Code review fixes: wardrobe migration, response validation, path traversal guard, deduplication
- Migrate 11 character JSONs from old wardrobe keys to _BODY_GROUP_KEYS format - Add is_favourite/is_nsfw columns to Preset model - Add HTTP response validation and timeouts to ComfyUI client - Add path traversal protection on replace cover route - Deduplicate services/mcp.py (4 functions → 2 generic + 2 wrappers) - Extract apply_library_filters() and clean_html_text() shared helpers - Add named constants for 17 ComfyUI workflow node IDs - Fix bare except clauses in services/llm.py - Fix tags schema in ensure_default_outfit() (list → dict) - Convert f-string logging to lazy % formatting - Add 5-minute polling timeout to frontend waitForJob() - Improve migration error handling (non-duplicate errors log at WARNING) - Update CLAUDE.md to reflect all changes Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Aodhan Collins
@@ -9,6 +9,27 @@ from services.prompts import _cross_dedup_prompts
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logger = logging.getLogger('gaze')
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# ---------------------------------------------------------------------------
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# ComfyUI workflow node IDs (must match comfy_workflow.json)
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# ---------------------------------------------------------------------------
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NODE_KSAMPLER = "3"
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NODE_CHECKPOINT = "4"
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NODE_LATENT = "5"
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NODE_POSITIVE = "6"
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NODE_NEGATIVE = "7"
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NODE_VAE_DECODE = "8"
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NODE_SAVE = "9"
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NODE_FACE_DETAILER = "11"
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NODE_HAND_DETAILER = "13"
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NODE_FACE_PROMPT = "14"
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NODE_HAND_PROMPT = "15"
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NODE_LORA_CHAR = "16"
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NODE_LORA_OUTFIT = "17"
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NODE_LORA_ACTION = "18"
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NODE_LORA_STYLE = "19"
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NODE_LORA_CHAR_B = "20"
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NODE_VAE_LOADER = "21"
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# Node IDs used by DetailerForEach in multi-char mode
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_SEGS_DETAILER_NODES = ['46', '47', '53', '54']
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# Node IDs for per-character CLIP prompts in multi-char mode
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@@ -22,7 +43,7 @@ def _log_workflow_prompts(label, workflow):
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lora_details = []
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# Collect detailed LoRA information
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for node_id, label_str in [("16", "char/look"), ("17", "outfit"), ("18", "action"), ("19", "style/detail/scene"), ("20", "char_b")]:
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for node_id, label_str in [(NODE_LORA_CHAR, "char/look"), (NODE_LORA_OUTFIT, "outfit"), (NODE_LORA_ACTION, "action"), (NODE_LORA_STYLE, "style/detail/scene"), (NODE_LORA_CHAR_B, "char_b")]:
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if node_id in workflow:
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name = workflow[node_id]["inputs"].get("lora_name", "")
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if name:
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@@ -41,13 +62,13 @@ def _log_workflow_prompts(label, workflow):
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# Extract VAE information
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vae_info = "(integrated)"
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if '21' in workflow:
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vae_info = workflow['21']['inputs'].get('vae_name', '(custom)')
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if NODE_VAE_LOADER in workflow:
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vae_info = workflow[NODE_VAE_LOADER]['inputs'].get('vae_name', '(custom)')
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# Extract adetailer information
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adetailer_info = []
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# Single-char mode: FaceDetailer nodes 11 + 13
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for node_id, node_name in [("11", "Face"), ("13", "Hand")]:
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for node_id, node_name in [(NODE_FACE_DETAILER, "Face"), (NODE_HAND_DETAILER, "Hand")]:
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if node_id in workflow:
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adetailer_info.append(f" {node_name} (Node {node_id}): steps={workflow[node_id]['inputs'].get('steps', '?')}, "
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f"cfg={workflow[node_id]['inputs'].get('cfg', '?')}, "
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@@ -59,24 +80,24 @@ def _log_workflow_prompts(label, workflow):
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f"cfg={workflow[node_id]['inputs'].get('cfg', '?')}, "
