Initial Swarm Assistent extension for Krea 2 + Ollama
This commit is contained in:
mrleo1nid
@@ -0,0 +1,6 @@
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bin/
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obj/
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.vs/
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*.user
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*.suo
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.DS_Store
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@@ -0,0 +1,214 @@
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.swarm-assistent-root {
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display: flex;
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flex-direction: column;
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height: calc(100vh - 8rem);
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min-height: 28rem;
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padding: 0.5rem;
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box-sizing: border-box;
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}
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.sa-gate {
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padding: 1.25rem;
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opacity: 0.9;
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}
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.sa-layout {
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display: flex;
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flex: 1;
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gap: 0.75rem;
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min-height: 0;
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}
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.sa-image-pane {
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flex: 0 0 32%;
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max-width: 22rem;
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display: flex;
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flex-direction: column;
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gap: 0.5rem;
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min-width: 12rem;
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}
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.sa-image-frame {
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flex: 1;
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display: flex;
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align-items: center;
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justify-content: center;
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border: 1px solid color-mix(in srgb, currentColor 25%, transparent);
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border-radius: 0.35rem;
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overflow: hidden;
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background: color-mix(in srgb, currentColor 6%, transparent);
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min-height: 14rem;
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}
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.sa-image-frame img {
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max-width: 100%;
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max-height: 100%;
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object-fit: contain;
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}
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.sa-image-empty {
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padding: 1rem;
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text-align: center;
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opacity: 0.65;
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font-size: 0.95rem;
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}
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.sa-image-actions {
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display: flex;
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flex-wrap: wrap;
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gap: 0.5rem;
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align-items: center;
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}
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.sa-chat-pane {
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flex: 1 1 68%;
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display: flex;
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flex-direction: column;
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min-width: 0;
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min-height: 0;
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border: 1px solid color-mix(in srgb, currentColor 25%, transparent);
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border-radius: 0.35rem;
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}
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.sa-chat-header {
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display: flex;
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align-items: center;
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justify-content: space-between;
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gap: 0.5rem;
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padding: 0.45rem 0.65rem;
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border-bottom: 1px solid color-mix(in srgb, currentColor 20%, transparent);
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}
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.sa-chat-title {
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font-weight: 600;
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letter-spacing: 0.02em;
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}
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.sa-header-right {
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display: flex;
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align-items: center;
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gap: 0.4rem;
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}
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.sa-icon-btn {
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min-width: 2rem;
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padding-left: 0.45rem;
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padding-right: 0.45rem;
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}
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.sa-settings {
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display: grid;
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gap: 0.45rem;
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padding: 0.65rem;
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border-bottom: 1px solid color-mix(in srgb, currentColor 20%, transparent);
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background: color-mix(in srgb, currentColor 5%, transparent);
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}
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.sa-settings label {
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display: flex;
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flex-direction: column;
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gap: 0.2rem;
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font-size: 0.9rem;
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}
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.sa-settings input[type="text"],
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.sa-select {
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width: 100%;
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box-sizing: border-box;
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}
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.sa-check {
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flex-direction: row !important;
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align-items: center;
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gap: 0.4rem !important;
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}
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.sa-messages {
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flex: 1;
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overflow: auto;
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padding: 0.75rem;
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display: flex;
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flex-direction: column;
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gap: 0.65rem;
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}
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.sa-msg {
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max-width: 95%;
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padding: 0.55rem 0.7rem;
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border-radius: 0.35rem;
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white-space: pre-wrap;
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word-break: break-word;
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line-height: 1.35;
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}
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.sa-msg.user {
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align-self: flex-end;
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background: color-mix(in srgb, currentColor 12%, transparent);
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}
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.sa-msg.assistant {
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align-self: flex-start;
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background: color-mix(in srgb, currentColor 7%, transparent);
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}
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.sa-msg.error {
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align-self: stretch;
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border: 1px solid color-mix(in srgb, #c44 50%, transparent);
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}
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.sa-patch {
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margin-top: 0.5rem;
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padding-top: 0.45rem;
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border-top: 1px dashed color-mix(in srgb, currentColor 25%, transparent);
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font-size: 0.9rem;
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}
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.sa-patch-actions {
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display: flex;
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flex-wrap: wrap;
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gap: 0.35rem;
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margin-top: 0.4rem;
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}
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.sa-composer {
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border-top: 1px solid color-mix(in srgb, currentColor 20%, transparent);
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padding: 0.55rem;
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display: flex;
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flex-direction: column;
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gap: 0.4rem;
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}
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.sa-composer textarea {
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width: 100%;
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resize: vertical;
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box-sizing: border-box;
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min-height: 4rem;
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}
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.sa-composer-actions {
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display: flex;
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flex-wrap: wrap;
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gap: 0.4rem;
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align-items: center;
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}
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.sa-status {
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font-size: 0.85rem;
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opacity: 0.75;
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}
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.sa-disabled {
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opacity: 0.45;
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pointer-events: none;
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}
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@media (max-width: 900px) {
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.sa-layout {
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flex-direction: column;
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}
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.sa-image-pane {
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flex: 0 0 auto;
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max-width: none;
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max-height: 40%;
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}
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}
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@@ -0,0 +1,546 @@
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/**
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* Swarm Assistent — Krea 2 collaborative chat (Ollama via Swarm API).
