diff --git a/Assets/assistent.css b/Assets/assistent.css
index df63fcd..8774d96 100644
--- a/Assets/assistent.css
+++ b/Assets/assistent.css
@@ -201,6 +201,11 @@
     max-width: 12rem;
 }
 
+.sa-header-right .sa-model-select {
+    min-width: 14rem;
+    max-width: 24rem;
+}
+
 .sa-check {
     flex-direction: row !important;
     align-items: center;
@@ -279,6 +284,55 @@
     margin-top: 0.45rem;
 }
 
+.sa-danger {
+    opacity: 0.85;
+}
+
+.sa-civitai-list {
+    display: flex;
+    flex-direction: column;
+    gap: 0.45rem;
+    margin-top: 0.55rem;
+}
+
+.sa-civitai-card {
+    padding: 0.5rem 0.6rem;
+    border-radius: 0.45rem;
+    border: 1px solid color-mix(in srgb, currentColor 22%, transparent);
+    background: color-mix(in srgb, currentColor 5%, transparent);
+    font-size: 0.88rem;
+}
+
+.sa-civitai-card.sa-installed {
+    opacity: 0.7;
+}
+
+.sa-civitai-title {
+    font-weight: 600;
+    margin-bottom: 0.2rem;
+}
+
+.sa-civitai-meta {
+    opacity: 0.8;
+    font-size: 0.82rem;
+    margin-bottom: 0.35rem;
+}
+
+.sa-civitai-actions {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 0.35rem;
+}
+
+.sa-streaming {
+    opacity: 0.9;
+    font-style: italic;
+}
+
+.sa-interrupt-active {
+    border-color: color-mix(in srgb, #c44 55%, transparent) !important;
+}
+
 .sa-composer {
     border-top: 1px solid color-mix(in srgb, currentColor 18%, transparent);
     padding: 0.6rem 0.7rem 0.7rem;
diff --git a/Assets/assistent.js b/Assets/assistent.js
index da7152e..1eec03d 100644
--- a/Assets/assistent.js
+++ b/Assets/assistent.js
@@ -1,11 +1,16 @@
 /**
  * Swarm Assistent — Krea 2 collaborative chat (Ollama via Swarm API).
+ * v0.3: inventory, streaming, auto-apply/generate, Civitai Confirm.
  */
 (function () {
     const LS_BASE = 'swarm_assistent_base_url';
     const LS_MODEL = 'swarm_assistent_model';
     const LS_PACK = 'swarm_assistent_pack';
     const LS_AUTO_VISION = 'swarm_assistent_auto_vision';
+    const LS_AUTO_APPLY = 'swarm_assistent_auto_apply';
+    const LS_AUTO_GENERATE = 'swarm_assistent_auto_generate';
+    const LS_AUTO_CRITIQUE = 'swarm_assistent_auto_critique';
+    const LS_AUTO_DOWNLOAD = 'swarm_assistent_auto_download';
     const LS_PANE_WIDTH = 'swarm_assistent_pane_width';
     const TAB_BUTTON_ID = 'maintab_assistent';
 
@@ -13,9 +18,13 @@
         history: [],
         packsLoaded: false,
         busy: false,
+        generating: false,
         lastImageDataUrl: null,
         preferredModel: null,
         dragDepth: 0,
+        inventory: { loras: [], checkpoints: [], wildcards: [], has_civitai_key: false },
+        streamEl: null,
+        critiqueHopUsed: false,
     };
 
     function $(id) {
@@ -29,12 +38,19 @@
         }
     }
 
+    function setInterruptVisible(on) {
+        const btn = $('sa_btn_interrupt');
+        if (btn) {
+            btn.hidden = !on;
+            btn.classList.toggle('sa-interrupt-active', !!on);
+        }
+    }
+
     function looksLikeKrea(text) {
         const s = String(text || '');
         return /krea\s*2|krea2|krea-2/i.test(s) || /krea/i.test(s);
     }
 
-    /** Resolve current checkpoint from modern SwarmUI APIs (getCurrentModel is gone). */
     function resolveCurrentCheckpoint() {
         const out = {
             name: null,
@@ -165,7 +181,6 @@
             tab.click();
             return true;
         }
-        // Fallback: try hash / pane
         const pane = document.getElementById('assistent');
         if (pane && typeof bootstrap !== 'undefined' && bootstrap.Tab) {
             try {
@@ -181,7 +196,6 @@
             return;
         }
         frame.classList.remove('sa-flash');
-        // reflow
         void frame.offsetWidth;
         frame.classList.add('sa-flash');
     }
@@ -246,7 +260,183 @@
         }
     }
 
+    function countPromptImages() {
+        try {
+            const box = document.getElementById('alt_prompt_textbox') || document.getElementById('input_prompt');
+            if (!box) {
+                return 0;
+            }
+            const text = box.value || '';
+            const matches = text.match(/<image(?:\s|\/|>)/gi) || text.match(/data:image\//gi);
+            return matches ? matches.length : 0;
+        } catch (e) {
+            return 0;
+        }
+    }
+
+    function triggerChangeForEl(el) {
+        if (!el) {
+            return;
+        }
+        if (typeof triggerChangeFor === 'function') {
+            triggerChangeFor(el);
+            return;
+        }
+        el.dispatchEvent(new Event('input', { bubbles: true }));
+        el.dispatchEvent(new Event('change', { bubbles: true }));
+    }
+
+    function openInitImageGroup() {
+        try {
+            const initEl = document.getElementById('input_initimage');
+            if (initEl && typeof toggleGroupOpen === 'function') {
+                toggleGroupOpen(initEl, true);
+            }
+        } catch (e) { /* ignore */ }
+        const toggler = document.getElementById('input_group_content_initimage_toggle');
+        if (toggler) {
+            toggler.checked = true;
+            triggerChangeForEl(toggler);
+        }
+    }
+
+    function hasFileParam(id) {
+        const el = document.getElementById(id);
+        return !!(el && el.files && el.files.length > 0);
+    }
+
+    function clearFileParam(id) {
+        const el = document.getElementById(id);
+        if (!el) {
+            return false;
+        }
+        try {
+            el.value = '';
+            if (el.files && typeof DataTransfer !== 'undefined') {
+                el.files = new DataTransfer().files;
+            }
+        } catch (e) { /* ignore */ }
+        triggerChangeForEl(el);
+        return true;
+    }
+
+    function guessImageMime(src) {
+        const s = String(src || '');
+        if (s.startsWith('data:image/')) {
+            const m = s.match(/^data:(image\/[a-z0-9.+-]+)/i);
+            return (m && m[1]) || 'image/png';
+        }
+        const path = s.split('?')[0];
+        const ext = path.substring(path.lastIndexOf('.') + 1).toLowerCase();
+        if (ext === 'jpg' || ext === 'jpeg') {
+            return 'image/jpeg';
+        }
+        if (ext === 'webp') {
+            return 'image/webp';
+        }
+        if (ext === 'gif') {
+            return 'image/gif';
+        }
+        return 'image/png';
+    }
+
+    async function srcToBlob(src) {
+        if (!src) {
+            return null;
+        }
+        const cleaned = String(src).trim().split(/\s+/)[0];
+        if (cleaned.startsWith('data:') || cleaned.startsWith('/') || cleaned.startsWith('View/') || cleaned.startsWith('http')) {
+            try {
+                const resp = await fetch(cleaned);
+                return await resp.blob();
+            } catch (e) {
+                console.warn('Assistent: fetch blob failed', e);
+            }
+        }
+        return await new Promise((resolve) => {
+            const tmpImg = new Image();
+            tmpImg.crossOrigin = 'Anonymous';
+            tmpImg.onload = () => {
+                try {
+                    const canvas = document.createElement('canvas');
+                    canvas.width = tmpImg.naturalWidth;
+                    canvas.height = tmpImg.naturalHeight;
+                    const ctx = canvas.getContext('2d');
+                    ctx.drawImage(tmpImg, 0, 0);
+                    canvas.toBlob((blob) => resolve(blob), 'image/png');
+                } catch (e) {
+                    resolve(null);
+                }
+            };
+            tmpImg.onerror = () => resolve(null);
+            tmpImg.src = cleaned;
+        });
+    }
+
+    async function setFileParamFromSrc(paramId, src, { filename = 'assistent.png' } = {}) {
+        const el = document.getElementById(paramId);
+        if (!el) {
+            setStatus(`Missing ${paramId} on Generate tab`);
+            return false;
+        }
+        const blob = await srcToBlob(src);
+        if (!blob) {
+            setStatus('Could not load image for init/mask');
+            return false;
+        }
+        const mime = blob.type || guessImageMime(src);
+        const file = new File([blob], filename, { type: mime });
+        const container = new DataTransfer();
+        container.items.add(file);
+        el.files = container.files;
+        triggerChangeForEl(el);
+        openInitImageGroup();
+        return true;
+    }
+
+    async function setInitFromSrc(src) {
+        const ok = await setFileParamFromSrc('input_initimage', src, { filename: 'assistent_init.png' });
+        if (ok) {
+            setStatus('Init Image set');
+        }
+        return ok;
+    }
+
+    async function setMaskFromSrc(src) {
+        const ok = await setFileParamFromSrc('input_maskimage', src, { filename: 'assistent_mask.png' });
+        if (ok) {
+            setStatus('Mask Image set (white = edit)');
+        }
+        return ok;
+    }
+
+    function clearInitAndMask() {
+        clearFileParam('input_initimage');
+        clearFileParam('input_maskimage');
+        const toggler = document.getElementById('input_group_content_initimage_toggle');
+        if (toggler) {
+            toggler.checked = false;
+            triggerChangeForEl(toggler);
+        }
+        setStatus('Init Image + Mask cleared');
+    }
+
+    function readInitContext() {
+        const creativityRaw = val('input_initimagecreativity');
+        const creativity = creativityRaw === '' ? null : parseFloat(creativityRaw);
+        return {
+            has_init_image: hasFileParam('input_initimage'),
+            has_mask_image: hasFileParam('input_maskimage'),
+            init_creativity: Number.isFinite(creativity) ? creativity : null,
+            mask_blur: parseFloat(val('input_maskblur') || '') || null,
+            mask_grow: parseInt(val('input_maskgrow') || val('input_maskshrinkgrow') || '', 10) || null,
+            init_group_on: !!(document.getElementById('input_group_content_initimage_toggle')?.checked),
+        };
+    }
+
     function collectLiveContext() {
+        const inv = state.inventory || {};
+        const initCtx = readInitContext();
         const ctx = {
             architecture_ok: isKreaSelected(),
             checkpoint: null,
@@ -258,9 +448,18 @@
             cfg: parseFloat(val('input_cfgscale') || val('input_cfg') || '') || null,
             sigma_shift: parseFloat(val('input_sigmashift') || '') || null,
             seed: val('input_seed') || null,
+            sampler: val('input_sampler') || val('input_samplerate') || null,
+            scheduler: val('input_scheduler') || null,
+            batch: parseInt(val('input_images') || val('input_batchsize') || '0', 10) || null,
+            prompt_image_count: countPromptImages(),
             selected_loras: [],
-            available_loras: [],
+            available_loras: (inv.loras || []).slice(0, 100),
+            wildcards: (inv.wildcards || []).map((w) => w.name || w).slice(0, 60),
             has_vision_image: !!state.lastImageDataUrl,
+            has_civitai_key: !!inv.has_civitai_key,
+            auto_apply: !!$('sa_auto_apply')?.checked,
+            auto_generate: !!$('sa_auto_generate')?.checked,
+            ...initCtx,
         };
 