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f"denoise={workflow[node_id]['inputs'].get('denoise', '?')}")
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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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face_text = workflow.get(NODE_FACE_PROMPT, {}).get('inputs', {}).get('text', '')
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hand_text = workflow.get(NODE_HAND_PROMPT, {}).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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" MODEL CONFIGURATION:",
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f" Checkpoint : {workflow['4']['inputs'].get('ckpt_name', '(not set)')}",
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f" Checkpoint : {workflow[NODE_CHECKPOINT]['inputs'].get('ckpt_name', '(not set)')}",
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f" VAE : {vae_info}",
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"",
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" GENERATION SETTINGS:",
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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', '?')}",
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f" Steps : {workflow['3']['inputs'].get('steps', '?')}",
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f" CFG Scale : {workflow['3']['inputs'].get('cfg', '?')}",
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f" Denoise : {workflow['3']['inputs'].get('denoise', '1.0')}",
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f" Seed : {workflow[NODE_KSAMPLER]['inputs'].get('seed', '(not set)')}",
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f" Resolution : {workflow[NODE_LATENT]['inputs'].get('width', '?')} x {workflow[NODE_LATENT]['inputs'].get('height', '?')}",
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f" Sampler : {workflow[NODE_KSAMPLER]['inputs'].get('sampler_name', '?')} / {workflow[NODE_KSAMPLER]['inputs'].get('scheduler', '?')}",
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f" Steps : {workflow[NODE_KSAMPLER]['inputs'].get('steps', '?')}",
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f" CFG Scale : {workflow[NODE_KSAMPLER]['inputs'].get('cfg', '?')}",
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f" Denoise : {workflow[NODE_KSAMPLER]['inputs'].get('denoise', '1.0')}",
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]
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# Add LoRA details
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@@ -98,8 +119,8 @@ def _log_workflow_prompts(label, workflow):
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lines.extend([
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"",
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" PROMPTS:",
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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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f" [+] Positive : {workflow[NODE_POSITIVE]['inputs'].get('text', '')}",
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f" [-] Negative : {workflow[NODE_NEGATIVE]['inputs'].get('text', '')}",
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])
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if face_text:
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@@ -128,17 +149,17 @@ def _apply_checkpoint_settings(workflow, ckpt_data):
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vae = ckpt_data.get('vae', 'integrated')
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# KSampler (node 3)
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if steps and '3' in workflow:
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workflow['3']['inputs']['steps'] = int(steps)
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if cfg and '3' in workflow:
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workflow['3']['inputs']['cfg'] = float(cfg)
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if sampler_name and '3' in workflow:
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workflow['3']['inputs']['sampler_name'] = sampler_name
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if scheduler and '3' in workflow:
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workflow['3']['inputs']['scheduler'] = scheduler
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if steps and NODE_KSAMPLER in workflow:
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workflow[NODE_KSAMPLER]['inputs']['steps'] = int(steps)
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if cfg and NODE_KSAMPLER in workflow:
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workflow[NODE_KSAMPLER]['inputs']['cfg'] = float(cfg)
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if sampler_name and NODE_KSAMPLER in workflow:
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workflow[NODE_KSAMPLER]['inputs']['sampler_name'] = sampler_name
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if scheduler and NODE_KSAMPLER in workflow:
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workflow[NODE_KSAMPLER]['inputs']['scheduler'] = scheduler
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# Face/hand detailers (nodes 11, 13) + multi-char SEGS detailers
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for node_id in ['11', '13'] + _SEGS_DETAILER_NODES:
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for node_id in [NODE_FACE_DETAILER, NODE_HAND_DETAILER] + _SEGS_DETAILER_NODES:
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if node_id in workflow:
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if steps:
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workflow[node_id]['inputs']['steps'] = int(steps)
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@@ -151,25 +172,25 @@ def _apply_checkpoint_settings(workflow, ckpt_data):
