5e4348ebc1
- Add extra positive/negative prompt textareas to all 9 detail pages with session persistence - Add Endless generation button to all detail pages (continuous preview generation until stopped) - Default character selector to "Random Character" on all secondary detail pages - Fix queue clear endpoint (remove spurious auth check) - Refactor app.py into routes/ and services/ modules - Update CLAUDE.md with new architecture documentation - Various data file updates and cleanup Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
343 lines
14 KiB
Python
343 lines
14 KiB
Python
import json
|
|
import logging
|
|
import random
|
|
|
|
from flask import session
|
|
from models import Settings, Checkpoint
|
|
from utils import _resolve_lora_weight
|
|
from services.prompts import _cross_dedup_prompts
|
|
|
|
logger = logging.getLogger('gaze')
|
|
|
|
|
|
def _log_workflow_prompts(label, workflow):
|
|
"""Log the final assembled ComfyUI prompts in a consistent, readable block."""
|
|
sep = "=" * 72
|
|
active_loras = []
|
|
lora_details = []
|
|
|
|
# Collect detailed LoRA information
|
|
for node_id, label_str in [("16", "char/look"), ("17", "outfit"), ("18", "action"), ("19", "style/detail/scene")]:
|
|
if node_id in workflow:
|
|
name = workflow[node_id]["inputs"].get("lora_name", "")
|
|
if name:
|
|
strength_model = workflow[node_id]["inputs"].get("strength_model", "?")
|
|
strength_clip = workflow[node_id]["inputs"].get("strength_clip", "?")
|
|
|
|
# Short version for summary
|
|
if isinstance(strength_model, float):
|
|
active_loras.append(f"{label_str}:{name.split('/')[-1]}@{strength_model:.3f}")
|
|
else:
|
|
active_loras.append(f"{label_str}:{name.split('/')[-1]}@{strength_model}")
|
|
|
|
# Detailed version
|
|
lora_details.append(f" Node {node_id} ({label_str}): {name}")
|
|
lora_details.append(f" strength_model={strength_model}, strength_clip={strength_clip}")
|
|
|
|
# Extract VAE information
|
|
vae_info = "(integrated)"
|
|
if '21' in workflow:
|
|
vae_info = workflow['21']['inputs'].get('vae_name', '(custom)')
|
|
|
|
# Extract adetailer information
|
|
adetailer_info = []
|
|
for node_id, node_name in [("11", "Face"), ("13", "Hand")]:
|
|
if node_id in workflow:
|
|
adetailer_info.append(f" {node_name} (Node {node_id}): steps={workflow[node_id]['inputs'].get('steps', '?')}, "
|
|
f"cfg={workflow[node_id]['inputs'].get('cfg', '?')}, "
|
|
f"denoise={workflow[node_id]['inputs'].get('denoise', '?')}")
|
|
|
|
face_text = workflow.get('14', {}).get('inputs', {}).get('text', '')
|
|
hand_text = workflow.get('15', {}).get('inputs', {}).get('text', '')
|
|
|
|
lines = [
|
|
sep,
|
|
f" WORKFLOW PROMPTS [{label}]",
|
|
sep,
|
|
" MODEL CONFIGURATION:",
|
|
f" Checkpoint : {workflow['4']['inputs'].get('ckpt_name', '(not set)')}",
|
|
f" VAE : {vae_info}",
|
|
"",
|
|
" GENERATION SETTINGS:",
|
|
f" Seed : {workflow['3']['inputs'].get('seed', '(not set)')}",
|
|
f" Resolution : {workflow['5']['inputs'].get('width', '?')} x {workflow['5']['inputs'].get('height', '?')}",
|
|
f" Sampler : {workflow['3']['inputs'].get('sampler_name', '?')} / {workflow['3']['inputs'].get('scheduler', '?')}",
|
|
f" Steps : {workflow['3']['inputs'].get('steps', '?')}",
|
|
f" CFG Scale : {workflow['3']['inputs'].get('cfg', '?')}",
|
|
f" Denoise : {workflow['3']['inputs'].get('denoise', '1.0')}",
|
|
]
|
|
|
|
# Add LoRA details
|
|
if active_loras:
|
|
lines.append("")
|
|
lines.append(" LORA CONFIGURATION:")
|
|
lines.extend(lora_details)
|
|
else:
|
|
lines.append("")
|
|
lines.append(" LORA CONFIGURATION: (none)")
|
|
|
|
# Add adetailer details
|
|
if adetailer_info:
|
|
lines.append("")
|
|
lines.append(" ADETAILER CONFIGURATION:")
|
|
lines.extend(adetailer_info)
|
|
|
|
# Add prompts
|
|
lines.extend([
|
|
"",
|
|
" PROMPTS:",
|
|
f" [+] Positive : {workflow['6']['inputs'].get('text', '')}",
|
|
f" [-] Negative : {workflow['7']['inputs'].get('text', '')}",
|
|
])
|
|
|
|
if face_text:
|
|
lines.append(f" [F] Face : {face_text}")
|
|
if hand_text:
|
|
lines.append(f" [H] Hand : {hand_text}")
|
|
|
|
lines.append(sep)
|
|
logger.info("\n%s", "\n".join(lines))
|
|
|
|
|
|
def _apply_checkpoint_settings(workflow, ckpt_data):
|
|
"""Apply checkpoint-specific sampler/prompt/VAE settings to the workflow."""
