Merge branch 'master' into feat/api-nodes/11labs-music

blueprints/.glsl/Brightness_and_Contrast_1.frag

@@ -0,0 +1,44 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform float u_float0; // Brightness slider -100..100
+uniform float u_float1; // Contrast slider -100..100
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+const float MID_GRAY = 0.18;  // 18% reflectance
+
+// sRGB gamma 2.2 approximation
+vec3 srgbToLinear(vec3 c) {
+    return pow(max(c, 0.0), vec3(2.2));
+}
+
+vec3 linearToSrgb(vec3 c) {
+    return pow(max(c, 0.0), vec3(1.0/2.2));
+}
+
+float mapBrightness(float b) {
+    return clamp(b / 100.0, -1.0, 1.0);
+}
+
+float mapContrast(float c) {
+    return clamp(c / 100.0 + 1.0, 0.0, 2.0);
+}
+
+void main() {
+    vec4 orig = texture(u_image0, v_texCoord);
+
+    float brightness = mapBrightness(u_float0);
+    float contrast   = mapContrast(u_float1);
+
+    vec3 lin = srgbToLinear(orig.rgb);
+
+    lin = (lin - MID_GRAY) * contrast + brightness + MID_GRAY;
+
+    // Convert back to sRGB
+    vec3 result = linearToSrgb(clamp(lin, 0.0, 1.0));
+
+    fragColor = vec4(result, orig.a);
+}

blueprints/.glsl/Chromatic_Aberration_16.frag

@@ -0,0 +1,72 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+uniform int u_int0;      // Mode
+uniform float u_float0;  // Amount (0 to 100)
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+const int MODE_LINEAR   = 0;
+const int MODE_RADIAL   = 1;
+const int MODE_BARREL   = 2;
+const int MODE_SWIRL    = 3;
+const int MODE_DIAGONAL = 4;
+
+const float AMOUNT_SCALE = 0.0005;
+const float RADIAL_MULT = 4.0;
+const float BARREL_MULT = 8.0;
+const float INV_SQRT2 = 0.70710678118;
+
+void main() {
+    vec2 uv = v_texCoord;
+    vec4 original = texture(u_image0, uv);
+
+    float amount = u_float0 * AMOUNT_SCALE;
+
+    if (amount < 0.000001) {
+        fragColor = original;
+        return;
+    }
+
+    // Aspect-corrected coordinates for circular effects
+    float aspect = u_resolution.x / u_resolution.y;
+    vec2 centered = uv - 0.5;
+    vec2 corrected = vec2(centered.x * aspect, centered.y);
+    float r = length(corrected);
+    vec2 dir = r > 0.0001 ? corrected / r : vec2(0.0);
+    vec2 offset = vec2(0.0);
+
+    if (u_int0 == MODE_LINEAR) {
+        // Horizontal shift (no aspect correction needed)
+        offset = vec2(amount, 0.0);
+    }
+    else if (u_int0 == MODE_RADIAL) {
+        // Outward from center, stronger at edges
+        offset = dir * r * amount * RADIAL_MULT;
+        offset.x /= aspect;  // Convert back to UV space
+    }
+    else if (u_int0 == MODE_BARREL) {
+        // Lens distortion simulation (r² falloff)
+        offset = dir * r * r * amount * BARREL_MULT;
+        offset.x /= aspect;  // Convert back to UV space
+    }
+    else if (u_int0 == MODE_SWIRL) {
+        // Perpendicular to radial (rotational aberration)
+        vec2 perp = vec2(-dir.y, dir.x);
+        offset = perp * r * amount * RADIAL_MULT;
+        offset.x /= aspect;  // Convert back to UV space
+    }
+    else if (u_int0 == MODE_DIAGONAL) {
+        // 45° offset (no aspect correction needed)
+        offset = vec2(amount, amount) * INV_SQRT2;
+    }
+    
+    float red = texture(u_image0, uv + offset).r;
+    float green = original.g;
+    float blue = texture(u_image0, uv - offset).b;
+    
+    fragColor = vec4(red, green, blue, original.a);
+}

blueprints/.glsl/Color_Adjustment_15.frag

@@ -0,0 +1,78 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform float u_float0; // temperature (-100 to 100)
+uniform float u_float1; // tint (-100 to 100)
+uniform float u_float2; // vibrance (-100 to 100)
+uniform float u_float3; // saturation (-100 to 100)
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+const float INPUT_SCALE = 0.01;
+const float TEMP_TINT_PRIMARY = 0.3;
+const float TEMP_TINT_SECONDARY = 0.15;
+const float VIBRANCE_BOOST = 2.0;
+const float SATURATION_BOOST = 2.0;
+const float SKIN_PROTECTION = 0.5;
+const float EPSILON = 0.001;
+const vec3 LUMA_WEIGHTS = vec3(0.299, 0.587, 0.114);
+
+void main() {
+    vec4 tex = texture(u_image0, v_texCoord);
+    vec3 color = tex.rgb;
+    
+    // Scale inputs: -100/100 → -1/1
+    float temperature = u_float0 * INPUT_SCALE;
+    float tint = u_float1 * INPUT_SCALE;
+    float vibrance = u_float2 * INPUT_SCALE;
+    float saturation = u_float3 * INPUT_SCALE;
+    
+    // Temperature (warm/cool): positive = warm, negative = cool
+    color.r += temperature * TEMP_TINT_PRIMARY;
+    color.b -= temperature * TEMP_TINT_PRIMARY;
+    
+    // Tint (green/magenta): positive = green, negative = magenta
+    color.g += tint * TEMP_TINT_PRIMARY;
+    color.r -= tint * TEMP_TINT_SECONDARY;
+    color.b -= tint * TEMP_TINT_SECONDARY;
+    
+    // Single clamp after temperature/tint
+    color = clamp(color, 0.0, 1.0);
+    
+    // Vibrance with skin protection
+    if (vibrance != 0.0) {
+        float maxC = max(color.r, max(color.g, color.b));
+        float minC = min(color.r, min(color.g, color.b));
+        float sat = maxC - minC;
+        float gray = dot(color, LUMA_WEIGHTS);
+        
+        if (vibrance < 0.0) {
+            // Desaturate: -100 → gray
+            color = mix(vec3(gray), color, 1.0 + vibrance);
+        } else {
+            // Boost less saturated colors more
+            float vibranceAmt = vibrance * (1.0 - sat);
+            
+            // Branchless skin tone protection
+            float isWarmTone = step(color.b, color.g) * step(color.g, color.r);
+            float warmth = (color.r - color.b) / max(maxC, EPSILON);
+            float skinTone = isWarmTone * warmth * sat * (1.0 - sat);
+            vibranceAmt *= (1.0 - skinTone * SKIN_PROTECTION);
+            
+            color = mix(vec3(gray), color, 1.0 + vibranceAmt * VIBRANCE_BOOST);
+        }
+    }
+    
+    // Saturation
+    if (saturation != 0.0) {
+        float gray = dot(color, LUMA_WEIGHTS);
+        float satMix = saturation < 0.0
+            ? 1.0 + saturation                      // -100 → gray
+            : 1.0 + saturation * SATURATION_BOOST;  // +100 → 3x boost
+        color = mix(vec3(gray), color, satMix);
+    }
+    
+    fragColor = vec4(clamp(color, 0.0, 1.0), tex.a);
+}

blueprints/.glsl/Edge-Preserving_Blur_128.frag

@@ -0,0 +1,94 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform float u_float0;   // Blur radius (0–20, default ~5)
+uniform float u_float1;   // Edge threshold (0–100, default ~30)
+uniform int u_int0;       // Step size (0/1 = every pixel, 2+ = skip pixels)
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+const int MAX_RADIUS = 20;
+const float EPSILON = 0.0001;
+
+// Perceptual luminance
+float getLuminance(vec3 rgb) {
+    return dot(rgb, vec3(0.299, 0.587, 0.114));
+}
+
+vec4 bilateralFilter(vec2 uv, vec2 texelSize, int radius,
+                     float sigmaSpatial, float sigmaColor)
+{
+    vec4 center = texture(u_image0, uv);
+    vec3 centerRGB = center.rgb;
+
+    float invSpatial2 = -0.5 / (sigmaSpatial * sigmaSpatial);
+    float invColor2   = -0.5 / (sigmaColor * sigmaColor + EPSILON);
+
+    vec3 sumRGB = vec3(0.0);
+    float sumWeight = 0.0;
+
+    int step = max(u_int0, 1);
+    float radius2 = float(radius * radius);
+
+    for (int dy = -MAX_RADIUS; dy <= MAX_RADIUS; dy++) {
+        if (dy < -radius || dy > radius) continue;
+        if (abs(dy) % step != 0) continue;
+
+        for (int dx = -MAX_RADIUS; dx <= MAX_RADIUS; dx++) {
+            if (dx < -radius || dx > radius) continue;
+            if (abs(dx) % step != 0) continue;
+
+            vec2 offset = vec2(float(dx), float(dy));
+            float dist2 = dot(offset, offset);
+            if (dist2 > radius2) continue;
+
+            vec3 sampleRGB = texture(u_image0, uv + offset * texelSize).rgb;
+
+            // Spatial Gaussian
+            float spatialWeight = exp(dist2 * invSpatial2);
+
+            // Perceptual color distance (weighted RGB)
+            vec3 diff = sampleRGB - centerRGB;
+            float colorDist = dot(diff * diff, vec3(0.299, 0.587, 0.114));
+            float colorWeight = exp(colorDist * invColor2);
+
+            float w = spatialWeight * colorWeight;
+            sumRGB += sampleRGB * w;
+            sumWeight += w;
+        }
+    }
+
+    vec3 resultRGB = sumRGB / max(sumWeight, EPSILON);
+    return vec4(resultRGB, center.a); // preserve center alpha
+}
+
+void main() {
+    vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));
+
+    float radiusF = clamp(u_float0, 0.0, float(MAX_RADIUS));
+    int radius = int(radiusF + 0.5);
+
+    if (radius == 0) {
+        fragColor = texture(u_image0, v_texCoord);
+        return;
+    }
+
+    // Edge threshold → color sigma
+    // Squared curve for better low-end control
+    float t = clamp(u_float1, 0.0, 100.0) / 100.0;
+    t *= t;
+    float sigmaColor = mix(0.01, 0.5, t);
+
+    // Spatial sigma tied to radius
+    float sigmaSpatial = max(radiusF * 0.75, 0.5);
+
+    fragColor = bilateralFilter(
+        v_texCoord,
+        texelSize,
+        radius,
+        sigmaSpatial,
+        sigmaColor
+    );
+}

blueprints/.glsl/Film_Grain_15.frag

@@ -0,0 +1,124 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+uniform float u_float0; // grain amount      [0.0 – 1.0]   typical: 0.2–0.8
+uniform float u_float1; // grain size        [0.3 – 3.0]   lower = finer grain
+uniform float u_float2; // color amount      [0.0 – 1.0]   0 = monochrome, 1 = RGB grain
+uniform float u_float3; // luminance bias    [0.0 – 1.0]   0 = uniform, 1 = shadows only
+uniform int   u_int0;   // noise mode        [0 or 1]      0 = smooth, 1 = grainy
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+
+// High-quality integer hash (pcg-like)
+uint pcg(uint v) {
+    uint state = v * 747796405u + 2891336453u;
+    uint word = ((state >> ((state >> 28u) + 4u)) ^ state) * 277803737u;
+    return (word >> 22u) ^ word;
+}
+
+// 2D -> 1D hash input
+uint hash2d(uvec2 p) {
+    return pcg(p.x + pcg(p.y));
+}
+
+// Hash to float [0, 1]
+float hashf(uvec2 p) {
+    return float(hash2d(p)) / float(0xffffffffu);
+}
+
+// Hash to float with offset (for RGB channels)
+float hashf(uvec2 p, uint offset) {
+    return float(pcg(hash2d(p) + offset)) / float(0xffffffffu);
+}
+
+// Convert uniform [0,1] to roughly Gaussian distribution
+// Using simple approximation: average of multiple samples
+float toGaussian(uvec2 p) {
+    float sum = hashf(p, 0u) + hashf(p, 1u) + hashf(p, 2u) + hashf(p, 3u);
+    return (sum - 2.0) * 0.7;  // Centered, scaled
+}
+
+float toGaussian(uvec2 p, uint offset) {
+    float sum = hashf(p, offset) + hashf(p, offset + 1u) 
+              + hashf(p, offset + 2u) + hashf(p, offset + 3u);
+    return (sum - 2.0) * 0.7;
+}
+
+// Smooth noise with better interpolation
+float smoothNoise(vec2 p) {
+    vec2 i = floor(p);
+    vec2 f = fract(p);
+    
+    // Quintic interpolation (less banding than cubic)
+    f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);
+    
+    uvec2 ui = uvec2(i);
+    float a = toGaussian(ui);
+    float b = toGaussian(ui + uvec2(1u, 0u));
+    float c = toGaussian(ui + uvec2(0u, 1u));
+    float d = toGaussian(ui + uvec2(1u, 1u));
+    
+    return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);
+}
+
+float smoothNoise(vec2 p, uint offset) {
+    vec2 i = floor(p);
+    vec2 f = fract(p);
+    
+    f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);
+    
+    uvec2 ui = uvec2(i);
+    float a = toGaussian(ui, offset);
+    float b = toGaussian(ui + uvec2(1u, 0u), offset);
+    float c = toGaussian(ui + uvec2(0u, 1u), offset);
+    float d = toGaussian(ui + uvec2(1u, 1u), offset);
+    
+    return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);
+}
+
+void main() {
+    vec4 color = texture(u_image0, v_texCoord);
+    
+    // Luminance (Rec.709)
+    float luma = dot(color.rgb, vec3(0.2126, 0.7152, 0.0722));
+    
+    // Grain UV (resolution-independent)
+    vec2 grainUV = v_texCoord * u_resolution / max(u_float1, 0.01);
+    uvec2 grainPixel = uvec2(grainUV);
+    
+    float g;
+    vec3 grainRGB;
+    
+    if (u_int0 == 1) {
+        // Grainy mode: pure hash noise (no interpolation = no banding)
+        g = toGaussian(grainPixel);
+        grainRGB = vec3(
+            toGaussian(grainPixel, 100u),
+            toGaussian(grainPixel, 200u),
+            toGaussian(grainPixel, 300u)
+        );
+    } else {
+        // Smooth mode: interpolated with quintic curve
+        g = smoothNoise(grainUV);
+        grainRGB = vec3(
+            smoothNoise(grainUV, 100u),
+            smoothNoise(grainUV, 200u),
+            smoothNoise(grainUV, 300u)
+        );
+    }
+    
+    // Luminance weighting (less grain in highlights)
+    float lumWeight = mix(1.0, 1.0 - luma, clamp(u_float3, 0.0, 1.0));
+    
+    // Strength
+    float strength = u_float0 * 0.15;
+    
+    // Color vs monochrome grain
+    vec3 grainColor = mix(vec3(g), grainRGB, clamp(u_float2, 0.0, 1.0));
+    
+    color.rgb += grainColor * strength * lumWeight;
+    fragColor0 = vec4(clamp(color.rgb, 0.0, 1.0), color.a);
+}

blueprints/.glsl/Glow_30.frag

@@ -0,0 +1,133 @@
+#version 300 es
+precision mediump float;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+uniform int u_int0;      // Blend mode
+uniform int u_int1;      // Color tint
+uniform float u_float0;  // Intensity
+uniform float u_float1;  // Radius
+uniform float u_float2;  // Threshold
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+const int BLEND_ADD      = 0;
+const int BLEND_SCREEN   = 1;
+const int BLEND_SOFT     = 2;
+const int BLEND_OVERLAY  = 3;
+const int BLEND_LIGHTEN  = 4;
+
+const float GOLDEN_ANGLE = 2.39996323;
+const int MAX_SAMPLES = 48;
+const vec3 LUMA = vec3(0.299, 0.587, 0.114);
+
+float hash(vec2 p) {
+    p = fract(p * vec2(123.34, 456.21));
+    p += dot(p, p + 45.32);
+    return fract(p.x * p.y);
+}
+
+vec3 hexToRgb(int h) {
+    return vec3(
+        float((h >> 16) & 255),
+        float((h >> 8) & 255),
+        float(h & 255)
+    ) * (1.0 / 255.0);
+}
+
+vec3 blend(vec3 base, vec3 glow, int mode) {
+    if (mode == BLEND_SCREEN) {
+        return 1.0 - (1.0 - base) * (1.0 - glow);
+    }
+    if (mode == BLEND_SOFT) {
+        return mix(
+            base - (1.0 - 2.0 * glow) * base * (1.0 - base),
+            base + (2.0 * glow - 1.0) * (sqrt(base) - base),
+            step(0.5, glow)
+        );
+    }
+    if (mode == BLEND_OVERLAY) {
+        return mix(
+            2.0 * base * glow,
+            1.0 - 2.0 * (1.0 - base) * (1.0 - glow),
+            step(0.5, base)
+        );
+    }
+    if (mode == BLEND_LIGHTEN) {
+        return max(base, glow);
+    }
+    return base + glow;
+}
+
+void main() {
+    vec4 original = texture(u_image0, v_texCoord);
+    
+    float intensity = u_float0 * 0.05;
+    float radius = u_float1 * u_float1 * 0.012;
+    
+    if (intensity < 0.001 || radius < 0.1) {
+        fragColor = original;
+        return;
+    }
+    
+    float threshold = 1.0 - u_float2 * 0.01;
+    float t0 = threshold - 0.15;
+    float t1 = threshold + 0.15;
+    
+    vec2 texelSize = 1.0 / u_resolution;
+    float radius2 = radius * radius;
+    
+    float sampleScale = clamp(radius * 0.75, 0.35, 1.0);
+    int samples = int(float(MAX_SAMPLES) * sampleScale);
+    
+    float noise = hash(gl_FragCoord.xy);
+    float angleOffset = noise * GOLDEN_ANGLE;
+    float radiusJitter = 0.85 + noise * 0.3;
+    
+    float ca = cos(GOLDEN_ANGLE);
+    float sa = sin(GOLDEN_ANGLE);
+    vec2 dir = vec2(cos(angleOffset), sin(angleOffset));
+    
+    vec3 glow = vec3(0.0);
+    float totalWeight = 0.0;
+    
+    // Center tap
+    float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA));
+    glow += original.rgb * centerMask * 2.0;
+    totalWeight += 2.0;
+    
+    for (int i = 1; i < MAX_SAMPLES; i++) {
+        if (i >= samples) break;
+        
+        float fi = float(i);
+        float dist = sqrt(fi / float(samples)) * radius * radiusJitter;
+        
+        vec2 offset = dir * dist * texelSize;
+        vec3 c = texture(u_image0, v_texCoord + offset).rgb;
+        float mask = smoothstep(t0, t1, dot(c, LUMA));
+        
+        float w = 1.0 - (dist * dist) / (radius2 * 1.5);
+        w = max(w, 0.0);
+        w *= w;
+        
+        glow += c * mask * w;
+        totalWeight += w;
+        
+        dir = vec2(
+            dir.x * ca - dir.y * sa,
+            dir.x * sa + dir.y * ca
+        );
+    }
+    
+    glow *= intensity / max(totalWeight, 0.001);
+    
+    if (u_int1 > 0) {
+        glow *= hexToRgb(u_int1);
+    }
+    
+    vec3 result = blend(original.rgb, glow, u_int0);
+    result += (noise - 0.5) * (1.0 / 255.0);
+    
+    fragColor = vec4(clamp(result, 0.0, 1.0), original.a);
+}

