nodes/goodguy1963-ComfyUI-ThinkingLLM
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Silence stream-mode status chatter

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This commit is contained in:
Marcel Hofer
2026-05-27 00:44:38 +02:00
parent 1189b6c4b3
commit 6faa6bade2
5 changed files with 48 additions and 39 deletions
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AILab_QwenVL.py
+8 -5
View File
@@ -411,6 +411,7 @@ def resolve_qwen_thinking_mode(
min_reasoning_tokens=QWEN_MIN_REASONING_BUDGET_TOKENS,
answer_reserve_tokens=QWEN_FINAL_ANSWER_RESERVE_TOKENS,
context_overhead_tokens=QWEN_CONTEXT_OVERHEAD_TOKENS,
quiet=False,
):
"""Enable thinking only when requested and enough generation budget remains."""
if not bool(requested_enable):
@@ -433,11 +434,12 @@ def resolve_qwen_thinking_mode(
)
else:
budget_note = f"max_tokens={requested_output}, available_output={available_output}"
print(
f"[{label}] Thinking requested but {budget_note} is below the required {minimum_required} tokens "
f"({int(min_reasoning_tokens)} reasoning + {int(answer_reserve_tokens)} answer reserve); "
"forcing non-thinking mode to preserve answer space."
)
if not quiet:
print(
f"[{label}] Thinking requested but {budget_note} is below the required {minimum_required} tokens "
f"({int(min_reasoning_tokens)} reasoning + {int(answer_reserve_tokens)} answer reserve); "
"forcing non-thinking mode to preserve answer space."
)
return False
return True
@@ -1668,6 +1670,7 @@ class QwenVLBase:
label="QwenVL HF",
prompt_tokens=prompt_tokens,
context_window=context_window,
quiet=stream_to_terminal,
)
if effective_thinking != requested_thinking:
_, processed = _build_processed(effective_thinking)
AILab_QwenVL_GGUF.py
+3 -1
View File
@@ -1130,7 +1130,8 @@ class QwenVLGGUFBase:
supports_soft_think = getattr(self, "supports_qwen_soft_think", False)
if supports_soft_think:
directive = "/think" if enable_thinking else "/no_think"
print(f"[QwenVL] Qwen3 GGUF detected: Thinking {'enabled' if enable_thinking else 'disabled'} via chat template and {directive}.")
if not stream_to_terminal:
print(f"[QwenVL] Qwen3 GGUF detected: Thinking {'enabled' if enable_thinking else 'disabled'} via chat template and {directive}.")
if self.llm is not None and hasattr(self.llm, "reset"):
try:
self.llm.reset()
@@ -1533,6 +1534,7 @@ class QwenVLGGUFBase:
label="QwenVL GGUF",
prompt_tokens=estimated_prompt_tokens,
context_window=context_window,
quiet=stream_to_terminal,
)
print(f"[QwenVL GGUF DEBUG] Loading model...")
self._load_model(
AILab_QwenVL_GGUF_PromptEnhancer.py
+29 -23
View File
@@ -489,8 +489,9 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
},
}
def clear(self):
print(f"[QwenVL PromptEnhancer DEBUG] Starting VRAM cleanup...")
def clear(self, quiet: bool = False):
if not quiet:
print(f"[QwenVL PromptEnhancer DEBUG] Starting VRAM cleanup...")
# Force cleanup of LLM model
if self.llm is not None:
@@ -503,7 +504,8 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
# Force garbage collection of the model
del self.llm
except Exception as e:
print(f"[QwenVL PromptEnhancer DEBUG] Error closing LLM: {e}")
if not quiet:
print(f"[QwenVL PromptEnhancer DEBUG] Error closing LLM: {e}")
finally:
self.llm = None
@@ -515,7 +517,8 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
# Force CUDA cache cleanup multiple times
if torch.cuda.is_available():
print(f"[QwenVL PromptEnhancer DEBUG] Clearing CUDA cache...")
if not quiet:
print(f"[QwenVL PromptEnhancer DEBUG] Clearing CUDA cache...")
