are reasoning models that
otherwise 'think out loud' in prose and never reach the JSON). Falls back gracefully."""
try:
return processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
except TypeError:
pass # template doesn't accept enable_thinking
except (ValueError, AttributeError):
return _format_chatml_qwenvl(messages)
try:
return processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
except (ValueError, AttributeError):
return _format_chatml_qwenvl(messages)
def _generate_from_messages(model, processor, messages, images, max_new_tokens, temperature):
"""Template + forward pass for a chat-message list; returns the decoded string."""
try:
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
except (ValueError, AttributeError):
# Processor/tokenizer carries no chat template -> build ChatML by hand.
text = _format_chatml_qwenvl(messages)
text = _apply_template(processor, messages)
inputs = processor(text=[text], images=images, return_tensors="pt")
inputs = inputs.to(model.device)
@@ -435,6 +449,8 @@ def _generate_from_messages(model, processor, messages, images, max_new_tokens,
out = model.generate(**inputs, **gen_kwargs)
trimmed = out[:, inputs.input_ids.shape[1]:]
decoded = processor.batch_decode(trimmed, skip_special_tokens=True)[0]
# Strip any <think>...</think> block a reasoning model may still emit.
decoded = re.sub(r"<think>.*?</think>", "", decoded, flags=re.DOTALL)
return decoded.strip()
@@ -486,7 +502,8 @@ def _build_describe_prompt(axes: list[str]) -> str:
"phrasing (the words a text-to-image prompt would use).\n\n"
"Axes and exactly what each one means:\n"
f"{_axis_definition_block(axes)}\n\n"
"Reply with STRICT JSON only, no prose, no markdown fences, exactly:\n"
"Output ONLY the JSON object — no reasoning, no analysis, no markdown, no "
"commentary. Do NOT think out loud. Start with '{' and end with '}', exactly:\n"
"{\n"
' "description": "<one detailed, self-consistent paragraph describing the whole scene as a generation prompt>",\n'
' "axes": {\n'
@@ -552,6 +569,39 @@ def _parse_json(raw: str) -> dict | None:
return None
def _parse_markdown_verdicts(raw: str, axes: list[str]) -> dict:
"""Fallback for reasoning models that emit prose instead of JSON. Reasoning models
reliably write a block per axis like:
**hair:**
- Ref: short, curly, brown
- Gen: long, straight, blonde
- Verdict: mismatch
Extract {verdict, ref, gen} per axis from that. Returns {} if nothing parseable."""
out = {}
for ax in axes:
m = re.search(rf"(?im)^[\s\d.>*\-]*\**\s*{re.escape(ax)}\s*\**\s*:?\s*$"
rf"|\**\s*{re.escape(ax)}\s*\**\s*:", raw)
if not m:
continue
seg = raw[m.end(): m.end() + 500]
vd = re.search(r"(?i)verdict[\s*:>-]*\**\s*(match|partial|mismatch)", seg)
if not vd:
continue
ref = re.search(r"(?im)^\W*ref[a-z]*\W*[:\-]\s*\**\s*(.+?)\s*$", seg)
gen = re.search(r"(?im)^\W*gen[a-z]*\W*[:\-]\s*\**\s*(.+?)\s*$", seg)
clean = lambda s: s.group(1).strip().strip("*").strip(" .") if s else ""
out[ax] = {"verdict": vd.group(1).lower(), "ref": clean(ref), "gen": clean(gen)}
return {"axes": out} if out else {}
def _parse_axes(raw: str, axes: list[str]) -> dict:
"""JSON first; if the model emitted prose instead, fall back to markdown verdicts."""
j = _parse_json(raw)
if j and isinstance(j.get("axes"), dict) and j["axes"]:
return j
return _parse_markdown_verdicts(raw, axes)
_VERDICT_ORDINAL = {"match": 1.0, "partial": 0.5, "mismatch": 0.0}
@@ -746,7 +796,7 @@ class QwenVLImageJudge:
"model_path": ("STRING", {"default": ""}), # manual override (local dir / HF repo / alias)
"precision": (["bf16", "fp8", "nf4"], {"default": "bf16"}),
"axes": ("STRING", {"default": "", "multiline": True}),
"max_new_tokens": ("INT", {"default": 1024, "min": 64, "max": 4096}),
"max_new_tokens": ("INT", {"default": 2048, "min": 64, "max": 8192}),
"temperature": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.5, "step": 0.05}),
"swap_eval": ("BOOLEAN", {"default": True}),
"keep_loaded": ("BOOLEAN", {"default": True}),
@@ -830,16 +880,16 @@ class QwenVLImageJudge:
# (single image), and the reference side stays identical across iterations.
raw_all = _run_anchored(model, processor, gen_pil, axis_list, max_new_tokens,
temperature, reference_description)
merged = _parse_json(raw_all) or {}
merged = _parse_axes(raw_all, axis_list)
else:
raw1 = _run_once(model, processor, ref_pil, gen_pil, axis_list, max_new_tokens, temperature)
parsed1 = _parse_json(raw1) or {}
parsed1 = _parse_axes(raw1, axis_list)
raw_all = raw1
merged = parsed1
if swap_eval:
# Swap which image is called REFERENCE to average out position bias.
raw2 = _run_once(model, processor, gen_pil, ref_pil, axis_list, max_new_tokens, temperature)
parsed2 = _parse_json(raw2) or {}
parsed2 = _parse_axes(raw2, axis_list)
merged = _merge_swapped(parsed1, parsed2)
raw_all = raw1 + "\n--- SWAPPED ---\n" + raw2
@@ -895,7 +945,8 @@ class QwenVLImageJudge:
del model
torch.cuda.empty_cache()
caption = (parsed.get("description") or parsed.get("caption") or "").strip()
# Fall back to the raw text as the caption if the model emitted prose, not JSON.
caption = (parsed.get("description") or parsed.get("caption") or raw).strip()
axes_spec = parsed.get("axes", {}) if isinstance(parsed.get("axes"), dict) else {}
axis_scores = json.dumps(axes_spec, ensure_ascii=False, indent=2)
# The canonical reference text the compare pass will anchor on: paragraph + axes.