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*/
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(function () {
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const LS_BASE = 'swarm_assistent_base_url';
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const LS_MODEL = 'swarm_assistent_model';
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const LS_PACK = 'swarm_assistent_pack';
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const LS_AUTO_VISION = 'swarm_assistent_auto_vision';
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const state = {
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history: [],
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packsLoaded: false,
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busy: false,
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lastImageDataUrl: null,
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};
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function $(id) {
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return document.getElementById(id);
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}
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function setStatus(text) {
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const el = $('sa_status');
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if (el) {
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el.textContent = text || '';
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}
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}
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function isKreaSelected() {
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try {
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const model = getCurrentModel && getCurrentModel();
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if (!model) {
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return false;
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}
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const arch = `${model.architecture || ''} ${model.title || ''} ${model.name || ''} ${model.class || ''}`;
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return /krea\s*2|krea2/i.test(arch) || /krea/i.test(arch);
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} catch (e) {
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return false;
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}
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}
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function updateGate() {
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const ok = isKreaSelected();
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const gate = $('sa_gate');
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const layout = $('sa_layout');
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if (gate) {
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gate.hidden = ok;
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}
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if (layout) {
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layout.classList.toggle('sa-disabled', !ok);
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}
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return ok;
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}
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function val(id) {
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const el = document.getElementById(id);
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return el ? el.value : '';
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}
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function setVal(id, value) {
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const el = document.getElementById(id);
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if (!el) {
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return;
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}
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el.value = value;
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el.dispatchEvent(new Event('input', { bubbles: true }));
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el.dispatchEvent(new Event('change', { bubbles: true }));
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}
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function collectLiveContext() {
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const ctx = {
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architecture_ok: isKreaSelected(),
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checkpoint: null,
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prompt: val('alt_prompt_textbox') || val('input_prompt') || '',
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negative: val('input_negativeprompt') || val('alt_negativeprompt_textbox') || '',
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width: parseInt(val('input_width') || '0', 10) || null,
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height: parseInt(val('input_height') || '0', 10) || null,
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steps: parseInt(val('input_steps') || '0', 10) || null,
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cfg: parseFloat(val('input_cfgscale') || val('input_cfg') || '') || null,
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sigma_shift: parseFloat(val('input_sigmashift') || '') || null,
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seed: val('input_seed') || null,
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selected_loras: [],
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available_loras: [],
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has_vision_image: !!state.lastImageDataUrl,
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};
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try {
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const model = getCurrentModel && getCurrentModel();
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if (model) {
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ctx.checkpoint = {
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name: model.name || model.title || null,
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architecture: model.architecture || model.class || null,
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title: model.title || null,
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};
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}
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} catch (e) { /* ignore */ }
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try {
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if (typeof loraHelper !== 'undefined' && loraHelper && Array.isArray(loraHelper.selected)) {
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ctx.selected_loras = loraHelper.selected.map((l) => ({
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name: l.name || l,
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weight: (loraHelper.loraWeightPref && loraHelper.loraWeightPref[l.name || l]) || 1,
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}));
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}
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} catch (e) { /* ignore */ }
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try {
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const models = (typeof allModels !== 'undefined' && allModels) || (typeof model_list !== 'undefined' && model_list) || [];
