chris.dumas/WhatDreamsCost-Dumas
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Allow custom character analyze prompts

Browse Source
This commit is contained in:
OpenClaw Agent
2026-07-20 14:02:28 +00:00
parent a711a9d34b
commit 3f24406697
2 changed files with 20 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
js/msr_character.js
View File

@@ -1,5 +1,6 @@
const { app } = window.comfyAPI.app;
const { api } = window.comfyAPI.api;
const DEFAULT_ANALYZE_PROMPT = "Describe the character's physical appearance in two concise sentences. Specify their hair color/style, face details, and their clothing type/color. Keep the entire response very brief.";
function findWidget(node, name) {
return (node.widgets || []).find((widget) => widget.name === name);
@@ -369,15 +370,19 @@ function buildCharacterUi(node) {
const providerWidget = findWidget(node, "analyze_provider");
const baseUrlWidget = findWidget(node, "analyze_base_url");
const modelWidget = findWidget(node, "analyze_model");
const promptWidget = findWidget(node, "analyze_prompt");
const provider = window.prompt("Analyze provider: ollama, lmstudio, custom, off", providerWidget?.value || "ollama");
if (provider == null) return;
const baseUrl = window.prompt("Analyze base URL (blank = default)", baseUrlWidget?.value || "");
if (baseUrl == null) return;
const model = window.prompt("Analyze model (blank = provider default)", modelWidget?.value || "");
if (model == null) return;
const prompt = window.prompt("Analyze prompt (blank = default)", promptWidget?.value || DEFAULT_ANALYZE_PROMPT);
if (prompt == null) return;
setWidgetValue(node, providerWidget, provider.trim() || "ollama");
setWidgetValue(node, baseUrlWidget, baseUrl.trim());
setWidgetValue(node, modelWidget, model.trim());
setWidgetValue(node, promptWidget, prompt.trim());
syncFormFromWidgets(node);
node.setDirtyCanvas?.(true, true);
});
@@ -461,6 +466,7 @@ async function analyzeCharacterNode(node, buttonWidget) {
const providerWidget = findWidget(node, "analyze_provider");
const baseUrlWidget = findWidget(node, "analyze_base_url");
const modelWidget = findWidget(node, "analyze_model");
const promptWidget = findWidget(node, "analyze_prompt");
buttonWidget.label = "Analyzing...";
if (node._msrAnalyzeButton) {
@@ -489,6 +495,7 @@ async function analyzeCharacterNode(node, buttonWidget) {
provider: providerWidget?.value || "ollama",
base_url: baseUrlWidget?.value || "",
model: modelWidget?.value || "",
prompt: promptWidget?.value || "",
image_b64: images,
image_debug: imageDebug,
}),
@@ -560,6 +567,9 @@ app.registerExtension({
if (!findWidget(this, "analyze_model")) {
this.addWidget("text", "analyze_model", "");
}
if (!findWidget(this, "analyze_prompt")) {
this.addWidget("text", "analyze_prompt", DEFAULT_ANALYZE_PROMPT);
}
if (!this._msrPreviewWidget) {
const previewContainer = buildCharacterUi(this);
@@ -587,7 +597,7 @@ app.registerExtension({
return result;
};
["alias", "description", "analyze_provider", "analyze_base_url", "analyze_model"].forEach((widgetName) => {
["alias", "description", "analyze_provider", "analyze_base_url", "analyze_model", "analyze_prompt"].forEach((widgetName) => {
hideNodeWidget(findWidget(this, widgetName));
});
hideNodeWidget((this.widgets || []).find((widget) => widget.msrAnalyze));

12
ltx_director.py
View File

@@ -512,6 +512,11 @@ _ANALYZE_PROMPT = (
)
def _resolve_analyze_prompt(data: dict) -> str:
prompt = (data.get("prompt") or "").strip()
return prompt or _ANALYZE_PROMPT
def _extract_ollama_generated_text(resp_json: dict) -> str:
if not isinstance(resp_json, dict):
return ""
@@ -621,6 +626,7 @@ async def analyze_character_endpoint(request):
image_debug = data.get("image_debug") or []
char_index = int(data.get("char_index", 0))
provider, base_url, model_name = _resolve_provider(data)
analyze_prompt = _resolve_analyze_prompt(data)
if provider == "off":
return web.json_response({"status": "error", "message": "Analyze is set to Off / Manual."})
@@ -652,7 +658,7 @@ async def analyze_character_endpoint(request):
payload = {
"model": model_name,
"system": _ANALYZE_SYSTEM_PROMPT,
"prompt": _ANALYZE_PROMPT,
"prompt": analyze_prompt,
"images": analysis_images,
"stream": False,
"keep_alive": 0,
@@ -691,7 +697,7 @@ async def analyze_character_endpoint(request):
},
{
"role": "user",
"content": _ANALYZE_PROMPT,
"content": analyze_prompt,
"images": analysis_images,
},
],
@@ -720,7 +726,7 @@ async def analyze_character_endpoint(request):
})
else:
# OpenAI-compatible vision chat (LM Studio / Custom).
content = [{"type": "text", "text": _ANALYZE_PROMPT}]
content = [{"type": "text", "text": analyze_prompt}]
for b64 in cleaned_b64_list:
content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{b64}"}})
payload = {