Add structured logging for job queue and workflow prompts
Replace bare print() calls with Python logging module. Job lifecycle (enqueue, start, ComfyUI acceptance, completion, failure) now emits timestamped INFO logs to stdout, captured by Docker. Failures use logger.exception() for full tracebacks. Workflow prompt block logs as a single INFO entry; LoRA chain details moved to DEBUG level. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Aodhan Collins
59
app.py
59
app.py
@@ -1,5 +1,6 @@
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import os
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import json
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import logging
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import time
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import re
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import requests
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@@ -42,6 +43,16 @@ app.config['SESSION_PERMANENT'] = False
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db.init_app(app)
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Session(app)
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# ---------------------------------------------------------------------------
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# Logging
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# ---------------------------------------------------------------------------
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S',
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)
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logger = logging.getLogger('gaze')
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# ---------------------------------------------------------------------------
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# Generation Job Queue
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# ---------------------------------------------------------------------------
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@@ -78,6 +89,7 @@ def _enqueue_job(label, workflow, finalize_fn):
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with _job_queue_lock:
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_job_queue.append(job)
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_job_history[job['id']] = job
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logger.info("Job queued: [%s] %s", job['id'][:8], label)
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_queue_worker_event.set()
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return job
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@@ -104,6 +116,8 @@ def _queue_worker():
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with _job_queue_lock:
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job['status'] = 'processing'
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logger.info("Job started: [%s] %s", job['id'][:8], job['label'])
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try:
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with app.app_context():
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# Send workflow to ComfyUI
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@@ -114,6 +128,7 @@ def _queue_worker():
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comfy_id = prompt_response['prompt_id']
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with _job_queue_lock:
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job['comfy_prompt_id'] = comfy_id
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logger.info("Job [%s] queued in ComfyUI as %s", job['id'][:8], comfy_id)
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# Poll until done (max ~10 minutes)
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max_retries = 300
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@@ -129,15 +144,17 @@ def _queue_worker():
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if not finished:
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raise Exception("ComfyUI generation timed out")
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logger.info("Job [%s] generation complete, finalizing...", job['id'][:8])
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# Run the finalize callback (saves image to disk / DB)
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# finalize_fn(comfy_prompt_id, job) — job is passed so callback can store result
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job['finalize_fn'](comfy_id, job)
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with _job_queue_lock:
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job['status'] = 'done'
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logger.info("Job done: [%s] %s", job['id'][:8], job['label'])
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except Exception as e:
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print(f"[Queue] Job {job['id']} failed: {e}")
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logger.exception("Job failed: [%s] %s — %s", job['id'][:8], job['label'], e)
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with _job_queue_lock:
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job['status'] = 'failed'
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job['error'] = str(e)
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@@ -2078,16 +2095,8 @@ def replace_cover_from_preview(slug):
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return redirect(url_for('detail', slug=slug))
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def _log_workflow_prompts(label, workflow):
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"""Print the final assembled ComfyUI prompts in a consistent, readable block."""
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"""Log the final assembled ComfyUI prompts in a consistent, readable block."""
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sep = "=" * 72
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print(f"\n{sep}")
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print(f" WORKFLOW PROMPTS [{label}]")
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print(sep)
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print(f" Checkpoint : {workflow['4']['inputs'].get('ckpt_name', '(not set)')}")
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print(f" Seed : {workflow['3']['inputs'].get('seed', '(not set)')}")
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print(f" Resolution : {workflow['5']['inputs'].get('width', '?')} x {workflow['5']['inputs'].get('height', '?')}")
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print(f" Sampler : {workflow['3']['inputs'].get('sampler_name', '?')} / {workflow['3']['inputs'].get('scheduler', '?')} steps={workflow['3']['inputs'].get('steps', '?')} cfg={workflow['3']['inputs'].get('cfg', '?')}")
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# LoRA chain summary
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active_loras = []
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for node_id, label_str in [("16", "char/look"), ("17", "outfit"), ("18", "action"), ("19", "style/detail/scene")]:
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if node_id in workflow:
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@@ -2095,16 +2104,26 @@ def _log_workflow_prompts(label, workflow):
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if name:
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w = workflow[node_id]["inputs"].get("strength_model", "?")