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# Prepend base_positive to all positive prompt nodes
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if base_positive:
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for node_id in ['6', '14', '15'] + _SEGS_PROMPT_NODES:
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for node_id in [NODE_POSITIVE, NODE_FACE_PROMPT, NODE_HAND_PROMPT] + _SEGS_PROMPT_NODES:
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if node_id in workflow:
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workflow[node_id]['inputs']['text'] = f"{base_positive}, {workflow[node_id]['inputs']['text']}"
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# Append base_negative to negative prompt (shared by main + detailers via node 7)
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if base_negative and '7' in workflow:
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workflow['7']['inputs']['text'] = f"{workflow['7']['inputs']['text']}, {base_negative}"
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if base_negative and NODE_NEGATIVE in workflow:
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workflow[NODE_NEGATIVE]['inputs']['text'] = f"{workflow[NODE_NEGATIVE]['inputs']['text']}, {base_negative}"
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# VAE: if not integrated, inject a VAELoader node and rewire
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if vae and vae != 'integrated':
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workflow['21'] = {
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workflow[NODE_VAE_LOADER] = {
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'inputs': {'vae_name': vae},
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'class_type': 'VAELoader'
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}
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if '8' in workflow:
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workflow['8']['inputs']['vae'] = ['21', 0]
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for node_id in ['11', '13'] + _SEGS_DETAILER_NODES:
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if NODE_VAE_DECODE in workflow:
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workflow[NODE_VAE_DECODE]['inputs']['vae'] = [NODE_VAE_LOADER, 0]
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for node_id in [NODE_FACE_DETAILER, NODE_HAND_DETAILER] + _SEGS_DETAILER_NODES:
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if node_id in workflow:
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workflow[node_id]['inputs']['vae'] = ['21', 0]
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workflow[node_id]['inputs']['vae'] = [NODE_VAE_LOADER, 0]
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return workflow
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@@ -190,7 +211,7 @@ def _get_default_checkpoint():
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try:
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with open('comfy_workflow.json', 'r') as f:
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workflow = json.load(f)
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ckpt_path = workflow.get('4', {}).get('inputs', {}).get('ckpt_name')
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ckpt_path = workflow.get(NODE_CHECKPOINT, {}).get('inputs', {}).get('ckpt_name')
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logger.debug("Loaded default checkpoint from workflow file: %s", ckpt_path)
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except Exception:
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pass
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@@ -231,11 +252,11 @@ def _inject_multi_char_detailers(workflow, prompts, model_source, clip_source):
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Image flow: VAEDecode(8) → PersonA(46) → PersonB(47) → FaceA(53) → FaceB(54) → Hand(13)
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"""
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vae_source = ["4", 2]
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vae_source = [NODE_CHECKPOINT, 2]
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# Remove old single face detailer and its prompt — we replace them
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workflow.pop('11', None)
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workflow.pop('14', None)
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workflow.pop(NODE_FACE_DETAILER, None)
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workflow.pop(NODE_FACE_PROMPT, None)
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# --- Person detection ---
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workflow['40'] = {
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@@ -246,7 +267,7 @@ def _inject_multi_char_detailers(workflow, prompts, model_source, clip_source):
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workflow['41'] = {
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'inputs': {
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'bbox_detector': ['40', 0],
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'image': ['8', 0],
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'image': [NODE_VAE_DECODE, 0],
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'threshold': 0.5,
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'dilation': 10,
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'crop_factor': 3.0,
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@@ -313,13 +334,13 @@ def _inject_multi_char_detailers(workflow, prompts, model_source, clip_source):
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workflow['46'] = {
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'inputs': {