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const list = Array.isArray(models) ? models : Object.values(models || {});
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for (const m of list) {
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if (!m) {
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continue;
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}
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const arch = `${m.architecture || ''} ${m.class || ''} ${m.name || ''} ${m.title || ''}`;
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const isLora = /lora/i.test(m.category || m.type || '') || (m.name && String(m.name).toLowerCase().includes('lora'));
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const folder = `${m.folder || m.path || ''}`;
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const inLoraFolder = /lora/i.test(folder);
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if (!(isLora || inLoraFolder)) {
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// Still include if metadata says lora
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if (!/lora/i.test(JSON.stringify(m).slice(0, 200))) {
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continue;
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}
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}
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// Prefer Krea-tagged or unknown; skip obvious FLUX/SDXL-only names when tagged
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ctx.available_loras.push({
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name: m.name || m.title,
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title: m.title || m.name,
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trigger_phrase: m.trigger_phrase || m.trigger || (m.metadata && (m.metadata.trigger_phrase || m.metadata.trigger)) || null,
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architecture: m.architecture || null,
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});
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}
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// Cap list size for context window
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if (ctx.available_loras.length > 80) {
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ctx.available_loras = ctx.available_loras.slice(0, 80);
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}
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} catch (e) { /* ignore */ }
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// Fallback: parse multi-select input_loras options as available names
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try {
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const sel = document.getElementById('input_loras');
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if (sel && sel.options && ctx.available_loras.length === 0) {
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for (const opt of sel.options) {
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if (opt.value) {
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ctx.available_loras.push({ name: opt.value, title: opt.text || opt.value, trigger_phrase: null });
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}
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}
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}
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} catch (e) { /* ignore */ }
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return ctx;
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}
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function extractPatch(text) {
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if (!text) {
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return { prose: text || '', patch: null };
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}
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const re = /```(?:json)?\s*([\s\S]*?)```/gi;
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let match;
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let lastPatch = null;
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let prose = text;
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while ((match = re.exec(text)) !== null) {
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const raw = match[1].trim();
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try {
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const obj = JSON.parse(raw);
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if (obj && typeof obj === 'object' && (obj.prompt != null || obj.loras || obj.width || obj.height || obj.steps || obj.cfg)) {
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lastPatch = obj;
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prose = (text.slice(0, match.index) + text.slice(match.index + match[0].length)).trim();
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}
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} catch (e) { /* not json */ }
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}
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return { prose, patch: lastPatch };
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}
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function applyPatch(patch, which) {
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if (!patch) {
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return;
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}
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const doPrompt = !which || which === 'all' || which === 'prompt';
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const doLoras = !which || which === 'all' || which === 'loras';
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const doSize = !which || which === 'all' || which === 'size';
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if (doPrompt && patch.prompt != null) {
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const box = document.getElementById('alt_prompt_textbox') || document.getElementById('input_prompt');
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if (box) {
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box.value = patch.prompt;
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box.dispatchEvent(new Event('input', { bubbles: true }));
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box.dispatchEvent(new Event('change', { bubbles: true }));
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}
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if (patch.negative != null) {
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setVal('input_negativeprompt', patch.negative);
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}
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// Ensure triggers present
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if (Array.isArray(patch.loras)) {
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for (const l of patch.loras) {
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const triggers = l.triggers || (l.trigger_phrase ? [l.trigger_phrase] : []);
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for (const t of triggers) {
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if (t && box && box.value && !box.value.includes(t)) {
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box.value = `${box.value.trim()}, ${t}`;
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box.dispatchEvent(new Event('input', { bubbles: true }));