blueprints/.glsl/Hue_and_Saturation_1.frag

@@ -0,0 +1,222 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform int u_int0;      // Mode: 0=Master, 1=Reds, 2=Yellows, 3=Greens, 4=Cyans, 5=Blues, 6=Magentas, 7=Colorize
+uniform int u_int1;      // Color Space: 0=HSL, 1=HSB/HSV
+uniform float u_float0;  // Hue (-180 to 180)
+uniform float u_float1;  // Saturation (-100 to 100)
+uniform float u_float2;  // Lightness/Brightness (-100 to 100)
+uniform float u_float3;  // Overlap (0 to 100) - feathering between adjacent color ranges
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+// Color range modes
+const int MODE_MASTER   = 0;
+const int MODE_RED      = 1;
+const int MODE_YELLOW   = 2;
+const int MODE_GREEN    = 3;
+const int MODE_CYAN     = 4;
+const int MODE_BLUE     = 5;
+const int MODE_MAGENTA  = 6;
+const int MODE_COLORIZE = 7;
+
+// Color space modes
+const int COLORSPACE_HSL = 0;
+const int COLORSPACE_HSB = 1;
+
+const float EPSILON = 0.0001;
+
+//=============================================================================
+// RGB <-> HSL Conversions
+//=============================================================================
+
+vec3 rgb2hsl(vec3 c) {
+    float maxC = max(max(c.r, c.g), c.b);
+    float minC = min(min(c.r, c.g), c.b);
+    float delta = maxC - minC;
+
+    float h = 0.0;
+    float s = 0.0;
+    float l = (maxC + minC) * 0.5;
+
+    if (delta > EPSILON) {
+        s = l < 0.5
+            ? delta / (maxC + minC)
+            : delta / (2.0 - maxC - minC);
+
+        if (maxC == c.r) {
+            h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);
+        } else if (maxC == c.g) {
+            h = (c.b - c.r) / delta + 2.0;
+        } else {
+            h = (c.r - c.g) / delta + 4.0;
+        }
+        h /= 6.0;
+    }
+
+    return vec3(h, s, l);
+}
+
+float hue2rgb(float p, float q, float t) {
+    t = fract(t);
+    if (t < 1.0/6.0) return p + (q - p) * 6.0 * t;
+    if (t < 0.5)       return q;
+    if (t < 2.0/3.0)   return p + (q - p) * (2.0/3.0 - t) * 6.0;
+    return p;
+}
+
+vec3 hsl2rgb(vec3 hsl) {
+    if (hsl.y < EPSILON) return vec3(hsl.z);
+
+    float q = hsl.z < 0.5
+        ? hsl.z * (1.0 + hsl.y)
+        : hsl.z + hsl.y - hsl.z * hsl.y;
+    float p = 2.0 * hsl.z - q;
+
+    return vec3(
+        hue2rgb(p, q, hsl.x + 1.0/3.0),
+        hue2rgb(p, q, hsl.x),
+        hue2rgb(p, q, hsl.x - 1.0/3.0)
+    );
+}
+
+vec3 rgb2hsb(vec3 c) {
+    float maxC = max(max(c.r, c.g), c.b);
+    float minC = min(min(c.r, c.g), c.b);
+    float delta = maxC - minC;
+
+    float h = 0.0;
+    float s = (maxC > EPSILON) ? delta / maxC : 0.0;
+    float b = maxC;
+
+    if (delta > EPSILON) {
+        if (maxC == c.r) {
+            h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);
+        } else if (maxC == c.g) {
+            h = (c.b - c.r) / delta + 2.0;
+        } else {
+            h = (c.r - c.g) / delta + 4.0;
+        }
+        h /= 6.0;
+    }
+
+    return vec3(h, s, b);
+}
+
+vec3 hsb2rgb(vec3 hsb) {
+    vec3 rgb = clamp(abs(mod(hsb.x * 6.0 + vec3(0.0, 4.0, 2.0), 6.0) - 3.0) - 1.0, 0.0, 1.0);
+    return hsb.z * mix(vec3(1.0), rgb, hsb.y);
+}
+
+//=============================================================================
+// Color Range Weight Calculation
+//=============================================================================
+
+float hueDistance(float a, float b) {
+    float d = abs(a - b);
+    return min(d, 1.0 - d);
+}
+
+float getHueWeight(float hue, float center, float overlap) {
+    float baseWidth = 1.0 / 6.0;
+    float feather = baseWidth * overlap;
+
+    float d = hueDistance(hue, center);
+
+    float inner = baseWidth * 0.5;
+    float outer = inner + feather;
+
+    return 1.0 - smoothstep(inner, outer, d);
+}
+
+float getModeWeight(float hue, int mode, float overlap) {
+    if (mode == MODE_MASTER || mode == MODE_COLORIZE) return 1.0;
+
+    if (mode == MODE_RED) {
+        return max(
+            getHueWeight(hue, 0.0, overlap),
+            getHueWeight(hue, 1.0, overlap)
+        );
+    }
+
+    float center = float(mode - 1) / 6.0;
+    return getHueWeight(hue, center, overlap);
+}
+
+//=============================================================================
+// Adjustment Functions
+//=============================================================================
+
+float adjustLightness(float l, float amount) {
+    return amount > 0.0
+        ? l + (1.0 - l) * amount
+        : l + l * amount;
+}
+
+float adjustBrightness(float b, float amount) {
+    return clamp(b + amount, 0.0, 1.0);
+}
+
+float adjustSaturation(float s, float amount) {
+    return amount > 0.0
+        ? s + (1.0 - s) * amount
+        : s + s * amount;
+}
+
+vec3 colorize(vec3 rgb, float hue, float sat, float light) {
+    float lum = dot(rgb, vec3(0.299, 0.587, 0.114));
+    float l = adjustLightness(lum, light);
+
+    vec3 hsl = vec3(fract(hue), clamp(sat, 0.0, 1.0), clamp(l, 0.0, 1.0));
+    return hsl2rgb(hsl);
+}
+
+//=============================================================================
+// Main
+//=============================================================================
+
+void main() {
+    vec4 original = texture(u_image0, v_texCoord);
+
+    float hueShift   = u_float0 / 360.0;   // -180..180 -> -0.5..0.5
+    float satAmount  = u_float1 / 100.0;   // -100..100 -> -1..1
+    float lightAmount= u_float2 / 100.0;   // -100..100 -> -1..1
+    float overlap    = u_float3 / 100.0;   // 0..100 -> 0..1
+
+    vec3 result;
+
+    if (u_int0 == MODE_COLORIZE) {
+        result = colorize(original.rgb, hueShift, satAmount, lightAmount);
+        fragColor = vec4(result, original.a);
+        return;
+    }
+
+    vec3 hsx = (u_int1 == COLORSPACE_HSL)
+        ? rgb2hsl(original.rgb)
+        : rgb2hsb(original.rgb);
+
+    float weight = getModeWeight(hsx.x, u_int0, overlap);
+
+    if (u_int0 != MODE_MASTER && hsx.y < EPSILON) {
+        weight = 0.0;
+    }
+
+    if (weight > EPSILON) {
+        float h = fract(hsx.x + hueShift * weight);
+        float s = clamp(adjustSaturation(hsx.y, satAmount * weight), 0.0, 1.0);
+        float v = (u_int1 == COLORSPACE_HSL)
+            ? clamp(adjustLightness(hsx.z, lightAmount * weight), 0.0, 1.0)
+            : clamp(adjustBrightness(hsx.z, lightAmount * weight), 0.0, 1.0);
+
+        vec3 adjusted = vec3(h, s, v);
+        result = (u_int1 == COLORSPACE_HSL)
+            ? hsl2rgb(adjusted)
+            : hsb2rgb(adjusted);
+    } else {
+        result = original.rgb;
+    }
+
+    fragColor = vec4(result, original.a);
+}

blueprints/.glsl/Image_Blur_1.frag

@@ -0,0 +1,111 @@
+#version 300 es
+#pragma passes 2
+precision highp float;
+
+// Blur type constants
+const int BLUR_GAUSSIAN = 0;
+const int BLUR_BOX = 1;
+const int BLUR_RADIAL = 2;
+
+// Radial blur config
+const int RADIAL_SAMPLES = 12;
+const float RADIAL_STRENGTH = 0.0003;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+uniform int u_int0;      // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)
+uniform float u_float0;  // Blur radius/amount
+uniform int u_pass;      // Pass index (0 = horizontal, 1 = vertical)
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+
+float gaussian(float x, float sigma) {
+    return exp(-(x * x) / (2.0 * sigma * sigma));
+}
+
+void main() {
+    vec2 texelSize = 1.0 / u_resolution;
+    float radius = max(u_float0, 0.0);
+
+    // Radial (angular) blur - single pass, doesn't use separable
+    if (u_int0 == BLUR_RADIAL) {
+        // Only execute on first pass
+        if (u_pass > 0) {
+            fragColor0 = texture(u_image0, v_texCoord);
+            return;
+        }
+
+        vec2 center = vec2(0.5);
+        vec2 dir = v_texCoord - center;
+        float dist = length(dir);
+
+        if (dist < 1e-4) {
+            fragColor0 = texture(u_image0, v_texCoord);
+            return;
+        }
+
+        vec4 sum = vec4(0.0);
+        float totalWeight = 0.0;
+        float angleStep = radius * RADIAL_STRENGTH;
+
+        dir /= dist;
+
+        float cosStep = cos(angleStep);
+        float sinStep = sin(angleStep);
+
+        float negAngle = -float(RADIAL_SAMPLES) * angleStep;
+        vec2 rotDir = vec2(
+            dir.x * cos(negAngle) - dir.y * sin(negAngle),
+            dir.x * sin(negAngle) + dir.y * cos(negAngle)
+        );
+
+        for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {
+            vec2 uv = center + rotDir * dist;
+            float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);
+            sum += texture(u_image0, uv) * w;
+            totalWeight += w;
+
+            rotDir = vec2(
+                rotDir.x * cosStep - rotDir.y * sinStep,
+                rotDir.x * sinStep + rotDir.y * cosStep
+            );
+        }
+
+        fragColor0 = sum / max(totalWeight, 0.001);
+        return;
+    }
+
+    // Separable Gaussian / Box blur
+    int samples = int(ceil(radius));
+
+    if (samples == 0) {
+        fragColor0 = texture(u_image0, v_texCoord);
+        return;
+    }
+
+    // Direction: pass 0 = horizontal, pass 1 = vertical
+    vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);
+
+    vec4 color = vec4(0.0);
+    float totalWeight = 0.0;
+    float sigma = radius / 2.0;
+
+    for (int i = -samples; i <= samples; i++) {
+        vec2 offset = dir * float(i) * texelSize;
+        vec4 sample_color = texture(u_image0, v_texCoord + offset);
+
+        float weight;
+        if (u_int0 == BLUR_GAUSSIAN) {
+            weight = gaussian(float(i), sigma);
+        } else {
+            // BLUR_BOX
+            weight = 1.0;
+        }
+
+        color += sample_color * weight;
+        totalWeight += weight;
+    }
+
+    fragColor0 = color / totalWeight;
+}

blueprints/.glsl/Image_Channels_23.frag

@@ -0,0 +1,19 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+layout(location = 1) out vec4 fragColor1;
+layout(location = 2) out vec4 fragColor2;
+layout(location = 3) out vec4 fragColor3;
+
+void main() {
+  vec4 color = texture(u_image0, v_texCoord);
+  // Output each channel as grayscale to separate render targets
+  fragColor0 = vec4(vec3(color.r), 1.0);  // Red channel
+  fragColor1 = vec4(vec3(color.g), 1.0);  // Green channel
+  fragColor2 = vec4(vec3(color.b), 1.0);  // Blue channel
+  fragColor3 = vec4(vec3(color.a), 1.0);  // Alpha channel
+}

blueprints/.glsl/Image_Levels_1.frag

@@ -0,0 +1,71 @@
+#version 300 es
+precision highp float;
+
+// Levels Adjustment
+// u_int0:   channel      (0=RGB, 1=R, 2=G, 3=B)         default: 0
+// u_float0: input black  (0-255)                        default: 0
+// u_float1: input white  (0-255)                        default: 255
+// u_float2: gamma        (0.01-9.99)                    default: 1.0
+// u_float3: output black (0-255)                        default: 0
+// u_float4: output white (0-255)                        default: 255
+
+uniform sampler2D u_image0;
+uniform int u_int0;
+uniform float u_float0;
+uniform float u_float1;
+uniform float u_float2;
+uniform float u_float3;
+uniform float u_float4;
+
+in vec2 v_texCoord;
+out vec4 fragColor;
+
+vec3 applyLevels(vec3 color, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {
+    float inRange = max(inWhite - inBlack, 0.0001);
+    vec3 result = clamp((color - inBlack) / inRange, 0.0, 1.0);
+    result = pow(result, vec3(1.0 / gamma));
+    result = mix(vec3(outBlack), vec3(outWhite), result);
+    return result;
+}
+
+float applySingleChannel(float value, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {
+    float inRange = max(inWhite - inBlack, 0.0001);
+    float result = clamp((value - inBlack) / inRange, 0.0, 1.0);
+    result = pow(result, 1.0 / gamma);
+    result = mix(outBlack, outWhite, result);
+    return result;
+}
+
+void main() {
+    vec4 texColor = texture(u_image0, v_texCoord);
+    vec3 color = texColor.rgb;
+    
+    float inBlack = u_float0 / 255.0;
+    float inWhite = u_float1 / 255.0;
+    float gamma = u_float2;
+    float outBlack = u_float3 / 255.0;
+    float outWhite = u_float4 / 255.0;
+    
+    vec3 result;
+    
+    if (u_int0 == 0) {
+        result = applyLevels(color, inBlack, inWhite, gamma, outBlack, outWhite);
+    }
+    else if (u_int0 == 1) {
+        result = color;
+        result.r = applySingleChannel(color.r, inBlack, inWhite, gamma, outBlack, outWhite);
+    }
+    else if (u_int0 == 2) {
+        result = color;
+        result.g = applySingleChannel(color.g, inBlack, inWhite, gamma, outBlack, outWhite);
+    }
+    else if (u_int0 == 3) {
+        result = color;
+        result.b = applySingleChannel(color.b, inBlack, inWhite, gamma, outBlack, outWhite);
+    }
+    else {
+        result = color;
+    }
+    
+    fragColor = vec4(result, texColor.a);
+}

blueprints/.glsl/README.md

@@ -0,0 +1,28 @@
+# GLSL Shader Sources
+
+This folder contains the GLSL fragment shaders extracted from blueprint JSON files for easier editing and version control.
+
+## File Naming Convention
+
+`{Blueprint_Name}_{node_id}.frag`
+
+- **Blueprint_Name**: The JSON filename with spaces/special chars replaced by underscores
+- **node_id**: The GLSLShader node ID within the subgraph
+
+## Usage
+
+```bash
+# Extract shaders from blueprint JSONs to this folder
+python update_blueprints.py extract
+
+# Patch edited shaders back into blueprint JSONs
+python update_blueprints.py patch
+```
+
+## Workflow
+
+1. Run `extract` to pull current shaders from JSONs
+2. Edit `.frag` files
+3. Run `patch` to update the blueprint JSONs
+4. Test
+5. Commit both `.frag` files and updated JSONs

blueprints/.glsl/Sharpen_23.frag

@@ -0,0 +1,28 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+uniform float u_float0;  // strength [0.0 – 2.0] typical: 0.3–1.0
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+
+void main() {
+    vec2 texel = 1.0 / u_resolution;
+    
+    // Sample center and neighbors
+    vec4 center = texture(u_image0, v_texCoord);
+    vec4 top    = texture(u_image0, v_texCoord + vec2( 0.0, -texel.y));
+    vec4 bottom = texture(u_image0, v_texCoord + vec2( 0.0,  texel.y));
+    vec4 left   = texture(u_image0, v_texCoord + vec2(-texel.x,  0.0));
+    vec4 right  = texture(u_image0, v_texCoord + vec2( texel.x,  0.0));
+    
+    // Edge enhancement (Laplacian)
+    vec4 edges = center * 4.0 - top - bottom - left - right;
+    
+    // Add edges back scaled by strength
+    vec4 sharpened = center + edges * u_float0;
+    
+    fragColor0 = vec4(clamp(sharpened.rgb, 0.0, 1.0), center.a);
+}

blueprints/.glsl/Unsharp_Mask_26.frag

@@ -0,0 +1,61 @@
+#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+uniform float u_float0;  // amount    [0.0 - 3.0]  typical: 0.5-1.5
+uniform float u_float1;  // radius    [0.5 - 10.0] blur radius in pixels
+uniform float u_float2;  // threshold [0.0 - 0.1]  min difference to sharpen
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+
+float gaussian(float x, float sigma) {
+    return exp(-(x * x) / (2.0 * sigma * sigma));
+}
+
+float getLuminance(vec3 color) {
+    return dot(color, vec3(0.2126, 0.7152, 0.0722));
+}
+
+void main() {
+    vec2 texel = 1.0 / u_resolution;
+    float radius = max(u_float1, 0.5);
+    float amount = u_float0;
+    float threshold = u_float2;
+
+    vec4 original = texture(u_image0, v_texCoord);
+
+    // Gaussian blur for the "unsharp" mask
+    int samples = int(ceil(radius));
+    float sigma = radius / 2.0;
+
+    vec4 blurred = vec4(0.0);
+    float totalWeight = 0.0;
+
+    for (int x = -samples; x <= samples; x++) {
+        for (int y = -samples; y <= samples; y++) {
+            vec2 offset = vec2(float(x), float(y)) * texel;
+            vec4 sample_color = texture(u_image0, v_texCoord + offset);
+
+            float dist = length(vec2(float(x), float(y)));
+            float weight = gaussian(dist, sigma);
+            blurred += sample_color * weight;
+            totalWeight += weight;
+        }
+    }
+    blurred /= totalWeight;
+
+    // Unsharp mask = original - blurred
+    vec3 mask = original.rgb - blurred.rgb;
+
+    // Luminance-based threshold with smooth falloff
+    float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb));
+    float thresholdScale = smoothstep(0.0, threshold, lumaDelta);
+    mask *= thresholdScale;
+
+    // Sharpen: original + mask * amount
+    vec3 sharpened = original.rgb + mask * amount;
+
+    fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a);
+}

blueprints/.glsl/update_blueprints.py

@@ -0,0 +1,159 @@
+#!/usr/bin/env python3
+"""
+Shader Blueprint Updater
+
+Syncs GLSL shader files between this folder and blueprint JSON files.
+
+File naming convention:
+    {Blueprint Name}_{node_id}.frag
+
+Usage:
+    python update_blueprints.py extract   # Extract shaders from JSONs to here
+    python update_blueprints.py patch     # Patch shaders back into JSONs
+    python update_blueprints.py           # Same as patch (default)
+"""
+
+import json
+import logging
+import sys
+import re
+from pathlib import Path
+
+logging.basicConfig(level=logging.INFO, format='%(message)s')
+logger = logging.getLogger(__name__)
+
+GLSL_DIR = Path(__file__).parent
+BLUEPRINTS_DIR = GLSL_DIR.parent
+
+
+def get_blueprint_files():
+    """Get all blueprint JSON files."""
+    return sorted(BLUEPRINTS_DIR.glob("*.json"))
+
+
+def sanitize_filename(name):
+    """Convert blueprint name to safe filename."""
+    return re.sub(r'[^\w\-]', '_', name)
+
+
+def extract_shaders():
+    """Extract all shaders from blueprint JSONs to this folder."""
+    extracted = 0
+    for json_path in get_blueprint_files():
+        blueprint_name = json_path.stem
+
+        try:
+            with open(json_path, 'r') as f:
+                data = json.load(f)
+        except (json.JSONDecodeError, IOError) as e:
+            logger.warning("Skipping %s: %s", json_path.name, e)
+            continue
+
+        # Find GLSLShader nodes in subgraphs
+        for subgraph in data.get('definitions', {}).get('subgraphs', []):
+            for node in subgraph.get('nodes', []):
+                if node.get('type') == 'GLSLShader':
+                    node_id = node.get('id')
+                    widgets = node.get('widgets_values', [])
+
+                    # Find shader code (first string that looks like GLSL)
+                    for widget in widgets:
+                        if isinstance(widget, str) and widget.startswith('#version'):
+                            safe_name = sanitize_filename(blueprint_name)
+                            frag_name = f"{safe_name}_{node_id}.frag"
+                            frag_path = GLSL_DIR / frag_name
+
+                            with open(frag_path, 'w') as f:
+                                f.write(widget)
+
+                            logger.info("  Extracted: %s", frag_name)
+                            extracted += 1
+                            break
+
+    logger.info("\nExtracted %d shader(s)", extracted)
+
+
+def patch_shaders():
+    """Patch shaders from this folder back into blueprint JSONs."""
+    # Build lookup: blueprint_name -> [(node_id, shader_code), ...]
+    shader_updates = {}
+
+    for frag_path in sorted(GLSL_DIR.glob("*.frag")):
+        # Parse filename: {blueprint_name}_{node_id}.frag
+        parts = frag_path.stem.rsplit('_', 1)
+        if len(parts) != 2:
+            logger.warning("Skipping %s: invalid filename format", frag_path.name)
+            continue
+
+        blueprint_name, node_id_str = parts
+
+        try:
+            node_id = int(node_id_str)
+        except ValueError:
+            logger.warning("Skipping %s: invalid node_id", frag_path.name)
+            continue
+
+        with open(frag_path, 'r') as f:
+            shader_code = f.read()
+
+        if blueprint_name not in shader_updates:
+            shader_updates[blueprint_name] = []
+        shader_updates[blueprint_name].append((node_id, shader_code))
+
+    # Apply updates to JSON files
+    patched = 0
+    for json_path in get_blueprint_files():
+        blueprint_name = sanitize_filename(json_path.stem)
+
+        if blueprint_name not in shader_updates:
+            continue
+
+        try:
+            with open(json_path, 'r') as f:
+                data = json.load(f)
+        except (json.JSONDecodeError, IOError) as e:
+            logger.error("Error reading %s: %s", json_path.name, e)
+            continue
+
+        modified = False
+        for node_id, shader_code in shader_updates[blueprint_name]:
+            # Find the node and update
+            for subgraph in data.get('definitions', {}).get('subgraphs', []):
+                for node in subgraph.get('nodes', []):
+                    if node.get('id') == node_id and node.get('type') == 'GLSLShader':
+                        widgets = node.get('widgets_values', [])
+                        if len(widgets) > 0 and widgets[0] != shader_code:
+                            widgets[0] = shader_code
+                            modified = True
+                            logger.info("  Patched: %s (node %d)", json_path.name, node_id)
+                            patched += 1
+
+        if modified:
+            with open(json_path, 'w') as f:
+                json.dump(data, f)
+
+    if patched == 0:
+        logger.info("No changes to apply.")
+    else:
+        logger.info("\nPatched %d shader(s)", patched)
+
+
+def main():
+    if len(sys.argv) < 2:
+        command = "patch"
+    else:
+        command = sys.argv[1].lower()
+
+    if command == "extract":
+        logger.info("Extracting shaders from blueprints...")
+        extract_shaders()
+    elif command in ("patch", "update", "apply"):
+        logger.info("Patching shaders into blueprints...")
+        patch_shaders()
+    else:
+        logger.info(__doc__)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