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**_person_base,
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'image': ['8', 0],
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'image': [NODE_VAE_DECODE, 0],
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'segs': ['42', 0],
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'model': model_source,
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'clip': clip_source,
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'vae': vae_source,
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'positive': ['44', 0],
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'negative': ['7', 0],
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'negative': [NODE_NEGATIVE, 0],
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},
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'class_type': 'DetailerForEach'
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}
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@@ -333,7 +354,7 @@ def _inject_multi_char_detailers(workflow, prompts, model_source, clip_source):
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'clip': clip_source,
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'vae': vae_source,
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'positive': ['45', 0],
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'negative': ['7', 0],
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'negative': [NODE_NEGATIVE, 0],
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},
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'class_type': 'DetailerForEach'
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}
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@@ -413,7 +434,7 @@ def _inject_multi_char_detailers(workflow, prompts, model_source, clip_source):
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'clip': clip_source,
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'vae': vae_source,
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'positive': ['51', 0],
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'negative': ['7', 0],
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'negative': [NODE_NEGATIVE, 0],
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},
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'class_type': 'DetailerForEach'
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}
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@@ -427,29 +448,29 @@ def _inject_multi_char_detailers(workflow, prompts, model_source, clip_source):
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'clip': clip_source,
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'vae': vae_source,
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'positive': ['52', 0],
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'negative': ['7', 0],
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'negative': [NODE_NEGATIVE, 0],
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},
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'class_type': 'DetailerForEach'
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}
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# Rewire hand detailer: image input from last face detailer instead of old node 11
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if '13' in workflow:
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workflow['13']['inputs']['image'] = ['54', 0]
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if NODE_HAND_DETAILER in workflow:
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workflow[NODE_HAND_DETAILER]['inputs']['image'] = ['54', 0]
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logger.debug("Injected multi-char SEGS detailers (nodes 40-54)")
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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, character_b=None):
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# 1. Update prompts using replacement to preserve embeddings
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workflow["6"]["inputs"]["text"] = workflow["6"]["inputs"]["text"].replace("{{POSITIVE_PROMPT}}", prompts["main"])
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workflow[NODE_POSITIVE]["inputs"]["text"] = workflow[NODE_POSITIVE]["inputs"]["text"].replace("{{POSITIVE_PROMPT}}", prompts["main"])
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if custom_negative:
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workflow["7"]["inputs"]["text"] = f"{custom_negative}, {workflow['7']['inputs']['text']}"
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workflow[NODE_NEGATIVE]["inputs"]["text"] = f"{custom_negative}, {workflow[NODE_NEGATIVE]['inputs']['text']}"
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if "14" in workflow:
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workflow["14"]["inputs"]["text"] = workflow["14"]["inputs"]["text"].replace("{{FACE_PROMPT}}", prompts["face"])
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if "15" in workflow:
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workflow["15"]["inputs"]["text"] = workflow["15"]["inputs"]["text"].replace("{{HAND_PROMPT}}", prompts["hand"])
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if NODE_FACE_PROMPT in workflow:
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workflow[NODE_FACE_PROMPT]["inputs"]["text"] = workflow[NODE_FACE_PROMPT]["inputs"]["text"].replace("{{FACE_PROMPT}}", prompts["face"])
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if NODE_HAND_PROMPT in workflow:
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workflow[NODE_HAND_PROMPT]["inputs"]["text"] = workflow[NODE_HAND_PROMPT]["inputs"]["text"].replace("{{HAND_PROMPT}}", prompts["hand"])