torch.cuda.empty_cache()
try:
torch.cuda.ipc_collect()
@@ -525,7 +528,8 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
# Additional cleanup
torch.cuda.empty_cache()
print(f"[QwenVL PromptEnhancer DEBUG] VRAM cleanup completed")
if not quiet:
print(f"[QwenVL PromptEnhancer DEBUG] VRAM cleanup completed")
def _resolve_model_path(self, model_name):
models = self.gguf_models.get("models") or {}
@@ -615,7 +619,7 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
if not resolved.exists():
raise FileNotFoundError(f"[QwenVL] GGUF model not found after download: {resolved} (tried: {', '.join(attempted)})")
def _load_model(self, model_name, device, enable_thinking=True, unique_id=None, hf_token: str | None = None, context_length_override: int | None = None):
def _load_model(self, model_name, device, enable_thinking=True, unique_id=None, hf_token: str | None = None, context_length_override: int | None = None, quiet: bool = False):
Llama = self._load_backend()
resolved = self._resolve_model_path(model_name)
self._maybe_download_model(model_name, resolved, unique_id=unique_id, hf_token=hf_token)
@@ -627,7 +631,7 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
raw_context_int = int(raw_context_length)
except Exception:
raw_context_int = context_length
if context_length < raw_context_int:
if context_length < raw_context_int and not quiet:
print(
f"[QwenVL PromptEnhancer GGUF] Using context length {context_length} "
f"instead of catalog {raw_context_int} to reduce KV-cache VRAM."
@@ -640,8 +644,9 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
release_other_gguf_loaders(self, resolved.name)
# Force aggressive cleanup before loading new model (especially for same model conflicts)
print(f"[QwenVL PromptEnhancer DEBUG] Forcing cleanup before model loading...")
self.clear()
if not quiet:
print(f"[QwenVL PromptEnhancer DEBUG] Forcing cleanup before model loading...")
self.clear(quiet=quiet)
# Additional wait for CUDA cleanup
if torch.cuda.is_available():
@@ -650,7 +655,8 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
resolved.parent.mkdir(parents=True, exist_ok=True)
if not resolved.exists():
raise FileNotFoundError(f"[QwenVL] GGUF model not found: {resolved}")
print(f"[QwenVL] Loading GGUF model from {resolved}")
if not quiet:
print(f"[QwenVL] Loading GGUF model from {resolved}")
if device == "auto":
device_choice = "cuda" if torch.cuda.is_available() else ("mps" if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available() else "cpu")
else:
@@ -679,7 +685,8 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
thinking_state = bool(enable_thinking)
kwargs["chat_template_kwargs"] = {"enable_thinking": thinking_state}
state_label = "enabled" if thinking_state else "disabled"
print(f"[QwenVL] Qwen architecture detected (arch={arch}): Thinking {state_label} via chat template.")
if not quiet:
print(f"[QwenVL] Qwen architecture detected (arch={arch}): Thinking {state_label} via chat template.")
else:
thinking_state = bool(enable_thinking)
if not thinking_state:
@@ -714,7 +721,8 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
supports_soft_think = getattr(self, "supports_qwen_soft_think", False)
if supports_soft_think:
directive = "/think" if enable_thinking else "/no_think"
print(f"[QwenVL] Qwen3 GGUF prompt enhancer: Thinking {'enabled' if enable_thinking else 'disabled'} via chat template and {directive}.")
if not stream_to_terminal:
print(f"[QwenVL] Qwen3 GGUF prompt enhancer: Thinking {'enabled' if enable_thinking else 'disabled'} via chat template and {directive}.")
def _call(
system: str,
@@ -990,9 +998,8 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
label="QwenVL PromptEnhancer GGUF",
prompt_tokens=estimated_prompt_tokens,
context_window=effective_context_window,
quiet=stream_tokens_to_terminal,
)
if stream_tokens_to_terminal:
print("[QwenVL GGUF] Prompt enhancer terminal stream shows readable progress only; reasoning text may be hidden or stripped from the final prompt.")
self._load_model(
model_name,
device,
@@ -1000,6 +1007,7 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
unique_id=unique_id,
hf_token=hf_token,
context_length_override=effective_context_window,
quiet=stream_tokens_to_terminal,
)
hf_token = ""
enhanced, raw_trace = self._invoke_llama(
@@ -1014,11 +1022,6 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
initial_stage_label="INITIAL GENERATION",
enable_thinking=effective_thinking,
)
if stream_tokens_to_terminal:
print(
f"[QwenVL GGUF] Thinking status: "
f"{_describe_prompt_enhancer_thinking(bool(enable_thinking), effective_thinking, raw_trace, retried='[FINALIZATION ATTEMPT ' in raw_trace)}"
)
full_raw_trace = raw_trace
if english_output:
translated, trans_trace = self._invoke_llama(
@@ -1054,18 +1057,21 @@ class ThinkingLLM_QwenVL_GGUF_PromptEnhancer:
}
save_prompt_cache()
print(f"[QwenVL PromptEnhancer GGUF] Cached new prompt for seed {seed}: {cache_key[:8]}...")
if not stream_tokens_to_terminal:
print(f"[QwenVL PromptEnhancer GGUF] Cached new prompt for seed {seed}: {cache_key[:8]}...")