blueprints/Brightness and Contrast.json

@@ -0,0 +1 @@
+{"revision":0,"last_node_id":140,"last_link_id":0,"nodes":[{"id":140,"type":"916dff42-6166-4d45-b028-04eaf69fbb35","pos":[500,1440],"size":[250,178],"flags":{},"order":2,"mode":0,"inputs":[{"label":"image","localized_name":"images.image0","name":"images.image0","type":"IMAGE","link":null}],"outputs":[{"label":"IMAGE","localized_name":"IMAGE0","name":"IMAGE0","type":"IMAGE","links":[]}],"properties":{"proxyWidgets":[["4","value"],["5","value"]]},"widgets_values":[],"title":"Brightness and Contrast"}],"links":[],"version":0.4,"definitions":{"subgraphs":[{"id":"916dff42-6166-4d45-b028-04eaf69fbb35","version":1,"state":{"lastGroupId":0,"lastNodeId":143,"lastLinkId":118,"lastRerouteId":0},"revision":0,"config":{},"name":"Brightness and Contrast","inputNode":{"id":-10,"bounding":[360,-176,120,60]},"outputNode":{"id":-20,"bounding":[1410,-176,120,60]},"inputs":[{"id":"a5aae7ea-b511-4045-b5da-94101e269cd7","name":"images.image0","type":"IMAGE","linkIds":[117],"localized_name":"images.image0","label":"image","pos":[460,-156]}],"outputs":[{"id":"30b72604-69b3-4944-b253-a9099bbd73a9","name":"IMAGE0","type":"IMAGE","linkIds":[118],"localized_name":"IMAGE0","label":"IMAGE","pos":[1430,-156]}],"widgets":[],"nodes":[{"id":4,"type":"PrimitiveFloat","pos":[540,-280],"size":[270,58],"flags":{},"order":0,"mode":0,"inputs":[{"label":"brightness","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[115]}],"properties":{"Node name for S&R":"PrimitiveFloat","min":0,"max":100,"precision":1,"step":1},"widgets_values":[50]},{"id":5,"type":"PrimitiveFloat","pos":[540,-170],"size":[270,58],"flags":{},"order":1,"mode":0,"inputs":[{"label":"contrast","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[116]}],"properties":{"Node name for S&R":"PrimitiveFloat","min":0,"max":100,"precision":1,"step":1},"widgets_values":[0]},{"id":143,"type":"GLSLShader","pos":[840,-280],"size":[400,212],"flags":{},"order":2,"mode":0,"inputs":[{"label":"image0","localized_name":"images.image0","name":"images.image0","type":"IMAGE","link":117},{"label":"image1","localized_name":"images.image1","name":"images.image1","shape":7,"type":"IMAGE","link":null},{"label":"u_float0","localized_name":"floats.u_float0","name":"floats.u_float0","shape":7,"type":"FLOAT","link":115},{"label":"u_float1","localized_name":"floats.u_float1","name":"floats.u_float1","shape":7,"type":"FLOAT","link":116},{"label":"u_float2","localized_name":"floats.u_float2","name":"floats.u_float2","shape":7,"type":"FLOAT","link":null},{"label":"u_int0","localized_name":"ints.u_int0","name":"ints.u_int0","shape":7,"type":"INT","link":null},{"localized_name":"fragment_shader","name":"fragment_shader","type":"STRING","widget":{"name":"fragment_shader"},"link":null},{"localized_name":"size_mode","name":"size_mode","type":"COMFY_DYNAMICCOMBO_V3","widget":{"name":"size_mode"},"link":null}],"outputs":[{"localized_name":"IMAGE0","name":"IMAGE0","type":"IMAGE","links":[118]},{"localized_name":"IMAGE1","name":"IMAGE1","type":"IMAGE","links":null},{"localized_name":"IMAGE2","name":"IMAGE2","type":"IMAGE","links":null},{"localized_name":"IMAGE3","name":"IMAGE3","type":"IMAGE","links":null}],"properties":{"Node name for S&R":"GLSLShader"},"widgets_values":["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // Brightness slider -100..100\nuniform float u_float1; // Contrast slider -100..100\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float MID_GRAY = 0.18;  // 18% reflectance\n\n// sRGB gamma 2.2 approximation\nvec3 srgbToLinear(vec3 c) {\n    return pow(max(c, 0.0), vec3(2.2));\n}\n\nvec3 linearToSrgb(vec3 c) {\n    return pow(max(c, 0.0), vec3(1.0/2.2));\n}\n\nfloat mapBrightness(float b) {\n    return clamp(b / 100.0, -1.0, 1.0);\n}\n\nfloat mapContrast(float c) {\n    return clamp(c / 100.0 + 1.0, 0.0, 2.0);\n}\n\nvoid main() {\n    vec4 orig = texture(u_image0, v_texCoord);\n\n    float brightness = mapBrightness(u_float0);\n    float contrast   = mapContrast(u_float1);\n\n    vec3 lin = srgbToLinear(orig.rgb);\n\n    lin = (lin - MID_GRAY) * contrast + brightness + MID_GRAY;\n\n    // Convert back to sRGB\n    vec3 result = linearToSrgb(clamp(lin, 0.0, 1.0));\n\n    fragColor = vec4(result, orig.a);\n}\n","from_input"]}],"groups":[],"links":[{"id":115,"origin_id":4,"origin_slot":0,"target_id":143,"target_slot":2,"type":"FLOAT"},{"id":116,"origin_id":5,"origin_slot":0,"target_id":143,"target_slot":3,"type":"FLOAT"},{"id":117,"origin_id":-10,"origin_slot":0,"target_id":143,"target_slot":0,"type":"IMAGE"},{"id":118,"origin_id":143,"origin_slot":0,"target_id":-20,"target_slot":0,"type":"IMAGE"}],"extra":{"workflowRendererVersion":"LG"}}]},"extra":{}}

blueprints/Image Channels.json

@@ -0,0 +1 @@
+{"revision": 0, "last_node_id": 29, "last_link_id": 0, "nodes": [{"id": 29, "type": "4c9d6ea4-b912-40e5-8766-6793a9758c53", "pos": [1970, -230], "size": [180, 86], "flags": {}, "order": 5, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], "outputs": [{"label": "R", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": []}, {"label": "G", "localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": []}, {"label": "B", "localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": []}, {"label": "A", "localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": []}], "title": "Image Channels", "properties": {"proxyWidgets": []}, "widgets_values": []}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "4c9d6ea4-b912-40e5-8766-6793a9758c53", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 28, "lastLinkId": 39, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Image Channels", "inputNode": {"id": -10, "bounding": [1820, -185, 120, 60]}, "outputNode": {"id": -20, "bounding": [2460, -215, 120, 120]}, "inputs": [{"id": "3522932b-2d86-4a1f-a02a-cb29f3a9d7fe", "name": "images.image0", "type": "IMAGE", "linkIds": [39], "localized_name": "images.image0", "label": "image", "pos": [1920, -165]}], "outputs": [{"id": "605cb9c3-b065-4d9b-81d2-3ec331889b2b", "name": "IMAGE0", "type": "IMAGE", "linkIds": [26], "localized_name": "IMAGE0", "label": "R", "pos": [2480, -195]}, {"id": "fb44a77e-0522-43e9-9527-82e7465b3596", "name": "IMAGE1", "type": "IMAGE", "linkIds": [27], "localized_name": "IMAGE1", "label": "G", "pos": [2480, -175]}, {"id": "81460ee6-0131-402a-874f-6bf3001fc4ff", "name": "IMAGE2", "type": "IMAGE", "linkIds": [28], "localized_name": "IMAGE2", "label": "B", "pos": [2480, -155]}, {"id": "ae690246-80d4-4951-b1d9-9306d8a77417", "name": "IMAGE3", "type": "IMAGE", "linkIds": [29], "localized_name": "IMAGE3", "label": "A", "pos": [2480, -135]}], "widgets": [], "nodes": [{"id": 23, "type": "GLSLShader", "pos": [2000, -330], "size": [400, 172], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": 39}, {"localized_name": "fragment_shader", "name": "fragment_shader", "type": "STRING", "widget": {"name": "fragment_shader"}, "link": null}, {"localized_name": "size_mode", "name": "size_mode", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "size_mode"}, "link": null}, {"label": "image1", "localized_name": "images.image1", "name": "images.image1", "shape": 7, "type": "IMAGE", "link": null}], "outputs": [{"label": "R", "localized_name": "IMAGE0", "name": "IMAGE0", "type": "IMAGE", "links": [26]}, {"label": "G", "localized_name": "IMAGE1", "name": "IMAGE1", "type": "IMAGE", "links": [27]}, {"label": "B", "localized_name": "IMAGE2", "name": "IMAGE2", "type": "IMAGE", "links": [28]}, {"label": "A", "localized_name": "IMAGE3", "name": "IMAGE3", "type": "IMAGE", "links": [29]}], "properties": {"Node name for S&R": "GLSLShader"}, "widgets_values": ["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\nlayout(location = 1) out vec4 fragColor1;\nlayout(location = 2) out vec4 fragColor2;\nlayout(location = 3) out vec4 fragColor3;\n\nvoid main() {\n  vec4 color = texture(u_image0, v_texCoord);\n  // Output each channel as grayscale to separate render targets\n  fragColor0 = vec4(vec3(color.r), 1.0);  // Red channel\n  fragColor1 = vec4(vec3(color.g), 1.0);  // Green channel\n  fragColor2 = vec4(vec3(color.b), 1.0);  // Blue channel\n  fragColor3 = vec4(vec3(color.a), 1.0);  // Alpha channel\n}\n", "from_input"]}], "groups": [], "links": [{"id": 39, "origin_id": -10, "origin_slot": 0, "target_id": 23, "target_slot": 0, "type": "IMAGE"}, {"id": 26, "origin_id": 23, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}, {"id": 27, "origin_id": 23, "origin_slot": 1, "target_id": -20, "target_slot": 1, "type": "IMAGE"}, {"id": 28, "origin_id": 23, "origin_slot": 2, "target_id": -20, "target_slot": 2, "type": "IMAGE"}, {"id": 29, "origin_id": 23, "origin_slot": 3, "target_id": -20, "target_slot": 3, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}}]}}

blueprints/Sharpen.json

@@ -0,0 +1 @@
+{"revision":0,"last_node_id":25,"last_link_id":0,"nodes":[{"id":25,"type":"621ba4e2-22a8-482d-a369-023753198b7b","pos":[4610,-790],"size":[230,58],"flags":{},"order":4,"mode":0,"inputs":[{"label":"image","localized_name":"images.image0","name":"images.image0","type":"IMAGE","link":null}],"outputs":[{"label":"IMAGE","localized_name":"IMAGE0","name":"IMAGE0","type":"IMAGE","links":[]}],"title":"Sharpen","properties":{"proxyWidgets":[["24","value"]]},"widgets_values":[]}],"links":[],"version":0.4,"definitions":{"subgraphs":[{"id":"621ba4e2-22a8-482d-a369-023753198b7b","version":1,"state":{"lastGroupId":0,"lastNodeId":24,"lastLinkId":36,"lastRerouteId":0},"revision":0,"config":{},"name":"Sharpen","inputNode":{"id":-10,"bounding":[4090,-825,120,60]},"outputNode":{"id":-20,"bounding":[5150,-825,120,60]},"inputs":[{"id":"37011fb7-14b7-4e0e-b1a0-6a02e8da1fd7","name":"images.image0","type":"IMAGE","linkIds":[34],"localized_name":"images.image0","label":"image","pos":[4190,-805]}],"outputs":[{"id":"e9182b3f-635c-4cd4-a152-4b4be17ae4b9","name":"IMAGE0","type":"IMAGE","linkIds":[35],"localized_name":"IMAGE0","label":"IMAGE","pos":[5170,-805]}],"widgets":[],"nodes":[{"id":24,"type":"PrimitiveFloat","pos":[4280,-1240],"size":[270,58],"flags":{},"order":0,"mode":0,"inputs":[{"label":"strength","localized_name":"value","name":"value","type":"FLOAT","widget":{"name":"value"},"link":null}],"outputs":[{"localized_name":"FLOAT","name":"FLOAT","type":"FLOAT","links":[36]}],"properties":{"Node name for S&R":"PrimitiveFloat","min":0,"max":3,"precision":2,"step":0.05},"widgets_values":[0.5]},{"id":23,"type":"GLSLShader","pos":[4570,-1240],"size":[370,192],"flags":{},"order":1,"mode":0,"inputs":[{"label":"image0","localized_name":"images.image0","name":"images.image0","type":"IMAGE","link":34},{"label":"image1","localized_name":"images.image1","name":"images.image1","shape":7,"type":"IMAGE","link":null},{"label":"u_float0","localized_name":"floats.u_float0","name":"floats.u_float0","shape":7,"type":"FLOAT","link":36},{"label":"u_float1","localized_name":"floats.u_float1","name":"floats.u_float1","shape":7,"type":"FLOAT","link":null},{"label":"u_int0","localized_name":"ints.u_int0","name":"ints.u_int0","shape":7,"type":"INT","link":null},{"localized_name":"fragment_shader","name":"fragment_shader","type":"STRING","widget":{"name":"fragment_shader"},"link":null},{"localized_name":"size_mode","name":"size_mode","type":"COMFY_DYNAMICCOMBO_V3","widget":{"name":"size_mode"},"link":null}],"outputs":[{"localized_name":"IMAGE0","name":"IMAGE0","type":"IMAGE","links":[35]},{"localized_name":"IMAGE1","name":"IMAGE1","type":"IMAGE","links":null},{"localized_name":"IMAGE2","name":"IMAGE2","type":"IMAGE","links":null},{"localized_name":"IMAGE3","name":"IMAGE3","type":"IMAGE","links":null}],"properties":{"Node name for S&R":"GLSLShader"},"widgets_values":["#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0;  // strength [0.0 – 2.0] typical: 0.3–1.0\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nvoid main() {\n    vec2 texel = 1.0 / u_resolution;\n    \n    // Sample center and neighbors\n    vec4 center = texture(u_image0, v_texCoord);\n    vec4 top    = texture(u_image0, v_texCoord + vec2( 0.0, -texel.y));\n    vec4 bottom = texture(u_image0, v_texCoord + vec2( 0.0,  texel.y));\n    vec4 left   = texture(u_image0, v_texCoord + vec2(-texel.x,  0.0));\n    vec4 right  = texture(u_image0, v_texCoord + vec2( texel.x,  0.0));\n    \n    // Edge enhancement (Laplacian)\n    vec4 edges = center * 4.0 - top - bottom - left - right;\n    \n    // Add edges back scaled by strength\n    vec4 sharpened = center + edges * u_float0;\n    \n    fragColor0 = vec4(clamp(sharpened.rgb, 0.0, 1.0), center.a);\n}","from_input"]}],"groups":[],"links":[{"id":36,"origin_id":24,"origin_slot":0,"target_id":23,"target_slot":2,"type":"FLOAT"},{"id":34,"origin_id":-10,"origin_slot":0,"target_id":23,"target_slot":0,"type":"IMAGE"},{"id":35,"origin_id":23,"origin_slot":0,"target_id":-20,"target_slot":0,"type":"IMAGE"}],"extra":{"workflowRendererVersion":"LG"}}]}}

comfy/sd.py

@@ -423,6 +423,17 @@ class CLIP:
     def get_key_patches(self):
         return self.patcher.get_key_patches()
 
+    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
+        self.cond_stage_model.reset_clip_options()
+
+        self.load_model()
+        self.cond_stage_model.set_clip_options({"layer": None})
+        self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
+        return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)
+
+    def decode(self, token_ids, skip_special_tokens=True):
+        return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
+
 class VAE:
     def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None):
         if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
@@ -1182,6 +1193,7 @@ class TEModel(Enum):
     JINA_CLIP_2 = 19
     QWEN3_8B = 20
     QWEN3_06B = 21
+    GEMMA_3_4B_VISION = 22
 
 
 def detect_te_model(sd):
@@ -1210,7 +1222,10 @@ def detect_te_model(sd):
         if 'model.layers.47.self_attn.q_norm.weight' in sd:
             return TEModel.GEMMA_3_12B
         if 'model.layers.0.self_attn.q_norm.weight' in sd:
-            return TEModel.GEMMA_3_4B
+            if 'vision_model.embeddings.patch_embedding.weight' in sd:
+                return TEModel.GEMMA_3_4B_VISION
+            else:
+                return TEModel.GEMMA_3_4B
         return TEModel.GEMMA_2_2B
     if 'model.layers.0.self_attn.k_proj.bias' in sd:
         weight = sd['model.layers.0.self_attn.k_proj.bias']
@@ -1270,6 +1285,8 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         else:
             if "text_projection" in clip_data[i]:
                 clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
+        if "lm_head.weight" in clip_data[i]:
+            clip_data[i]["model.lm_head.weight"] = clip_data[i].pop("lm_head.weight") # prefix missing in some models
 
     tokenizer_data = {}
     clip_target = EmptyClass()
@@ -1335,6 +1352,14 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type="gemma3_4b")
             clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer
             tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
+        elif te_model == TEModel.GEMMA_3_4B_VISION:
+            clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type="gemma3_4b_vision")
+            clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer
+            tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
+        elif te_model == TEModel.GEMMA_3_12B:
+            clip_target.clip = comfy.text_encoders.lt.gemma3_te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.lt.Gemma3_12BTokenizer
+            tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
         elif te_model == TEModel.LLAMA3_8:
             clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data),
                                                                         clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None)

comfy/sd1_clip.py

@@ -308,6 +308,15 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     def load_sd(self, sd):
         return self.transformer.load_state_dict(sd, strict=False, assign=getattr(self, "can_assign_sd", False))
 
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
+        if isinstance(tokens, dict):
+            tokens_only = next(iter(tokens.values())) # todo: get this better?
+        else:
+            tokens_only = tokens
+        tokens_only = [[t[0] for t in b] for b in tokens_only]
+        embeds = self.process_tokens(tokens_only, device=self.execution_device)[0]
+        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)
+
 def parse_parentheses(string):
     result = []
     current_item = ""
@@ -564,6 +573,8 @@ class SDTokenizer:
         min_length = tokenizer_options.get("{}_min_length".format(self.embedding_key), self.min_length)
         min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding)
 
+        min_length = kwargs.get("min_length", min_length)
+
         text = escape_important(text)
         if kwargs.get("disable_weights", self.disable_weights):
             parsed_weights = [(text, 1.0)]
@@ -663,6 +674,9 @@ class SDTokenizer:
     def state_dict(self):
         return {}
 
+    def decode(self, token_ids, skip_special_tokens=True):
+        return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
+
 class SD1Tokenizer:
     def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer, name=None):
         if name is not None:
@@ -686,6 +700,9 @@ class SD1Tokenizer:
     def state_dict(self):
         return getattr(self, self.clip).state_dict()
 
+    def decode(self, token_ids, skip_special_tokens=True):
+        return getattr(self, self.clip).decode(token_ids, skip_special_tokens=skip_special_tokens)
+
 class SD1CheckpointClipModel(SDClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}):
         super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options)
@@ -722,3 +739,6 @@ class SD1ClipModel(torch.nn.Module):
 
     def load_sd(self, sd):
         return getattr(self, self.clip).load_sd(sd)
+
+    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
+        return getattr(self, self.clip).generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)

comfy/text_encoders/anima.py

@@ -33,6 +33,8 @@ class AnimaTokenizer:
     def state_dict(self):
         return {}
 
+    def decode(self, token_ids, **kwargs):
+        return self.qwen3_06b.decode(token_ids, **kwargs)
 
 class Qwen3_06BModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):

comfy/text_encoders/llama.py

@@ -3,6 +3,8 @@ import torch.nn as nn
 from dataclasses import dataclass
 from typing import Optional, Any, Tuple
 import math
+from tqdm import tqdm
+import comfy.utils
 
 from comfy.ldm.modules.attention import optimized_attention_for_device
 import comfy.model_management
@@ -103,6 +105,7 @@ class Qwen3_06BConfig:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [151643, 151645]
 
 @dataclass
 class Qwen3_06B_ACE15_Config:
@@ -126,6 +129,7 @@ class Qwen3_06B_ACE15_Config:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [151643, 151645]
 
 @dataclass
 class Qwen3_2B_ACE15_lm_Config:
@@ -149,6 +153,7 @@ class Qwen3_2B_ACE15_lm_Config:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [151643, 151645]
 
 @dataclass
 class Qwen3_4B_ACE15_lm_Config:
@@ -172,6 +177,7 @@ class Qwen3_4B_ACE15_lm_Config:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [151643, 151645]
 
 @dataclass
 class Qwen3_4BConfig:
@@ -195,6 +201,7 @@ class Qwen3_4BConfig:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [151643, 151645]
 
 @dataclass
 class Qwen3_8BConfig:
@@ -218,6 +225,7 @@ class Qwen3_8BConfig:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [151643, 151645]
 
 @dataclass
 class Ovis25_2BConfig:
@@ -288,6 +296,7 @@ class Gemma2_2B_Config:
     rope_scale = None
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [1]
 
 @dataclass
 class Gemma3_4B_Config:
@@ -312,6 +321,14 @@ class Gemma3_4B_Config:
     rope_scale = [8.0, 1.0]
     final_norm: bool = True
     lm_head: bool = False
+    stop_tokens = [1, 106]
+
+GEMMA3_VISION_CONFIG = {"num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14}
+
+@dataclass
+class Gemma3_4B_Vision_Config(Gemma3_4B_Config):
+    vision_config = GEMMA3_VISION_CONFIG
+    mm_tokens_per_image = 256
 
 @dataclass
 class Gemma3_12B_Config:
@@ -336,8 +353,9 @@ class Gemma3_12B_Config:
     rope_scale = [8.0, 1.0]
     final_norm: bool = True
     lm_head: bool = False
-    vision_config = {"num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14}
+    vision_config = GEMMA3_VISION_CONFIG
     mm_tokens_per_image = 256
+    stop_tokens = [1, 106]
 
 class RMSNorm(nn.Module):
     def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):
@@ -441,8 +459,10 @@ class Attention(nn.Module):
         freqs_cis: Optional[torch.Tensor] = None,
         optimized_attention=None,
         past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        sliding_window: Optional[int] = None,
     ):
         batch_size, seq_length, _ = hidden_states.shape
+
         xq = self.q_proj(hidden_states)
         xk = self.k_proj(hidden_states)
         xv = self.v_proj(hidden_states)
@@ -477,6 +497,11 @@ class Attention(nn.Module):
             else:
                 present_key_value = (xk, xv, index + num_tokens)
 