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||||
}
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||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
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if (doLoras && Array.isArray(patch.loras) && typeof loraHelper !== 'undefined' && loraHelper) {
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try {
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if (typeof loraHelper.clearLoras === 'function') {
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loraHelper.clearLoras();
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}
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||||
} catch (e) { /* ignore */ }
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||||
for (const l of patch.loras) {
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||||
const name = l.name;
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||||
if (!name) {
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continue;
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||||
}
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||||
try {
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||||
if (typeof loraHelper.selectLora === 'function') {
|
||||
loraHelper.selectLora(name);
|
||||
}
|
||||
if (loraHelper.loraWeightPref && l.weight != null) {
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||||
loraHelper.loraWeightPref[name] = l.weight;
|
||||
}
|
||||
} catch (e) {
|
||||
console.warn('Assistent: selectLora failed', name, e);
|
||||
}
|
||||
}
|
||||
try {
|
||||
if (typeof loraHelper.rebuildUI === 'function') {
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||||
loraHelper.rebuildUI();
|
||||
}
|
||||
} catch (e) { /* ignore */ }
|
||||
}
|
||||
|
||||
if (doSize) {
|
||||
if (patch.width != null) {
|
||||
setVal('input_width', String(patch.width));
|
||||
}
|
||||
if (patch.height != null) {
|
||||
setVal('input_height', String(patch.height));
|
||||
}
|
||||
if (patch.steps != null) {
|
||||
setVal('input_steps', String(patch.steps));
|
||||
}
|
||||
if (patch.cfg != null) {
|
||||
if (document.getElementById('input_cfgscale')) {
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||||
setVal('input_cfgscale', String(patch.cfg));
|
||||
} else {
|
||||
setVal('input_cfg', String(patch.cfg));
|
||||
}
|
||||
}
|
||||
}
|
||||
setStatus('Applied patch');
|
||||
}
|
||||
|
||||
function appendMessage(role, text, patch) {
|
||||
const box = $('sa_messages');
|
||||
if (!box) {
|
||||
return;
|
||||
}
|
||||
const div = document.createElement('div');
|
||||
div.className = `sa-msg ${role}`;
|
||||
const { prose, patch: extracted } = role === 'assistant' ? extractPatch(text) : { prose: text, patch: null };
|
||||
const finalPatch = patch || extracted;
|
||||
div.textContent = prose || text || '';
|
||||
if (finalPatch) {
|
||||
const wrap = document.createElement('div');
|
||||
wrap.className = 'sa-patch';
|
||||
const pre = document.createElement('pre');
|
||||
pre.textContent = JSON.stringify(finalPatch, null, 2);
|
||||
wrap.appendChild(pre);
|
||||
const actions = document.createElement('div');
|
||||
actions.className = 'sa-patch-actions';
|
||||
for (const [label, which] of [
|
||||
['Apply all', 'all'],
|
||||
['Prompt', 'prompt'],
|
||||
['LoRAs', 'loras'],
|
||||
['Size', 'size'],
|
||||
]) {
|
||||
const btn = document.createElement('button');
|
||||
btn.type = 'button';
|
||||
btn.className = 'basic-button';
|
||||
btn.textContent = label;
|
||||
btn.addEventListener('click', () => applyPatch(finalPatch, which));
|
||||
actions.appendChild(btn);
|
||||
}
|
||||
wrap.appendChild(actions);
|
||||
div.appendChild(wrap);
|
||||
}
|
||||
box.appendChild(div);
|
||||
box.scrollTop = box.scrollHeight;
|
||||
}
|
||||
|
||||
function refreshImagePreview() {
|
||||
let src = null;
|
||||
try {
|
||||
const cur = document.getElementById('current_image_img') || document.querySelector('#current_image img') || document.querySelector('.current-image img');
|
||||
if (cur && cur.src) {
|
||||
src = cur.src;
|
||||
}
|
||||
} catch (e) { /* ignore */ }
|
||||
try {
|
||||
if (!src && typeof currentMetadataMap !== 'undefined' && currentMetadataMap && currentMetadataMap.image) {
|
||||
src = currentMetadataMap.image;
|
||||
}
|
||||
} catch (e) { /* ignore */ }
|
||||
|
||||
const img = $('sa_image_preview');
|
||||
const empty = $('sa_image_empty');
|
||||
if (src) {
|
||||
state.lastImageDataUrl = src;
|
||||
if (img) {
|
||||
img.src = src;
|
||||
img.hidden = false;
|
||||
}
|
||||
if (empty) {
|
||||
empty.hidden = true;
|
||||
}
|
||||
} else {
|
||||
state.lastImageDataUrl = null;
|
||||
if (img) {
|
||||
img.hidden = true;
|
||||
}
|
||||
if (empty) {
|
||||
empty.hidden = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async function imageToBase64ForOllama(src) {
|
||||
if (!src) {
|
||||
return null;
|
||||
}
|
||||
// Already data URL
|
||||
if (src.startsWith('data:')) {
|
||||
const i = src.indexOf(',');
|
||||
return i >= 0 ? src.slice(i + 1) : null;
|
||||
}
|
||||
try {
|
||||
const resp = await fetch(src);
|
||||
const blob = await resp.blob();
|
||||
return await new Promise((resolve, reject) => {
|
||||
const reader = new FileReader();
|
||||
reader.onload = () => {
|
||||
const data = String(reader.result || '');
|
||||
const i = data.indexOf(',');
|
||||
resolve(i >= 0 ? data.slice(i + 1) : null);
|
||||
};
|
||||
reader.onerror = reject;
|
||||
reader.readAsDataURL(blob);
|
||||
});
|
||||
} catch (e) {
|
||||
console.warn('Assistent: vision fetch failed', e);
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
function loadSettings() {
|
||||
const base = localStorage.getItem(LS_BASE);
|
||||
const model = localStorage.getItem(LS_MODEL);
|
||||
const pack = localStorage.getItem(LS_PACK);
|
||||
const auto = localStorage.getItem(LS_AUTO_VISION);
|
||||
if (base && $('sa_base_url')) {
|
||||
$('sa_base_url').value = base;
|
||||
}
|
||||
if (pack && $('sa_pack')) {
|
||||
$('sa_pack').value = pack;
|
||||
}
|
||||
if (auto != null && $('sa_auto_vision')) {
|
||||
$('sa_auto_vision').checked = auto === '1';
|
||||
}
|
||||
if (model) {
|
||||
state.preferredModel = model;
|
||||
}
|
||||
}
|
||||
|
||||
function saveSettings() {
|
||||
localStorage.setItem(LS_BASE, $('sa_base_url')?.value || '');
|
||||
localStorage.setItem(LS_MODEL, $('sa_model')?.value || '');
|
||||
localStorage.setItem(LS_PACK, $('sa_pack')?.value || 'write_prompt');
|
||||
localStorage.setItem(LS_AUTO_VISION, $('sa_auto_vision')?.checked ? '1' : '0');
|
||||
}
|
||||
|
||||
function refreshModels() {
|
||||
const baseUrl = $('sa_base_url')?.value || 'http://127.0.0.1:11434';
|
||||
setStatus('Loading models…');
|
||||
genericRequest('AssistentListModels', { baseUrl }, (data) => {
|
||||
if (data.error) {
|
||||
setStatus(data.error);
|
||||
appendMessage('error', data.error);
|
||||
return;
|
||||
}
|
||||
const sel = $('sa_model');
|
||||
if (!sel) {
|
||||
return;
|
||||
}
|
||||
sel.innerHTML = '';
|
||||
const models = data.models || [];
|
||||
for (const name of models) {
|
||||
if (!name) {
|
||||
continue;
|
||||
}
|
||||
const opt = document.createElement('option');
|
||||
opt.value = name;
|
||||
opt.textContent = name;
|
||||
sel.appendChild(opt);
|
||||
}
|
||||
const prefer = state.preferredModel || localStorage.getItem(LS_MODEL);
|
||||
if (prefer && models.includes(prefer)) {
|
||||
sel.value = prefer;
|
||||
}
|
||||
setStatus(models.length ? `${models.length} models` : 'No Ollama models');