         try {
@@ -283,47 +482,65 @@
             }
         } catch (e) { /* ignore */ }
 
-        try {
-            const models = (typeof allModels !== 'undefined' && allModels) || (typeof model_list !== 'undefined' && model_list) || [];
-            const list = Array.isArray(models) ? models : Object.values(models || {});
-            for (const m of list) {
-                if (!m) {
-                    continue;
-                }
-                const folder = `${m.folder || m.path || ''}`;
-                const isLora = /lora/i.test(m.category || m.type || '') || (m.name && String(m.name).toLowerCase().includes('lora'));
-                const inLoraFolder = /lora/i.test(folder);
-                if (!(isLora || inLoraFolder)) {
-                    if (!/lora/i.test(JSON.stringify(m).slice(0, 200))) {
+        // Fallback if inventory empty
+        if (!ctx.available_loras.length) {
+            try {
+                const models = (typeof allModels !== 'undefined' && allModels) || (typeof model_list !== 'undefined' && model_list) || [];
+                const list = Array.isArray(models) ? models : Object.values(models || {});
+                for (const m of list) {
+                    if (!m) {
                         continue;
                     }
-                }
-                ctx.available_loras.push({
-                    name: m.name || m.title,
-                    title: m.title || m.name,
-                    trigger_phrase: m.trigger_phrase || m.trigger || (m.metadata && (m.metadata.trigger_phrase || m.metadata.trigger)) || null,
-                    architecture: m.architecture || null,
-                });
-            }
-            if (ctx.available_loras.length > 80) {
-                ctx.available_loras = ctx.available_loras.slice(0, 80);
-            }
-        } catch (e) { /* ignore */ }
-
-        try {
-            const sel = document.getElementById('input_loras');
-            if (sel && sel.options && ctx.available_loras.length === 0) {
-                for (const opt of sel.options) {
-                    if (opt.value) {
-                        ctx.available_loras.push({ name: opt.value, title: opt.text || opt.value, trigger_phrase: null });
+                    const folder = `${m.folder || m.path || ''}`;
+                    const isLora = /lora/i.test(m.category || m.type || '') || (m.name && String(m.name).toLowerCase().includes('lora'));
+                    const inLoraFolder = /lora/i.test(folder);
+                    if (!(isLora || inLoraFolder)) {
+                        continue;
                     }
+                    ctx.available_loras.push({
+                        name: m.name || m.title,
+                        title: m.title || m.name,
+                        trigger_phrase: m.trigger_phrase || m.trigger || (m.metadata && (m.metadata.trigger_phrase || m.metadata.trigger)) || null,
+                        architecture: m.architecture || null,
+                    });
                 }
-            }
-        } catch (e) { /* ignore */ }
+                if (ctx.available_loras.length > 80) {
+                    ctx.available_loras = ctx.available_loras.slice(0, 80);
+                }
+            } catch (e) { /* ignore */ }
+        }
 
         return ctx;
     }
 
+    function isPatchObject(obj) {
+        if (!obj || typeof obj !== 'object') {
+            return false;
+        }
+        return (
+            obj.prompt != null ||
+            obj.loras ||
+            obj.width ||
+            obj.height ||
+            obj.steps ||
+            obj.cfg ||
+            obj.seed != null ||
+            obj.sigma_shift != null ||
+            obj.sampler ||
+            obj.actions ||
+            obj.search_query ||
+            obj.civitai_query ||
+            obj.use_init_image != null ||
+            obj.clear_init_image != null ||
+            obj.init_creativity != null ||
+            obj.denoise != null ||
+            obj.use_mask_image != null ||
+            obj.clear_mask_image != null ||
+            obj.mask_blur != null ||
+            obj.mask_grow != null
+        );
+    }
+
     function extractPatch(text) {
         if (!text) {
             return { prose: text || '', patch: null };
@@ -336,7 +553,7 @@
             const raw = match[1].trim();
             try {
                 const obj = JSON.parse(raw);
-                if (obj && typeof obj === 'object' && (obj.prompt != null || obj.loras || obj.width || obj.height || obj.steps || obj.cfg)) {
+                if (isPatchObject(obj)) {
                     lastPatch = obj;
                     prose = (text.slice(0, match.index) + text.slice(match.index + match[0].length)).trim();
                 }
@@ -345,13 +562,39 @@
         return { prose, patch: lastPatch };
     }
 
-    function applyPatch(patch, which) {
+    function patchHasGenTrigger(patch) {
+        if (!patch) {
+            return false;
+        }
+        if (Array.isArray(patch.actions) && patch.actions.map(String).includes('generate')) {
+            return true;
+        }
+        return (
+            patch.prompt != null ||
+            patch.loras ||
+            patch.width != null ||
+            patch.height != null ||
+            patch.steps != null ||
+            patch.cfg != null ||
+            patch.seed != null ||
+            patch.sigma_shift != null ||
+            patch.use_init_image ||
+            patch.clear_init_image ||
+            patch.init_creativity != null ||
+            patch.denoise != null ||
+            patch.use_mask_image ||
+            patch.clear_mask_image
+        );
+    }
+
+    async function applyPatch(patch, which) {
         if (!patch) {
             return;
         }
         const doPrompt = !which || which === 'all' || which === 'prompt';
         const doLoras = !which || which === 'all' || which === 'loras';
-        const doSize = !which || which === 'all' || which === 'size';
+        const doParams = !which || which === 'all' || which === 'size' || which === 'params';
+        const doInit = !which || which === 'all' || which === 'params' || which === 'init';
 
         if (doPrompt && patch.prompt != null) {
             const box = document.getElementById('alt_prompt_textbox') || document.getElementById('input_prompt');
@@ -405,7 +648,7 @@
             } catch (e) { /* ignore */ }
         }
 
-        if (doSize) {
+        if (doParams) {
             if (patch.width != null) {
                 setVal('input_width', String(patch.width));
             }
@@ -422,14 +665,172 @@
                     setVal('input_cfg', String(patch.cfg));
                 }
             }
+            if (patch.seed != null) {
+                setVal('input_seed', String(patch.seed));
+            }
+            if (patch.sigma_shift != null) {
+                setVal('input_sigmashift', String(patch.sigma_shift));
+            }
+            if (patch.sampler != null) {
+                if (document.getElementById('input_sampler')) {
+                    setVal('input_sampler', String(patch.sampler));
+                }
+            }
+            if (patch.scheduler != null && document.getElementById('input_scheduler')) {
+                setVal('input_scheduler', String(patch.scheduler));
+            }
+        }
+
+        if (doInit) {
+            const creativity = patch.init_creativity != null ? patch.init_creativity : patch.denoise;
+            if (creativity != null && document.getElementById('input_initimagecreativity')) {
+                setVal('input_initimagecreativity', String(creativity));
+                openInitImageGroup();
+            }
+            if (patch.mask_blur != null && document.getElementById('input_maskblur')) {
+                setVal('input_maskblur', String(patch.mask_blur));
+            }
+            if (patch.mask_grow != null) {
+                if (document.getElementById('input_maskgrow')) {
+                    setVal('input_maskgrow', String(patch.mask_grow));
+                } else if (document.getElementById('input_maskshrinkgrow')) {
+                    setVal('input_maskshrinkgrow', String(patch.mask_grow));
+                }
+            }
+            if (patch.clear_init_image || patch.clear_mask_image) {
+                if (patch.clear_init_image) {
+                    clearFileParam('input_initimage');
+                }
+                if (patch.clear_mask_image) {
+                    clearFileParam('input_maskimage');
+                }
+                if (patch.clear_init_image && patch.clear_mask_image) {
+                    const toggler = document.getElementById('input_group_content_initimage_toggle');
+                    if (toggler) {
+                        toggler.checked = false;
+                        triggerChangeForEl(toggler);
+                    }
+                }
+            }
+            const src = state.lastImageDataUrl || findCurrentGenerateSrc();
+            const wantInit = patch.use_init_image === true
+                || (Array.isArray(patch.actions) && patch.actions.map(String).includes('use_init'));
+            const wantMask = patch.use_mask_image === true
+                || (Array.isArray(patch.actions) && patch.actions.map(String).includes('use_mask'));
+            if (wantInit) {
+                if (src) {
+                    await setInitFromSrc(src);
+                } else {
+                    setStatus('No image for Init — drop/Send to Assistent first');
+                }
+            }
+            if (wantMask) {
+                if (src) {
+                    await setMaskFromSrc(src);
+                } else {
+                    setStatus('No image for Mask — drop a mask (white=edit) first');
+                }
+            }
         }
         setStatus('Applied patch');
     }
 