+            if sliding_window is not None and xk.shape[2] > sliding_window:
+                xk = xk[:, :, -sliding_window:]
+                xv = xv[:, :, -sliding_window:]
+                attention_mask = attention_mask[..., -sliding_window:] if attention_mask is not None else None
+
         xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
         xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
 
@@ -559,10 +584,12 @@ class TransformerBlockGemma2(nn.Module):
         optimized_attention=None,
         past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ):
+        sliding_window = None
         if self.transformer_type == 'gemma3':
             if self.sliding_attention:
+                sliding_window = self.sliding_attention
                 if x.shape[1] > self.sliding_attention:
-                    sliding_mask = torch.full((x.shape[1], x.shape[1]), float("-inf"), device=x.device, dtype=x.dtype)
+                    sliding_mask = torch.full((x.shape[1], x.shape[1]), torch.finfo(x.dtype).min, device=x.device, dtype=x.dtype)
                     sliding_mask.tril_(diagonal=-self.sliding_attention)
                     if attention_mask is not None:
                         attention_mask = attention_mask + sliding_mask
@@ -581,6 +608,7 @@ class TransformerBlockGemma2(nn.Module):
             freqs_cis=freqs_cis,
             optimized_attention=optimized_attention,
             past_key_value=past_key_value,
+            sliding_window=sliding_window,
         )
 
         x = self.post_attention_layernorm(x)
@@ -765,6 +793,107 @@ class BaseLlama:
     def forward(self, input_ids, *args, **kwargs):
         return self.model(input_ids, *args, **kwargs)
 
+class BaseGenerate:
+    def logits(self, x):
+        input = x[:, -1:]
+        if hasattr(self.model, "lm_head"):
+            module = self.model.lm_head
+        else:
+            module = self.model.embed_tokens
+
+        offload_stream = None
+        if module.comfy_cast_weights:
+            weight, _, offload_stream = comfy.ops.cast_bias_weight(module, input, offloadable=True)
+        else:
+            weight = self.model.embed_tokens.weight.to(x)
+
+        x = torch.nn.functional.linear(input, weight, None)
+
+        comfy.ops.uncast_bias_weight(module, weight, None, offload_stream)
+        return x
+
+    def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0):
+        device = embeds.device
+        model_config = self.model.config
+
+        if stop_tokens is None:
+            stop_tokens = self.model.config.stop_tokens
+
+        if execution_dtype is None:
+            if comfy.model_management.should_use_bf16(device):
+                execution_dtype = torch.bfloat16
+            else:
+                execution_dtype = torch.float32
+        embeds = embeds.to(execution_dtype)
+
+        if embeds.ndim == 2:
+            embeds = embeds.unsqueeze(0)
+
+        past_key_values = [] #kv_cache init
+        max_cache_len = embeds.shape[1] + max_length
+        for x in range(model_config.num_hidden_layers):
+            past_key_values.append((torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
+                                    torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0))
+
+        generator = torch.Generator(device=device).manual_seed(seed) if do_sample else None
+
+        generated_token_ids = []
+        pbar = comfy.utils.ProgressBar(max_length)
+
+        # Generation loop
+        for step in tqdm(range(max_length), desc="Generating tokens"):
+            x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values)
+            logits = self.logits(x)[:, -1]
+            next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample)
+            token_id = next_token[0].item()
+            generated_token_ids.append(token_id)
+
+            embeds = self.model.embed_tokens(next_token).to(execution_dtype)
+            pbar.update(1)
+
+            if token_id in stop_tokens:
+                break
+
+        return generated_token_ids
+
+    def sample_token(self, logits, temperature, top_k, top_p, min_p, repetition_penalty, token_history, generator, do_sample=True):
+
+        if not do_sample or temperature == 0.0:
+            return torch.argmax(logits, dim=-1, keepdim=True)
+
+        # Sampling mode
+        if repetition_penalty != 1.0:
+            for i in range(logits.shape[0]):
+                for token_id in set(token_history):
+                    logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty
+
+        if temperature != 1.0:
+            logits = logits / temperature
+
+        if top_k > 0:
+            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+            logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+        if min_p > 0.0:
+            probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)
+            top_probs, _ = probs_before_filter.max(dim=-1, keepdim=True)
+            min_threshold = min_p * top_probs
+            indices_to_remove = probs_before_filter < min_threshold
+            logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+        if top_p < 1.0:
+            sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+            cumulative_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)
+            sorted_indices_to_remove = cumulative_probs > top_p
+            sorted_indices_to_remove[..., 0] = False
+            indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)
+            indices_to_remove.scatter_(1, sorted_indices, sorted_indices_to_remove)
+            logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+        probs = torch.nn.functional.softmax(logits, dim=-1)
+
+        return torch.multinomial(probs, num_samples=1, generator=generator)
+
 class BaseQwen3:
     def logits(self, x):
         input = x[:, -1:]
@@ -808,7 +937,7 @@ class Qwen25_3B(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
-class Qwen3_06B(BaseLlama, BaseQwen3, torch.nn.Module):
+class Qwen3_06B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Qwen3_06BConfig(**config_dict)
@@ -835,7 +964,7 @@ class Qwen3_2B_ACE15_lm(BaseLlama, BaseQwen3, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
-class Qwen3_4B(BaseLlama, BaseQwen3, torch.nn.Module):
+class Qwen3_4B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Qwen3_4BConfig(**config_dict)
@@ -853,7 +982,7 @@ class Qwen3_4B_ACE15_lm(BaseLlama, BaseQwen3, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
-class Qwen3_8B(BaseLlama, BaseQwen3, torch.nn.Module):
+class Qwen3_8B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Qwen3_8BConfig(**config_dict)
@@ -871,7 +1000,7 @@ class Ovis25_2B(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
-class Qwen25_7BVLI(BaseLlama, torch.nn.Module):
+class Qwen25_7BVLI(BaseLlama, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Qwen25_7BVLI_Config(**config_dict)
@@ -881,6 +1010,9 @@ class Qwen25_7BVLI(BaseLlama, torch.nn.Module):
         self.visual = qwen_vl.Qwen2VLVisionTransformer(hidden_size=1280, output_hidden_size=config.hidden_size, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
+        # todo: should this be tied or not?
+        #self.lm_head = operations.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
+
     def preprocess_embed(self, embed, device):
         if embed["type"] == "image":
             image, grid = qwen_vl.process_qwen2vl_images(embed["data"])
@@ -914,7 +1046,7 @@ class Qwen25_7BVLI(BaseLlama, torch.nn.Module):
 
         return super().forward(x, attention_mask=attention_mask, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=final_layer_norm_intermediate, dtype=dtype, position_ids=position_ids)
 
-class Gemma2_2B(BaseLlama, torch.nn.Module):
+class Gemma2_2B(BaseLlama, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Gemma2_2B_Config(**config_dict)
@@ -923,7 +1055,7 @@ class Gemma2_2B(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
-class Gemma3_4B(BaseLlama, torch.nn.Module):
+class Gemma3_4B(BaseLlama, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Gemma3_4B_Config(**config_dict)
@@ -932,7 +1064,25 @@ class Gemma3_4B(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
-class Gemma3_12B(BaseLlama, torch.nn.Module):
+class Gemma3_4B_Vision(BaseLlama, BaseGenerate, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Gemma3_4B_Vision_Config(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+        self.multi_modal_projector = Gemma3MultiModalProjector(config, dtype, device, operations)
+        self.vision_model = comfy.clip_model.CLIPVision(config.vision_config, dtype, device, operations)
+        self.image_size = config.vision_config["image_size"]
+
+    def preprocess_embed(self, embed, device):
+        if embed["type"] == "image":
+            image = comfy.clip_model.clip_preprocess(embed["data"], size=self.image_size, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], crop=True)
+            return self.multi_modal_projector(self.vision_model(image.to(device, dtype=torch.float32))[0]), None
+        return None, None
+
+class Gemma3_12B(BaseLlama, BaseGenerate, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Gemma3_12B_Config(**config_dict)

comfy/text_encoders/lt.py

@@ -6,6 +6,7 @@ import comfy.text_encoders.genmo
 from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
 import torch
 import comfy.utils
+import math
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -22,40 +23,79 @@ def ltxv_te(*args, **kwargs):
     return comfy.text_encoders.genmo.mochi_te(*args, **kwargs)
 
 
-class Gemma3_12BTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, embedding_directory=None, tokenizer_data={}):
-        tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_left=True, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
-
+class Gemma3_Tokenizer():
     def state_dict(self):
         return {"spiece_model": self.tokenizer.serialize_model()}
 
+    def tokenize_with_weights(self, text, return_word_ids=False, image=None, llama_template=None, skip_template=True, **kwargs):
+        self.llama_template = "<start_of_turn>system\nYou are a helpful assistant.<end_of_turn>\n<start_of_turn>user\n{}<end_of_turn>\n<start_of_turn>model\n"
+        self.llama_template_images = "<start_of_turn>system\nYou are a helpful assistant.<end_of_turn>\n<start_of_turn>user\n\n<image_soft_token>{}<end_of_turn>\n\n<start_of_turn>model\n"
+
+        if image is None:
+            images = []
+        else:
+            samples = image.movedim(-1, 1)
+            total = int(896 * 896)
+
+            scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+            width = round(samples.shape[3] * scale_by)
+            height = round(samples.shape[2] * scale_by)
+
+            s = comfy.utils.common_upscale(samples, width, height, "area", "disabled").movedim(1, -1)
+            images = [s[:, :, :, :3]]
+
+        if text.startswith('<start_of_turn>'):
+            skip_template = True
+
+        if skip_template:
+            llama_text = text
+        else:
+            if llama_template is None:
+                if len(images) > 0:
+                    llama_text = self.llama_template_images.format(text)
+                else:
+                    llama_text = self.llama_template.format(text)
+            else:
+                llama_text = llama_template.format(text)
+
+        text_tokens = super().tokenize_with_weights(llama_text, return_word_ids)
+
+        if len(images) > 0:
+            embed_count = 0
+            for r in text_tokens:
+                for i, token in enumerate(r):
+                    if token[0] == 262144 and embed_count < len(images):
+                        r[i] = ({"type": "image", "data": images[embed_count]},) + token[1:]
+                        embed_count += 1
+        return text_tokens
+
+class Gemma3_12BTokenizer(Gemma3_Tokenizer, sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer = tokenizer_data.get("spiece_model", None)
+        special_tokens = {"<image_soft_token>": 262144, "<end_of_turn>": 106}
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_left=True, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False, "special_tokens": special_tokens}, tokenizer_data=tokenizer_data)
+
+
 class LTXAVGemmaTokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma3_12b", tokenizer=Gemma3_12BTokenizer)
 
+
 class Gemma3_12BModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="all", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
         llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
         if llama_quantization_metadata is not None:
             model_options = model_options.copy()
             model_options["quantization_metadata"] = llama_quantization_metadata
-
+        self.dtypes = set()
+        self.dtypes.add(dtype)
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_12B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
-    def tokenize_with_weights(self, text, return_word_ids=False, llama_template="{}", image_embeds=None, **kwargs):
-        text = llama_template.format(text)
-        text_tokens = super().tokenize_with_weights(text, return_word_ids)
-        embed_count = 0
-        for k in text_tokens:
-            tt = text_tokens[k]
-            for r in tt:
-                for i in range(len(r)):
-                    if r[i][0] == 262144:
-                        if image_embeds is not None and embed_count < image_embeds.shape[0]:
-                            r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image"},) + r[i][1:]
-                            embed_count += 1
-        return text_tokens
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
+        tokens_only = [[t[0] for t in b] for b in tokens]
+        embeds, _, _, embeds_info = self.process_tokens(tokens_only, self.execution_device)
+        comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5)
+        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106])  # 106 is <end_of_turn>
 
 class LTXAVTEModel(torch.nn.Module):
     def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}):
@@ -112,6 +152,9 @@ class LTXAVTEModel(torch.nn.Module):
 
         return out.to(out_device), pooled
 
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
+        return self.gemma3_12b.generate(tokens["gemma3_12b"], do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)
+
     def load_sd(self, sd):
         if "model.layers.47.self_attn.q_norm.weight" in sd:
             return self.gemma3_12b.load_sd(sd)
@@ -152,3 +195,14 @@ def ltxav_te(dtype_llama=None, llama_quantization_metadata=None):
                 dtype = dtype_llama
             super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
     return LTXAVTEModel_
+
+def gemma3_te(dtype_llama=None, llama_quantization_metadata=None):
+    class Gemma3_12BModel_(Gemma3_12BModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["llama_quantization_metadata"] = llama_quantization_metadata
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return Gemma3_12BModel_

comfy/text_encoders/lumina2.py

@@ -1,23 +1,23 @@
 from comfy import sd1_clip
 from .spiece_tokenizer import SPieceTokenizer
 import comfy.text_encoders.llama
-
+from comfy.text_encoders.lt import Gemma3_Tokenizer
+import comfy.utils
 
 class Gemma2BTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
+        special_tokens = {"<end_of_turn>": 107}
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False, "special_tokens": special_tokens}, tokenizer_data=tokenizer_data)
 
     def state_dict(self):
         return {"spiece_model": self.tokenizer.serialize_model()}
 
-class Gemma3_4BTokenizer(sd1_clip.SDTokenizer):
+class Gemma3_4BTokenizer(Gemma3_Tokenizer, sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, disable_weights=True, tokenizer_data=tokenizer_data)
-
-    def state_dict(self):
-        return {"spiece_model": self.tokenizer.serialize_model()}
+        special_tokens = {"<image_soft_token>": 262144, "<end_of_turn>": 106}
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False, "special_tokens": special_tokens}, disable_weights=True, tokenizer_data=tokenizer_data)
 
 class LuminaTokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -40,6 +40,20 @@ class Gemma3_4BModel(sd1_clip.SDClipModel):
 
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
+class Gemma3_4B_Vision_Model(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
+        llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
+        if llama_quantization_metadata is not None:
+            model_options = model_options.copy()
+            model_options["quantization_metadata"] = llama_quantization_metadata
+
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B_Vision, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+    def process_tokens(self, tokens, device):
+        embeds, _, _, embeds_info = super().process_tokens(tokens, device)
+        comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5)
+        return embeds
+
 class LuminaModel(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}, name="gemma2_2b", clip_model=Gemma2_2BModel):
         super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options)
@@ -50,6 +64,8 @@ def te(dtype_llama=None, llama_quantization_metadata=None, model_type="gemma2_2b
         model = Gemma2_2BModel
     elif model_type == "gemma3_4b":
         model = Gemma3_4BModel
+    elif model_type == "gemma3_4b_vision":
+        model = Gemma3_4B_Vision_Model
 
     class LuminaTEModel_(LuminaModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):

comfy/text_encoders/spiece_tokenizer.py

@@ -6,9 +6,10 @@ class SPieceTokenizer:
     def from_pretrained(path, **kwargs):
         return SPieceTokenizer(path, **kwargs)
 
-    def __init__(self, tokenizer_path, add_bos=False, add_eos=True):
+    def __init__(self, tokenizer_path, add_bos=False, add_eos=True, special_tokens=None):
         self.add_bos = add_bos
         self.add_eos = add_eos
+        self.special_tokens = special_tokens
         import sentencepiece
         if torch.is_tensor(tokenizer_path):
             tokenizer_path = tokenizer_path.numpy().tobytes()
@@ -27,8 +28,32 @@ class SPieceTokenizer:
         return out
 
     def __call__(self, string):
+        if self.special_tokens is not None:
+            import re
+            special_tokens_pattern = '|'.join(re.escape(token) for token in self.special_tokens.keys())
+            if special_tokens_pattern and re.search(special_tokens_pattern, string):
+                parts = re.split(f'({special_tokens_pattern})', string)
+                result = []
+                for part in parts:
+                    if not part:
+                        continue
+                    if part in self.special_tokens:
+                        result.append(self.special_tokens[part])
+                    else:
+                        encoded = self.tokenizer.encode(part, add_bos=False, add_eos=False)
+                        result.extend(encoded)
+                return {"input_ids": result}
+
         out = self.tokenizer.encode(string)
         return {"input_ids": out}
 
+    def decode(self, token_ids, skip_special_tokens=False):
+
+        if skip_special_tokens and self.special_tokens:
+            special_token_ids = set(self.special_tokens.values())
+            token_ids = [tid for tid in token_ids if tid not in special_token_ids]
+
+        return self.tokenizer.decode(token_ids)
+
     def serialize_model(self):
         return torch.ByteTensor(list(self.tokenizer.serialized_model_proto()))

comfy/utils.py

@@ -1418,3 +1418,11 @@ def deepcopy_list_dict(obj, memo=None):
 
     memo[obj_id] = res
     return res
+
+def normalize_image_embeddings(embeds, embeds_info, scale_factor):
+    """Normalize image embeddings to match text embedding scale"""
+    for info in embeds_info:
+        if info.get("type") == "image":
+            start_idx = info["index"]
+            end_idx = start_idx + info["size"]
+            embeds[:, start_idx:end_idx, :] /= scale_factor

comfy_api/latest/_io.py

@@ -1339,6 +1339,7 @@ class NodeInfoV1:
     api_node: bool=None
     price_badge: dict | None = None
     search_aliases: list[str]=None
+    essentials_category: str=None
 
 
 @dataclass
@@ -1460,6 +1461,8 @@ class Schema:
     """Flags a node as expandable, allowing NodeOutput to include 'expand' property."""
     accept_all_inputs: bool=False
     """When True, all inputs from the prompt will be passed to the node as kwargs, even if not defined in the schema."""
+    essentials_category: str | None = None
+    """Optional category for the Essentials tab. Path-based like category field (e.g., 'Basic', 'Image Tools/Editing')."""
 
     def validate(self):
         '''Validate the schema:
@@ -1566,6 +1569,7 @@ class Schema:
             python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes"),
             price_badge=self.price_badge.as_dict(self.inputs) if self.price_badge is not None else None,
             search_aliases=self.search_aliases if self.search_aliases else None,
+            essentials_category=self.essentials_category,
         )
         return info
 

comfy_api_nodes/apis/gemini.py

@@ -116,9 +116,15 @@ class GeminiGenerationConfig(BaseModel):
     topP: float | None = Field(None, ge=0.0, le=1.0)
 
 
+class GeminiImageOutputOptions(BaseModel):
+    mimeType: str = Field("image/png")
+    compressionQuality: int | None = Field(None)
+
+
 class GeminiImageConfig(BaseModel):
     aspectRatio: str | None = Field(None)
     imageSize: str | None = Field(None)
+    imageOutputOptions: GeminiImageOutputOptions = Field(default_factory=GeminiImageOutputOptions)
 
 
 class GeminiImageGenerationConfig(GeminiGenerationConfig):

comfy_api_nodes/nodes_bfl.py

@@ -57,6 +57,7 @@ class FluxProUltraImageNode(IO.ComfyNode):
                     tooltip="Whether to perform upsampling on the prompt. "
                     "If active, automatically modifies the prompt for more creative generation, "
                     "but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -200,6 +201,7 @@ class FluxKontextProImageNode(IO.ComfyNode):
                     "prompt_upsampling",
                     default=False,
                     tooltip="Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
                 ),
                 IO.Image.Input(
                     "input_image",
@@ -296,6 +298,7 @@ class FluxProExpandNode(IO.ComfyNode):
                     tooltip="Whether to perform upsampling on the prompt. "
                     "If active, automatically modifies the prompt for more creative generation, "
                     "but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "top",
@@ -433,6 +436,7 @@ class FluxProFillNode(IO.ComfyNode):
                     tooltip="Whether to perform upsampling on the prompt. "
                     "If active, automatically modifies the prompt for more creative generation, "
                     "but results are nondeterministic (same seed will not produce exactly the same result).",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "guidance",
@@ -577,6 +581,7 @@ class Flux2ProImageNode(IO.ComfyNode):
                     default=True,
                     tooltip="Whether to perform upsampling on the prompt. "
                     "If active, automatically modifies the prompt for more creative generation.",
+                    advanced=True,
                 ),
                 IO.Image.Input("images", optional=True, tooltip="Up to 9 images to be used as references."),
             ],

comfy_api_nodes/nodes_bytedance.py

@@ -114,6 +114,7 @@ class ByteDanceImageNode(IO.ComfyNode):
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the image',
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -259,12 +260,14 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the image.',
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "fail_on_partial",
                     default=True,
                     tooltip="If enabled, abort execution if any requested images are missing or return an error.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -432,18 +435,21 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
                     tooltip="Specifies whether to fix the camera. The platform appends an instruction "
                     "to fix the camera to your prompt, but does not guarantee the actual effect.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "generate_audio",
                     default=False,
                     tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -561,18 +567,21 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
                     tooltip="Specifies whether to fix the camera. The platform appends an instruction "
                     "to fix the camera to your prompt, but does not guarantee the actual effect.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "generate_audio",
                     default=False,
                     tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -694,18 +703,21 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
                     tooltip="Specifies whether to fix the camera. The platform appends an instruction "
                     "to fix the camera to your prompt, but does not guarantee the actual effect.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "generate_audio",
                     default=False,
                     tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -834,6 +846,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
                     default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_api_nodes/nodes_gemini.py