|
|
steps = ckpt_data.get('steps')
|
|
cfg = ckpt_data.get('cfg')
|
|
sampler_name = ckpt_data.get('sampler_name')
|
|
scheduler = ckpt_data.get('scheduler')
|
|
base_positive = ckpt_data.get('base_positive', '')
|
|
base_negative = ckpt_data.get('base_negative', '')
|
|
vae = ckpt_data.get('vae', 'integrated')
|
|
|
|
# KSampler (node 3)
|
|
if steps and '3' in workflow:
|
|
workflow['3']['inputs']['steps'] = int(steps)
|
|
if cfg and '3' in workflow:
|
|
workflow['3']['inputs']['cfg'] = float(cfg)
|
|
if sampler_name and '3' in workflow:
|
|
workflow['3']['inputs']['sampler_name'] = sampler_name
|
|
if scheduler and '3' in workflow:
|
|
workflow['3']['inputs']['scheduler'] = scheduler
|
|
|
|
# Face/hand detailers (nodes 11, 13)
|
|
for node_id in ['11', '13']:
|
|
if node_id in workflow:
|
|
if steps:
|
|
workflow[node_id]['inputs']['steps'] = int(steps)
|
|
if cfg:
|
|
workflow[node_id]['inputs']['cfg'] = float(cfg)
|
|
if sampler_name:
|
|
workflow[node_id]['inputs']['sampler_name'] = sampler_name
|
|
if scheduler:
|
|
workflow[node_id]['inputs']['scheduler'] = scheduler
|
|
|
|
# Prepend base_positive to positive prompts (main + face/hand detailers)
|
|
if base_positive:
|
|
for node_id in ['6', '14', '15']:
|
|
if node_id in workflow:
|
|
workflow[node_id]['inputs']['text'] = f"{base_positive}, {workflow[node_id]['inputs']['text']}"
|
|
|
|
# Append base_negative to negative prompt (shared by main + detailers via node 7)
|
|
if base_negative and '7' in workflow:
|
|
workflow['7']['inputs']['text'] = f"{workflow['7']['inputs']['text']}, {base_negative}"
|
|
|
|
# VAE: if not integrated, inject a VAELoader node and rewire
|
|
if vae and vae != 'integrated':
|
|
workflow['21'] = {
|
|
'inputs': {'vae_name': vae},
|
|
'class_type': 'VAELoader'
|
|
}
|
|
if '8' in workflow:
|
|
workflow['8']['inputs']['vae'] = ['21', 0]
|
|
for node_id in ['11', '13']:
|
|
if node_id in workflow:
|
|
workflow[node_id]['inputs']['vae'] = ['21', 0]
|
|
|
|
return workflow
|
|
|
|
|
|
def _get_default_checkpoint():
|
|
"""Return (checkpoint_path, checkpoint_data) from the database Settings, session, or fall back to workflow file."""