|
||||
saveSettings();
|
||||
});
|
||||
}
|
||||
|
||||
async function sendChat() {
|
||||
if (state.busy) {
|
||||
return;
|
||||
}
|
||||
if (!updateGate()) {
|
||||
setStatus('Select a Krea 2 model');
|
||||
return;
|
||||
}
|
||||
const text = ($('sa_input')?.value || '').trim();
|
||||
if (!text) {
|
||||
return;
|
||||
}
|
||||
const pack = $('sa_pack')?.value || 'write_prompt';
|
||||
const model = $('sa_model')?.value;
|
||||
if (!model) {
|
||||
setStatus('Pick an Ollama model in ⚙');
|
||||
refreshModels();
|
||||
return;
|
||||
}
|
||||
|
||||
refreshImagePreview();
|
||||
const attach = ($('sa_attach_vision')?.checked || $('sa_auto_vision')?.checked) && state.lastImageDataUrl;
|
||||
let images = null;
|
||||
if (attach) {
|
||||
setStatus('Encoding image…');
|
||||
const b64 = await imageToBase64ForOllama(state.lastImageDataUrl);
|
||||
if (b64) {
|
||||
images = [b64];
|
||||
}
|
||||
}
|
||||
|
||||
const userMsg = { role: 'user', content: text };
|
||||
if (images) {
|
||||
userMsg.images = images;
|
||||
}
|
||||
state.history.push({ role: 'user', content: text });
|
||||
appendMessage('user', text);
|
||||
$('sa_input').value = '';
|
||||
|
||||
const context = collectLiveContext();
|
||||
context.has_vision_image = !!images;
|
||||
// Strip huge fields from history replay — only send recent turns without images in history payload
|
||||
const messages = state.history.slice(-12).map((m) => ({ role: m.role, content: m.content }));
|
||||
// Last user message may include images
|
||||
if (images && messages.length) {
|
||||
messages[messages.length - 1].images = images;
|
||||
}
|
||||
|
||||
state.busy = true;
|
||||
setStatus('Thinking…');
|
||||
saveSettings();
|
||||
|
||||
const payload = {
|
||||
baseUrl: $('sa_base_url')?.value || 'http://127.0.0.1:11434',
|
||||
model,
|
||||
pack,
|
||||
includeBase: true,
|
||||
raw: {
|
||||
messages,
|
||||
context_json: JSON.stringify(context),
|
||||
pack,
|
||||
base_url: $('sa_base_url')?.value || 'http://127.0.0.1:11434',
|
||||
model,
|
||||
},
|
||||
};
|
||||
|
||||
genericRequest('AssistentChat', payload, (data) => {
|
||||
state.busy = false;
|
||||
if (data.error) {
|
||||
setStatus(data.error);
|
||||
appendMessage('error', data.error);
|
||||
return;
|
||||
}
|
||||
const reply = data.reply || '';
|
||||
state.history.push({ role: 'assistant', content: reply });
|
||||
appendMessage('assistant', reply);
|
||||
setStatus('Done');
|
||||
});
|
||||
}
|
||||
|
||||
function wire() {
|
||||
if (!$('swarm_assistent_root')) {
|
||||
return;
|
||||
}
|
||||
loadSettings();
|
||||
updateGate();
|
||||
refreshImagePreview();
|
||||
refreshModels();
|
||||
|
||||
$('sa_btn_settings')?.addEventListener('click', () => {
|
||||
const s = $('sa_settings');
|
||||
if (s) {
|
||||
s.hidden = !s.hidden;
|
||||
}
|
||||
});
|
||||
$('sa_btn_refresh_models')?.addEventListener('click', () => {
|
||||
saveSettings();
|
||||
refreshModels();
|
||||
});
|
||||
$('sa_btn_refresh_image')?.addEventListener('click', refreshImagePreview);
|
||||
$('sa_btn_send')?.addEventListener('click', () => sendChat());
|
||||
$('sa_btn_clear')?.addEventListener('click', () => {
|
||||
state.history = [];
|
||||
const box = $('sa_messages');
|
||||
if (box) {
|
||||
box.innerHTML = '';
|
||||
}
|
||||
setStatus('');
|
||||
});
|
||||
$('sa_input')?.addEventListener('keydown', (e) => {
|
||||
if (e.key === 'Enter' && (e.ctrlKey || e.metaKey)) {
|
||||
e.preventDefault();
|
||||
sendChat();
|
||||
}
|
||||
});
|
||||
$('sa_base_url')?.addEventListener('change', saveSettings);
|
||||
$('sa_model')?.addEventListener('change', saveSettings);
|
||||
$('sa_pack')?.addEventListener('change', saveSettings);
|
||||
$('sa_auto_vision')?.addEventListener('change', saveSettings);
|
||||
|
||||
// Re-check Krea gate when user may swap models
|
||||
setInterval(updateGate, 2000);
|
||||
setInterval(refreshImagePreview, 4000);
|
||||
}
|
||||
|
||||
if (document.readyState === 'loading') {
|
||||
document.addEventListener('DOMContentLoaded', wire);
|
||||
} else {
|
||||
wire();
|
||||
}
|
||||
})();
|
||||
@@ -0,0 +1,21 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2026 mrleo1nid
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
@@ -0,0 +1,47 @@
|
||||
# Base: Krea 2 + Swarm Assistent
|
||||
|
||||
You are **Swarm Assistent**, a collaborative art director for **Krea 2** image generation inside SwarmUI.
|
||||
|
||||
## Model facts (do not contradict)
|
||||
|
||||
- Architecture: Krea 2 (12B DiT). Not FLUX, not SDXL, not FLUX.1-Krea.
|
||||
- Text encoder: Qwen3-VL 4B. VAE: Qwen Image VAE.
|
||||
- **Turbo** defaults: steps **8**, CFG **1**, sigma shift **1.15**, side length ~**1024**.
|
||||
- **Prompt Images** (refs in the prompt box) often **overpower** the text prompt — suggest them sparingly and warn the user.
|
||||
- Built-in NSFW text-refiner may strip risque words; LoRAs may change that — do not lecture; stay practical.
|
||||
- LoRAs: **only Krea2-trained**. Never suggest FLUX/SDXL LoRAs.
|
||||
|
||||
## Live context
|
||||
|
||||
A JSON block named "Live SwarmUI context" is attached. Treat it as ground truth:
|
||||
|
||||
- Use only LoRAs listed in `available_loras` (by exact `name`).
|
||||
- Prefer listed `trigger_phrase` / `triggers` — **never invent** trigger words.
|
||||
- When enabling a LoRA, include its triggers in `prompt` if missing.
|
||||
- Respect current width/height/steps/cfg unless the user asks to change them or the pack is `fix_params`.
|
||||
|
||||
## Output contract (mandatory)
|
||||
|
||||
1. Write a short helpful reply in the user's language (RU or EN).
|
||||
2. Then emit **one** fenced JSON patch (and only fields you want to change):
|
||||
|
||||
```json
|
||||
{
|
||||
"prompt": "...",
|
||||
"negative": null,
|
||||
"loras": [{"name": "exact_name_from_list", "weight": 0.8, "triggers": ["..."]}],
|
||||
"width": 1024,
|
||||
"height": 1280,
|
||||
"steps": 8,
|
||||
"cfg": 1,
|
||||
"notes": "one-line why"
|
||||
}
|
||||
```
|
||||
|
||||
Rules for the patch:
|
||||
|
||||
- Omit keys you are not changing.
|
||||
- `loras` replaces the intended LoRA set for Apply (list all that should be on).
|
||||
- width/height between 128 and 4096; prefer multiples near 1024 for Turbo.
|
||||
- Do not invent model or LoRA filenames.
|
||||
- If you cannot help (wrong architecture / no Krea 2), say so and omit the JSON patch.
|
||||
@@ -0,0 +1,14 @@
|
||||
# Mode: compose_scene
|
||||
|
||||
Goal: co-create a scene / moodboard direction for **Krea 2**.
|
||||
|
||||
## Approach
|
||||
|
||||
- Clarify subject, setting, time of day, camera distance, style.
|
||||
- Propose one strong prompt (not five weak ones).
|
||||
- Optionally suggest which available LoRAs fit — only from the live list, with triggers.
|
||||
- Mention Prompt Images only if a reference would help, and warn that refs can overpower text.
|
||||
|
||||
## Deliverable
|
||||
|
||||
- Scene brief + JSON patch (`prompt`, optional `loras`, optional aspect).
|
||||
@@ -0,0 +1,16 @@
|
||||
# Mode: critique_image
|
||||
|
||||
Goal: look at the attached image (vision) and improve the next generation for **Krea 2**.
|
||||
|
||||
## How to critique
|
||||
|
||||
- Describe what you see: subject, composition, lighting, defects (anatomy, blur, wrong style).
|
||||
- Tie feedback to **actionable** prompt / LoRA / size changes.
|
||||
- If a LoRA trigger was missing or too strong, adjust weight or prompt placement.
|
||||
- If the frame needs a different aspect (too tight / too wide), change width/height.