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# 2. Update Checkpoint - always set one, fall back to default if not provided
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if not checkpoint:
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@@ -458,20 +479,20 @@ def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_nega
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if not checkpoint_data:
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checkpoint_data = default_ckpt_data
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if checkpoint:
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workflow["4"]["inputs"]["ckpt_name"] = checkpoint
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workflow[NODE_CHECKPOINT]["inputs"]["ckpt_name"] = checkpoint
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else:
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raise ValueError("No checkpoint specified and no default checkpoint configured")
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# 3. Handle LoRAs - Node 16 for character, Node 17 for outfit, Node 18 for action, Node 19 for style/detailer
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# Start with direct checkpoint connections
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model_source = ["4", 0]
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clip_source = ["4", 1]
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model_source = [NODE_CHECKPOINT, 0]
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clip_source = [NODE_CHECKPOINT, 1]
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# Look negative prompt (applied before character LoRA)
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if look:
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look_negative = look.data.get('negative', '')
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if look_negative:
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workflow["7"]["inputs"]["text"] = f"{look_negative}, {workflow['7']['inputs']['text']}"
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workflow[NODE_NEGATIVE]["inputs"]["text"] = f"{look_negative}, {workflow[NODE_NEGATIVE]['inputs']['text']}"
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# Character LoRA (Node 16) — look LoRA overrides character LoRA when present
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if look:
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@@ -480,47 +501,47 @@ def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_nega
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char_lora_data = character.data.get('lora', {}) if character else {}
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char_lora_name = char_lora_data.get('lora_name')
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if char_lora_name and "16" in workflow:
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if char_lora_name and NODE_LORA_CHAR in workflow:
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_w16 = _resolve_lora_weight(char_lora_data)
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workflow["16"]["inputs"]["lora_name"] = char_lora_name
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workflow["16"]["inputs"]["strength_model"] = _w16
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workflow["16"]["inputs"]["strength_clip"] = _w16
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workflow["16"]["inputs"]["model"] = ["4", 0] # From checkpoint
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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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workflow[NODE_LORA_CHAR]["inputs"]["lora_name"] = char_lora_name
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workflow[NODE_LORA_CHAR]["inputs"]["strength_model"] = _w16
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workflow[NODE_LORA_CHAR]["inputs"]["strength_clip"] = _w16
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workflow[NODE_LORA_CHAR]["inputs"]["model"] = [NODE_CHECKPOINT, 0] # From checkpoint
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workflow[NODE_LORA_CHAR]["inputs"]["clip"] = [NODE_CHECKPOINT, 1] # From checkpoint
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model_source = [NODE_LORA_CHAR, 0]
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clip_source = [NODE_LORA_CHAR, 1]
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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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outfit_lora_name = outfit_lora_data.get('lora_name')
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if outfit_lora_name and "17" in workflow:
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if outfit_lora_name and NODE_LORA_OUTFIT in workflow:
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_w17 = _resolve_lora_weight({**{'lora_weight': 0.8}, **outfit_lora_data})
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workflow["17"]["inputs"]["lora_name"] = outfit_lora_name
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workflow["17"]["inputs"]["strength_model"] = _w17
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workflow["17"]["inputs"]["strength_clip"] = _w17
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workflow[NODE_LORA_OUTFIT]["inputs"]["lora_name"] = outfit_lora_name
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workflow[NODE_LORA_OUTFIT]["inputs"]["strength_model"] = _w17
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workflow[NODE_LORA_OUTFIT]["inputs"]["strength_clip"] = _w17
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# Chain from character LoRA (node 16) or checkpoint (node 4)
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workflow["17"]["inputs"]["model"] = model_source