|
|
ckpt_path = session.get('default_checkpoint')
|
|
|
|
# If no session checkpoint, try to read from database Settings
|
|
if not ckpt_path:
|
|
settings = Settings.query.first()
|
|
if settings and settings.default_checkpoint:
|
|
ckpt_path = settings.default_checkpoint
|
|
logger.debug("Loaded default checkpoint from database: %s", ckpt_path)
|
|
|
|
# If still no checkpoint, try to read from the workflow file
|
|
if not ckpt_path:
|
|
try:
|
|
with open('comfy_workflow.json', 'r') as f:
|
|
workflow = json.load(f)
|
|
ckpt_path = workflow.get('4', {}).get('inputs', {}).get('ckpt_name')
|
|
logger.debug("Loaded default checkpoint from workflow file: %s", ckpt_path)
|
|
except Exception:
|
|
pass
|
|
|
|
if not ckpt_path:
|
|
return None, None
|
|
|
|
ckpt = Checkpoint.query.filter_by(checkpoint_path=ckpt_path).first()
|
|
if not ckpt:
|
|
# Checkpoint path exists but not in DB - return path with empty data
|
|
return ckpt_path, {}
|
|
return ckpt.checkpoint_path, ckpt.data or {}
|
|
|
|
|
|
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):
|
|
# 1. Update prompts using replacement to preserve embeddings
|
|
workflow["6"]["inputs"]["text"] = workflow["6"]["inputs"]["text"].replace("{{POSITIVE_PROMPT}}", prompts["main"])
|
|
|
|
if custom_negative:
|
|
workflow["7"]["inputs"]["text"] = f"{workflow['7']['inputs']['text']}, {custom_negative}"
|
|
|
|
if "14" in workflow:
|
|
workflow["14"]["inputs"]["text"] = workflow["14"]["inputs"]["text"].replace("{{FACE_PROMPT}}", prompts["face"])
|
|
if "15" in workflow:
|
|
workflow["15"]["inputs"]["text"] = workflow["15"]["inputs"]["text"].replace("{{HAND_PROMPT}}", prompts["hand"])
|
|
|
|
# 2. Update Checkpoint - always set one, fall back to default if not provided
|
|
if not checkpoint:
|
|
default_ckpt, default_ckpt_data = _get_default_checkpoint()
|
|
checkpoint = default_ckpt
|
|
if not checkpoint_data:
|
|
checkpoint_data = default_ckpt_data
|
|
if checkpoint:
|
|
workflow["4"]["inputs"]["ckpt_name"] = checkpoint
|
|
else:
|
|
raise ValueError("No checkpoint specified and no default checkpoint configured")
|
|
|
|
# 3. Handle LoRAs - Node 16 for character, Node 17 for outfit, Node 18 for action, Node 19 for style/detailer
|
|
# Start with direct checkpoint connections
|
|
model_source = ["4", 0]
|
|
clip_source = ["4", 1]
|
|
|
|
# Look negative prompt (applied before character LoRA)
|
|
if look:
|
|
look_negative = look.data.get('negative', '')
|
|
if look_negative:
|
|
workflow["7"]["inputs"]["text"] = f"{look_negative}, {workflow['7']['inputs']['text']}"
|
|
|
|
# Character LoRA (Node 16) — look LoRA overrides character LoRA when present
|
|
if look:
|
|
char_lora_data = look.data.get('lora', {})
|
|
else:
|
|
char_lora_data = character.data.get('lora', {}) if character else {}
|
|
char_lora_name = char_lora_data.get('lora_name')
|
|
|
|
if char_lora_name and "16" in workflow:
|
|
_w16 = _resolve_lora_weight(char_lora_data)
|
|
workflow["16"]["inputs"]["lora_name"] = char_lora_name
|
|
workflow["16"]["inputs"]["strength_model"] = _w16
|
|
workflow["16"]["inputs"]["strength_clip"] = _w16
|
|
workflow["16"]["inputs"]["model"] = ["4", 0] # From checkpoint
|
|
workflow["16"]["inputs"]["clip"] = ["4", 1] # From checkpoint
|
|
model_source = ["16", 0]
|
|
clip_source = ["16", 1]
|
|
logger.debug("Character LoRA: %s @ %s", char_lora_name, _w16)
|
|
|
|
# Outfit LoRA (Node 17) - chains from character LoRA or checkpoint
|
|
outfit_lora_data = outfit.data.get('lora', {}) if outfit else {}
|
|
outfit_lora_name = outfit_lora_data.get('lora_name')
|
|
|
|
if outfit_lora_name and "17" in workflow:
|
|
_w17 = _resolve_lora_weight({**{'lora_weight': 0.8}, **outfit_lora_data})
|
|
workflow["17"]["inputs"]["lora_name"] = outfit_lora_name
|
|
workflow["17"]["inputs"]["strength_model"] = _w17
|
|
workflow["17"]["inputs"]["strength_clip"] = _w17
|
|
# Chain from character LoRA (node 16) or checkpoint (node 4)
|
|