|
||||
- Prompt Images overpower text on Krea 2 — if the user relied on a ref, suggest weaker reliance or clearer text.
|
||||
|
||||
## Deliverable
|
||||
|
||||
- Short critique in the user's language.
|
||||
- JSON patch with improved `prompt` and any `loras` / size tweaks.
|
||||
@@ -0,0 +1,16 @@
|
||||
# Mode: fix_params
|
||||
|
||||
Goal: adjust **generation parameters** for Krea 2 Turbo (or Raw if context says so).
|
||||
|
||||
## Guidelines
|
||||
|
||||
- Turbo: prefer steps 4–12 (default 8), CFG ~1, sigma shift ~1.15.
|
||||
- Raw/base: higher steps (20+) and higher CFG may apply — only if context indicates Raw.
|
||||
- Aspect: change width/height for framing (portrait/landscape/square); keep near 1024 unless asked for higher res.
|
||||
- Do not change the prompt unless needed to match the new framing.
|
||||
- Keep LoRAs unless the user asks to drop them.
|
||||
|
||||
## Deliverable
|
||||
|
||||
- Explain the param change.
|
||||
- JSON patch focusing on `width`, `height`, `steps`, `cfg` (and `prompt` only if necessary).
|
||||
@@ -0,0 +1,17 @@
|
||||
# Mode: write_prompt
|
||||
|
||||
Goal: craft or improve a **Krea 2** prompt that will generate well on Turbo.
|
||||
|
||||
## How to write the prompt
|
||||
|
||||
- Natural language + concrete visual details (subject, lighting, lens/mood, composition).
|
||||
- Put **LoRA trigger phrases** near the subject they affect; do not dump unrelated tags.
|
||||
- Prefer clarity over keyword stuffing. Krea 2 understands sentences.
|
||||
- If the user wants a style covered by an available LoRA, enable that LoRA and weave its triggers in.
|
||||
- Keep Turbo defaults unless the user asks otherwise (steps 8, cfg 1).
|
||||
|
||||
## Deliverable
|
||||
|
||||
- Explain briefly what you changed.
|
||||
- Emit a JSON patch with at least `prompt`, and `loras` when relevant.
|
||||
- Include `width`/`height` only if aspect should change for the scene (e.g. portrait → taller).
|
||||
@@ -0,0 +1,45 @@
|
||||
# Swarm Assistent
|
||||
|
||||
SwarmUI extension for **collaborative Krea 2** prompting via **Ollama**: chat + vision, LoRA/trigger awareness, and applyable prompt/size patches.
|
||||
|
||||
## Layout
|
||||
|
||||
- **Left:** current / selected image preview (attach for vision)
|
||||
- **Right (wider):** chat
|
||||
- **Top-right:** settings (Ollama URL, model, auto-attach)
|
||||
|
||||
## Requirements
|
||||
|
||||
- SwarmUI with a **Krea 2** checkpoint selected
|
||||
- Ollama on `http://127.0.0.1:11434` (gpu-rent default when `LLM_RUNTIME=ollama`)
|
||||
- A chat+vision-capable Ollama model recommended for critique mode
|
||||
|
||||
## Install
|
||||
|
||||
Clone into SwarmUI `src/Extensions/swarm-assistent` (or let **gpu-rent** seed it from `extensions.yaml` with `requires: ollama`):
|
||||
|
||||
```yaml
|
||||
swarmui:
|
||||
- url: https://gitea.hsrv.site/mrleo1nid/swarm-assistent.git
|
||||
ref: main
|
||||
dir: swarm-assistent
|
||||
requires: ollama
|
||||
```
|
||||
|
||||
Restart / rebuild SwarmUI after clone.
|
||||
|
||||
## Prompt packs
|
||||
|
||||
| Pack | Role |
|
||||
| --- | --- |
|
||||
| `base_krea2` | Always injected: Krea 2 rules + JSON patch contract |
|
||||
| `write_prompt` | Craft / improve prompts |
|
||||
| `critique_image` | Vision critique → fixes |
|
||||
| `compose_scene` | Scene / moodboard |
|
||||
| `fix_params` | Width/height/steps/CFG |
|
||||
|
||||
Live context (checkpoint, LoRAs + triggers, current params) is injected every request.