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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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workflow[NODE_LORA_OUTFIT]["inputs"]["model"] = model_source
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workflow[NODE_LORA_OUTFIT]["inputs"]["clip"] = clip_source
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model_source = [NODE_LORA_OUTFIT, 0]
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clip_source = [NODE_LORA_OUTFIT, 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:
|
||||
if action_lora_name and NODE_LORA_ACTION 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
|
||||
workflow[NODE_LORA_ACTION]["inputs"]["lora_name"] = action_lora_name
|
||||
workflow[NODE_LORA_ACTION]["inputs"]["strength_model"] = _w18
|
||||
workflow[NODE_LORA_ACTION]["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]
|
||||
workflow[NODE_LORA_ACTION]["inputs"]["model"] = model_source
|
||||
workflow[NODE_LORA_ACTION]["inputs"]["clip"] = clip_source
|
||||
model_source = [NODE_LORA_ACTION, 0]
|
||||
clip_source = [NODE_LORA_ACTION, 1]
|
||||
logger.debug("Action LoRA: %s @ %s", action_lora_name, _w18)
|
||||
|
||||
# Style/Detailer/Scene LoRA (Node 19) - chains from previous LoRA or checkpoint
|
||||
@@ -529,31 +550,31 @@ def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_nega
|
||||
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:
|
||||
if style_lora_name and NODE_LORA_STYLE 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
|
||||
workflow[NODE_LORA_STYLE]["inputs"]["lora_name"] = style_lora_name
|
||||
workflow[NODE_LORA_STYLE]["inputs"]["strength_model"] = _w19
|
||||
workflow[NODE_LORA_STYLE]["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]
|
||||
workflow[NODE_LORA_STYLE]["inputs"]["model"] = model_source
|
||||
workflow[NODE_LORA_STYLE]["inputs"]["clip"] = clip_source
|
||||
model_source = [NODE_LORA_STYLE, 0]
|
||||
clip_source = [NODE_LORA_STYLE, 1]
|
||||
logger.debug("Style/Detailer LoRA: %s @ %s", style_lora_name, _w19)
|
||||
|
||||
# Second character LoRA (Node 20) - for multi-character generation
|
||||
if character_b:
|
||||
char_b_lora_data = character_b.data.get('lora', {})
|
||||
char_b_lora_name = char_b_lora_data.get('lora_name')
|
||||
if char_b_lora_name and "20" in workflow:
|
||||
if char_b_lora_name and NODE_LORA_CHAR_B in workflow:
|
||||
_w20 = _resolve_lora_weight(char_b_lora_data)
|
||||
workflow["20"]["inputs"]["lora_name"] = char_b_lora_name
|
||||
workflow["20"]["inputs"]["strength_model"] = _w20
|
||||
workflow["20"]["inputs"]["strength_clip"] = _w20
|
||||
workflow["20"]["inputs"]["model"] = model_source
|
||||
workflow["20"]["inputs"]["clip"] = clip_source
|
||||
model_source = ["20", 0]
|
||||
clip_source = ["20", 1]
|
||||
workflow[NODE_LORA_CHAR_B]["inputs"]["lora_name"] = char_b_lora_name
|
||||
workflow[NODE_LORA_CHAR_B]["inputs"]["strength_model"] = _w20
|
||||
workflow[NODE_LORA_CHAR_B]["inputs"]["strength_clip"] = _w20
|
||||
workflow[NODE_LORA_CHAR_B]["inputs"]["model"] = model_source
|
||||
workflow[NODE_LORA_CHAR_B]["inputs"]["clip"] = clip_source
|
||||
model_source = [NODE_LORA_CHAR_B, 0]
|
||||
clip_source = [NODE_LORA_CHAR_B, 1]
|
||||
logger.debug("Character B LoRA: %s @ %s", char_b_lora_name, _w20)
|
||||
|
||||
# 3b. Multi-char: inject per-character SEGS detailers (replaces node 11/14)
|
||||
@@ -561,35 +582,35 @@ def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_nega
|
||||
_inject_multi_char_detailers(workflow, prompts, model_source, clip_source)
|
||||
|
||||
# Apply connections to all model/clip consumers (conditional on node existence)
|
||||
for nid in ["3", "11", "13"] + _SEGS_DETAILER_NODES:
|
||||
for nid in [NODE_KSAMPLER, NODE_FACE_DETAILER, NODE_HAND_DETAILER] + _SEGS_DETAILER_NODES:
|
||||
if nid in workflow:
|
||||
workflow[nid]["inputs"]["model"] = model_source
|
||||
|
||||
for nid in ["6", "7", "11", "13", "14", "15"] + _SEGS_PROMPT_NODES:
|
||||
for nid in [NODE_POSITIVE, NODE_NEGATIVE, NODE_FACE_DETAILER, NODE_HAND_DETAILER, NODE_FACE_PROMPT, NODE_HAND_PROMPT] + _SEGS_PROMPT_NODES:
|
||||
if nid in workflow:
|
||||
workflow[nid]["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)
|
||||
for nid in ["3", "11", "13"] + _SEGS_DETAILER_NODES:
|
||||
for nid in [NODE_KSAMPLER, NODE_FACE_DETAILER, NODE_HAND_DETAILER] + _SEGS_DETAILER_NODES:
|
||||
if nid in workflow:
|
||||
workflow[nid]["inputs"]["seed"] = gen_seed
|
||||
|
||||
# 5. Set image dimensions
|
||||
if "5" in workflow:
|
||||
if NODE_LATENT in workflow:
|
||||
if width:
|
||||
workflow["5"]["inputs"]["width"] = int(width)
|
||||
workflow[NODE_LATENT]["inputs"]["width"] = int(width)
|
||||
if height:
|
||||
workflow["5"]["inputs"]["height"] = int(height)
|
||||
workflow[NODE_LATENT]["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"] + _SEGS_DETAILER_NODES:
|
||||
sampler_name = workflow[NODE_KSAMPLER]["inputs"].get("sampler_name")
|
||||
scheduler = workflow[NODE_KSAMPLER]["inputs"].get("scheduler")
|
||||
for node_id in [NODE_FACE_DETAILER, NODE_HAND_DETAILER] + _SEGS_DETAILER_NODES:
|
||||
if node_id in workflow:
|
||||
if sampler_name:
|
||||
workflow[node_id]["inputs"]["sampler_name"] = sampler_name
|
||||
@@ -598,11 +619,11 @@ def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_nega
|
||||
|
||||
# 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[NODE_POSITIVE]["inputs"]["text"],
|
||||
workflow[NODE_NEGATIVE]["inputs"]["text"]
|
||||
)
|
||||
workflow["6"]["inputs"]["text"] = pos_text
|
||||
workflow["7"]["inputs"]["text"] = neg_text
|
||||
workflow[NODE_POSITIVE]["inputs"]["text"] = pos_text
|
||||
workflow[NODE_NEGATIVE]["inputs"]["text"] = neg_text
|
||||
|
||||
# 9. Final prompt debug — logged after all transformations are complete
|
||||
_log_workflow_prompts("_prepare_workflow", workflow)
|
||||
|
||||
Reference in New Issue
Block a user