@@ -316,6 +316,7 @@ class GeminiNode(IO.ComfyNode):
                     default="",
                     optional=True,
                     tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -593,6 +594,7 @@ class GeminiImage(IO.ComfyNode):
                     tooltip="Choose 'IMAGE' for image-only output, or "
                     "'IMAGE+TEXT' to return both the generated image and a text response.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.String.Input(
                     "system_prompt",
@@ -600,6 +602,7 @@ class GeminiImage(IO.ComfyNode):
                     default=GEMINI_IMAGE_SYS_PROMPT,
                     optional=True,
                     tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -634,7 +637,7 @@ class GeminiImage(IO.ComfyNode):
 
         if not aspect_ratio:
             aspect_ratio = "auto"  # for backward compatability with old workflows; to-do remove this in December
-        image_config = GeminiImageConfig(aspectRatio=aspect_ratio)
+        image_config = GeminiImageConfig() if aspect_ratio == "auto" else GeminiImageConfig(aspectRatio=aspect_ratio)
 
         if images is not None:
             parts.extend(await create_image_parts(cls, images))
@@ -654,7 +657,7 @@ class GeminiImage(IO.ComfyNode):
                 ],
                 generationConfig=GeminiImageGenerationConfig(
                     responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
-                    imageConfig=None if aspect_ratio == "auto" else image_config,
+                    imageConfig=image_config,
                 ),
                 systemInstruction=gemini_system_prompt,
             ),
@@ -714,6 +717,7 @@ class GeminiImage2(IO.ComfyNode):
                     options=["IMAGE+TEXT", "IMAGE"],
                     tooltip="Choose 'IMAGE' for image-only output, or "
                     "'IMAGE+TEXT' to return both the generated image and a text response.",
+                    advanced=True,
                 ),
                 IO.Image.Input(
                     "images",
@@ -733,6 +737,7 @@ class GeminiImage2(IO.ComfyNode):
                     default=GEMINI_IMAGE_SYS_PROMPT,
                     optional=True,
                     tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_api_nodes/nodes_hunyuan3d.py

@@ -54,6 +54,7 @@ class TencentTextToModelNode(IO.ComfyNode):
             node_id="TencentTextToModelNode",
             display_name="Hunyuan3D: Text to Model",
             category="api node/3d/Tencent",
+            essentials_category="3D",
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -168,6 +169,7 @@ class TencentImageToModelNode(IO.ComfyNode):
             node_id="TencentImageToModelNode",
             display_name="Hunyuan3D: Image(s) to Model",
             category="api node/3d/Tencent",
+            essentials_category="3D",
             inputs=[
                 IO.Combo.Input(
                     "model",

comfy_api_nodes/nodes_ideogram.py

@@ -261,6 +261,7 @@ class IdeogramV1(IO.ComfyNode):
                     default="AUTO",
                     tooltip="Determine if MagicPrompt should be used in generation",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -394,6 +395,7 @@ class IdeogramV2(IO.ComfyNode):
                     default="AUTO",
                     tooltip="Determine if MagicPrompt should be used in generation",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -411,6 +413,7 @@ class IdeogramV2(IO.ComfyNode):
                     default="NONE",
                     tooltip="Style type for generation (V2 only)",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.String.Input(
                     "negative_prompt",
@@ -564,6 +567,7 @@ class IdeogramV3(IO.ComfyNode):
                     default="AUTO",
                     tooltip="Determine if MagicPrompt should be used in generation",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -590,6 +594,7 @@ class IdeogramV3(IO.ComfyNode):
                     default="DEFAULT",
                     tooltip="Controls the trade-off between generation speed and quality",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Image.Input(
                     "character_image",

comfy_api_nodes/nodes_kling.py

@@ -2262,6 +2262,7 @@ class KlingLipSyncAudioToVideoNode(IO.ComfyNode):
             node_id="KlingLipSyncAudioToVideoNode",
             display_name="Kling Lip Sync Video with Audio",
             category="api node/video/Kling",
+            essentials_category="Video Generation",
             description="Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length.",
             inputs=[
                 IO.Video.Input("video"),
@@ -2333,6 +2334,7 @@ class KlingLipSyncTextToVideoNode(IO.ComfyNode):
                     max=2.0,
                     display_mode=IO.NumberDisplay.slider,
                     tooltip="Speech Rate. Valid range: 0.8~2.0, accurate to one decimal place.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -2454,6 +2456,7 @@ class KlingImageGenerationNode(IO.ComfyNode):
                 IO.Combo.Input(
                     "image_type",
                     options=[i.value for i in KlingImageGenImageReferenceType],
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "image_fidelity",
@@ -2463,6 +2466,7 @@ class KlingImageGenerationNode(IO.ComfyNode):
                     step=0.01,
                     display_mode=IO.NumberDisplay.slider,
                     tooltip="Reference intensity for user-uploaded images",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "human_fidelity",
@@ -2472,6 +2476,7 @@ class KlingImageGenerationNode(IO.ComfyNode):
                     step=0.01,
                     display_mode=IO.NumberDisplay.slider,
                     tooltip="Subject reference similarity",
+                    advanced=True,
                 ),
                 IO.Combo.Input("model_name", options=["kling-v3", "kling-v2", "kling-v1-5"]),
                 IO.Combo.Input(
@@ -2587,7 +2592,7 @@ class TextToVideoWithAudio(IO.ComfyNode):
                 IO.Combo.Input("mode", options=["pro"]),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
                 IO.Combo.Input("duration", options=[5, 10]),
-                IO.Boolean.Input("generate_audio", default=True),
+                IO.Boolean.Input("generate_audio", default=True, advanced=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -2655,7 +2660,7 @@ class ImageToVideoWithAudio(IO.ComfyNode):
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt."),
                 IO.Combo.Input("mode", options=["pro"]),
                 IO.Combo.Input("duration", options=[5, 10]),
-                IO.Boolean.Input("generate_audio", default=True),
+                IO.Boolean.Input("generate_audio", default=True, advanced=True),
             ],
             outputs=[
                 IO.Video.Output(),

comfy_api_nodes/nodes_ltxv.py

@@ -74,6 +74,7 @@ class TextToVideoNode(IO.ComfyNode):
                     default=False,
                     optional=True,
                     tooltip="When true, the generated video will include AI-generated audio matching the scene.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -151,6 +152,7 @@ class ImageToVideoNode(IO.ComfyNode):
                     default=False,
                     optional=True,
                     tooltip="When true, the generated video will include AI-generated audio matching the scene.",
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_api_nodes/nodes_magnific.py

@@ -110,11 +110,13 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
                 IO.Combo.Input(
                     "engine",
                     options=["automatic", "magnific_illusio", "magnific_sharpy", "magnific_sparkle"],
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "auto_downscale",
                     default=False,
                     tooltip="Automatically downscale input image if output would exceed maximum pixel limit.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -280,6 +282,7 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
                     "auto_downscale",
                     default=False,
                     tooltip="Automatically downscale input image if output would exceed maximum resolution.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -440,6 +443,7 @@ class MagnificImageStyleTransferNode(IO.ComfyNode):
                         "softy",
                     ],
                     tooltip="Processing engine selection.",
+                    advanced=True,
                 ),
                 IO.DynamicCombo.Input(
                     "portrait_mode",
@@ -468,6 +472,7 @@ class MagnificImageStyleTransferNode(IO.ComfyNode):
                     default=True,
                     tooltip="When disabled, expect each generation to introduce a degree of randomness, "
                     "leading to more diverse outcomes.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -582,16 +587,19 @@ class MagnificImageRelightNode(IO.ComfyNode):
                     "interpolate_from_original",
                     default=False,
                     tooltip="Restricts generation freedom to match original more closely.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "change_background",
                     default=True,
                     tooltip="Modifies background based on prompt/reference.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "preserve_details",
                     default=True,
                     tooltip="Maintains texture and fine details from original.",
+                    advanced=True,
                 ),
                 IO.DynamicCombo.Input(
                     "advanced_settings",

comfy_api_nodes/nodes_meshy.py

@@ -58,11 +58,12 @@ class MeshyTextToModelNode(IO.ComfyNode):
                     ],
                     tooltip="When set to false, returns an unprocessed triangular mesh.",
                 ),
-                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"]),
+                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"], advanced=True),
                 IO.Combo.Input(
                     "pose_mode",
                     options=["", "A-pose", "T-pose"],
                     tooltip="Specify the pose mode for the generated model.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -155,6 +156,7 @@ class MeshyRefineNode(IO.ComfyNode):
                     tooltip="Generate PBR Maps (metallic, roughness, normal) in addition to the base color. "
                     "Note: this should be set to false when using Sculpture style, "
                     "as Sculpture style generates its own set of PBR maps.",
+                    advanced=True,
                 ),
                 IO.String.Input(
                     "texture_prompt",
@@ -299,6 +301,7 @@ class MeshyImageToModelNode(IO.ComfyNode):
                     "pose_mode",
                     options=["", "A-pose", "T-pose"],
                     tooltip="Specify the pose mode for the generated model.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -429,7 +432,7 @@ class MeshyMultiImageToModelNode(IO.ComfyNode):
                     ],
                     tooltip="When set to false, returns an unprocessed triangular mesh.",
                 ),
-                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"]),
+                IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"], advanced=True),
                 IO.DynamicCombo.Input(
                     "should_texture",
                     options=[
@@ -466,6 +469,7 @@ class MeshyMultiImageToModelNode(IO.ComfyNode):
                     "pose_mode",
                     options=["", "A-pose", "T-pose"],
                     tooltip="Specify the pose mode for the generated model.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -728,8 +732,9 @@ class MeshyTextureNode(IO.ComfyNode):
                     tooltip="Use the original UV of the model instead of generating new UVs. "
                     "When enabled, Meshy preserves existing textures from the uploaded model. "
                     "If the model has no original UV, the quality of the output might not be as good.",
+                    advanced=True,
                 ),
-                IO.Boolean.Input("pbr", default=False),
+                IO.Boolean.Input("pbr", default=False, advanced=True),
                 IO.String.Input(
                     "text_style_prompt",
                     default="",

comfy_api_nodes/nodes_openai.py

@@ -575,6 +575,7 @@ class OpenAIChatNode(IO.ComfyNode):
             node_id="OpenAIChatNode",
             display_name="OpenAI ChatGPT",
             category="api node/text/OpenAI",
+            essentials_category="Text Generation",
             description="Generate text responses from an OpenAI model.",
             inputs=[
                 IO.String.Input(
@@ -587,6 +588,7 @@ class OpenAIChatNode(IO.ComfyNode):
                     "persist_context",
                     default=False,
                     tooltip="This parameter is deprecated and has no effect.",
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "model",
@@ -856,6 +858,7 @@ class OpenAIChatConfig(IO.ComfyNode):
                     options=["auto", "disabled"],
                     default="auto",
                     tooltip="The truncation strategy to use for the model response. auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.disabled: If a model response will exceed the context window size for a model, the request will fail with a 400 error",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "max_output_tokens",
@@ -864,6 +867,7 @@ class OpenAIChatConfig(IO.ComfyNode):
                     max=16384,
                     tooltip="An upper bound for the number of tokens that can be generated for a response, including visible output tokens",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.String.Input(
                     "instructions",

comfy_api_nodes/nodes_recraft.py

@@ -963,6 +963,7 @@ class RecraftRemoveBackgroundNode(IO.ComfyNode):
             node_id="RecraftRemoveBackgroundNode",
             display_name="Recraft Remove Background",
             category="api node/image/Recraft",
+            essentials_category="Image Tools",
             description="Remove background from image, and return processed image and mask.",
             inputs=[
                 IO.Image.Input("image"),

comfy_api_nodes/nodes_rodin.py

@@ -493,7 +493,7 @@ class Rodin3D_Gen2(IO.ComfyNode):
                     default="500K-Triangle",
                     optional=True,
                 ),
-                IO.Boolean.Input("TAPose", default=False),
+                IO.Boolean.Input("TAPose", default=False, advanced=True),
             ],
             outputs=[
                 IO.String.Output(display_name="3D Model Path"),  # for backward compatibility only

comfy_api_nodes/nodes_stability.py

@@ -86,6 +86,7 @@ class StabilityStableImageUltraNode(IO.ComfyNode):
                     "style_preset",
                     options=get_stability_style_presets(),
                     tooltip="Optional desired style of generated image.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -107,6 +108,7 @@ class StabilityStableImageUltraNode(IO.ComfyNode):
                     tooltip="A blurb of text describing what you do not wish to see in the output image. This is an advanced feature.",
                     force_input=True,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "image_denoise",
@@ -218,6 +220,7 @@ class StabilityStableImageSD_3_5Node(IO.ComfyNode):
                     "style_preset",
                     options=get_stability_style_presets(),
                     tooltip="Optional desired style of generated image.",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "cfg_scale",
@@ -247,6 +250,7 @@ class StabilityStableImageSD_3_5Node(IO.ComfyNode):
                     tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
                     force_input=True,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "image_denoise",
@@ -384,6 +388,7 @@ class StabilityUpscaleConservativeNode(IO.ComfyNode):
                     tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
                     force_input=True,
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -474,6 +479,7 @@ class StabilityUpscaleCreativeNode(IO.ComfyNode):
                     "style_preset",
                     options=get_stability_style_presets(),
                     tooltip="Optional desired style of generated image.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -491,6 +497,7 @@ class StabilityUpscaleCreativeNode(IO.ComfyNode):
                     tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
                     force_input=True,
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -624,6 +631,7 @@ class StabilityTextToAudio(IO.ComfyNode):
             node_id="StabilityTextToAudio",
             display_name="Stability AI Text To Audio",
             category="api node/audio/Stability AI",
+            essentials_category="Audio",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.Combo.Input(
@@ -659,6 +667,7 @@ class StabilityTextToAudio(IO.ComfyNode):
                     step=1,
                     tooltip="Controls the number of sampling steps.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -736,6 +745,7 @@ class StabilityAudioToAudio(IO.ComfyNode):
                     step=1,
                     tooltip="Controls the number of sampling steps.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "strength",
@@ -829,6 +839,7 @@ class StabilityAudioInpaint(IO.ComfyNode):
                     step=1,
                     tooltip="Controls the number of sampling steps.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "mask_start",
@@ -837,6 +848,7 @@ class StabilityAudioInpaint(IO.ComfyNode):
                     max=190,
                     step=1,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "mask_end",
@@ -845,6 +857,7 @@ class StabilityAudioInpaint(IO.ComfyNode):
                     max=190,
                     step=1,
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_api_nodes/nodes_topaz.py

@@ -63,12 +63,14 @@ class TopazImageEnhance(IO.ComfyNode):
                     "subject_detection",
                     options=["All", "Foreground", "Background"],
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "face_enhancement",
                     default=True,
                     optional=True,
                     tooltip="Enhance faces (if present) during processing.",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "face_enhancement_creativity",
@@ -79,6 +81,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     optional=True,
                     tooltip="Set the creativity level for face enhancement.",
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "face_enhancement_strength",
@@ -89,6 +92,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     optional=True,
                     tooltip="Controls how sharp enhanced faces are relative to the background.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "crop_to_fill",
@@ -96,6 +100,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     optional=True,
                     tooltip="By default, the image is letterboxed when the output aspect ratio differs. "
                     "Enable to crop the image to fill the output dimensions.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "output_width",
@@ -106,6 +111,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     optional=True,
                     tooltip="Zero value means to calculate automatically (usually it will be original size or output_height if specified).",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "output_height",
@@ -116,6 +122,7 @@ class TopazImageEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     optional=True,
                     tooltip="Zero value means to output in the same height as original or output width.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "creativity",
@@ -131,12 +138,14 @@ class TopazImageEnhance(IO.ComfyNode):
                     default=True,
                     optional=True,
                     tooltip="Preserve subjects' facial identity.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "color_preservation",
                     default=True,
                     optional=True,
                     tooltip="Preserve the original colors.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -234,9 +243,10 @@ class TopazVideoEnhance(IO.ComfyNode):
                     default="low",
                     tooltip="Creativity level (applies only to Starlight (Astra) Creative).",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input("interpolation_enabled", default=False, optional=True),
-                IO.Combo.Input("interpolation_model", options=["apo-8"], default="apo-8", optional=True),
+                IO.Combo.Input("interpolation_model", options=["apo-8"], default="apo-8", optional=True, advanced=True),
                 IO.Int.Input(
                     "interpolation_slowmo",
                     default=1,
@@ -246,6 +256,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                     tooltip="Slow-motion factor applied to the input video. "
                     "For example, 2 makes the output twice as slow and doubles the duration.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "interpolation_frame_rate",
@@ -261,6 +272,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                     default=False,
                     tooltip="Analyze the input for duplicate frames and remove them.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Float.Input(
                     "interpolation_duplicate_threshold",
@@ -271,6 +283,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     tooltip="Detection sensitivity for duplicate frames.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "dynamic_compression_level",
@@ -278,6 +291,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                     default="Low",
                     tooltip="CQP level.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_api_nodes/nodes_tripo.py

@@ -89,13 +89,13 @@ class TripoTextToModelNode(IO.ComfyNode):
                 IO.Combo.Input("style", options=TripoStyle, default="None", optional=True),
                 IO.Boolean.Input("texture", default=True, optional=True),
                 IO.Boolean.Input("pbr", default=True, optional=True),
-                IO.Int.Input("image_seed", default=42, optional=True),
-                IO.Int.Input("model_seed", default=42, optional=True),
-                IO.Int.Input("texture_seed", default=42, optional=True),
-                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True),
-                IO.Int.Input("face_limit", default=-1, min=-1, max=2000000, optional=True),
-                IO.Boolean.Input("quad", default=False, optional=True),
-                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("image_seed", default=42, optional=True, advanced=True),
+                IO.Int.Input("model_seed", default=42, optional=True, advanced=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
+                IO.Int.Input("face_limit", default=-1, min=-1, max=2000000, optional=True, advanced=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
+                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
             ],
             outputs=[
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
@@ -210,18 +210,18 @@ class TripoImageToModelNode(IO.ComfyNode):
                 IO.Combo.Input("style", options=TripoStyle, default="None", optional=True),
                 IO.Boolean.Input("texture", default=True, optional=True),
                 IO.Boolean.Input("pbr", default=True, optional=True),
-                IO.Int.Input("model_seed", default=42, optional=True),
+                IO.Int.Input("model_seed", default=42, optional=True, advanced=True),
                 IO.Combo.Input(
-                    "orientation", options=TripoOrientation, default=TripoOrientation.DEFAULT, optional=True
+                    "orientation", options=TripoOrientation, default=TripoOrientation.DEFAULT, optional=True, advanced=True
                 ),
-                IO.Int.Input("texture_seed", default=42, optional=True),
-                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
                 IO.Combo.Input(
-                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True
+                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True, advanced=True
                 ),
-                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True),
-                IO.Boolean.Input("quad", default=False, optional=True),
-                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True, advanced=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
+                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
             ],
             outputs=[
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
@@ -347,18 +347,19 @@ class TripoMultiviewToModelNode(IO.ComfyNode):
                     options=TripoOrientation,
                     default=TripoOrientation.DEFAULT,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input("texture", default=True, optional=True),
                 IO.Boolean.Input("pbr", default=True, optional=True),
-                IO.Int.Input("model_seed", default=42, optional=True),
-                IO.Int.Input("texture_seed", default=42, optional=True),
-                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("model_seed", default=42, optional=True, advanced=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
                 IO.Combo.Input(
-                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True
+                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True, advanced=True
                 ),
-                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True),
-                IO.Boolean.Input("quad", default=False, optional=True),
-                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True, advanced=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
+                IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
             ],
             outputs=[
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
@@ -474,10 +475,10 @@ class TripoTextureNode(IO.ComfyNode):
                 IO.Custom("MODEL_TASK_ID").Input("model_task_id"),
                 IO.Boolean.Input("texture", default=True, optional=True),
                 IO.Boolean.Input("pbr", default=True, optional=True),
-                IO.Int.Input("texture_seed", default=42, optional=True),
-                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True),
+                IO.Int.Input("texture_seed", default=42, optional=True, advanced=True),
+                IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
                 IO.Combo.Input(
-                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True
+                    "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True, advanced=True
                 ),
             ],
             outputs=[
@@ -682,13 +683,14 @@ class TripoConversionNode(IO.ComfyNode):
             inputs=[
                 IO.Custom("MODEL_TASK_ID,RIG_TASK_ID,RETARGET_TASK_ID").Input("original_model_task_id"),
                 IO.Combo.Input("format", options=["GLTF", "USDZ", "FBX", "OBJ", "STL", "3MF"]),
-                IO.Boolean.Input("quad", default=False, optional=True),
+                IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
                 IO.Int.Input(
                     "face_limit",
                     default=-1,
                     min=-1,
                     max=2000000,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "texture_size",
@@ -696,47 +698,53 @@ class TripoConversionNode(IO.ComfyNode):
                     min=128,
                     max=4096,
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "texture_format",
                     options=["BMP", "DPX", "HDR", "JPEG", "OPEN_EXR", "PNG", "TARGA", "TIFF", "WEBP"],
                     default="JPEG",
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("force_symmetry", default=False, optional=True),
-                IO.Boolean.Input("flatten_bottom", default=False, optional=True),
+                IO.Boolean.Input("force_symmetry", default=False, optional=True, advanced=True),
+                IO.Boolean.Input("flatten_bottom", default=False, optional=True, advanced=True),
                 IO.Float.Input(
                     "flatten_bottom_threshold",
                     default=0.0,
                     min=0.0,
                     max=1.0,
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("pivot_to_center_bottom", default=False, optional=True),
+                IO.Boolean.Input("pivot_to_center_bottom", default=False, optional=True, advanced=True),
                 IO.Float.Input(
                     "scale_factor",
                     default=1.0,
                     min=0.0,
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("with_animation", default=False, optional=True),
-                IO.Boolean.Input("pack_uv", default=False, optional=True),
-                IO.Boolean.Input("bake", default=False, optional=True),
-                IO.String.Input("part_names", default="", optional=True),  # comma-separated list
+                IO.Boolean.Input("with_animation", default=False, optional=True, advanced=True),
+                IO.Boolean.Input("pack_uv", default=False, optional=True, advanced=True),
+                IO.Boolean.Input("bake", default=False, optional=True, advanced=True),
+                IO.String.Input("part_names", default="", optional=True, advanced=True),  # comma-separated list
                 IO.Combo.Input(
                     "fbx_preset",
                     options=["blender", "mixamo", "3dsmax"],
                     default="blender",
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("export_vertex_colors", default=False, optional=True),
+                IO.Boolean.Input("export_vertex_colors", default=False, optional=True, advanced=True),
                 IO.Combo.Input(
                     "export_orientation",
                     options=["align_image", "default"],
                     default="default",
                     optional=True,
+                    advanced=True,
                 ),
-                IO.Boolean.Input("animate_in_place", default=False, optional=True),
+                IO.Boolean.Input("animate_in_place", default=False, optional=True, advanced=True),
             ],
             outputs=[],
             hidden=[