|
||||
|
||||
## License
|
||||
|
||||
MIT
|
||||
@@ -0,0 +1,236 @@
|
||||
using System;
|
||||
using System.Collections.Generic;
|
||||
using System.IO;
|
||||
using System.Linq;
|
||||
using System.Net.Http;
|
||||
using System.Text;
|
||||
using System.Threading.Tasks;
|
||||
using FreneticUtilities.FreneticExtensions;
|
||||
using Newtonsoft.Json.Linq;
|
||||
using SwarmUI.Accounts;
|
||||
using SwarmUI.Core;
|
||||
using SwarmUI.Utils;
|
||||
using SwarmUI.WebAPI;
|
||||
|
||||
namespace Mrleo1nid.SwarmAssistent;
|
||||
|
||||
/// <summary>Krea 2 collaborative assistant: Ollama chat + vision + prompt/LoRA/params patches.</summary>
|
||||
public class SwarmAssistentExtension : Extension
|
||||
{
|
||||
public static PermInfo PermUse = Permissions.Register(new(
|
||||
"swarm_assistent_use",
|
||||
"[Swarm Assistent] Use",
|
||||
"Allows using the Swarm Assistent chat (Ollama proxy).",
|
||||
PermissionDefault.USER,
|
||||
Permissions.GroupUser));
|
||||
|
||||
public static HttpClient HttpClient;
|
||||
|
||||
public static readonly string[] PackNames =
|
||||
[
|
||||
"base_krea2",
|
||||
"write_prompt",
|
||||
"critique_image",
|
||||
"compose_scene",
|
||||
"fix_params",
|
||||
];
|
||||
|
||||
public override void OnPreInit()
|
||||
{
|
||||
ScriptFiles.Add("Assets/assistent.js");
|
||||
StyleSheetFiles.Add("Assets/assistent.css");
|
||||
ExtensionAuthor = "mrleo1nid";
|
||||
Description = "Collaborative Krea 2 assistant via Ollama: chat, vision, prompts, LoRA triggers, size patches.";
|
||||
License = "MIT";
|
||||
Version = "0.1.0";
|
||||
Tags = ["tabs", "ui", "llm", "ollama", "krea"];
|
||||
}
|
||||
|
||||
public override void OnInit()
|
||||
{
|
||||
HttpClient ??= new HttpClient { Timeout = TimeSpan.FromMinutes(10) };
|
||||
API.RegisterAPICall(AssistentListModels, false, PermUse);
|
||||
API.RegisterAPICall(AssistentGetPacks, false, PermUse);
|
||||
API.RegisterAPICall(AssistentChat, true, PermUse);
|
||||
Logs.Init("Swarm Assistent extension loaded (Ollama proxy + Krea 2 packs)");
|
||||
}
|
||||
|
||||
static string Clip(string text, int max)
|
||||
{
|
||||
if (string.IsNullOrEmpty(text) || text.Length <= max)
|
||||
{
|
||||
return text ?? "";
|
||||
}
|
||||
return text[..max] + "…";
|
||||
}
|
||||
|
||||
public static string NormalizeBaseUrl(string raw)
|
||||
{
|
||||
string url = (raw ?? "").Trim();
|
||||
if (string.IsNullOrWhiteSpace(url))
|
||||
{
|
||||
url = "http://127.0.0.1:11434";
|
||||
}
|
||||
return url.TrimEnd('/');
|
||||
}
|
||||
|
||||
public string ReadPackFile(string name)
|
||||
{
|
||||
string safe = name.Replace('\\', '/').AfterLast('/').Replace("..", "");
|
||||
if (!PackNames.Contains(safe))
|
||||
{
|
||||
return null;
|
||||
}
|
||||
string path = Path.Combine(FilePath, "Prompts", $"{safe}.md");
|
||||
if (!File.Exists(path))
|
||||
{
|
||||
return null;
|
||||
}
|
||||
return File.ReadAllText(path, Encoding.UTF8);
|
||||
}
|
||||
|
||||
public async Task<JObject> AssistentListModels(Session session, string baseUrl)
|
||||
{
|
||||
string root = NormalizeBaseUrl(baseUrl);
|
||||
try
|
||||
{
|
||||
using HttpResponseMessage resp = await HttpClient.GetAsync($"{root}/api/tags");
|
||||
string body = await resp.Content.ReadAsStringAsync();
|
||||
if (!resp.IsSuccessStatusCode)
|
||||
{
|
||||
return new JObject { ["error"] = $"Ollama /api/tags HTTP {(int)resp.StatusCode}: {Clip(body, 400)}" };
|
||||
}
|
||||
JObject parsed = JObject.Parse(body);
|
||||
JArray models = [];
|
||||
foreach (JToken m in parsed["models"] as JArray ?? [])
|
||||
{
|
||||
models.Add(m["name"]?.ToString() ?? "");
|
||||
}
|
||||
return new JObject { ["success"] = true, ["base_url"] = root, ["models"] = models };
|
||||
}
|
||||
catch (Exception ex)
|
||||
{
|
||||
return new JObject { ["error"] = $"Ollama unreachable at {root}: {ex.Message}" };
|
||||
}
|
||||
}
|
||||
|
||||
public async Task<JObject> AssistentGetPacks(Session session)
|
||||
{
|
||||
JObject packs = new();
|
||||
foreach (string name in PackNames)
|
||||
{
|
||||
string text = ReadPackFile(name);
|
||||
if (text is not null)
|
||||
{
|
||||
packs[name] = text;
|
||||
}
|
||||
}
|
||||
return new JObject { ["success"] = true, ["packs"] = packs, ["order"] = new JArray(PackNames) };
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Proxy to Ollama /api/chat (non-stream).
|
||||
/// <paramref name="raw"/> must include messages (JArray) and optional context_json.