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active_loras.append(f"{label_str}:{name.split('/')[-1]}@{w:.3f}" if isinstance(w, float) else f"{label_str}:{name.split('/')[-1]}@{w}")
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print(f" LoRAs : {' | '.join(active_loras) if active_loras else '(none)'}")
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print(f" [+] Positive : {workflow['6']['inputs'].get('text', '')}")
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print(f" [-] Negative : {workflow['7']['inputs'].get('text', '')}")
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face_text = workflow.get('14', {}).get('inputs', {}).get('text', '')
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hand_text = workflow.get('15', {}).get('inputs', {}).get('text', '')
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lines = [
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sep,
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f" WORKFLOW PROMPTS [{label}]",
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sep,
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f" Checkpoint : {workflow['4']['inputs'].get('ckpt_name', '(not set)')}",
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f" Seed : {workflow['3']['inputs'].get('seed', '(not set)')}",
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f" Resolution : {workflow['5']['inputs'].get('width', '?')} x {workflow['5']['inputs'].get('height', '?')}",
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f" Sampler : {workflow['3']['inputs'].get('sampler_name', '?')} / {workflow['3']['inputs'].get('scheduler', '?')} steps={workflow['3']['inputs'].get('steps', '?')} cfg={workflow['3']['inputs'].get('cfg', '?')}",
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f" LoRAs : {' | '.join(active_loras) if active_loras else '(none)'}",
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f" [+] Positive : {workflow['6']['inputs'].get('text', '')}",
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f" [-] Negative : {workflow['7']['inputs'].get('text', '')}",
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]
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if face_text:
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print(f" [F] Face : {face_text}")
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lines.append(f" [F] Face : {face_text}")
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if hand_text:
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print(f" [H] Hand : {hand_text}")
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print(f"{sep}\n")
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lines.append(f" [H] Hand : {hand_text}")
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lines.append(sep)
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logger.info("\n%s", "\n".join(lines))
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def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_negative=None, outfit=None, action=None, style=None, detailer=None, scene=None, width=None, height=None, checkpoint_data=None, look=None, fixed_seed=None):
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@@ -2150,7 +2169,7 @@ def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_nega
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workflow["16"]["inputs"]["clip"] = ["4", 1] # From checkpoint
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model_source = ["16", 0]
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clip_source = ["16", 1]
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print(f"Character LoRA: {char_lora_name} @ {_w16}")
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logger.debug("Character LoRA: %s @ %s", char_lora_name, _w16)
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# Outfit LoRA (Node 17) - chains from character LoRA or checkpoint
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outfit_lora_data = outfit.data.get('lora', {}) if outfit else {}
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@@ -2166,7 +2185,7 @@ def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_nega
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workflow["17"]["inputs"]["clip"] = clip_source
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model_source = ["17", 0]
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clip_source = ["17", 1]
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print(f"Outfit LoRA: {outfit_lora_name} @ {_w17}")
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logger.debug("Outfit LoRA: %s @ %s", outfit_lora_name, _w17)
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# Action LoRA (Node 18) - chains from previous LoRA or checkpoint
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action_lora_data = action.data.get('lora', {}) if action else {}
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@@ -2182,7 +2201,7 @@ def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_nega
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workflow["18"]["inputs"]["clip"] = clip_source
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model_source = ["18", 0]
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clip_source = ["18", 1]
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print(f"Action LoRA: {action_lora_name} @ {_w18}")
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logger.debug("Action LoRA: %s @ %s", action_lora_name, _w18)
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# Style/Detailer/Scene LoRA (Node 19) - chains from previous LoRA or checkpoint
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# Priority: Style > Detailer > Scene (Scene LoRAs are rare but supported)
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@@ -2200,7 +2219,7 @@ def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_nega
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workflow["19"]["inputs"]["clip"] = clip_source
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model_source = ["19", 0]
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clip_source = ["19", 1]
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print(f"Style/Detailer LoRA: {style_lora_name} @ {_w19}")
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logger.debug("Style/Detailer LoRA: %s @ %s", style_lora_name, _w19)
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# Apply connections to all model/clip consumers
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workflow["3"]["inputs"]["model"] = model_source
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