comfy_api_nodes/nodes_veo2.py

@@ -81,6 +81,7 @@ class VeoVideoGenerationNode(IO.ComfyNode):
                     default=True,
                     tooltip="Whether to enhance the prompt with AI assistance",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "person_generation",
@@ -88,6 +89,7 @@ class VeoVideoGenerationNode(IO.ComfyNode):
                     default="ALLOW",
                     tooltip="Whether to allow generating people in the video",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",
@@ -299,6 +301,7 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                     default=True,
                     tooltip="This parameter is deprecated and ignored.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "person_generation",
@@ -306,6 +309,7 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                     default="ALLOW",
                     tooltip="Whether to allow generating people in the video",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "seed",

comfy_api_nodes/nodes_vidu.py

@@ -112,12 +112,14 @@ class ViduTextToVideoNode(IO.ComfyNode):
                     options=["1080p"],
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -208,12 +210,14 @@ class ViduImageToVideoNode(IO.ComfyNode):
                     options=["1080p"],
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -314,12 +318,14 @@ class ViduReferenceVideoNode(IO.ComfyNode):
                     options=["1080p"],
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -426,12 +432,14 @@ class ViduStartEndToVideoNode(IO.ComfyNode):
                     options=["1080p"],
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -511,11 +519,12 @@ class Vidu2TextToVideoNode(IO.ComfyNode):
                     control_after_generate=True,
                 ),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "3:4", "4:3", "1:1"]),
-                IO.Combo.Input("resolution", options=["720p", "1080p"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p"], advanced=True),
                 IO.Boolean.Input(
                     "background_music",
                     default=False,
                     tooltip="Whether to add background music to the generated video.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -609,11 +618,13 @@ class Vidu2ImageToVideoNode(IO.ComfyNode):
                 IO.Combo.Input(
                     "resolution",
                     options=["720p", "1080p"],
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -727,6 +738,7 @@ class Vidu2ReferenceVideoNode(IO.ComfyNode):
                     "audio",
                     default=False,
                     tooltip="When enabled video will contain generated speech and background music based on the prompt.",
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "duration",
@@ -746,11 +758,12 @@ class Vidu2ReferenceVideoNode(IO.ComfyNode):
                     control_after_generate=True,
                 ),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "4:3", "3:4", "1:1"]),
-                IO.Combo.Input("resolution", options=["720p", "1080p"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p"], advanced=True),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -864,11 +877,12 @@ class Vidu2StartEndToVideoNode(IO.ComfyNode):
                     display_mode=IO.NumberDisplay.number,
                     control_after_generate=True,
                 ),
-                IO.Combo.Input("resolution", options=["720p", "1080p"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p"], advanced=True),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],
                     tooltip="The movement amplitude of objects in the frame.",
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_api_nodes/nodes_wan.py

@@ -227,12 +227,14 @@ class WanTextToImageApi(IO.ComfyNode):
                     default=True,
                     tooltip="Whether to enhance the prompt with AI assistance.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -355,6 +357,7 @@ class WanImageToImageApi(IO.ComfyNode):
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -495,18 +498,21 @@ class WanTextToVideoApi(IO.ComfyNode):
                     default=False,
                     optional=True,
                     tooltip="If no audio input is provided, generate audio automatically.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "prompt_extend",
                     default=True,
                     tooltip="Whether to enhance the prompt with AI assistance.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "shot_type",
@@ -515,6 +521,7 @@ class WanTextToVideoApi(IO.ComfyNode):
                     "single continuous shot or multiple shots with cuts. "
                     "This parameter takes effect only when prompt_extend is True.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -667,18 +674,21 @@ class WanImageToVideoApi(IO.ComfyNode):
                     default=False,
                     optional=True,
                     tooltip="If no audio input is provided, generate audio automatically.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "prompt_extend",
                     default=True,
                     tooltip="Whether to enhance the prompt with AI assistance.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
+                    advanced=True,
                 ),
                 IO.Combo.Input(
                     "shot_type",
@@ -687,6 +697,7 @@ class WanImageToVideoApi(IO.ComfyNode):
                     "single continuous shot or multiple shots with cuts. "
                     "This parameter takes effect only when prompt_extend is True.",
                     optional=True,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -839,11 +850,13 @@ class WanReferenceVideoApi(IO.ComfyNode):
                     options=["single", "multi"],
                     tooltip="Specifies the shot type for the generated video, that is, whether the video is a "
                     "single continuous shot or multiple shots with cuts.",
+                    advanced=True,
                 ),
                 IO.Boolean.Input(
                     "watermark",
                     default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_extras/nodes_advanced_samplers.py

@@ -47,8 +47,8 @@ class SamplerLCMUpscale(io.ComfyNode):
             node_id="SamplerLCMUpscale",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Float.Input("scale_ratio", default=1.0, min=0.1, max=20.0, step=0.01),
-                io.Int.Input("scale_steps", default=-1, min=-1, max=1000, step=1),
+                io.Float.Input("scale_ratio", default=1.0, min=0.1, max=20.0, step=0.01, advanced=True),
+                io.Int.Input("scale_steps", default=-1, min=-1, max=1000, step=1, advanced=True),
                 io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS),
             ],
             outputs=[io.Sampler.Output()],
@@ -94,7 +94,7 @@ class SamplerEulerCFGpp(io.ComfyNode):
             display_name="SamplerEulerCFG++",
             category="_for_testing",  # "sampling/custom_sampling/samplers"
             inputs=[
-                io.Combo.Input("version", options=["regular", "alternative"]),
+                io.Combo.Input("version", options=["regular", "alternative"], advanced=True),
             ],
             outputs=[io.Sampler.Output()],
             is_experimental=True,

comfy_extras/nodes_apg.py

@@ -26,6 +26,7 @@ class APG(io.ComfyNode):
                     max=10.0,
                     step=0.01,
                     tooltip="Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1.",
+                    advanced=True,
                 ),
                 io.Float.Input(
                     "norm_threshold",
@@ -34,6 +35,7 @@ class APG(io.ComfyNode):
                     max=50.0,
                     step=0.1,
                     tooltip="Normalize guidance vector to this value, normalization disable at a setting of 0.",
+                    advanced=True,
                 ),
                 io.Float.Input(
                     "momentum",
@@ -42,6 +44,7 @@ class APG(io.ComfyNode):
                     max=1.0,
                     step=0.01,
                     tooltip="Controls a running average of guidance during diffusion, disabled at a setting of 0.",
+                    advanced=True,
                 ),
             ],
             outputs=[io.Model.Output()],

comfy_extras/nodes_attention_multiply.py

@@ -28,10 +28,10 @@ class UNetSelfAttentionMultiply(io.ComfyNode):
             category="_for_testing/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[io.Model.Output()],
             is_experimental=True,
@@ -51,10 +51,10 @@ class UNetCrossAttentionMultiply(io.ComfyNode):
             category="_for_testing/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[io.Model.Output()],
             is_experimental=True,
@@ -75,10 +75,10 @@ class CLIPAttentionMultiply(io.ComfyNode):
             category="_for_testing/attention_experiments",
             inputs=[
                 io.Clip.Input("clip"),
-                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[io.Clip.Output()],
             is_experimental=True,
@@ -109,10 +109,10 @@ class UNetTemporalAttentionMultiply(io.ComfyNode):
             category="_for_testing/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("self_temporal", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("cross_structural", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("cross_temporal", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("self_temporal", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("cross_structural", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("cross_temporal", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[io.Model.Output()],
             is_experimental=True,

comfy_extras/nodes_audio.py

@@ -22,7 +22,7 @@ class EmptyLatentAudio(IO.ComfyNode):
             inputs=[
                 IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
                 IO.Int.Input(
-                    "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
+                    "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch.",
                 ),
             ],
             outputs=[IO.Latent.Output()],
@@ -159,6 +159,7 @@ class SaveAudio(IO.ComfyNode):
             search_aliases=["export flac"],
             display_name="Save Audio (FLAC)",
             category="audio",
+            essentials_category="Audio",
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.String.Input("filename_prefix", default="audio/ComfyUI"),
@@ -300,6 +301,7 @@ class LoadAudio(IO.ComfyNode):
             search_aliases=["import audio", "open audio", "audio file"],
             display_name="Load Audio",
             category="audio",
+            essentials_category="Audio",
             inputs=[
                 IO.Combo.Input("audio", upload=IO.UploadType.audio, options=sorted(files)),
             ],
@@ -677,6 +679,7 @@ class EmptyAudio(IO.ComfyNode):
                     tooltip="Sample rate of the empty audio clip.",
                     min=1,
                     max=192000,
+                    advanced=True,
                 ),
                 IO.Int.Input(
                     "channels",
@@ -684,6 +687,7 @@ class EmptyAudio(IO.ComfyNode):
                     min=1,
                     max=2,
                     tooltip="Number of audio channels (1 for mono, 2 for stereo).",
+                    advanced=True,
                 ),
             ],
             outputs=[IO.Audio.Output()],
@@ -698,6 +702,67 @@ class EmptyAudio(IO.ComfyNode):
     create_empty_audio = execute  # TODO: remove
 
 
+class AudioEqualizer3Band(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="AudioEqualizer3Band",
+            search_aliases=["eq", "bass boost", "treble boost", "equalizer"],
+            display_name="Audio Equalizer (3-Band)",
+            category="audio",
+            is_experimental=True,
+            inputs=[
+                IO.Audio.Input("audio"),
+                IO.Float.Input("low_gain_dB", default=0.0, min=-24.0, max=24.0, step=0.1, tooltip="Gain for Low frequencies (Bass)"),
+                IO.Int.Input("low_freq", default=100, min=20, max=500, tooltip="Cutoff frequency for Low shelf"),
+                IO.Float.Input("mid_gain_dB", default=0.0, min=-24.0, max=24.0, step=0.1, tooltip="Gain for Mid frequencies"),
+                IO.Int.Input("mid_freq", default=1000, min=200, max=4000, tooltip="Center frequency for Mids"),
+                IO.Float.Input("mid_q", default=0.707, min=0.1, max=10.0, step=0.1, tooltip="Q factor (bandwidth) for Mids"),
+                IO.Float.Input("high_gain_dB", default=0.0, min=-24.0, max=24.0, step=0.1, tooltip="Gain for High frequencies (Treble)"),
+                IO.Int.Input("high_freq", default=5000, min=1000, max=15000, tooltip="Cutoff frequency for High shelf"),
+            ],
+            outputs=[IO.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, audio, low_gain_dB, low_freq, mid_gain_dB, mid_freq, mid_q, high_gain_dB, high_freq) -> IO.NodeOutput:
+        waveform = audio["waveform"]
+        sample_rate = audio["sample_rate"]
+        eq_waveform = waveform.clone()
+
+        # 1. Apply Low Shelf (Bass)
+        if low_gain_dB != 0:
+            eq_waveform = torchaudio.functional.bass_biquad(
+                eq_waveform,
+                sample_rate,
+                gain=low_gain_dB,
+                central_freq=float(low_freq),
+                Q=0.707
+            )
+
+        # 2. Apply Peaking EQ (Mids)
+        if mid_gain_dB != 0:
+            eq_waveform = torchaudio.functional.equalizer_biquad(
+                eq_waveform,
+                sample_rate,
+                center_freq=float(mid_freq),
+                gain=mid_gain_dB,
+                Q=mid_q
+            )
+
+        # 3. Apply High Shelf (Treble)
+        if high_gain_dB != 0:
+            eq_waveform = torchaudio.functional.treble_biquad(
+                eq_waveform,
+                sample_rate,
+                gain=high_gain_dB,
+                central_freq=float(high_freq),
+                Q=0.707
+            )
+
+        return IO.NodeOutput({"waveform": eq_waveform, "sample_rate": sample_rate})
+
+
 class AudioExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -720,6 +785,7 @@ class AudioExtension(ComfyExtension):
             AudioMerge,
             AudioAdjustVolume,
             EmptyAudio,
+            AudioEqualizer3Band,
         ]
 
 async def comfy_entrypoint() -> AudioExtension:

comfy_extras/nodes_camera_trajectory.py

@@ -174,10 +174,10 @@ class WanCameraEmbedding(io.ComfyNode):
                 io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
                 io.Float.Input("speed", default=1.0, min=0, max=10.0, step=0.1, optional=True),
-                io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True),
-                io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True),
-                io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True),
-                io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True),
+                io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True, advanced=True),
+                io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True, advanced=True),
+                io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True, advanced=True),
+                io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True, advanced=True),
             ],
             outputs=[
                 io.WanCameraEmbedding.Output(display_name="camera_embedding"),

comfy_extras/nodes_canny.py

@@ -12,6 +12,7 @@ class Canny(io.ComfyNode):
             node_id="Canny",
             search_aliases=["edge detection", "outline", "contour detection", "line art"],
             category="image/preprocessors",
+            essentials_category="Image Tools",
             inputs=[
                 io.Image.Input("image"),
                 io.Float.Input("low_threshold", default=0.4, min=0.01, max=0.99, step=0.01),

comfy_extras/nodes_chroma_radiance.py

@@ -48,6 +48,7 @@ class ChromaRadianceOptions(io.ComfyNode):
                     min=0.0,
                     max=1.0,
                     tooltip="First sigma that these options will be in effect.",
+                    advanced=True,
                 ),
                 io.Float.Input(
                     id="end_sigma",
@@ -55,12 +56,14 @@ class ChromaRadianceOptions(io.ComfyNode):
                     min=0.0,
                     max=1.0,
                     tooltip="Last sigma that these options will be in effect.",
+                    advanced=True,
                 ),
                 io.Int.Input(
                     id="nerf_tile_size",
                     default=-1,
                     min=-1,
                     tooltip="Allows overriding the default NeRF tile size. -1 means use the default (32). 0 means use non-tiling mode (may require a lot of VRAM).",
+                    advanced=True,
                 ),
             ],
             outputs=[io.Model.Output()],

comfy_extras/nodes_clip_sdxl.py

@@ -35,8 +35,8 @@ class CLIPTextEncodeSDXL(io.ComfyNode):
                 io.Clip.Input("clip"),
                 io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                 io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
-                io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION),
-                io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION, advanced=True),
+                io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION, advanced=True),
                 io.Int.Input("target_width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                 io.Int.Input("target_height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                 io.String.Input("text_g", multiline=True, dynamic_prompts=True),

comfy_extras/nodes_cond.py

@@ -38,8 +38,8 @@ class T5TokenizerOptions(io.ComfyNode):
             category="_for_testing/conditioning",
             inputs=[
                 io.Clip.Input("clip"),
-                io.Int.Input("min_padding", default=0, min=0, max=10000, step=1),
-                io.Int.Input("min_length", default=0, min=0, max=10000, step=1),
+                io.Int.Input("min_padding", default=0, min=0, max=10000, step=1, advanced=True),
+                io.Int.Input("min_length", default=0, min=0, max=10000, step=1, advanced=True),
             ],
             outputs=[io.Clip.Output()],
             is_experimental=True,

comfy_extras/nodes_context_windows.py

@@ -14,15 +14,15 @@ class ContextWindowsManualNode(io.ComfyNode):
             description="Manually set context windows.",
             inputs=[
                 io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
-                io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window."),
-                io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window."),
+                io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window.", advanced=True),
+                io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window.", advanced=True),
                 io.Combo.Input("context_schedule", options=[
                     comfy.context_windows.ContextSchedules.STATIC_STANDARD,
                     comfy.context_windows.ContextSchedules.UNIFORM_STANDARD,
                     comfy.context_windows.ContextSchedules.UNIFORM_LOOPED,
                     comfy.context_windows.ContextSchedules.BATCHED,
                     ], tooltip="The stride of the context window."),
-                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."),
+                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True),
                 io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
                 io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
                 io.Int.Input("dim", min=0, max=5, default=0, tooltip="The dimension to apply the context windows to."),
@@ -67,15 +67,15 @@ class WanContextWindowsManualNode(ContextWindowsManualNode):
         schema.description = "Manually set context windows for WAN-like models (dim=2)."
         schema.inputs = [
             io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
-                io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window."),
-                io.Int.Input("context_overlap", min=0, default=30, tooltip="The overlap of the context window."),
+                io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window.", advanced=True),
+                io.Int.Input("context_overlap", min=0, default=30, tooltip="The overlap of the context window.", advanced=True),
                 io.Combo.Input("context_schedule", options=[
                     comfy.context_windows.ContextSchedules.STATIC_STANDARD,
                     comfy.context_windows.ContextSchedules.UNIFORM_STANDARD,
                     comfy.context_windows.ContextSchedules.UNIFORM_LOOPED,
                     comfy.context_windows.ContextSchedules.BATCHED,
                     ], tooltip="The stride of the context window."),
-                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."),
+                io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True),
                 io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
                 io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
                 io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."),

comfy_extras/nodes_controlnet.py

@@ -48,8 +48,8 @@ class ControlNetInpaintingAliMamaApply(io.ComfyNode):
                 io.Image.Input("image"),
                 io.Mask.Input("mask"),
                 io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
             outputs=[
                 io.Conditioning.Output(display_name="positive"),

comfy_extras/nodes_custom_sampler.py

@@ -50,9 +50,9 @@ class KarrasScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("rho", default=7.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("rho", default=7.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -72,8 +72,8 @@ class ExponentialScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -93,9 +93,9 @@ class PolyexponentialScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("rho", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("rho", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -115,10 +115,10 @@ class LaplaceScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.1, round=False),
-                io.Float.Input("beta", default=0.5, min=0.0, max=10.0, step=0.1, round=False),
+                io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.1, round=False, advanced=True),
+                io.Float.Input("beta", default=0.5, min=0.0, max=10.0, step=0.1, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -164,8 +164,8 @@ class BetaSamplingScheduler(io.ComfyNode):
             inputs=[
                 io.Model.Input("model"),
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("alpha", default=0.6, min=0.0, max=50.0, step=0.01, round=False),
-                io.Float.Input("beta", default=0.6, min=0.0, max=50.0, step=0.01, round=False),
+                io.Float.Input("alpha", default=0.6, min=0.0, max=50.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("beta", default=0.6, min=0.0, max=50.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -185,9 +185,9 @@ class VPScheduler(io.ComfyNode):
             category="sampling/custom_sampling/schedulers",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
-                io.Float.Input("beta_d", default=19.9, min=0.0, max=5000.0, step=0.01, round=False), #TODO: fix default values
-                io.Float.Input("beta_min", default=0.1, min=0.0, max=5000.0, step=0.01, round=False),
-                io.Float.Input("eps_s", default=0.001, min=0.0, max=1.0, step=0.0001, round=False),
+                io.Float.Input("beta_d", default=19.9, min=0.0, max=5000.0, step=0.01, round=False, advanced=True), #TODO: fix default values
+                io.Float.Input("beta_min", default=0.1, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("eps_s", default=0.001, min=0.0, max=1.0, step=0.0001, round=False, advanced=True),
             ],
             outputs=[io.Sigmas.Output()]
         )
@@ -398,9 +398,9 @@ class SamplerDPMPP_3M_SDE(io.ComfyNode):
             node_id="SamplerDPMPP_3M_SDE",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Combo.Input("noise_device", options=['gpu', 'cpu']),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -424,9 +424,9 @@ class SamplerDPMPP_2M_SDE(io.ComfyNode):
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Combo.Input("solver_type", options=['midpoint', 'heun']),
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Combo.Input("noise_device", options=['gpu', 'cpu']),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -450,10 +450,10 @@ class SamplerDPMPP_SDE(io.ComfyNode):
             node_id="SamplerDPMPP_SDE",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("r", default=0.5, min=0.0, max=100.0, step=0.01, round=False),
-                io.Combo.Input("noise_device", options=['gpu', 'cpu']),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("r", default=0.5, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -496,8 +496,8 @@ class SamplerEulerAncestral(io.ComfyNode):
             node_id="SamplerEulerAncestral",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -538,7 +538,7 @@ class SamplerLMS(io.ComfyNode):
         return io.Schema(
             node_id="SamplerLMS",
             category="sampling/custom_sampling/samplers",
-            inputs=[io.Int.Input("order", default=4, min=1, max=100)],
+            inputs=[io.Int.Input("order", default=4, min=1, max=100, advanced=True)],
             outputs=[io.Sampler.Output()]
         )
 