-    function appendMessage(role, text, patch) {
+    function triggerGenerate() {
+        try {
+            if (typeof mainGenHandler !== 'undefined' && mainGenHandler && typeof mainGenHandler.doGenerate === 'function') {
+                mainGenHandler.doGenerate();
+                return true;
+            }
+        } catch (e) {
+            console.warn('Assistent: doGenerate failed', e);
+        }
+        const btn =
+            document.getElementById('generate_button') ||
+            document.getElementById('alt_generate_button') ||
+            document.querySelector('button.generate-button') ||
+            document.querySelector('#generate_button, button[id*="generate"]');
+        if (btn) {
+            btn.click();
+            return true;
+        }
+        return false;
+    }
+
+    function doInterruptNow() {
+        try {
+            if (typeof doInterrupt === 'function') {
+                doInterrupt(false);
+                return;
+            }
+        } catch (e) { /* ignore */ }
+        if (typeof genericRequest === 'function') {
+            genericRequest('InterruptAll', { other_sessions: false }, () => {}, 0, () => {});
+        }
+    }
+
+    function waitForNewImage(prevSrc, timeoutMs = 180000) {
+        return new Promise((resolve) => {
+            const start = Date.now();
+            const timer = setInterval(() => {
+                const src = findCurrentGenerateSrc();
+                if (src && src !== prevSrc) {
+                    clearInterval(timer);
+                    resolve(src);
+                } else if (Date.now() - start > timeoutMs) {
+                    clearInterval(timer);
+                    resolve(null);
+                }
+            }, 400);
+        });
+    }
+
+    async function runGenerateFromPatch(patch) {
+        if (!$('sa_auto_generate')?.checked || !patchHasGenTrigger(patch)) {
+            return null;
+        }
+        const prev = findCurrentGenerateSrc();
+        setStatus('Generating…');
+        state.generating = true;
+        setInterruptVisible(true);
+        const ok = triggerGenerate();
+        if (!ok) {
+            state.generating = false;
+            setInterruptVisible(state.busy);
+            setStatus('Could not start Generate (UI hook missing)');
+            return null;
+        }
+        const src = await waitForNewImage(prev);
+        state.generating = false;
+        setInterruptVisible(state.busy);
+        if (src) {
+            setImageFromSrc(src, { note: 'Result → vision' });
+            return src;
+        }
+        setStatus('Generate finished (no new image detected)');
+        return null;
+    }
+
+    async function maybeAutoCritique(imageSrc) {
+        if (!$('sa_auto_critique')?.checked || state.critiqueHopUsed || !imageSrc) {
+            return;
+        }
+        state.critiqueHopUsed = true;
+        const pack = $('sa_pack');
+        if (pack) {
+            pack.value = 'critique_image';
+            saveSettings();
+        }
+        if ($('sa_input')) {
+            $('sa_input').value = 'Critique this result and improve the prompt for the next generation.';
+        }
+        setStatus('Auto-critique…');
+        await sendChat({ fromAutoCritique: true });
+    }
+
+    function appendMessage(role, text, patch, civitaiResults) {
         const box = $('sa_messages');
         if (!box) {
-            return;
+            return null;
         }
         const div = document.createElement('div');
         div.className = `sa-msg ${role}`;
@@ -448,7 +849,7 @@
                 ['Apply all', 'all'],
                 ['Prompt', 'prompt'],
                 ['LoRAs', 'loras'],
-                ['Size', 'size'],
+                ['Params', 'params'],
             ]) {
                 const btn = document.createElement('button');
                 btn.type = 'button';
@@ -457,11 +858,209 @@
                 btn.addEventListener('click', () => applyPatch(finalPatch, which));
                 actions.appendChild(btn);
             }
+            const genBtn = document.createElement('button');
+            genBtn.type = 'button';
+            genBtn.className = 'basic-button';
+            genBtn.textContent = 'Apply + Generate';
+            genBtn.addEventListener('click', async () => {
+                await applyPatch(finalPatch, 'all');
+                await runGenerateFromPatch({ ...finalPatch, actions: ['generate'] });
+            });
+            actions.appendChild(genBtn);
             wrap.appendChild(actions);
             div.appendChild(wrap);
         }
+        if (civitaiResults && civitaiResults.length) {
+            div.appendChild(buildCivitaiCards(civitaiResults));
+        }
         box.appendChild(div);
         box.scrollTop = box.scrollHeight;
+        return div;
+    }
+
+    function beginStreamMessage() {
+        const box = $('sa_messages');
+        if (!box) {
+            return null;
+        }
+        const div = document.createElement('div');
+        div.className = 'sa-msg assistant sa-streaming';
+        div.textContent = '';
+        box.appendChild(div);
+        box.scrollTop = box.scrollHeight;
+        state.streamEl = div;
+        return div;
+    }
+
+    function appendStreamDelta(delta) {
+        if (!state.streamEl) {
+            beginStreamMessage();
+        }
+        if (state.streamEl) {
+            state.streamEl.textContent += delta;
+            const box = $('sa_messages');
+            if (box) {
+                box.scrollTop = box.scrollHeight;
+            }
+        }
+    }
+
+    function finalizeStreamMessage(fullReply, civitaiResults) {
+        const el = state.streamEl;
+        state.streamEl = null;
+        if (!el) {
+            appendMessage('assistant', fullReply, null, civitaiResults);
+            return;
+        }
+        el.classList.remove('sa-streaming');
+        const { prose, patch } = extractPatch(fullReply);
+        el.textContent = prose || fullReply || '';
+        if (patch) {
+            const wrap = document.createElement('div');
+            wrap.className = 'sa-patch';
+            const pre = document.createElement('pre');
+            pre.textContent = JSON.stringify(patch, 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'],
+                ['Params', 'params'],
+            ]) {
+                const btn = document.createElement('button');
+                btn.type = 'button';
+                btn.className = 'basic-button';
+                btn.textContent = label;
+                btn.addEventListener('click', () => applyPatch(patch, which));
+                actions.appendChild(btn);
+            }
+            const genBtn = document.createElement('button');
+            genBtn.type = 'button';
+            genBtn.className = 'basic-button';
+            genBtn.textContent = 'Apply + Generate';
+            genBtn.addEventListener('click', async () => {
+                await applyPatch(patch, 'all');
+                await runGenerateFromPatch({ ...patch, actions: ['generate'] });
+            });
+            actions.appendChild(genBtn);
+            wrap.appendChild(actions);
+            el.appendChild(wrap);
+        }
+        if (civitaiResults && civitaiResults.length) {
+            el.appendChild(buildCivitaiCards(civitaiResults));
+        }
+        const box = $('sa_messages');
+        if (box) {
+            box.scrollTop = box.scrollHeight;
+        }
+    }
+
+    function buildCivitaiCards(results) {
+        const list = document.createElement('div');
+        list.className = 'sa-civitai-list';
+        for (const r of results) {
+            const card = document.createElement('div');
+            card.className = 'sa-civitai-card' + (r.already_installed ? ' sa-installed' : '');
+            const title = document.createElement('div');
+            title.className = 'sa-civitai-title';
+            title.textContent = r.name || r.file_name || 'LoRA';
+            card.appendChild(title);
+            const meta = document.createElement('div');
+            meta.className = 'sa-civitai-meta';
+            const bits = [
+                r.base_model || '?',
+                r.krea_likely ? 'Krea?' : null,
+                r.already_installed ? 'already installed' : null,
+                (r.triggers || []).slice(0, 3).join(', ') || null,
+            ].filter(Boolean);
+            meta.textContent = bits.join(' · ');
+            card.appendChild(meta);
+            const actions = document.createElement('div');
+            actions.className = 'sa-civitai-actions';
+            if (r.already_installed) {
+                const note = document.createElement('span');
+                note.textContent = 'Installed';
+                actions.appendChild(note);
+            } else if (r.download_url) {
+                const btn = document.createElement('button');
+                btn.type = 'button';
+                btn.className = 'basic-button sa-primary';
+                btn.textContent = 'Confirm download';
+                btn.addEventListener('click', () => downloadCivitaiLoRA(r, btn));
+                actions.appendChild(btn);
+            } else {
+                const note = document.createElement('span');
+                note.textContent = 'No download URL';
+                actions.appendChild(note);
+            }
+            card.appendChild(actions);
+            list.appendChild(card);
+        }
+        return list;
+    }
+
+    function downloadCivitaiLoRA(card, btn) {
+        if (!card.download_url) {
+            return;
+        }
+        if (btn) {
+            btn.disabled = true;
+            btn.textContent = 'Downloading…';
+        }
+        setStatus(`Downloading ${card.file_name || card.name}…`);
+        setInterruptVisible(true);
+        const payload = {
+            url: card.download_url,
+            type: 'LoRA',
+            name: card.file_name || card.name || 'lora',
+        };
+        const onDone = (ok, msg) => {
+            setInterruptVisible(state.busy || state.generating);
+            if (ok) {
+                setStatus(`Downloaded ${payload.name}`);
+                if (btn) {
+                    btn.textContent = 'Downloaded';
+                }
+                refreshInventory(() => {
+                    if ($('sa_input')) {
+                        $('sa_input').value = `LoRA "${payload.name}" is now installed. Enable it with its triggers and improve the prompt.`;
+                    }
+                    sendChat({ fromDownload: true });
+                });
+            } else {
+                setStatus(msg || 'Download failed');
+                if (btn) {
+                    btn.disabled = false;
+                    btn.textContent = 'Confirm download';
+                }
+                appendMessage('error', msg || 'Download failed');
+            }
+        };
+        if (typeof makeWSRequest === 'function') {
+            makeWSRequest(
+                'DoModelDownloadWS',
+                payload,
+                (data) => {
+                    if (data.error) {
+                        onDone(false, String(data.error));
+                        return;
+                    }
+                    if (data.success || data.overall_percent >= 1 || data.current_percent >= 1) {
+                        if (data.success || data.overall_percent >= 0.99) {
+                            onDone(true);
+                        } else if (data.current_percent != null) {
+                            setStatus(`Download ${(data.current_percent * 100).toFixed(0)}%`);
+                        }
+                    }
+                },
+                0,
+                (err) => onDone(false, String(err || 'Download failed')),
+            );
+        } else {
+            onDone(false, 'makeWSRequest unavailable');
+        }
     }
 
     function findCurrentGenerateSrc() {
@@ -530,7 +1129,6 @@
                 return setImageFromSrc(first, { note: 'Image from drag' });
             }
         }
-        // HTML img drag
         const html = dt.getData('text/html') || '';
         const m = html.match(/src=["']([^"']+)["']/i);
         if (m && m[1]) {
@@ -571,6 +1169,10 @@
         const model = localStorage.getItem(LS_MODEL);
         const pack = localStorage.getItem(LS_PACK);
         const auto = localStorage.getItem(LS_AUTO_VISION);
+        const autoApply = localStorage.getItem(LS_AUTO_APPLY);
+        const autoGen = localStorage.getItem(LS_AUTO_GENERATE);
+        const autoCrit = localStorage.getItem(LS_AUTO_CRITIQUE);
+        const autoDl = localStorage.getItem(LS_AUTO_DOWNLOAD);
         const paneW = localStorage.getItem(LS_PANE_WIDTH);
         if (base && $('sa_base_url')) {
             $('sa_base_url').value = base;
@@ -581,6 +1183,18 @@
         if (auto != null && $('sa_auto_vision')) {
             $('sa_auto_vision').checked = auto === '1';
         }
+        if ($('sa_auto_apply')) {
+            $('sa_auto_apply').checked = autoApply == null ? true : autoApply === '1';
+        }
+        if ($('sa_auto_generate')) {
+            $('sa_auto_generate').checked = autoGen == null ? true : autoGen === '1';
+        }
+        if ($('sa_auto_critique') && autoCrit != null) {
+            $('sa_auto_critique').checked = autoCrit === '1';
+        }
+        if ($('sa_auto_download') && autoDl != null) {
+            $('sa_auto_download').checked = autoDl === '1';
+        }
         if (model) {
             state.preferredModel = model;
         }
@@ -594,42 +1208,134 @@
         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');
+        localStorage.setItem(LS_AUTO_APPLY, $('sa_auto_apply')?.checked ? '1' : '0');
+        localStorage.setItem(LS_AUTO_GENERATE, $('sa_auto_generate')?.checked ? '1' : '0');
+        localStorage.setItem(LS_AUTO_CRITIQUE, $('sa_auto_critique')?.checked ? '1' : '0');
+        localStorage.setItem(LS_AUTO_DOWNLOAD, $('sa_auto_download')?.checked ? '1' : '0');
+    }
+
+    function setModelOptions(models, { error } = {}) {
+        const sel = $('sa_model');
+        if (!sel) {
+            return;
+        }
+        const names = (models || []).map((n) => String(n || '').trim()).filter(Boolean);
+        sel.innerHTML = '';
+        if (error) {
+            const opt = document.createElement('option');
+            opt.value = '';
+            opt.textContent = `⚠ ${String(error).replace(/\s+/g, ' ').slice(0, 90)}`;
+            sel.appendChild(opt);
+            sel.disabled = true;
+            return;
+        }
+        sel.disabled = false;
+        if (!names.length) {
+            const opt = document.createElement('option');
+            opt.value = '';
+            opt.textContent = 'No Ollama models — pull / Refresh';
+            sel.appendChild(opt);
+            return;
+        }
+        for (const name of names) {
+            const opt = document.createElement('option');
+            opt.value = name;
+            opt.textContent = name;
+            sel.appendChild(opt);
+        }
+        const prefer = state.preferredModel || localStorage.getItem(LS_MODEL);
+        if (prefer && names.includes(prefer)) {
+            sel.value = prefer;
+        }
     }
 