try:
# Persist per-node for future keep_last_prompt=True
set_node_saved_prompt(node_class, unique_id, extra_pnginfo, final, raw_trace=full_raw_trace, seed=seed, max_tokens=max_tokens, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty, input_signature=input_signature)
print(f"[QwenVL PromptEnhancer GGUF] Saved per-node prompt: {final[:50]}...")
if not stream_tokens_to_terminal:
print(f"[QwenVL PromptEnhancer GGUF] Saved per-node prompt: {final[:50]}...")
return (final, full_raw_trace)
finally:
if not keep_model_loaded:
self.clear()
print(f"[QwenVL PromptEnhancer GGUF] keep_model_loaded=False — cleaning up model...")
self.clear(quiet=stream_tokens_to_terminal)
if not stream_tokens_to_terminal:
print(f"[QwenVL PromptEnhancer GGUF] keep_model_loaded=False — cleaning up model...")
@staticmethod
def _is_english(text):
AILab_QwenVL_PromptEnhancer.py
+7 -10
View File
@@ -301,7 +301,8 @@ class ThinkingLLM_QwenVL_PromptEnhancer(QwenVLBase):
# Persist per-node for future keep_last_prompt=True
set_node_saved_prompt(node_class, unique_id, extra_pnginfo, final, raw_trace=raw_trace, seed=seed, max_tokens=max_tokens, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty, input_signature=input_signature)
print(f"[QwenVL PromptEnhancer HF] Saved per-node prompt: {final[:50]}...")
if not stream_tokens_to_terminal:
print(f"[QwenVL PromptEnhancer HF] Saved per-node prompt: {final[:50]}...")
return (final, raw_trace)
@@ -522,19 +523,19 @@ class ThinkingLLM_QwenVL_PromptEnhancer(QwenVLBase):
label="QwenVL PromptEnhancer HF",
prompt_tokens=prompt_tokens,
context_window=context_window,
quiet=stream_to_terminal,
)
if effective_thinking != requested_thinking:
_, inputs = _build_inputs(effective_thinking)
if stream_to_terminal:
print("[QwenVL HF] Prompt enhancer terminal stream shows readable progress only; reasoning text may be hidden or stripped from the final prompt.")
if is_qwen35 or supports_soft_think:
if supports_soft_think:
directive = "/think" if effective_thinking else "/no_think"
print(f"[QwenVL] Qwen3 HF prompt enhancer: Thinking {'enabled' if effective_thinking else 'disabled'} via chat template and {directive}.")
if not stream_to_terminal:
print(f"[QwenVL] Qwen3 HF prompt enhancer: Thinking {'enabled' if effective_thinking else 'disabled'} via chat template and {directive}.")
else:
print(f"[QwenVL] Non-Qwen model in HF text path: thinking toggle is advisory (model may ignore).")
if not stream_to_terminal:
print(f"[QwenVL] Non-Qwen model in HF text path: thinking toggle is advisory (model may ignore).")
gen_kwargs = {
"max_new_tokens": max_tokens,
"repetition_penalty": repetition_penalty,
@@ -577,7 +578,6 @@ class ThinkingLLM_QwenVL_PromptEnhancer(QwenVLBase):
raise RuntimeError("[QwenVL] HF streaming returned empty response")
raw_text = full_streamed.strip()
result = clean_model_output(raw_text, OutputCleanConfig(mode="prompt")) or raw_text
print(f"[QwenVL HF] Thinking status: {_describe_prompt_enhancer_thinking(requested_thinking, effective_thinking, raw_text)}")
if not keep_model_loaded:
self.text_model = None
self.text_processor = None
@@ -600,9 +600,6 @@ class ThinkingLLM_QwenVL_PromptEnhancer(QwenVLBase):
raw_text = self.text_tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()
result = clean_model_output(raw_text, OutputCleanConfig(mode="prompt")) or raw_text
if stream_to_terminal:
print(f"[QwenVL HF] Thinking status: {_describe_prompt_enhancer_thinking(requested_thinking, effective_thinking, raw_text)}")
if not keep_model_loaded:
self.text_model = None
self.text_processor = None
tests/test_new_features.py
+1
View File
@@ -411,6 +411,7 @@ class TestBufferedStreamingIntegration(unittest.TestCase):
self.assertNotIn("last_status_at = _maybe_emit_hf_stream_heartbeat", source)
self.assertNotIn("last_status_at = _maybe_emit_hf_prompt_stream_heartbeat", source)
self.assertNotIn("last_status_at = _maybe_emit_prompt_stream_heartbeat", source)
self.assertNotIn("terminal stream shows readable progress", source)
class TestPromptEnhancerMetadata(unittest.TestCase):