workflow["17"]["inputs"]["model"] = model_source
|
|
workflow["17"]["inputs"]["clip"] = clip_source
|
|
model_source = ["17", 0]
|
|
clip_source = ["17", 1]
|
|
logger.debug("Outfit LoRA: %s @ %s", outfit_lora_name, _w17)
|
|
|
|
# Action LoRA (Node 18) - chains from previous LoRA or checkpoint
|
|
action_lora_data = action.data.get('lora', {}) if action else {}
|
|
action_lora_name = action_lora_data.get('lora_name')
|
|
|
|
if action_lora_name and "18" in workflow:
|
|
_w18 = _resolve_lora_weight(action_lora_data)
|
|
workflow["18"]["inputs"]["lora_name"] = action_lora_name
|
|
workflow["18"]["inputs"]["strength_model"] = _w18
|
|
workflow["18"]["inputs"]["strength_clip"] = _w18
|
|
# Chain from previous source
|
|
workflow["18"]["inputs"]["model"] = model_source
|
|
workflow["18"]["inputs"]["clip"] = clip_source
|
|
model_source = ["18", 0]
|
|
clip_source = ["18", 1]
|
|
logger.debug("Action LoRA: %s @ %s", action_lora_name, _w18)
|
|
|
|
# Style/Detailer/Scene LoRA (Node 19) - chains from previous LoRA or checkpoint
|
|
# Priority: Style > Detailer > Scene (Scene LoRAs are rare but supported)
|
|
target_obj = style or detailer or scene
|
|
style_lora_data = target_obj.data.get('lora', {}) if target_obj else {}
|
|
style_lora_name = style_lora_data.get('lora_name')
|
|
|
|
if style_lora_name and "19" in workflow:
|
|
_w19 = _resolve_lora_weight(style_lora_data)
|
|
workflow["19"]["inputs"]["lora_name"] = style_lora_name
|
|
workflow["19"]["inputs"]["strength_model"] = _w19
|
|
workflow["19"]["inputs"]["strength_clip"] = _w19
|
|
# Chain from previous source
|
|
workflow["19"]["inputs"]["model"] = model_source
|
|
workflow["19"]["inputs"]["clip"] = clip_source
|
|
model_source = ["19", 0]
|
|
clip_source = ["19", 1]
|
|
logger.debug("Style/Detailer LoRA: %s @ %s", style_lora_name, _w19)
|
|
|
|
# Apply connections to all model/clip consumers
|
|
workflow["3"]["inputs"]["model"] = model_source
|
|
workflow["11"]["inputs"]["model"] = model_source
|
|
workflow["13"]["inputs"]["model"] = model_source
|
|
|
|
workflow["6"]["inputs"]["clip"] = clip_source
|
|
workflow["7"]["inputs"]["clip"] = clip_source
|
|
workflow["11"]["inputs"]["clip"] = clip_source
|
|
workflow["13"]["inputs"]["clip"] = clip_source
|
|
workflow["14"]["inputs"]["clip"] = clip_source
|
|
workflow["15"]["inputs"]["clip"] = clip_source
|
|
|
|
# 4. Randomize seeds (or use a fixed seed for reproducible batches like Strengths Gallery)
|
|
gen_seed = fixed_seed if fixed_seed is not None else random.randint(1, 10**15)
|
|
workflow["3"]["inputs"]["seed"] = gen_seed
|
|
if "11" in workflow: workflow["11"]["inputs"]["seed"] = gen_seed
|
|
if "13" in workflow: workflow["13"]["inputs"]["seed"] = gen_seed
|
|
|
|
# 5. Set image dimensions
|
|
if "5" in workflow:
|
|
if width:
|
|
workflow["5"]["inputs"]["width"] = int(width)
|
|
if height:
|
|
workflow["5"]["inputs"]["height"] = int(height)
|
|
|
|
# 6. Apply checkpoint-specific settings (steps, cfg, sampler, base prompts, VAE)
|
|
if checkpoint_data:
|
|
workflow = _apply_checkpoint_settings(workflow, checkpoint_data)
|
|
|
|
# 7. Sync sampler/scheduler from main KSampler to adetailer nodes
|
|
sampler_name = workflow["3"]["inputs"].get("sampler_name")
|
|
scheduler = workflow["3"]["inputs"].get("scheduler")
|
|
for node_id in ["11", "13"]:
|
|
if node_id in workflow:
|
|
if sampler_name:
|
|
workflow[node_id]["inputs"]["sampler_name"] = sampler_name
|
|
if scheduler:
|
|
workflow[node_id]["inputs"]["scheduler"] = scheduler
|
|
|
|
# 8. Cross-deduplicate: remove tags shared between positive and negative
|
|
pos_text, neg_text = _cross_dedup_prompts(
|
|
workflow["6"]["inputs"]["text"],
|
|
workflow["7"]["inputs"]["text"]
|
|
)
|
|
workflow["6"]["inputs"]["text"] = pos_text
|
|
workflow["7"]["inputs"]["text"] = neg_text
|
|
|
|
# 9. Final prompt debug — logged after all transformations are complete
|
|
_log_workflow_prompts("_prepare_workflow", workflow)
|
|
|
|
return workflow
|