     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;
+        if (typeof genericRequest !== 'function') {
+            setStatus('SwarmUI API not ready');
+            setModelOptions([], { error: 'SwarmUI API not ready' });
+            return;
+        }
+        genericRequest(
+            'AssistentListModels',
+            { baseUrl },
+            (data) => {
+                const models = data.models || [];
+                setModelOptions(models);
+                const prefer = state.preferredModel || localStorage.getItem(LS_MODEL);
+                if (prefer && models.includes(prefer) && $('sa_model')) {
+                    $('sa_model').value = prefer;
                 }
-                const opt = document.createElement('option');
-                opt.value = name;
-                opt.textContent = name;
-                sel.appendChild(opt);
-            }
-            const prefer = state.preferredModel || localStorage.getItem(LS_MODEL);
-            if (prefer && models.includes(prefer)) {
-                sel.value = prefer;
-            }
-            setStatus(models.length ? `${models.length} models` : 'No Ollama models');
-            saveSettings();
-        });
+                setStatus(models.length ? `${models.length} models` : 'No Ollama models (gpu-rent: ollama pull)');
+                saveSettings();
+            },
+            0,
+            (err) => {
+                const msg = String(err || 'Ollama unreachable');
+                setStatus(msg);
+                setModelOptions([], { error: msg });
+                appendMessage('error', msg);
+            },
+        );
     }
 
-    async function sendChat() {
+    function refreshInventory(done) {
+        if (typeof genericRequest !== 'function') {
+            if (done) {
+                done();
+            }
+            return;
+        }
+        genericRequest(
+            'AssistentListInventory',
+            {},
+            (data) => {
+                state.inventory = {
+                    loras: data.loras || [],
+                    checkpoints: data.checkpoints || [],
+                    wildcards: data.wildcards || [],
+                    has_civitai_key: !!data.has_civitai_key,
+                };
+                setStatus(`Inventory: ${state.inventory.loras.length} LoRAs, ${state.inventory.wildcards.length} wildcards`);
+                if (done) {
+                    done();
+                }
+            },
+            0,
+            (err) => {
+                console.warn('Assistent inventory', err);
+                if (done) {
+                    done();
+                }
+            },
+        );
+    }
+
+    async function handleReplySideEffects(reply, civitaiResults, { fromAutoCritique } = {}) {
+        const { patch } = extractPatch(reply);
+        if (Array.isArray(patch?.actions) && patch.actions.map(String).includes('interrupt')) {
+            doInterruptNow();
+        }
+        if (civitaiResults && civitaiResults.length && $('sa_auto_download')?.checked) {
+            const pick = civitaiResults.find((r) => !r.already_installed && r.download_url && r.krea_likely)
+                || civitaiResults.find((r) => !r.already_installed && r.download_url);
+            if (pick) {
+                downloadCivitaiLoRA(pick, null);
+            }
+        }
+        if (patch && $('sa_auto_apply')?.checked) {
+            await applyPatch(patch, 'all');
+            if (!fromAutoCritique) {
+                const src = await runGenerateFromPatch(patch);
+                if (src) {
+                    await maybeAutoCritique(src);
+                }
+            }
+        }
+    }
+
+    async function sendChat(opts = {}) {
         if (state.busy) {
             return;
         }
@@ -662,6 +1368,10 @@
             }
         }
 
+        if (!opts.fromAutoCritique && !opts.fromDownload) {
+            state.critiqueHopUsed = false;
+        }
+
         state.history.push({ role: 'user', content: text });
         appendMessage('user', text);
         $('sa_input').value = '';
@@ -674,35 +1384,122 @@
         }
 
         state.busy = true;
+        setInterruptVisible(true);
         setStatus('Thinking…');
         saveSettings();
 
+        const baseUrl = $('sa_base_url')?.value || 'http://127.0.0.1:11434';
+        // Flat fields: SwarmUI JObject params receive the whole request body.
         const payload = {
-            baseUrl: $('sa_base_url')?.value || 'http://127.0.0.1:11434',
+            baseUrl,
             model,
             pack,
             includeBase: true,
+            messages,
+            context_json: JSON.stringify(context),
             raw: {
                 messages,
                 context_json: JSON.stringify(context),
                 pack,
-                base_url: $('sa_base_url')?.value || 'http://127.0.0.1:11434',
+                base_url: baseUrl,
                 model,
             },
         };
 
-        genericRequest('AssistentChat', payload, (data) => {
+        const finishOk = async (reply, civitaiResults) => {
             state.busy = false;
-            if (data.error) {
-                setStatus(data.error);
-                appendMessage('error', data.error);
-                return;
-            }
-            const reply = data.reply || '';
+            setInterruptVisible(state.generating);
             state.history.push({ role: 'assistant', content: reply });
-            appendMessage('assistant', reply);
             setStatus('Done');
-        });
+            await handleReplySideEffects(reply, civitaiResults, opts);
+        };
+
+        const finishErr = (msg) => {
+            state.busy = false;
+            setInterruptVisible(state.generating);
+            setStatus(msg);
+            if (state.streamEl) {
+                state.streamEl.classList.remove('sa-streaming');
+                state.streamEl.classList.add('error');
+                state.streamEl.textContent = msg;
+                state.streamEl = null;
+            } else {
+                appendMessage('error', msg);
+            }
+        };
+
+        if (typeof makeWSRequest === 'function') {
+            beginStreamMessage();
+            makeWSRequest(
+                'AssistentChatWS',
+                payload,
+                (data) => {
+                    if (data.error) {
+                        finishErr(String(data.error));
+                        return;
+                    }
+                    if (data.clear_stream) {
+                        if (state.streamEl) {
+                            state.streamEl.textContent = '';
+                        }
+                        setStatus(data.notice || 'Refining…');
+                        return;
+                    }
+                    if (data.delta) {
+                        appendStreamDelta(data.delta);
+                        setStatus('Thinking…');
+                        return;
+                    }
+                    if (data.done || data.reply != null) {
+                        const reply = data.reply || (state.streamEl && state.streamEl.textContent) || '';
+                        const civitai = data.civitai_results || [];
+                        finalizeStreamMessage(reply, civitai);
+                        finishOk(reply, civitai);
+                    }
+                },
+                0,
+                (err) => {
+                    // Fallback to HTTP AssistentChat
+                    console.warn('AssistentChatWS failed, falling back', err);
+                    if (state.streamEl) {
+                        state.streamEl.remove();
+                        state.streamEl = null;
+                    }
+                    genericRequest(
+                        'AssistentChat',
+                        payload,
+                        (data) => {
+                            if (data.error) {
+                                finishErr(String(data.error));
+                                return;
+                            }
+                            const reply = data.reply || '';
+                            appendMessage('assistant', reply, null, data.civitai_results || []);
+                            finishOk(reply, data.civitai_results || []);
+                        },
+                        0,
+                        (err2) => finishErr(String(err2 || err || 'Chat failed')),
+                    );
+                },
+            );
+            return;
+        }
+
+        genericRequest(
+            'AssistentChat',
+            payload,
+            (data) => {
+                if (data.error) {
+                    finishErr(String(data.error));
+                    return;
+                }
+                const reply = data.reply || '';
+                appendMessage('assistant', reply, null, data.civitai_results || []);
+                finishOk(reply, data.civitai_results || []);
+            },
+            0,
+            (err) => finishErr(String(err || 'Chat failed')),
+        );
     }
 
     function wireDropZone() {
@@ -745,7 +1542,6 @@
             await handleDropDataTransfer(e.dataTransfer);
         });
 
-        // Also accept drops on the whole layout (easier target)
         const layout = $('sa_layout');
         layout?.addEventListener('dragover', (e) => {
             if (e.dataTransfer?.types?.includes('Files') || e.dataTransfer?.types?.includes('text/uri-list')) {
@@ -760,31 +1556,12 @@
             await handleDropDataTransfer(e.dataTransfer);
         });
 
-        frame.addEventListener('paste', async (e) => {
-            const items = e.clipboardData?.items;
-            if (!items) {
-                return;
-            }
-            for (const item of items) {
-                if (item.type.startsWith('image/')) {
-                    e.preventDefault();
-                    const file = item.getAsFile();
-                    if (file) {
-                        await acceptImageFile(file);
-                    }
-                    return;
-                }
-            }
-        });
-
-        // Global paste when Assistent tab is visible
         document.addEventListener('paste', async (e) => {
             const pane = document.getElementById('assistent');
             if (!pane || !pane.classList.contains('active')) {
                 return;
             }
             if (e.target && (e.target.tagName === 'TEXTAREA' || e.target.tagName === 'INPUT')) {
-                // Still allow image paste over text fields when clipboard has image
                 const items = e.clipboardData?.items;
                 let hasImage = false;
                 if (items) {
@@ -855,11 +1632,9 @@
 
     function registerSendButton() {
         if (typeof registerMediaButton !== 'function') {
-            // SwarmUI core not ready yet — retry briefly
             setTimeout(registerSendButton, 500);
             return;
         }
-        // Avoid duplicate registrations on hot-reload
         if (window.__swarmAssistentMediaRegistered) {
             return;
         }
@@ -871,7 +1646,6 @@
                     switchTab: true,
                     note: 'Image sent to Assistent',
                 });
-                // Prefer critique pack when coming from an image
                 const pack = $('sa_pack');
                 if (pack && pack.value === 'write_prompt') {
                     pack.value = 'critique_image';
@@ -889,10 +1663,19 @@
         if (!$('swarm_assistent_root')) {
             return;
         }
+        if (typeof genericRequest !== 'function') {
+            setTimeout(wire, 300);
+            return;
+        }
+        if (window.__swarmAssistentWired) {
+            return;
+        }
+        window.__swarmAssistentWired = true;
         loadSettings();
         updateGate();
         refreshImagePreview();
         refreshModels();
+        refreshInventory();
         wireDropZone();
         wireSplitter();
         registerSendButton();
@@ -907,6 +1690,7 @@
             saveSettings();
             refreshModels();
         });
+        $('sa_btn_refresh_inventory')?.addEventListener('click', () => refreshInventory());
         $('sa_btn_use_current')?.addEventListener('click', () => {
             const src = findCurrentGenerateSrc();
             if (src) {
@@ -915,10 +1699,48 @@
                 setStatus('No current Generate image');
             }
         });
+        $('sa_btn_as_init')?.addEventListener('click', async () => {
+            const src = state.lastImageDataUrl || findCurrentGenerateSrc();
+            if (!src) {
+                setStatus('No vision/Generate image for Init');
+                return;
+            }
+            if (!state.lastImageDataUrl) {
+                setImageFromSrc(src);
+            }
+            await setInitFromSrc(src);
+            const pack = $('sa_pack');
+            if (pack && pack.value === 'write_prompt') {
+                pack.value = 'inpaint_edit';
+                saveSettings();
+            }
+        });
+        $('sa_btn_as_mask')?.addEventListener('click', async () => {
+            const src = state.lastImageDataUrl || findCurrentGenerateSrc();
+            if (!src) {
+                setStatus('No vision image for Mask');
+                return;
+            }
+            await setMaskFromSrc(src);
+            const pack = $('sa_pack');
+            if (pack) {
+                pack.value = 'inpaint_edit';
+                saveSettings();
+            }
+        });
+        $('sa_btn_clear_init')?.addEventListener('click', clearInitAndMask);
         $('sa_btn_clear_image')?.addEventListener('click', clearVisionImage);
         $('sa_btn_send')?.addEventListener('click', () => sendChat());
+        $('sa_btn_interrupt')?.addEventListener('click', () => {
+            doInterruptNow();
+            setStatus('Interrupted');
+            state.busy = false;
+            state.generating = false;
+            setInterruptVisible(false);
+        });
         $('sa_btn_clear')?.addEventListener('click', () => {
             state.history = [];
+            state.critiqueHopUsed = false;
             const box = $('sa_messages');
             if (box) {
                 box.innerHTML = '';
@@ -935,12 +1757,19 @@
         $('sa_model')?.addEventListener('change', saveSettings);
         $('sa_pack')?.addEventListener('change', saveSettings);
         $('sa_auto_vision')?.addEventListener('change', saveSettings);
+        $('sa_auto_apply')?.addEventListener('change', saveSettings);
+        $('sa_auto_generate')?.addEventListener('change', saveSettings);
+        $('sa_auto_critique')?.addEventListener('change', saveSettings);
+        $('sa_auto_download')?.addEventListener('change', saveSettings);
 