@@ -556,16 +556,16 @@ class SamplerDPMAdaptative(io.ComfyNode):
             node_id="SamplerDPMAdaptative",
             category="sampling/custom_sampling/samplers",
             inputs=[
-                io.Int.Input("order", default=3, min=2, max=3),
-                io.Float.Input("rtol", default=0.05, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("atol", default=0.0078, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("h_init", default=0.05, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("pcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("icoeff", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("dcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("accept_safety", default=0.81, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("eta", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Int.Input("order", default=3, min=2, max=3, advanced=True),
+                io.Float.Input("rtol", default=0.05, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("atol", default=0.0078, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("h_init", default=0.05, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("pcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("icoeff", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("dcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("accept_safety", default=0.81, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("eta", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -588,9 +588,9 @@ class SamplerER_SDE(io.ComfyNode):
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Combo.Input("solver_type", options=["ER-SDE", "Reverse-time SDE", "ODE"]),
-                io.Int.Input("max_stage", default=3, min=1, max=3),
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type."),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
+                io.Int.Input("max_stage", default=3, min=1, max=3, advanced=True),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type.", advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -626,14 +626,14 @@ class SamplerSASolver(io.ComfyNode):
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False),
-                io.Float.Input("sde_start_percent", default=0.2, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("sde_end_percent", default=0.8, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
-                io.Int.Input("predictor_order", default=3, min=1, max=6),
-                io.Int.Input("corrector_order", default=4, min=0, max=6),
-                io.Boolean.Input("use_pece"),
-                io.Boolean.Input("simple_order_2"),
+                io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False, advanced=True),
+                io.Float.Input("sde_start_percent", default=0.2, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("sde_end_percent", default=0.8, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
+                io.Int.Input("predictor_order", default=3, min=1, max=6, advanced=True),
+                io.Int.Input("corrector_order", default=4, min=0, max=6, advanced=True),
+                io.Boolean.Input("use_pece", advanced=True),
+                io.Boolean.Input("simple_order_2", advanced=True),
             ],
             outputs=[io.Sampler.Output()]
         )
@@ -671,9 +671,9 @@ class SamplerSEEDS2(io.ComfyNode):
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Combo.Input("solver_type", options=["phi_1", "phi_2"]),
-                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength"),
-                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="SDE noise multiplier"),
-                io.Float.Input("r", default=0.5, min=0.01, max=1.0, step=0.01, round=False, tooltip="Relative step size for the intermediate stage (c2 node)"),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength", advanced=True),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="SDE noise multiplier", advanced=True),
+                io.Float.Input("r", default=0.5, min=0.01, max=1.0, step=0.01, round=False, tooltip="Relative step size for the intermediate stage (c2 node)", advanced=True),
             ],
             outputs=[io.Sampler.Output()],
             description=(
@@ -730,7 +730,7 @@ class SamplerCustom(io.ComfyNode):
             category="sampling/custom_sampling",
             inputs=[
                 io.Model.Input("model"),
-                io.Boolean.Input("add_noise", default=True),
+                io.Boolean.Input("add_noise", default=True, advanced=True),
                 io.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True),
                 io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
                 io.Conditioning.Input("positive"),

comfy_extras/nodes_dataset.py

@@ -222,6 +222,7 @@ class SaveImageDataSetToFolderNode(io.ComfyNode):
                     "filename_prefix",
                     default="image",
                     tooltip="Prefix for saved image filenames.",
+                    advanced=True,
                 ),
             ],
             outputs=[],
@@ -262,6 +263,7 @@ class SaveImageTextDataSetToFolderNode(io.ComfyNode):
                     "filename_prefix",
                     default="image",
                     tooltip="Prefix for saved image filenames.",
+                    advanced=True,
                 ),
             ],
             outputs=[],
@@ -741,6 +743,7 @@ class NormalizeImagesNode(ImageProcessingNode):
             min=0.0,
             max=1.0,
             tooltip="Mean value for normalization.",
+            advanced=True,
         ),
         io.Float.Input(
             "std",
@@ -748,6 +751,7 @@ class NormalizeImagesNode(ImageProcessingNode):
             min=0.001,
             max=1.0,
             tooltip="Standard deviation for normalization.",
+            advanced=True,
         ),
     ]
 
@@ -961,6 +965,7 @@ class ImageDeduplicationNode(ImageProcessingNode):
             min=0.0,
             max=1.0,
             tooltip="Similarity threshold (0-1). Higher means more similar. Images above this threshold are considered duplicates.",
+            advanced=True,
         ),
     ]
 
@@ -1039,6 +1044,7 @@ class ImageGridNode(ImageProcessingNode):
             min=32,
             max=2048,
             tooltip="Width of each cell in the grid.",
+            advanced=True,
         ),
         io.Int.Input(
             "cell_height",
@@ -1046,9 +1052,10 @@ class ImageGridNode(ImageProcessingNode):
             min=32,
             max=2048,
             tooltip="Height of each cell in the grid.",
+            advanced=True,
         ),
         io.Int.Input(
-            "padding", default=4, min=0, max=50, tooltip="Padding between images."
+            "padding", default=4, min=0, max=50, tooltip="Padding between images.", advanced=True
         ),
     ]
 
@@ -1339,6 +1346,7 @@ class SaveTrainingDataset(io.ComfyNode):
                     min=1,
                     max=100000,
                     tooltip="Number of samples per shard file.",
+                    advanced=True,
                 ),
             ],
             outputs=[],

comfy_extras/nodes_easycache.py

@@ -367,10 +367,10 @@ class EasyCacheNode(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model", tooltip="The model to add EasyCache to."),
-                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."),
-                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of EasyCache."),
-                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of EasyCache."),
-                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
+                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps.", advanced=True),
+                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of EasyCache.", advanced=True),
+                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of EasyCache.", advanced=True),
+                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information.", advanced=True),
             ],
             outputs=[
                 io.Model.Output(tooltip="The model with EasyCache."),
@@ -500,10 +500,10 @@ class LazyCacheNode(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model", tooltip="The model to add LazyCache to."),
-                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."),
-                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of LazyCache."),
-                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of LazyCache."),
-                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
+                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps.", advanced=True),
+                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of LazyCache.", advanced=True),
+                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of LazyCache.", advanced=True),
+                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information.", advanced=True),
             ],
             outputs=[
                 io.Model.Output(tooltip="The model with LazyCache."),

comfy_extras/nodes_eps.py

@@ -28,6 +28,7 @@ class EpsilonScaling(io.ComfyNode):
                     max=1.5,
                     step=0.001,
                     display_mode=io.NumberDisplay.number,
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -97,6 +98,7 @@ class TemporalScoreRescaling(io.ComfyNode):
                     max=100.0,
                     step=0.001,
                     display_mode=io.NumberDisplay.number,
+                    advanced=True,
                 ),
                 io.Float.Input(
                     "tsr_sigma",
@@ -109,6 +111,7 @@ class TemporalScoreRescaling(io.ComfyNode):
                     max=100.0,
                     step=0.001,
                     display_mode=io.NumberDisplay.number,
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_extras/nodes_flux.py

@@ -161,6 +161,7 @@ class FluxKontextMultiReferenceLatentMethod(io.ComfyNode):
                 io.Combo.Input(
                     "reference_latents_method",
                     options=["offset", "index", "uxo/uno", "index_timestep_zero"],
+                    advanced=True,
                 ),
             ],
             outputs=[

comfy_extras/nodes_freelunch.py

@@ -32,10 +32,10 @@ class FreeU(IO.ComfyNode):
             category="model_patches/unet",
             inputs=[
                 IO.Model.Input("model"),
-                IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("b2", default=1.2, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01),
+                IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("b2", default=1.2, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[
                 IO.Model.Output(),
@@ -79,10 +79,10 @@ class FreeU_V2(IO.ComfyNode):
             category="model_patches/unet",
             inputs=[
                 IO.Model.Input("model"),
-                IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("b2", default=1.4, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01),
-                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01),
+                IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("b2", default=1.4, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),
+                IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),
             ],
             outputs=[
                 IO.Model.Output(),

comfy_extras/nodes_fresca.py

@@ -65,11 +65,11 @@ class FreSca(io.ComfyNode):
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("scale_low", default=1.0, min=0, max=10, step=0.01,
-                               tooltip="Scaling factor for low-frequency components"),
+                               tooltip="Scaling factor for low-frequency components", advanced=True),
                 io.Float.Input("scale_high", default=1.25, min=0, max=10, step=0.01,
-                               tooltip="Scaling factor for high-frequency components"),
+                               tooltip="Scaling factor for high-frequency components", advanced=True),
                 io.Int.Input("freq_cutoff", default=20, min=1, max=10000, step=1,
-                             tooltip="Number of frequency indices around center to consider as low-frequency"),
+                             tooltip="Number of frequency indices around center to consider as low-frequency", advanced=True),
             ],
             outputs=[
                 io.Model.Output(),

comfy_extras/nodes_gits.py

@@ -342,7 +342,7 @@ class GITSScheduler(io.ComfyNode):
             node_id="GITSScheduler",
             category="sampling/custom_sampling/schedulers",
             inputs=[
-                io.Float.Input("coeff", default=1.20, min=0.80, max=1.50, step=0.05),
+                io.Float.Input("coeff", default=1.20, min=0.80, max=1.50, step=0.05, advanced=True),
                 io.Int.Input("steps", default=10, min=2, max=1000),
                 io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
             ],