|
||||
/// </summary>
|
||||
public async Task<JObject> AssistentChat(Session session, string baseUrl, string model, string pack, bool includeBase, JObject raw)
|
||||
{
|
||||
string root = NormalizeBaseUrl(baseUrl ?? raw?["base_url"]?.ToString());
|
||||
string modelName = (model ?? raw?["model"]?.ToString() ?? "").Trim();
|
||||
if (string.IsNullOrWhiteSpace(modelName))
|
||||
{
|
||||
return new JObject { ["error"] = "model is required" };
|
||||
}
|
||||
JArray userMessages = raw?["messages"] as JArray;
|
||||
if (userMessages is null || userMessages.Count == 0)
|
||||
{
|
||||
return new JObject { ["error"] = "messages required" };
|
||||
}
|
||||
|
||||
List<JObject> ollamaMessages = [];
|
||||
StringBuilder system = new();
|
||||
if (includeBase)
|
||||
{
|
||||
string basePack = ReadPackFile("base_krea2");
|
||||
if (!string.IsNullOrWhiteSpace(basePack))
|
||||
{
|
||||
system.AppendLine(basePack);
|
||||
}
|
||||
}
|
||||
string packName = (pack ?? raw?["pack"]?.ToString() ?? "write_prompt").Trim();
|
||||
if (!string.IsNullOrWhiteSpace(packName) && packName != "base_krea2")
|
||||
{
|
||||
string situational = ReadPackFile(packName);
|
||||
if (!string.IsNullOrWhiteSpace(situational))
|
||||
{
|
||||
system.AppendLine();
|
||||
system.AppendLine($"## Active mode: {packName}");
|
||||
system.AppendLine(situational);
|
||||
}
|
||||
}
|
||||
string contextJson = raw?["context_json"]?.ToString();
|
||||
if (!string.IsNullOrWhiteSpace(contextJson))
|
||||
{
|
||||
system.AppendLine();
|
||||
system.AppendLine("## Live SwarmUI context (JSON — trust this over guesses)");
|
||||
system.AppendLine("```json");
|
||||
system.AppendLine(contextJson);
|
||||
system.AppendLine("```");
|
||||
}
|
||||
if (system.Length > 0)
|
||||
{
|
||||
ollamaMessages.Add(new JObject
|
||||
{
|
||||
["role"] = "system",
|
||||
["content"] = system.ToString(),
|
||||
});
|
||||
}
|
||||
foreach (JToken msg in userMessages)
|
||||
{
|
||||
if (msg is not JObject mo)
|
||||
{
|
||||
continue;
|
||||
}
|
||||
JObject copy = new()
|
||||
{
|
||||
["role"] = mo["role"]?.ToString() ?? "user",
|
||||
["content"] = mo["content"]?.ToString() ?? "",
|
||||
};
|
||||
if (mo["images"] is JArray images && images.Count > 0)
|
||||
{
|
||||
copy["images"] = images;
|
||||
}
|
||||
ollamaMessages.Add(copy);
|
||||
}
|
||||
|
||||
JObject payload = new()
|
||||
{
|
||||
["model"] = modelName,
|
||||
["stream"] = false,
|
||||
["messages"] = new JArray(ollamaMessages),
|
||||
};
|
||||
try
|
||||
{
|
||||
using StringContent content = new(payload.ToString(Newtonsoft.Json.Formatting.None), Encoding.UTF8, "application/json");
|
||||
using HttpResponseMessage resp = await HttpClient.PostAsync($"{root}/api/chat", content);
|
||||
string body = await resp.Content.ReadAsStringAsync();
|
||||
if (!resp.IsSuccessStatusCode)
|
||||
{
|
||||
return new JObject { ["error"] = $"Ollama /api/chat HTTP {(int)resp.StatusCode}: {Clip(body, 800)}" };
|
||||
}
|
||||
JObject parsed = JObject.Parse(body);
|
||||
string reply = parsed["message"]?["content"]?.ToString() ?? parsed["response"]?.ToString() ?? "";
|
||||
return new JObject
|
||||
{
|
||||
["success"] = true,
|
||||
["reply"] = reply,
|
||||
["model"] = modelName,
|
||||
["pack"] = packName,
|
||||
["raw"] = parsed,
|
||||
};
|
||||
}
|
||||
catch (Exception ex)
|
||||
{
|
||||
return new JObject { ["error"] = $"Ollama chat failed: {ex.Message}" };
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,6 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk.Web">
|
||||
<PropertyGroup>
|
||||
<AssemblyName>SwarmAssistentExtension</AssemblyName>
|
||||
</PropertyGroup>
|
||||
<Import Project="../../SwarmUI.extension.props" />
|
||||
</Project>
|
||||
@@ -0,0 +1,48 @@
|
||||
<div class="swarm-assistent-root" id="swarm_assistent_root">
|
||||
<div class="sa-gate" id="sa_gate" hidden>
|
||||
<p>Swarm Assistent is for <strong>Krea 2</strong> models only. Select a Krea 2 checkpoint to enable the chat.</p>
|
||||
</div>
|
||||
<div class="sa-layout" id="sa_layout">
|
||||
<aside class="sa-image-pane">
|
||||
<div class="sa-image-frame" id="sa_image_frame">
|
||||
<img id="sa_image_preview" alt="" hidden />
|
||||
<div class="sa-image-empty" id="sa_image_empty">Generate or select an image</div>
|
||||
</div>
|
||||
<div class="sa-image-actions">
|
||||
<button type="button" class="basic-button" id="sa_btn_refresh_image">Refresh preview</button>
|
||||
<label class="sa-check"><input type="checkbox" id="sa_attach_vision" checked /> Attach to next message</label>
|
||||
</div>
|
||||
</aside>
|
||||
<section class="sa-chat-pane">
|
||||
<header class="sa-chat-header">
|
||||
<div class="sa-chat-title">Assistent</div>
|
||||
<div class="sa-header-right">
|
||||
<select id="sa_pack" class="sa-select" title="Prompt pack">
|
||||
<option value="write_prompt">Write prompt</option>
|
||||
<option value="critique_image">Critique image</option>
|
||||
<option value="compose_scene">Compose scene</option>
|
||||
<option value="fix_params">Fix params</option>
|
||||
</select>
|
||||
<button type="button" class="basic-button sa-icon-btn" id="sa_btn_settings" title="Settings" aria-label="Settings">⚙</button>
|
||||
</div>
|
||||
</header>
|
||||
<div class="sa-settings" id="sa_settings" hidden>
|
||||
<label>Ollama URL <input type="text" id="sa_base_url" value="http://127.0.0.1:11434" /></label>
|
||||
<label>Model
|
||||
<select id="sa_model" class="sa-select"></select>
|
||||
</label>
|
||||
<button type="button" class="basic-button" id="sa_btn_refresh_models">Refresh models</button>
|
||||
<label class="sa-check"><input type="checkbox" id="sa_auto_vision" /> Auto-attach current image</label>
|
||||
</div>
|
||||
<div class="sa-messages" id="sa_messages"></div>
|
||||
<div class="sa-composer">
|
||||
<textarea id="sa_input" rows="3" placeholder="Ask for a prompt, critique the image, change aspect…"></textarea>
|
||||
<div class="sa-composer-actions">
|
||||
<button type="button" class="basic-button" id="sa_btn_send">Send</button>
|
||||
<button type="button" class="basic-button" id="sa_btn_clear">Clear chat</button>
|
||||
<span class="sa-status" id="sa_status"></span>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
</div>
|
||||
</div>
|
||||
Reference in New Issue
Block a user