         setInterval(updateGate, 2000);
-        // Soft sync from Generate only when we have no pinned vision image
         setInterval(() => refreshImagePreview({ onlyIfEmpty: true }), 5000);
+        setInterval(() => {
+            if (!state.busy) {
+                refreshInventory();
+            }
+        }, 120000);
 
-        // Expose for console / other extensions
         window.swarmAssistent = {
             setImageFromSrc,
             clearVisionImage,
@@ -948,6 +1777,12 @@
             sendToAssistent: (src) => setImageFromSrc(src, { switchTab: true, note: 'Image sent to Assistent' }),
             isKreaSelected,
             resolveCurrentCheckpoint,
+            refreshInventory,
+            applyPatch,
+            triggerGenerate,
+            setInitFromSrc,
+            setMaskFromSrc,
+            clearInitAndMask,
         };
     }
 
diff --git a/Prompts/base_krea2.md b/Prompts/base_krea2.md
index 3709796..2e58099 100644
--- a/Prompts/base_krea2.md
+++ b/Prompts/base_krea2.md
@@ -15,15 +15,18 @@ You are **Swarm Assistent**, a collaborative art director for **Krea 2** image g
 
 A JSON block named "Live SwarmUI context" is attached. Treat it as ground truth:
 
-- Use only LoRAs listed in `available_loras` (by exact `name`).
+- Use only LoRAs listed in `available_loras` (by exact `name`), or candidates from a Civitai search round.
 - 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`.
+- Respect current width/height/steps/cfg/seed/sigma_shift/sampler unless the user asks to change them or the pack is `fix_params`.
+- `wildcards` lists installed wildcard names (`__name__` syntax in prompts).
+- `prompt_image_count` > 0 means Prompt Images are attached — warn if they may dominate.
+- **Init / inpaint:** `has_init_image`, `has_mask_image`, `init_creativity` (aka denoise, 0–1), `mask_blur`, `mask_grow`. `has_vision_image` is the Assistent pane reference (can become Init/Mask).
 
 ## 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):
+2. Then emit **one** fenced JSON patch (only fields you want to change):
 
 ```json
 {
@@ -34,14 +37,45 @@ A JSON block named "Live SwarmUI context" is attached. Treat it as ground truth:
   "height": 1280,
   "steps": 8,
   "cfg": 1,
+  "seed": -1,
+  "sigma_shift": 1.15,
+  "sampler": null,
+  "use_init_image": false,
+  "clear_init_image": false,
+  "init_creativity": 0.45,
+  "use_mask_image": false,
+  "clear_mask_image": false,
+  "mask_blur": null,
+  "mask_grow": null,
+  "actions": ["generate"],
+  "search_query": null,
   "notes": "one-line why"
 }
 ```
 
-Rules for the patch:
+### Patch rules
 
 - 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.
+
+### Init image / inpaint
+
+- **img2img:** set `use_init_image: true` (uses Assistent vision / current Generate image) and `init_creativity` (0 = almost copy, 1 = almost new). Typical edits: **0.25–0.45**; restyle: **0.5–0.7**. Alias `denoise` is accepted.
+- **Inpaint:** needs Init + Mask. Set `use_init_image: true` and `use_mask_image: true` only when the vision pane holds a proper mask (white = edit, black = keep). If the user has not painted a mask, tell them to use Swarm **Edit Image** / paint a mask, or press **As Mask** with a prepared mask — do not invent pixel masks.
+- `clear_init_image` / `clear_mask_image` to leave img2img mode.
+- Prompt Images ≠ Init Image. Prefer Init for structural edits; Prompt Images for style refs (and warn they can dominate).
+
+### Actions (auto-safe)
+
+- `"generate"` — after Apply, start generation (UI auto-generate is on by default).
+- `"use_init"` / `"use_mask"` — same as the boolean flags (optional).
+- `"search_civitai"` — Civitai search; user must **Confirm** downloads.
+- `"interrupt"` — stop generation.
+- Pure Q&A with no prompt/param change: omit the JSON patch entirely (do not burn GPU).
+
+### Auto-apply note
+
+The UI may auto-apply your patch and auto-generate when `actions` contains `generate` or when you change prompt/loras/size/init. Keep patches intentional.
diff --git a/Prompts/compose_scene.md b/Prompts/compose_scene.md
index 3de266b..b185253 100644
--- a/Prompts/compose_scene.md
+++ b/Prompts/compose_scene.md
@@ -8,7 +8,9 @@ Goal: co-create a scene / moodboard direction for **Krea 2**.
 - 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.
+- Missing style LoRA → `search_civitai` + `search_query` (Krea-compatible).
 
 ## Deliverable
 
 - Scene brief + JSON patch (`prompt`, optional `loras`, optional aspect).
+- Add `actions: ["generate"]` when ready to try the scene.
diff --git a/Prompts/critique_image.md b/Prompts/critique_image.md
index d1b5b0f..8163e15 100644
--- a/Prompts/critique_image.md
+++ b/Prompts/critique_image.md
@@ -9,8 +9,11 @@ Goal: look at the attached image (vision) and improve the next generation for **
 - 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.
+- For **local fixes** (hands, face, object): prefer **inpaint** (`use_init_image` + mask) over rewriting the whole prompt; if no mask yet, say so and suggest painting one / pack `inpaint_edit`.
+- For **global restyle**: img2img with moderate `init_creativity` (≈0.4–0.6) can be better than from-scratch.
 
 ## Deliverable
 
 - Short critique in the user's language.
-- JSON patch with improved `prompt` and any `loras` / size tweaks.
+- JSON patch with improved `prompt` and any `loras` / size / init tweaks.
+- Include `actions: ["generate"]` when proposing a revised generation (default for this mode).
diff --git a/Prompts/fix_params.md b/Prompts/fix_params.md
index 492340b..57d1c77 100644
--- a/Prompts/fix_params.md
+++ b/Prompts/fix_params.md
@@ -7,10 +7,14 @@ Goal: adjust **generation parameters** for Krea 2 Turbo (or Raw if context says
 - 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.
+- Seed: set `seed` when the user wants reproducibility; `-1` for random.
+- Sampler: only change if the form exposes it and the user asks.
+- **Init creativity** (`init_creativity` / denoise 0–1): only when `has_init_image` or enabling img2img — see pack `inpaint_edit`.
 - 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).
+- JSON patch focusing on `width`, `height`, `steps`, `cfg`, `seed`, `sigma_shift`, optional `init_creativity` (and `prompt` only if necessary).
+- Include `actions: ["generate"]` if the user wants to re-roll with the new params.
diff --git a/Prompts/inpaint_edit.md b/Prompts/inpaint_edit.md
new file mode 100644
index 0000000..ba05d64
--- /dev/null
+++ b/Prompts/inpaint_edit.md
@@ -0,0 +1,37 @@
+# Mode: inpaint_edit
+
+Goal: guide **img2img** (Init Image) and **inpainting** (Init + Mask) on **Krea 2**.
+
+## When to use which
+
+| Need | Setup |
+| --- | --- |
+| Soft edit / restyle whole frame | Init only + `init_creativity` |
+| Change one region (face, hand, logo) | Init + Mask (white = edit) |
+| Fresh image from text | Clear init/mask; normal t2i |
+
+## Creativity (denoise)
+
+- **0.2–0.35** — small fixes, keep composition
+- **0.4–0.55** — noticeable edit, still related
+- **0.6–0.8** — strong restyle; structure may drift
+- Always set `use_init_image: true` when enabling img2img from the Assistent vision / current image.
+
+## Mask rules
+
+- White = regenerate, black = preserve. Gray = partial.
+- Only set `use_mask_image: true` if context shows a vision image that is meant as a mask, or the user said they prepared one.
+- If `has_mask_image` is false and the user wants regional edit: ask them to paint a mask in Swarm Image Editor (or Assistent **As Mask**), then continue.
+- Optional: `mask_blur` / `mask_grow` for softer edges.
+
+## Prompting
+
+- Describe **what should appear in the edited region**, not the whole scene dump.
+- Keep LoRA triggers if the subject depends on them.
+- Match width/height to the init image when possible.
+
+## Deliverable
+
+- Short plan (img2img vs inpaint) in the user's language.
+- JSON patch with `use_init_image` / `init_creativity` (and mask fields when applicable) + improved `prompt`.
+- `actions: ["generate"]` when ready to run.
diff --git a/Prompts/write_prompt.md b/Prompts/write_prompt.md
index 5844280..6b6cc90 100644
--- a/Prompts/write_prompt.md
+++ b/Prompts/write_prompt.md
@@ -9,9 +9,11 @@ Goal: craft or improve a **Krea 2** prompt that will generate well on Turbo.
 - 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).
+- If a needed LoRA is missing from `available_loras`, use `actions: ["search_civitai"]` with a clear `search_query` (and prefer Krea base).
 
 ## Deliverable
 
 - Explain briefly what you changed.
 - Emit a JSON patch with at least `prompt`, and `loras` when relevant.
+- Include `actions: ["generate"]` when the user wants to see a new image.
 - Include `width`/`height` only if aspect should change for the scene (e.g. portrait → taller).
diff --git a/README.md b/README.md
index 409b966..9b7c90e 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,13 @@
 # Swarm Assistent
 
-SwarmUI extension for **collaborative Krea 2** prompting via **Ollama**: chat + vision, LoRA/trigger awareness, and applyable prompt/size patches.
+SwarmUI extension for **collaborative Krea 2** prompting via **Ollama**: chat + vision, LoRA/trigger awareness, applyable patches, **img2img / inpaint**, auto Generate, and Civitai search with Confirm.
 
 ## Layout
 
 - **Left:** vision reference (drop / paste / *Send to Assistent* from Generate)
 - **Splitter:** drag to resize panes
 - **Right (wider):** chat
-- **Top-right:** prompt pack + settings (Ollama URL, model, auto-attach)
+- **Top-right:** prompt pack + settings (Ollama URL, model, auto-apply / auto-generate)
 
 ## UX
 
@@ -15,13 +15,19 @@ SwarmUI extension for **collaborative Krea 2** prompting via **Ollama**: chat +
 - Drag images from Generate/History or the OS onto the left pane
 - Paste (`Ctrl+V`) while the Assistent tab is open
 - **Use current** / **Clear** for the vision reference
+- **As Init** / **As Mask** / **Clear Init** — wire the vision image into Swarm `Init Image` / `Mask Image` (img2img & inpaint)
 - Enter sends; Shift+Enter newline
+- **Auto-apply** + **Auto-generate** (default on): patch from the LLM is applied and Generate runs when the patch changes prompt/params or includes `actions: ["generate"]`
+- Pure Q&A without a patch does **not** start Generate
+- **Interrupt** stops Swarm generation / clears busy state
+- **Civitai** search cards require **Confirm download** (uses Swarm `DoModelDownloadWS` + stored `civitai_api` key). Auto-download is off by default.
 
 ## 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
+- Ollama on `http://127.0.0.1:11434` **on the GPU VM** (gpu-rent `LLM_RUNTIME=ollama`). The browser talks to SwarmUI; SwarmUI proxies `/api/tags` and `/api/chat`. URL in settings must stay `127.0.0.1:11434`, not the laptop tunnel port 17811.
+- At least one pulled model (`ollama pull` / `ollama-models.yaml`). Empty `/api/tags` → empty Model dropdown.
+- Optional: Civitai API key in SwarmUI User Settings for search/download.
 
 ## Install
 
@@ -41,13 +47,48 @@ Restart / rebuild SwarmUI after clone.
 
 | Pack | Role |
 | --- | --- |
-| `base_krea2` | Always injected: Krea 2 rules + JSON patch contract |
+| `base_krea2` | Always injected: Krea 2 rules + JSON patch / actions contract |
 | `write_prompt` | Craft / improve prompts |
 | `critique_image` | Vision critique → fixes |
 | `compose_scene` | Scene / moodboard |
-| `fix_params` | Width/height/steps/CFG |
+| `fix_params` | Width/height/steps/CFG/seed/σ-shift |
+| `inpaint_edit` | Init Image img2img + Mask inpaint |
 
-Live context (checkpoint, LoRAs + triggers, current params) is injected every request.
+Live context (checkpoint, server inventory LoRAs + triggers, wildcards, current params) is injected every request.
+
+### Patch actions
+
+```json
+{
+  "prompt": "...",
+  "loras": [{"name": "exact", "weight": 0.8, "triggers": ["..."]}],
+  "width": 1024, "height": 1280, "steps": 8, "cfg": 1,
+  "seed": -1, "sigma_shift": 1.15,
+  "use_init_image": true,
+  "init_creativity": 0.45,
+  "use_mask_image": false,
+  "actions": ["generate"],
+  "search_query": null
+}
+```
+
+- `generate` — auto-generate after apply (when enabled)
+- `use_init` / `use_mask` — set Assistent vision (or current Generate) as Init / Mask
+- `search_civitai` + `search_query` — server searches Civitai, second LLM hop, Confirm cards in UI
+- `interrupt` — stop current generation
+
+Mask convention: **white = edit**, black = keep. Creativity ≈ denoise (0–1).
+
+## API routes
+
+| Route | Role |
+| --- | --- |
+| `AssistentListModels` | Ollama `/api/tags` |
+| `AssistentListInventory` | LoRA / checkpoint / wildcard inventory from Swarm |
+| `AssistentSearchCivitai` | Civitai LoRA search |
+| `AssistentGetPacks` | Prompt pack texts |
+| `AssistentChat` | HTTP chat (+ Civitai hop) |
+| `AssistentChatWS` | Streaming chat WebSocket |
 
 ## License
 
diff --git a/SwarmAssistentExtension.cs b/SwarmAssistentExtension.cs
index 28584be..4efa5e3 100644
--- a/SwarmAssistentExtension.cs
+++ b/SwarmAssistentExtension.cs
@@ -3,18 +3,22 @@ using System.Collections.Generic;
 using System.IO;
 using System.Linq;
 using System.Net.Http;
+using System.Net.Http.Headers;
+using System.Net.WebSockets;
 using System.Text;
+using System.Text.RegularExpressions;
 using System.Threading.Tasks;
 using FreneticUtilities.FreneticExtensions;
 using Newtonsoft.Json.Linq;
 using SwarmUI.Accounts;
 using SwarmUI.Core;
+using SwarmUI.Text2Image;
 using SwarmUI.Utils;
 using SwarmUI.WebAPI;
 
 namespace Mrleo1nid.SwarmAssistent;
 
-/// <summary>Krea 2 collaborative assistant: Ollama chat + vision + prompt/LoRA/params patches.</summary>
+/// <summary>Krea 2 collaborative assistant: Ollama chat + vision + prompt/LoRA/params patches + Generate/Civitai.</summary>
 public class SwarmAssistentExtension : Extension
 {
     public static PermInfo PermUse = Permissions.Register(new(
@@ -33,17 +37,24 @@ public class SwarmAssistentExtension : Extension
         "critique_image",
         "compose_scene",
         "fix_params",
+        "inpaint_edit",
     ];
 
+    const int MaxCivitaiHops = 2;
+    const int MaxLorasInInventory = 120;
+    const int MaxWildcardsInInventory = 80;
+
+    static readonly Regex JsonFenceRe = new(@"```(?:json)?\s*([\s\S]*?)```", RegexOptions.IgnoreCase | RegexOptions.Compiled);
+
     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.";
+        Description = "Collaborative Krea 2 assistant via Ollama: chat, vision, img2img/inpaint, Generate loop, Civitai Confirm.";
         License = "MIT";
-        Version = "0.2.0";
-        Tags = ["tabs", "ui", "llm", "ollama", "krea"];
+        Version = "0.3.1";
+        Tags = ["tabs", "ui", "llm", "ollama", "krea", "inpaint"];
     }
 
     public override void OnInit()
@@ -51,8 +62,11 @@ public class SwarmAssistentExtension : Extension
         HttpClient ??= new HttpClient { Timeout = TimeSpan.FromMinutes(10) };
         API.RegisterAPICall(AssistentListModels, false, PermUse);
         API.RegisterAPICall(AssistentGetPacks, false, PermUse);
+        API.RegisterAPICall(AssistentListInventory, false, PermUse);
+        API.RegisterAPICall(AssistentSearchCivitai, false, PermUse);
         API.RegisterAPICall(AssistentChat, true, PermUse);
-        Logs.Init("Swarm Assistent extension loaded (Ollama proxy + Krea 2 packs)");
+        API.RegisterAPICall(AssistentChatWS, true, PermUse);
+        Logs.Init("Swarm Assistent extension loaded (Ollama proxy + Krea 2 packs + inventory/Civitai)");
     }
 
     static string Clip(string text, int max)
@@ -128,24 +142,231 @@ public class SwarmAssistentExtension : Extension
         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)
+    /// <summary>Server-side LoRA / checkpoint / wildcard inventory (not DOM scrape).</summary>
+    public async Task<JObject> AssistentListInventory(Session session)
     {
-        string root = NormalizeBaseUrl(baseUrl ?? raw?["base_url"]?.ToString());
-        string modelName = (model ?? raw?["model"]?.ToString() ?? "").Trim();
-        if (string.IsNullOrWhiteSpace(modelName))
+        await Task.CompletedTask;
+        JArray loras = [];
+        JArray checkpoints = [];
+        JArray wildcards = [];
+
+        if (Program.T2IModelSets.TryGetValue("LoRA", out T2IModelHandler loraHandler))
         {
-            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" };
+            foreach (T2IModel model in loraHandler.Models.Values.OrderBy(m => m.Name).Take(MaxLorasInInventory))
+            {
+                loras.Add(new JObject
+                {
+                    ["name"] = model.Name,
+                    ["title"] = model.Metadata?.Title ?? model.Title ?? model.Name,
+                    ["trigger_phrase"] = model.Metadata?.TriggerPhrase,
+                    ["architecture"] = model.ModelClass?.ID,
+                    ["compat_class"] = model.ModelClass?.CompatClass?.ID,
+                    ["hash"] = model.Metadata?.Hash ?? "",
+                });
+            }
         }
 
+        if (Program.T2IModelSets.TryGetValue("Stable-Diffusion", out T2IModelHandler ckptHandler))
+        {
+            foreach (T2IModel model in ckptHandler.Models.Values.OrderBy(m => m.Name).Take(60))
+            {
+                checkpoints.Add(new JObject
+                {
+                    ["name"] = model.Name,
+                    ["title"] = model.Metadata?.Title ?? model.Title ?? model.Name,
+                    ["architecture"] = model.ModelClass?.ID,
+                    ["compat_class"] = model.ModelClass?.CompatClass?.ID,
+                });
+            }
+        }
+
+        try
+        {
+            foreach (string name in WildcardsHelper.ListFiles.OrderBy(n => n).Take(MaxWildcardsInInventory))
+            {
+                wildcards.Add(new JObject { ["name"] = name });
+            }
+        }
+        catch (Exception ex)
+        {
+            Logs.Debug($"AssistentListInventory wildcards: {ex.Message}");
+        }
+
+        bool hasCivitaiKey = !string.IsNullOrWhiteSpace(session.User.GetGenericData("civitai_api", "key"));
+
+        return new JObject
+        {
+            ["success"] = true,
+            ["loras"] = loras,
+            ["checkpoints"] = checkpoints,
+            ["wildcards"] = wildcards,
+            ["has_civitai_key"] = hasCivitaiKey,
+        };
+    }
+
+    /// <summary>Search Civitai for LoRAs (prefers Krea 2 base). Uses Swarm-stored civitai_api key.</summary>
+    public async Task<JObject> AssistentSearchCivitai(Session session, string query, int limit = 8)
+    {
+        string q = (query ?? "").Trim();
+        if (string.IsNullOrWhiteSpace(q))
+        {
+            return new JObject { ["error"] = "query is required" };
+        }
+        limit = Math.Clamp(limit, 1, 20);
+        string apiKey = session.User.GetGenericData("civitai_api", "key") ?? "";
+        HashSet<string> installedNames = CollectInstalledLoraNames();
+        HashSet<string> installedHashes = CollectInstalledLoraHashes();
+
+        string[] hosts = ["civitai.red", "civitai.com"];
+        Exception lastEx = null;
+        foreach (string host in hosts)
+        {
+            try
+            {
+                string url = $"https://{host}/api/v1/models?limit={limit}&types=LORA&query={Uri.EscapeDataString(q)}";
+                using HttpRequestMessage req = new(HttpMethod.Get, url);
+                if (!string.IsNullOrWhiteSpace(apiKey))
+                {
+                    req.Headers.Authorization = new AuthenticationHeaderValue("Bearer", apiKey.Trim());
+                }
+                using HttpResponseMessage resp = await HttpClient.SendAsync(req);
+                string body = await resp.Content.ReadAsStringAsync();
+                if (!resp.IsSuccessStatusCode)
+                {
+                    lastEx = new Exception($"HTTP {(int)resp.StatusCode}: {Clip(body, 200)}");
+                    continue;
+                }
+                JObject parsed = JObject.Parse(body);
+                JArray items = parsed["items"] as JArray ?? [];
+                JArray results = [];
+                foreach (JToken item in items)
+                {
+                    if (item is not JObject mo)
+                    {
+                        continue;
+                    }
+                    JObject card = BuildCivitaiCard(mo, installedNames, installedHashes);
+                    if (card is not null)
+                    {
+                        results.Add(card);
+                    }
+                }
+                // Prefer Krea-compatible first
+                JArray sorted = new(results.OrderByDescending(t => LooksLikeKrea(t["base_model"]?.ToString())).ThenBy(t => t["name"]?.ToString()));
+                return new JObject
+                {
+                    ["success"] = true,
+                    ["query"] = q,
+                    ["host"] = host,
+                    ["results"] = sorted,
+                    ["has_civitai_key"] = !string.IsNullOrWhiteSpace(apiKey),
+                };
+            }
+            catch (Exception ex)
+            {
+                lastEx = ex;
+            }
+        }
+        return new JObject { ["error"] = $"Civitai search failed: {lastEx?.Message ?? "unknown"}" };
+    }
+
+    static bool LooksLikeKrea(string text) => !string.IsNullOrEmpty(text) && Regex.IsMatch(text, @"krea", RegexOptions.IgnoreCase);
+
+    static HashSet<string> CollectInstalledLoraNames()
+    {
+        HashSet<string> names = new(StringComparer.OrdinalIgnoreCase);
+        if (!Program.T2IModelSets.TryGetValue("LoRA", out T2IModelHandler handler))
+        {
+            return names;
+        }
+        foreach (T2IModel m in handler.Models.Values)
+        {
+            names.Add(m.Name);
+            string leaf = m.Name.Replace('\\', '/').AfterLast('/');
+            if (!string.IsNullOrEmpty(leaf))
+            {
+                names.Add(leaf);
+                names.Add(Path.GetFileNameWithoutExtension(leaf));
+            }
+        }
+        return names;
+    }
+
+    static HashSet<string> CollectInstalledLoraHashes()
+    {
+        HashSet<string> hashes = new(StringComparer.OrdinalIgnoreCase);
+        if (!Program.T2IModelSets.TryGetValue("LoRA", out T2IModelHandler handler))
+        {
+            return hashes;
+        }
+        foreach (T2IModel m in handler.Models.Values)
+        {
+            string h = m.Metadata?.Hash;
+            if (!string.IsNullOrWhiteSpace(h))
+            {
+                hashes.Add(h.Trim().ToLowerInvariant());
+            }
+        }
+        return hashes;
+    }
+
+    static JObject BuildCivitaiCard(JObject model, HashSet<string> installedNames, HashSet<string> installedHashes)
+    {
+        string name = model["name"]?.ToString() ?? "";
+        JArray versions = model["modelVersions"] as JArray;
+        JObject ver = versions?.FirstOrDefault() as JObject;
+        if (ver is null)
+        {
+            return null;
+        }
+        string baseModel = ver["baseModel"]?.ToString() ?? "";
+        JArray trained = ver["trainedWords"] as JArray ?? [];
+        List<string> triggers = trained.Select(t => t?.ToString()).Where(s => !string.IsNullOrWhiteSpace(s)).Take(8).ToList();
+        JObject file = null;
+        foreach (JToken f in ver["files"] as JArray ?? [])
+        {
+            if (f is JObject fo && (fo["primary"]?.Value<bool>() == true || (fo["name"]?.ToString() ?? "").EndsWith(".safetensors", StringComparison.OrdinalIgnoreCase)))
+            {
+                file = fo;
+                break;
+            }
+        }
+        file ??= (ver["files"] as JArray)?.FirstOrDefault() as JObject;
+        string downloadUrl = file?["downloadUrl"]?.ToString() ?? ver["downloadUrl"]?.ToString() ?? "";
+        string fileName = file?["name"]?.ToString() ?? "";
+        string sha = file?["hashes"]?["SHA256"]?.ToString() ?? file?["hashes"]?["AutoV2"]?.ToString() ?? "";
+        string saveName = string.IsNullOrWhiteSpace(fileName)
+            ? Regex.Replace(name, @"[^\w\-.]+", "_").Trim('_')
+            : Path.GetFileNameWithoutExtension(fileName);
+
+        bool already = false;
+        if (!string.IsNullOrWhiteSpace(sha) && installedHashes.Contains(sha.Trim().ToLowerInvariant()))
+        {
+            already = true;
+        }
+        else if (installedNames.Contains(saveName) || installedNames.Contains(name) || installedNames.Contains(fileName))
+        {
+            already = true;
+        }
+
+        return new JObject
+        {
+            ["id"] = model["id"],
+            ["version_id"] = ver["id"],
+            ["name"] = name,
+            ["base_model"] = baseModel,
+            ["krea_likely"] = LooksLikeKrea(baseModel),
+            ["triggers"] = new JArray(triggers),
+            ["download_url"] = downloadUrl,
+            ["file_name"] = saveName,
+            ["sha256"] = sha,
+            ["already_installed"] = already,
+            ["n_sfw"] = model["nsfw"]?.Value<bool>() ?? false,
+        };
+    }
+
+    List<JObject> BuildOllamaMessages(string packName, bool includeBase, string contextJson, JArray userMessages, string extraSystem = null)
+    {
         List<JObject> ollamaMessages = [];
         StringBuilder system = new();
         if (includeBase)
@@ -156,7 +377,6 @@ public class SwarmAssistentExtension : Extension
                 system.AppendLine(basePack);
             }
         }
-        string packName = (pack ?? raw?["pack"]?.ToString() ?? "write_prompt").Trim();
         if (!string.IsNullOrWhiteSpace(packName) && packName != "base_krea2")
         {
             string situational = ReadPackFile(packName);
@@ -167,7 +387,6 @@ public class SwarmAssistentExtension : Extension
                 system.AppendLine(situational);
             }
         }
-        string contextJson = raw?["context_json"]?.ToString();
         if (!string.IsNullOrWhiteSpace(contextJson))
         {
             system.AppendLine();
@@ -176,6 +395,11 @@ public class SwarmAssistentExtension : Extension
             system.AppendLine(contextJson);
             system.AppendLine("```");
         }
+        if (!string.IsNullOrWhiteSpace(extraSystem))
+        {
+            system.AppendLine();
+            system.AppendLine(extraSystem);
+        }
         if (system.Length > 0)
         {
             ollamaMessages.Add(new JObject
@@ -184,7 +408,7 @@ public class SwarmAssistentExtension : Extension
                 ["content"] = system.ToString(),
             });
         }
-        foreach (JToken msg in userMessages)
+        foreach (JToken msg in userMessages ?? [])
         {
             if (msg is not JObject mo)
             {
@@ -201,24 +425,264 @@ public class SwarmAssistentExtension : Extension
             }
             ollamaMessages.Add(copy);
         }
+        return ollamaMessages;
+    }
 
+    static JObject TryParsePatch(string reply)
+    {
+        if (string.IsNullOrWhiteSpace(reply))
+        {
+            return null;
+        }
+        foreach (Match match in JsonFenceRe.Matches(reply))
+        {
+            string raw = match.Groups[1].Value.Trim();
+            try
+            {
+                JObject obj = JObject.Parse(raw);
+                if (obj is not null && (obj["prompt"] != null || obj["loras"] != null || obj["width"] != null
+                    || obj["height"] != null || obj["steps"] != null || obj["cfg"] != null
+                    || obj["seed"] != null || obj["sigma_shift"] != null || obj["sampler"] != null
+                    || obj["actions"] != null || obj["search_query"] != null || obj["civitai_query"] != null
+                    || obj["use_init_image"] != null || obj["clear_init_image"] != null
+                    || obj["init_creativity"] != null || obj["denoise"] != null
+                    || obj["use_mask_image"] != null || obj["clear_mask_image"] != null
+                    || obj["mask_blur"] != null || obj["mask_grow"] != null))
+                {
+                    return obj;
+                }
+            }
+            catch
+            {
+                // not json
+            }
+        }
+        return null;
+    }
+
+    static string ExtractSearchQuery(JObject patch)
+    {
+        if (patch is null)
+        {
+            return null;
+        }
+        string q = (patch["search_query"] ?? patch["civitai_query"])?.ToString()?.Trim();
+        if (!string.IsNullOrWhiteSpace(q))
+        {
+            return q;
+        }
+        if (patch["actions"] is JArray acts)
+        {
+            foreach (JToken a in acts)
+            {
+                if (string.Equals(a?.ToString(), "search_civitai", StringComparison.OrdinalIgnoreCase))
+                {
+                    return q; // may still be null — caller checks
+                }
+            }
+        }
+        return null;
+    }
+
+    static bool WantsCivitaiSearch(JObject patch)
+    {
+        if (patch is null)
+        {
+            return false;
+        }
+        if (!string.IsNullOrWhiteSpace(ExtractSearchQuery(patch)))
+        {
+            return true;
+        }
+        if (patch["actions"] is JArray acts)
+        {
+            foreach (JToken a in acts)
+            {
+                if (string.Equals(a?.ToString(), "search_civitai", StringComparison.OrdinalIgnoreCase))
+                {
+                    return true;
+                }
+            }
+        }
+        return false;
+    }
+
+    async Task<(string reply, JObject raw, JArray civitaiResults)> RunChatWithHops(
+        Session session,
+        string root,
+        string modelName,
+        string packName,
+        bool includeBase,
+        string contextJson,
+        JArray userMessages,
+        Func<string, Task> onDelta = null,
+        Func<int, Task> onHopStart = null)
+    {
+        List<JObject> messages = BuildOllamaMessages(packName, includeBase, contextJson, userMessages);
+        JArray civitaiResults = [];
+        string reply = "";
+        JObject lastRaw = null;
+        for (int hop = 0; hop < MaxCivitaiHops; hop++)
+        {
+            if (onHopStart is not null)
+            {
+                await onHopStart(hop);
+            }
+            (reply, lastRaw) = await CallOllamaChat(root, modelName, messages, stream: onDelta is not null, onDelta);
+            JObject patch = TryParsePatch(reply);
+            if (hop + 1 >= MaxCivitaiHops || !WantsCivitaiSearch(patch))
+            {
+                break;
+            }
+            string query = ExtractSearchQuery(patch);
+            if (string.IsNullOrWhiteSpace(query))
+            {
+                query = userMessages.LastOrDefault(m => m["role"]?.ToString() == "user")?["content"]?.ToString() ?? "";
+            }
+            if (string.IsNullOrWhiteSpace(query))
+            {
+                break;
+            }
+            JObject search = await AssistentSearchCivitai(session, query, 8);
+            if (search["error"] is not null)
+            {
+                messages.Add(new JObject { ["role"] = "assistant", ["content"] = reply });
+                messages.Add(new JObject
+                {
+                    ["role"] = "user",
+                    ["content"] = $"Civitai search failed: {search["error"]}. Continue without download — use only available_loras from context.",
+                });
+                continue;
+            }
+            civitaiResults = search["results"] as JArray ?? [];
+            messages.Add(new JObject { ["role"] = "assistant", ["content"] = reply });
+            messages.Add(new JObject
+            {
+                ["role"] = "user",
+                ["content"] =
+                    "Civitai search results (JSON). Prefer `krea_likely: true`. Do NOT download yourself — the UI shows Confirm cards. " +
+                    "Pick useful LoRAs from results or available_loras, emit a normal patch (prompt/loras). " +
+                    "Omit search_civitai from actions unless you need a different query.\n```json\n" +
+                    civitaiResults.ToString(Newtonsoft.Json.Formatting.None) + "\n```",
+            });
+        }
+        return (reply, lastRaw, civitaiResults);
+    }
+
+    async Task<(string reply, JObject raw)> CallOllamaChat(
+        string root,
+        string modelName,
+        List<JObject> ollamaMessages,
+        bool stream,
+        Func<string, Task> onDelta)
+    {
         JObject payload = new()
         {
             ["model"] = modelName,
-            ["stream"] = false,
+            ["stream"] = stream,
             ["messages"] = new JArray(ollamaMessages),
         };
-        try
+        using StringContent content = new(payload.ToString(Newtonsoft.Json.Formatting.None), Encoding.UTF8, "application/json");
+        using HttpRequestMessage req = new(HttpMethod.Post, $"{root}/api/chat") { Content = content };
+        using HttpResponseMessage resp = await HttpClient.SendAsync(req, stream
+            ? HttpCompletionOption.ResponseHeadersRead
+            : HttpCompletionOption.ResponseContentRead);
+        if (!resp.IsSuccessStatusCode)
+        {
+            string errBody = await resp.Content.ReadAsStringAsync();
+            throw new Exception($"Ollama /api/chat HTTP {(int)resp.StatusCode}: {Clip(errBody, 800)}");
+        }
+        if (!stream)
         {
-            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 (reply, parsed);
+        }
+        StringBuilder full = new();
+        await using Stream streamBody = await resp.Content.ReadAsStreamAsync();
+        using StreamReader reader = new(streamBody, Encoding.UTF8);
+        JObject last = null;
+        while (true)
+        {
+            string line = await reader.ReadLineAsync();
+            if (line is null)
+            {
+                break;
+            }
+            if (string.IsNullOrWhiteSpace(line))
+            {
+                continue;
+            }
+            JObject chunk = JObject.Parse(line);
+            last = chunk;
+            string delta = chunk["message"]?["content"]?.ToString() ?? "";
+            if (!string.IsNullOrEmpty(delta))
+            {
+                full.Append(delta);
+                if (onDelta is not null)
+                {
+                    await onDelta(delta);
+                }
+            }
+            if (chunk["done"]?.Value<bool>() == true)
+            {
+                break;
+            }
+        }
+        return (full.ToString(), last ?? new JObject());
+    }
+
+    /// <summary>
+    /// SwarmUI passes the whole request as the JObject param (not only a nested key).
+    /// Support both flat fields and legacy nested <c>raw</c>.
+    /// </summary>
+    static void ExtractChatPayload(JObject raw, ref string baseUrl, ref string model, ref string pack, ref bool includeBase, out JArray userMessages, out string contextJson)
+    {
+        JObject whole = raw ?? [];
+        JObject nested = whole["raw"] as JObject;
+        if (string.IsNullOrWhiteSpace(baseUrl))
+        {
+            baseUrl = whole["base_url"]?.ToString()
+                ?? whole["baseUrl"]?.ToString()
+                ?? nested?["base_url"]?.ToString()
+                ?? nested?["baseUrl"]?.ToString();
+        }
+        if (string.IsNullOrWhiteSpace(model))
+        {
+            model = whole["model"]?.ToString() ?? nested?["model"]?.ToString();
+        }
+        if (string.IsNullOrWhiteSpace(pack))
+        {
+            pack = whole["pack"]?.ToString() ?? nested?["pack"]?.ToString();
+        }
+        if (whole["includeBase"] is not null)
+        {
+            includeBase = whole.Value<bool?>("includeBase") ?? includeBase;
+        }
+        userMessages = (whole["messages"] as JArray) ?? (nested?["messages"] as JArray);
+        contextJson = whole["context_json"]?.ToString() ?? nested?["context_json"]?.ToString();
+    }
+
+    /// <summary>Proxy to Ollama /api/chat (non-stream), with optional Civitai search hop.</summary>
+    public async Task<JObject> AssistentChat(Session session, string baseUrl, string model, string pack, bool includeBase, JObject raw)
+    {
+        ExtractChatPayload(raw, ref baseUrl, ref model, ref pack, ref includeBase, out JArray userMessages, out string contextJson);
+        string root = NormalizeBaseUrl(baseUrl);
+        string modelName = (model ?? "").Trim();
+        if (string.IsNullOrWhiteSpace(modelName))
+        {
+            return new JObject { ["error"] = "model is required" };
+        }
+        if (userMessages is null || userMessages.Count == 0)
+        {
+            return new JObject { ["error"] = "messages required" };
+        }
+        string packName = (pack ?? "write_prompt").Trim();
+        try
+        {
+            (string reply, JObject parsed, JArray civitai) = await RunChatWithHops(
+                session, root, modelName, packName, includeBase, contextJson, userMessages);
             return new JObject
             {
                 ["success"] = true,
@@ -226,6 +690,7 @@ public class SwarmAssistentExtension : Extension
                 ["model"] = modelName,
                 ["pack"] = packName,
                 ["raw"] = parsed,
+                ["civitai_results"] = civitai,
             };
         }
         catch (Exception ex)
@@ -233,4 +698,62 @@ public class SwarmAssistentExtension : Extension
             return new JObject { ["error"] = $"Ollama chat failed: {ex.Message}" };
         }
     }
+
+    /// <summary>WebSocket streaming chat (Ollama stream:true) + Civitai hops.</summary>
+    public async Task<JObject> AssistentChatWS(Session session, WebSocket ws, string baseUrl, string model, string pack, bool includeBase, JObject raw)
+    {
+        ExtractChatPayload(raw, ref baseUrl, ref model, ref pack, ref includeBase, out JArray userMessages, out string contextJson);
+        string root = NormalizeBaseUrl(baseUrl);
+        string modelName = (model ?? "").Trim();
+        if (string.IsNullOrWhiteSpace(modelName))
+        {
+            await ws.SendJson(new JObject { ["error"] = "model is required" }, API.WebsocketTimeout);
+            return null;
+        }
+        if (userMessages is null || userMessages.Count == 0)
+        {
+            await ws.SendJson(new JObject { ["error"] = "messages required" }, API.WebsocketTimeout);
+            return null;
+        }
+        string packName = (pack ?? "write_prompt").Trim();
+        try
+        {
+            async Task OnDelta(string delta)
+            {
+                if (ws.State == WebSocketState.Open)
+                {
+                    await ws.SendJson(new JObject { ["delta"] = delta }, API.WebsocketTimeout);
+                }
+            }
+            async Task OnHopStart(int hop)
+            {
+                if (ws.State == WebSocketState.Open && hop > 0)
+                {
+                    await ws.SendJson(new JObject
+                    {
+                        ["clear_stream"] = true,
+                        ["hop"] = hop + 1,
+                        ["notice"] = "Civitai search done — refining…",
+                    }, API.WebsocketTimeout);
+                }
+            }
+            (string reply, JObject parsed, JArray civitai) = await RunChatWithHops(
+                session, root, modelName, packName, includeBase, contextJson, userMessages, OnDelta, OnHopStart);
+            await ws.SendJson(new JObject
+            {
+                ["success"] = true,
+                ["done"] = true,
+                ["reply"] = reply,
+                ["model"] = modelName,
+                ["pack"] = packName,
+                ["raw"] = parsed,
+                ["civitai_results"] = civitai,
+            }, API.WebsocketTimeout);
+        }
+        catch (Exception ex)
+        {
+            await ws.SendJson(new JObject { ["error"] = $"Ollama chat failed: {ex.Message}" }, API.WebsocketTimeout);
+        }
+        return null;
+    }
 }
diff --git a/Tabs/Text2Image/Assistent.html b/Tabs/Text2Image/Assistent.html
index b934645..dc256af 100644
--- a/Tabs/Text2Image/Assistent.html
+++ b/Tabs/Text2Image/Assistent.html
@@ -15,6 +15,9 @@
             </div>
             <div class="sa-image-actions">
                 <button type="button" class="basic-button" id="sa_btn_use_current" title="Grab the current Generate preview">Use current</button>
+                <button type="button" class="basic-button" id="sa_btn_as_init" title="Set vision image as Swarm Init Image (img2img)">As Init</button>
+                <button type="button" class="basic-button" id="sa_btn_as_mask" title="Set vision image as Mask (inpaint — white=edit)">As Mask</button>
+                <button type="button" class="basic-button" id="sa_btn_clear_init" title="Clear Init Image + Mask on Generate tab">Clear Init</button>
                 <button type="button" class="basic-button" id="sa_btn_clear_image" title="Clear vision image">Clear</button>
                 <label class="sa-check"><input type="checkbox" id="sa_attach_vision" checked /> Attach next</label>
             </div>
@@ -29,23 +32,30 @@
                         <option value="critique_image">Critique image</option>
                         <option value="compose_scene">Compose scene</option>
                         <option value="fix_params">Fix params</option>
+                        <option value="inpaint_edit">Inpaint / img2img</option>
+                    </select>
+                    <select id="sa_model" class="sa-select sa-model-select" title="Ollama model">
+                        <option value="">Loading models…</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>
+                <button type="button" class="basic-button" id="sa_btn_refresh_inventory">Refresh inventory</button>
                 <label class="sa-check"><input type="checkbox" id="sa_auto_vision" /> Auto-attach current image</label>
+                <label class="sa-check"><input type="checkbox" id="sa_auto_apply" checked /> Auto-apply patch</label>
+                <label class="sa-check"><input type="checkbox" id="sa_auto_generate" checked /> Auto-generate after patch</label>
+                <label class="sa-check"><input type="checkbox" id="sa_auto_critique" /> Auto-critique after generate</label>
+                <label class="sa-check sa-danger" title="Dangerous — downloads without Confirm"><input type="checkbox" id="sa_auto_download" /> Auto-download Civitai (off)</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… (Enter = send, Shift+Enter = newline)"></textarea>
+                <textarea id="sa_input" rows="3" placeholder="Ask for a prompt, img2img, inpaint, critique… (Enter = send, Shift+Enter = newline)"></textarea>
                 <div class="sa-composer-actions">
                     <button type="button" class="basic-button sa-primary" id="sa_btn_send">Send</button>
+                    <button type="button" class="basic-button" id="sa_btn_interrupt" title="Interrupt generation / chat" hidden>Interrupt</button>
                     <button type="button" class="basic-button" id="sa_btn_clear">Clear chat</button>
                     <span class="sa-status" id="sa_status"></span>
                 </div>