comfy_extras/nodes_glsl.py

@@ -0,0 +1,895 @@
+import os
+import sys
+import re
+import logging
+import ctypes.util
+import importlib.util
+from typing import TypedDict
+
+import numpy as np
+import torch
+
+import nodes
+from comfy_api.latest import ComfyExtension, io, ui
+from typing_extensions import override
+from utils.install_util import get_missing_requirements_message
+
+logger = logging.getLogger(__name__)
+
+
+def _check_opengl_availability():
+    """Early check for OpenGL availability. Raises RuntimeError if unlikely to work."""
+    logger.debug("_check_opengl_availability: starting")
+    missing = []
+
+    # Check Python packages (using find_spec to avoid importing)
+    logger.debug("_check_opengl_availability: checking for glfw package")
+    if importlib.util.find_spec("glfw") is None:
+        missing.append("glfw")
+
+    logger.debug("_check_opengl_availability: checking for OpenGL package")
+    if importlib.util.find_spec("OpenGL") is None:
+        missing.append("PyOpenGL")
+
+    if missing:
+        raise RuntimeError(
+            f"OpenGL dependencies not available.\n{get_missing_requirements_message()}\n"
+        )
+
+    # On Linux without display, check if headless backends are available
+    logger.debug(f"_check_opengl_availability: platform={sys.platform}")
+    if sys.platform.startswith("linux"):
+        has_display = os.environ.get("DISPLAY") or os.environ.get("WAYLAND_DISPLAY")
+        logger.debug(f"_check_opengl_availability: has_display={bool(has_display)}")
+        if not has_display:
+            # Check for EGL or OSMesa libraries
+            logger.debug("_check_opengl_availability: checking for EGL library")
+            has_egl = ctypes.util.find_library("EGL")
+            logger.debug("_check_opengl_availability: checking for OSMesa library")
+            has_osmesa = ctypes.util.find_library("OSMesa")
+
+            # Error disabled for CI as it fails this check
+            # if not has_egl and not has_osmesa:
+            #     raise RuntimeError(
+            #         "GLSL Shader node: No display and no headless backend (EGL/OSMesa) found.\n"
+            #         "See error below for installation instructions."
+            #     )
+            logger.debug(f"Headless mode: EGL={'yes' if has_egl else 'no'}, OSMesa={'yes' if has_osmesa else 'no'}")
+
+    logger.debug("_check_opengl_availability: completed")
+
+
+# Run early check at import time
+logger.debug("nodes_glsl: running _check_opengl_availability at import time")
+_check_opengl_availability()
+
+# OpenGL modules - initialized lazily when context is created
+gl = None
+glfw = None
+EGL = None
+
+
+def _import_opengl():
+    """Import OpenGL module. Called after context is created."""
+    global gl
+    if gl is None:
+        logger.debug("_import_opengl: importing OpenGL.GL")
+        import OpenGL.GL as _gl
+        gl = _gl
+        logger.debug("_import_opengl: import completed")
+    return gl
+
+
+class SizeModeInput(TypedDict):
+    size_mode: str
+    width: int
+    height: int
+
+
+MAX_IMAGES = 5      # u_image0-4
+MAX_UNIFORMS = 5    # u_float0-4, u_int0-4
+MAX_OUTPUTS = 4     # fragColor0-3 (MRT)
+
+# Vertex shader using gl_VertexID trick - no VBO needed.
+# Draws a single triangle that covers the entire screen:
+#
+#     (-1,3)
+#       /|
+#      / |  <- visible area is the unit square from (-1,-1) to (1,1)
+#     /  |     parts outside get clipped away
+# (-1,-1)---(3,-1)
+#
+# v_texCoord is computed from clip space: * 0.5 + 0.5 maps (-1,1) -> (0,1)
+VERTEX_SHADER = """#version 330 core
+out vec2 v_texCoord;
+void main() {
+    vec2 verts[3] = vec2[](vec2(-1, -1), vec2(3, -1), vec2(-1, 3));
+    v_texCoord = verts[gl_VertexID] * 0.5 + 0.5;
+    gl_Position = vec4(verts[gl_VertexID], 0, 1);
+}
+"""
+
+DEFAULT_FRAGMENT_SHADER = """#version 300 es
+precision highp float;
+
+uniform sampler2D u_image0;
+uniform vec2 u_resolution;
+
+in vec2 v_texCoord;
+layout(location = 0) out vec4 fragColor0;
+
+void main() {
+    fragColor0 = texture(u_image0, v_texCoord);
+}
+"""
+
+
+def _convert_es_to_desktop(source: str) -> str:
+    """Convert GLSL ES (WebGL) shader source to desktop GLSL 330 core."""
+    # Remove any existing #version directive
+    source = re.sub(r"#version\s+\d+(\s+es)?\s*\n?", "", source, flags=re.IGNORECASE)
+    # Remove precision qualifiers (not needed in desktop GLSL)
+    source = re.sub(r"precision\s+(lowp|mediump|highp)\s+\w+\s*;\s*\n?", "", source)
+    # Prepend desktop GLSL version
+    return "#version 330 core\n" + source
+
+
+def _detect_output_count(source: str) -> int:
+    """Detect how many fragColor outputs are used in the shader.
+
+    Returns the count of outputs needed (1 to MAX_OUTPUTS).
+    """
+    matches = re.findall(r"fragColor(\d+)", source)
+    if not matches:
+        return 1  # Default to 1 output if none found
+    max_index = max(int(m) for m in matches)
+    return min(max_index + 1, MAX_OUTPUTS)
+
+
+def _detect_pass_count(source: str) -> int:
+    """Detect multi-pass rendering from #pragma passes N directive.
+
+    Returns the number of passes (1 if not specified).
+    """
+    match = re.search(r'#pragma\s+passes\s+(\d+)', source)
+    if match:
+        return max(1, int(match.group(1)))
+    return 1
+
+
+def _init_glfw():
+    """Initialize GLFW. Returns (window, glfw_module). Raises RuntimeError on failure."""
+    logger.debug("_init_glfw: starting")
+    # On macOS, glfw.init() must be called from main thread or it hangs forever
+    if sys.platform == "darwin":
+        logger.debug("_init_glfw: skipping on macOS")
+        raise RuntimeError("GLFW backend not supported on macOS")
+
+    logger.debug("_init_glfw: importing glfw module")
+    import glfw as _glfw
+
+    logger.debug("_init_glfw: calling glfw.init()")
+    if not _glfw.init():
+        raise RuntimeError("glfw.init() failed")
+
+    try:
+        logger.debug("_init_glfw: setting window hints")
+        _glfw.window_hint(_glfw.VISIBLE, _glfw.FALSE)
+        _glfw.window_hint(_glfw.CONTEXT_VERSION_MAJOR, 3)
+        _glfw.window_hint(_glfw.CONTEXT_VERSION_MINOR, 3)
+        _glfw.window_hint(_glfw.OPENGL_PROFILE, _glfw.OPENGL_CORE_PROFILE)
+
+        logger.debug("_init_glfw: calling create_window()")
+        window = _glfw.create_window(64, 64, "ComfyUI GLSL", None, None)
+        if not window:
+            raise RuntimeError("glfw.create_window() failed")
+
+        logger.debug("_init_glfw: calling make_context_current()")
+        _glfw.make_context_current(window)
+        logger.debug("_init_glfw: completed successfully")
+        return window, _glfw
+    except Exception:
+        logger.debug("_init_glfw: failed, terminating glfw")
+        _glfw.terminate()
+        raise
+
+
+def _init_egl():
+    """Initialize EGL for headless rendering. Returns (display, context, surface, EGL_module). Raises RuntimeError on failure."""
+    logger.debug("_init_egl: starting")
+    from OpenGL import EGL as _EGL
+    from OpenGL.EGL import (
+        eglGetDisplay, eglInitialize, eglChooseConfig, eglCreateContext,
+        eglMakeCurrent, eglCreatePbufferSurface, eglBindAPI,
+        eglTerminate, eglDestroyContext, eglDestroySurface,
+        EGL_DEFAULT_DISPLAY, EGL_NO_CONTEXT, EGL_NONE,
+        EGL_SURFACE_TYPE, EGL_PBUFFER_BIT, EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT,
+        EGL_RED_SIZE, EGL_GREEN_SIZE, EGL_BLUE_SIZE, EGL_ALPHA_SIZE, EGL_DEPTH_SIZE,
+        EGL_WIDTH, EGL_HEIGHT, EGL_OPENGL_API,
+    )
+    logger.debug("_init_egl: imports completed")
+
+    display = None
+    context = None
+    surface = None
+
+    try:
+        logger.debug("_init_egl: calling eglGetDisplay()")
+        display = eglGetDisplay(EGL_DEFAULT_DISPLAY)
+        if display == _EGL.EGL_NO_DISPLAY:
+            raise RuntimeError("eglGetDisplay() failed")
+
+        logger.debug("_init_egl: calling eglInitialize()")
+        major, minor = _EGL.EGLint(), _EGL.EGLint()
+        if not eglInitialize(display, major, minor):
+            display = None  # Not initialized, don't terminate
+            raise RuntimeError("eglInitialize() failed")
+        logger.debug(f"_init_egl: EGL version {major.value}.{minor.value}")
+
+        config_attribs = [
+            EGL_SURFACE_TYPE, EGL_PBUFFER_BIT,
+            EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT,
+            EGL_RED_SIZE, 8, EGL_GREEN_SIZE, 8, EGL_BLUE_SIZE, 8, EGL_ALPHA_SIZE, 8,
+            EGL_DEPTH_SIZE, 0, EGL_NONE
+        ]
+        configs = (_EGL.EGLConfig * 1)()
+        num_configs = _EGL.EGLint()
+        if not eglChooseConfig(display, config_attribs, configs, 1, num_configs) or num_configs.value == 0:
+            raise RuntimeError("eglChooseConfig() failed")
+        config = configs[0]
+        logger.debug(f"_init_egl: config chosen, num_configs={num_configs.value}")
+
+        if not eglBindAPI(EGL_OPENGL_API):
+            raise RuntimeError("eglBindAPI() failed")
+
+        logger.debug("_init_egl: calling eglCreateContext()")
+        context_attribs = [
+            _EGL.EGL_CONTEXT_MAJOR_VERSION, 3,
+            _EGL.EGL_CONTEXT_MINOR_VERSION, 3,
+            _EGL.EGL_CONTEXT_OPENGL_PROFILE_MASK, _EGL.EGL_CONTEXT_OPENGL_CORE_PROFILE_BIT,
+            EGL_NONE
+        ]
+        context = eglCreateContext(display, config, EGL_NO_CONTEXT, context_attribs)
+        if context == EGL_NO_CONTEXT:
+            raise RuntimeError("eglCreateContext() failed")
+
+        logger.debug("_init_egl: calling eglCreatePbufferSurface()")
+        pbuffer_attribs = [EGL_WIDTH, 64, EGL_HEIGHT, 64, EGL_NONE]
+        surface = eglCreatePbufferSurface(display, config, pbuffer_attribs)
+        if surface == _EGL.EGL_NO_SURFACE:
+            raise RuntimeError("eglCreatePbufferSurface() failed")
+
+        logger.debug("_init_egl: calling eglMakeCurrent()")
+        if not eglMakeCurrent(display, surface, surface, context):
+            raise RuntimeError("eglMakeCurrent() failed")
+
+        logger.debug("_init_egl: completed successfully")
+        return display, context, surface, _EGL
+
+    except Exception:
+        logger.debug("_init_egl: failed, cleaning up")
+        # Clean up any resources on failure
+        if surface is not None:
+            eglDestroySurface(display, surface)
+        if context is not None:
+            eglDestroyContext(display, context)
+        if display is not None:
+            eglTerminate(display)
+        raise
+
+
+def _init_osmesa():
+    """Initialize OSMesa for software rendering. Returns (context, buffer). Raises RuntimeError on failure."""
+    import ctypes
+
+    logger.debug("_init_osmesa: starting")
+    os.environ["PYOPENGL_PLATFORM"] = "osmesa"
+
+    logger.debug("_init_osmesa: importing OpenGL.osmesa")
+    from OpenGL import GL as _gl
+    from OpenGL.osmesa import (
+        OSMesaCreateContextExt, OSMesaMakeCurrent, OSMesaDestroyContext,
+        OSMESA_RGBA,
+    )
+    logger.debug("_init_osmesa: imports completed")
+
+    ctx = OSMesaCreateContextExt(OSMESA_RGBA, 24, 0, 0, None)
+    if not ctx:
+        raise RuntimeError("OSMesaCreateContextExt() failed")
+
+    width, height = 64, 64
+    buffer = (ctypes.c_ubyte * (width * height * 4))()
+
+    logger.debug("_init_osmesa: calling OSMesaMakeCurrent()")
+    if not OSMesaMakeCurrent(ctx, buffer, _gl.GL_UNSIGNED_BYTE, width, height):
+        OSMesaDestroyContext(ctx)
+        raise RuntimeError("OSMesaMakeCurrent() failed")
+
+    logger.debug("_init_osmesa: completed successfully")
+    return ctx, buffer
+
+
+class GLContext:
+    """Manages OpenGL context and resources for shader execution.
+
+    Tries backends in order: GLFW (desktop) → EGL (headless GPU) → OSMesa (software).
+    """
+
+    _instance = None
+    _initialized = False
+
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __init__(self):
+        if GLContext._initialized:
+            logger.debug("GLContext.__init__: already initialized, skipping")
+            return
+
+        logger.debug("GLContext.__init__: starting initialization")
+
+        global glfw, EGL
+
+        import time
+        start = time.perf_counter()
+
+        self._backend = None
+        self._window = None
+        self._egl_display = None
+        self._egl_context = None
+        self._egl_surface = None
+        self._osmesa_ctx = None
+        self._osmesa_buffer = None
+        self._vao = None
+
+        # Try backends in order: GLFW → EGL → OSMesa
+        errors = []
+
+        logger.debug("GLContext.__init__: trying GLFW backend")
+        try:
+            self._window, glfw = _init_glfw()
+            self._backend = "glfw"
+            logger.debug("GLContext.__init__: GLFW backend succeeded")
+        except Exception as e:
+            logger.debug(f"GLContext.__init__: GLFW backend failed: {e}")
+            errors.append(("GLFW", e))
+
+        if self._backend is None:
+            logger.debug("GLContext.__init__: trying EGL backend")
+            try:
+                self._egl_display, self._egl_context, self._egl_surface, EGL = _init_egl()
+                self._backend = "egl"
+                logger.debug("GLContext.__init__: EGL backend succeeded")
+            except Exception as e:
+                logger.debug(f"GLContext.__init__: EGL backend failed: {e}")
+                errors.append(("EGL", e))
+
+        if self._backend is None:
+            logger.debug("GLContext.__init__: trying OSMesa backend")
+            try:
+                self._osmesa_ctx, self._osmesa_buffer = _init_osmesa()
+                self._backend = "osmesa"
+                logger.debug("GLContext.__init__: OSMesa backend succeeded")
+            except Exception as e:
+                logger.debug(f"GLContext.__init__: OSMesa backend failed: {e}")
+                errors.append(("OSMesa", e))
+
+        if self._backend is None:
+            if sys.platform == "win32":
+                platform_help = (
+                    "Windows: Ensure GPU drivers are installed and display is available.\n"
+                    "         CPU-only/headless mode is not supported on Windows."
+                )
+            elif sys.platform == "darwin":
+                platform_help = (
+                    "macOS: GLFW is not supported.\n"
+                    "  Install OSMesa via Homebrew: brew install mesa\n"
+                    "  Then: pip install PyOpenGL PyOpenGL-accelerate"
+                )
+            else:
+                platform_help = (
+                    "Linux: Install one of these backends:\n"
+                    "  Desktop:           sudo apt install libgl1-mesa-glx libglfw3\n"
+                    "  Headless with GPU: sudo apt install libegl1-mesa libgl1-mesa-dri\n"
+                    "  Headless (CPU):    sudo apt install libosmesa6"
+                )
+
+            error_details = "\n".join(f"  {name}: {err}" for name, err in errors)
+            raise RuntimeError(
+                f"Failed to create OpenGL context.\n\n"
+                f"Backend errors:\n{error_details}\n\n"
+                f"{platform_help}"
+            )
+
+        # Now import OpenGL.GL (after context is current)
+        logger.debug("GLContext.__init__: importing OpenGL.GL")
+        _import_opengl()
+
+        # Create VAO (required for core profile, but OSMesa may use compat profile)
+        logger.debug("GLContext.__init__: creating VAO")
+        try:
+            vao = gl.glGenVertexArrays(1)
+            gl.glBindVertexArray(vao)
+            self._vao = vao  # Only store after successful bind
+            logger.debug("GLContext.__init__: VAO created successfully")
+        except Exception as e:
+            logger.debug(f"GLContext.__init__: VAO creation failed (may be expected for OSMesa): {e}")
+            # OSMesa with older Mesa may not support VAOs
+            # Clean up if we created but couldn't bind
+            if vao:
+                try:
+                    gl.glDeleteVertexArrays(1, [vao])
+                except Exception:
+                    pass
+
+        elapsed = (time.perf_counter() - start) * 1000
+
+        # Log device info
+        renderer = gl.glGetString(gl.GL_RENDERER)
+        vendor = gl.glGetString(gl.GL_VENDOR)
+        version = gl.glGetString(gl.GL_VERSION)
+        renderer = renderer.decode() if renderer else "Unknown"
+        vendor = vendor.decode() if vendor else "Unknown"
+        version = version.decode() if version else "Unknown"
+
+        GLContext._initialized = True
+        logger.info(f"GLSL context initialized in {elapsed:.1f}ms ({self._backend}) - {renderer} ({vendor}), GL {version}")
+
+    def make_current(self):
+        if self._backend == "glfw":
+            glfw.make_context_current(self._window)
+        elif self._backend == "egl":
+            from OpenGL.EGL import eglMakeCurrent
+            eglMakeCurrent(self._egl_display, self._egl_surface, self._egl_surface, self._egl_context)
+        elif self._backend == "osmesa":
+            from OpenGL.osmesa import OSMesaMakeCurrent
+            OSMesaMakeCurrent(self._osmesa_ctx, self._osmesa_buffer, gl.GL_UNSIGNED_BYTE, 64, 64)
+
+        if self._vao is not None:
+            gl.glBindVertexArray(self._vao)
+
+
+def _compile_shader(source: str, shader_type: int) -> int:
+    """Compile a shader and return its ID."""
+    shader = gl.glCreateShader(shader_type)
+    gl.glShaderSource(shader, source)
+    gl.glCompileShader(shader)
+
+    if gl.glGetShaderiv(shader, gl.GL_COMPILE_STATUS) != gl.GL_TRUE:
+        error = gl.glGetShaderInfoLog(shader).decode()
+        gl.glDeleteShader(shader)
+        raise RuntimeError(f"Shader compilation failed:\n{error}")
+
+    return shader
+
+
+def _create_program(vertex_source: str, fragment_source: str) -> int:
+    """Create and link a shader program."""
+    vertex_shader = _compile_shader(vertex_source, gl.GL_VERTEX_SHADER)
+    try:
+        fragment_shader = _compile_shader(fragment_source, gl.GL_FRAGMENT_SHADER)
+    except RuntimeError:
+        gl.glDeleteShader(vertex_shader)
+        raise
+
+    program = gl.glCreateProgram()
+    gl.glAttachShader(program, vertex_shader)
+    gl.glAttachShader(program, fragment_shader)
+    gl.glLinkProgram(program)
+
+    gl.glDeleteShader(vertex_shader)
+    gl.glDeleteShader(fragment_shader)
+
+    if gl.glGetProgramiv(program, gl.GL_LINK_STATUS) != gl.GL_TRUE:
+        error = gl.glGetProgramInfoLog(program).decode()
+        gl.glDeleteProgram(program)
+        raise RuntimeError(f"Program linking failed:\n{error}")
+
+    return program
+
+
+def _render_shader_batch(
+    fragment_code: str,
+    width: int,
+    height: int,
+    image_batches: list[list[np.ndarray]],
+    floats: list[float],
+    ints: list[int],
+) -> list[list[np.ndarray]]:
+    """
+    Render a fragment shader for multiple batches efficiently.
+
+    Compiles shader once, reuses framebuffer/textures across batches.
+    Supports multi-pass rendering via #pragma passes N directive.
+
+    Args:
+        fragment_code: User's fragment shader code
+        width: Output width
+        height: Output height
+        image_batches: List of batches, each batch is a list of input images (H, W, C) float32 [0,1]
+        floats: List of float uniforms
+        ints: List of int uniforms
+
+    Returns:
+        List of batch outputs, each is a list of output images (H, W, 4) float32 [0,1]
+    """
+    import time
+    start_time = time.perf_counter()
+
+    if not image_batches:
+        return []
+
+    ctx = GLContext()
+    ctx.make_current()
+
+    # Convert from GLSL ES to desktop GLSL 330
+    fragment_source = _convert_es_to_desktop(fragment_code)
+
+    # Detect how many outputs the shader actually uses
+    num_outputs = _detect_output_count(fragment_code)
+
+    # Detect multi-pass rendering
+    num_passes = _detect_pass_count(fragment_code)
+
+    # Track resources for cleanup
+    program = None
+    fbo = None
+    output_textures = []
+    input_textures = []
+    ping_pong_textures = []
+    ping_pong_fbos = []
+
+    num_inputs = len(image_batches[0])
+
+    try:
+        # Compile shaders (once for all batches)
+        try:
+            program = _create_program(VERTEX_SHADER, fragment_source)
+        except RuntimeError:
+            logger.error(f"Fragment shader:\n{fragment_source}")
+            raise
+
+        gl.glUseProgram(program)
+
+        # Create framebuffer with only the needed color attachments
+        fbo = gl.glGenFramebuffers(1)
+        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
+
+        draw_buffers = []
+        for i in range(num_outputs):
+            tex = gl.glGenTextures(1)
+            output_textures.append(tex)
+            gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
+            gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA32F, width, height, 0, gl.GL_RGBA, gl.GL_FLOAT, None)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
+            gl.glFramebufferTexture2D(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0 + i, gl.GL_TEXTURE_2D, tex, 0)
+            draw_buffers.append(gl.GL_COLOR_ATTACHMENT0 + i)
+
+        gl.glDrawBuffers(num_outputs, draw_buffers)
+
+        if gl.glCheckFramebufferStatus(gl.GL_FRAMEBUFFER) != gl.GL_FRAMEBUFFER_COMPLETE:
+            raise RuntimeError("Framebuffer is not complete")
+
+        # Create ping-pong resources for multi-pass rendering
+        if num_passes > 1:
+            for _ in range(2):
+                pp_tex = gl.glGenTextures(1)
+                ping_pong_textures.append(pp_tex)
+                gl.glBindTexture(gl.GL_TEXTURE_2D, pp_tex)
+                gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA32F, width, height, 0, gl.GL_RGBA, gl.GL_FLOAT, None)
+                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
+                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
+                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE)
+                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE)
+
+                pp_fbo = gl.glGenFramebuffers(1)
+                ping_pong_fbos.append(pp_fbo)
+                gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, pp_fbo)
+                gl.glFramebufferTexture2D(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0, gl.GL_TEXTURE_2D, pp_tex, 0)
+                gl.glDrawBuffers(1, [gl.GL_COLOR_ATTACHMENT0])
+
+                if gl.glCheckFramebufferStatus(gl.GL_FRAMEBUFFER) != gl.GL_FRAMEBUFFER_COMPLETE:
+                    raise RuntimeError("Ping-pong framebuffer is not complete")
+
+        # Create input textures (reused for all batches)
+        for i in range(num_inputs):
+            tex = gl.glGenTextures(1)
+            input_textures.append(tex)
+            gl.glActiveTexture(gl.GL_TEXTURE0 + i)
+            gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE)
+            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE)
+
+            loc = gl.glGetUniformLocation(program, f"u_image{i}")
+            if loc >= 0:
+                gl.glUniform1i(loc, i)
+
+        # Set static uniforms (once for all batches)
+        loc = gl.glGetUniformLocation(program, "u_resolution")
+        if loc >= 0:
+            gl.glUniform2f(loc, float(width), float(height))
+
+        for i, v in enumerate(floats):
+            loc = gl.glGetUniformLocation(program, f"u_float{i}")
+            if loc >= 0:
+                gl.glUniform1f(loc, v)
+
+        for i, v in enumerate(ints):
+            loc = gl.glGetUniformLocation(program, f"u_int{i}")
+            if loc >= 0:
+                gl.glUniform1i(loc, v)
+
+        # Get u_pass uniform location for multi-pass
+        pass_loc = gl.glGetUniformLocation(program, "u_pass")
+
+        gl.glViewport(0, 0, width, height)
+        gl.glDisable(gl.GL_BLEND)  # Ensure no alpha blending - write output directly
+
+        # Process each batch
+        all_batch_outputs = []
+        for images in image_batches:
+            # Update input textures with this batch's images
+            for i, img in enumerate(images):
+                gl.glActiveTexture(gl.GL_TEXTURE0 + i)
+                gl.glBindTexture(gl.GL_TEXTURE_2D, input_textures[i])
+
+                # Flip vertically for GL coordinates, ensure RGBA
+                h, w, c = img.shape
+                if c == 3:
+                    img_upload = np.empty((h, w, 4), dtype=np.float32)
+                    img_upload[:, :, :3] = img[::-1, :, :]
+                    img_upload[:, :, 3] = 1.0
+                else:
+                    img_upload = np.ascontiguousarray(img[::-1, :, :])
+
+                gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA32F, w, h, 0, gl.GL_RGBA, gl.GL_FLOAT, img_upload)
+
+            if num_passes == 1:
+                # Single pass - render directly to output FBO
+                gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
+                if pass_loc >= 0:
+                    gl.glUniform1i(pass_loc, 0)
+                gl.glClearColor(0, 0, 0, 0)
+                gl.glClear(gl.GL_COLOR_BUFFER_BIT)
+                gl.glDrawArrays(gl.GL_TRIANGLES, 0, 3)
+            else:
+                # Multi-pass rendering with ping-pong
+                for p in range(num_passes):
+                    is_last_pass = (p == num_passes - 1)
+
+                    # Set pass uniform
+                    if pass_loc >= 0:
+                        gl.glUniform1i(pass_loc, p)
+
+                    if is_last_pass:
+                        # Last pass renders to the main output FBO
+                        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
+                    else:
+                        # Intermediate passes render to ping-pong FBO
+                        target_fbo = ping_pong_fbos[p % 2]
+                        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, target_fbo)
+
+                    # Set input texture for this pass
+                    gl.glActiveTexture(gl.GL_TEXTURE0)
+                    if p == 0:
+                        # First pass reads from original input
+                        gl.glBindTexture(gl.GL_TEXTURE_2D, input_textures[0])
+                    else:
+                        # Subsequent passes read from previous pass output
+                        source_tex = ping_pong_textures[(p - 1) % 2]
+                        gl.glBindTexture(gl.GL_TEXTURE_2D, source_tex)
+
+                    gl.glClearColor(0, 0, 0, 0)
+                    gl.glClear(gl.GL_COLOR_BUFFER_BIT)
+                    gl.glDrawArrays(gl.GL_TRIANGLES, 0, 3)
+
+            # Read back outputs for this batch
+            # (glGetTexImage is synchronous, implicitly waits for rendering)
+            batch_outputs = []
+            for tex in output_textures:
+                gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
+                data = gl.glGetTexImage(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA, gl.GL_FLOAT)
+                img = np.frombuffer(data, dtype=np.float32).reshape(height, width, 4)
+                batch_outputs.append(img[::-1, :, :].copy())
+
+            # Pad with black images for unused outputs
+            black_img = np.zeros((height, width, 4), dtype=np.float32)
+            for _ in range(num_outputs, MAX_OUTPUTS):
+                batch_outputs.append(black_img)
+
+            all_batch_outputs.append(batch_outputs)
+
+        elapsed = (time.perf_counter() - start_time) * 1000
+        num_batches = len(image_batches)
+        pass_info = f", {num_passes} passes" if num_passes > 1 else ""
+        logger.info(f"GLSL shader executed in {elapsed:.1f}ms ({num_batches} batch{'es' if num_batches != 1 else ''}, {width}x{height}{pass_info})")
+
+        return all_batch_outputs
+
+    finally:
+        # Unbind before deleting
+        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, 0)
+        gl.glUseProgram(0)
+
+        if input_textures:
+            gl.glDeleteTextures(len(input_textures), input_textures)
+        if output_textures:
+            gl.glDeleteTextures(len(output_textures), output_textures)
+        if ping_pong_textures:
+            gl.glDeleteTextures(len(ping_pong_textures), ping_pong_textures)
+        if fbo is not None:
+            gl.glDeleteFramebuffers(1, [fbo])
+        for pp_fbo in ping_pong_fbos:
+            gl.glDeleteFramebuffers(1, [pp_fbo])
+        if program is not None:
+            gl.glDeleteProgram(program)
+
+class GLSLShader(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        image_template = io.Autogrow.TemplatePrefix(
+            io.Image.Input("image"),
+            prefix="image",
+            min=1,
+            max=MAX_IMAGES,
+        )
+
+        float_template = io.Autogrow.TemplatePrefix(
+            io.Float.Input("float", default=0.0),
+            prefix="u_float",
+            min=0,
+            max=MAX_UNIFORMS,
+        )
+
+        int_template = io.Autogrow.TemplatePrefix(
+            io.Int.Input("int", default=0),
+            prefix="u_int",
+            min=0,
+            max=MAX_UNIFORMS,
+        )
+
+        return io.Schema(
+            node_id="GLSLShader",
+            display_name="GLSL Shader",
+            category="image/shader",
+            description=(
+                "Apply GLSL ES fragment shaders to images. "
+                "u_resolution (vec2) is always available."
+            ),
+            inputs=[
+                io.String.Input(
+                    "fragment_shader",
+                    default=DEFAULT_FRAGMENT_SHADER,
+                    multiline=True,
+                    tooltip="GLSL fragment shader source code (GLSL ES 3.00 / WebGL 2.0 compatible)",
+                ),
+                io.DynamicCombo.Input(
+                    "size_mode",
+                    options=[
+                        io.DynamicCombo.Option("from_input", []),
+                        io.DynamicCombo.Option(
+                            "custom",
+                            [
+                                io.Int.Input(
+                                    "width",
+                                    default=512,
+                                    min=1,
+                                    max=nodes.MAX_RESOLUTION,
+                                ),
+                                io.Int.Input(
+                                    "height",
+                                    default=512,
+                                    min=1,
+                                    max=nodes.MAX_RESOLUTION,
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Output size: 'from_input' uses first input image dimensions, 'custom' allows manual size",
+                ),
+                io.Autogrow.Input("images", template=image_template, tooltip=f"Images are available as u_image0-{MAX_IMAGES-1} (sampler2D) in the shader code"),
+                io.Autogrow.Input("floats", template=float_template, tooltip=f"Floats are available as u_float0-{MAX_UNIFORMS-1} in the shader code"),
+                io.Autogrow.Input("ints", template=int_template, tooltip=f"Ints are available as u_int0-{MAX_UNIFORMS-1} in the shader code"),
+            ],
+            outputs=[
+                io.Image.Output(display_name="IMAGE0", tooltip="Available via layout(location = 0) out vec4 fragColor0 in the shader code"),
+                io.Image.Output(display_name="IMAGE1", tooltip="Available via layout(location = 1) out vec4 fragColor1 in the shader code"),
+                io.Image.Output(display_name="IMAGE2", tooltip="Available via layout(location = 2) out vec4 fragColor2 in the shader code"),
+                io.Image.Output(display_name="IMAGE3", tooltip="Available via layout(location = 3) out vec4 fragColor3 in the shader code"),
+            ],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        fragment_shader: str,
+        size_mode: SizeModeInput,
+        images: io.Autogrow.Type,
+        floats: io.Autogrow.Type = None,
+        ints: io.Autogrow.Type = None,
+        **kwargs,
+    ) -> io.NodeOutput:
+        image_list = [v for v in images.values() if v is not None]
+        float_list = (
+            [v if v is not None else 0.0 for v in floats.values()] if floats else []
+        )
+        int_list = [v if v is not None else 0 for v in ints.values()] if ints else []
+
+        if not image_list:
+            raise ValueError("At least one input image is required")
+
+        # Determine output dimensions
+        if size_mode["size_mode"] == "custom":
+            out_width = size_mode["width"]
+            out_height = size_mode["height"]
+        else:
+            out_height, out_width = image_list[0].shape[1:3]
+
+        batch_size = image_list[0].shape[0]
+
+        # Prepare batches
+        image_batches = []
+        for batch_idx in range(batch_size):
+            batch_images = [img_tensor[batch_idx].cpu().numpy().astype(np.float32) for img_tensor in image_list]
+            image_batches.append(batch_images)
+
+        all_batch_outputs = _render_shader_batch(
+            fragment_shader,
+            out_width,
+            out_height,
+            image_batches,
+            float_list,
+            int_list,
+        )
+
+        # Collect outputs into tensors
+        all_outputs = [[] for _ in range(MAX_OUTPUTS)]
+        for batch_outputs in all_batch_outputs:
+            for i, out_img in enumerate(batch_outputs):
+                all_outputs[i].append(torch.from_numpy(out_img))
+
+        output_tensors = [torch.stack(all_outputs[i], dim=0) for i in range(MAX_OUTPUTS)]
+        return io.NodeOutput(
+            *output_tensors,
+            ui=cls._build_ui_output(image_list, output_tensors[0]),
+        )
+
+    @classmethod
+    def _build_ui_output(
+        cls, image_list: list[torch.Tensor], output_batch: torch.Tensor
+    ) -> dict[str, list]:
+        """Build UI output with input and output images for client-side shader execution."""
+        combined_inputs = torch.cat(image_list, dim=0)
+        input_images_ui = ui.ImageSaveHelper.save_images(
+            combined_inputs,
+            filename_prefix="GLSLShader_input",
+            folder_type=io.FolderType.temp,
+            cls=None,
+            compress_level=1,
+        )
+
+        output_images_ui = ui.ImageSaveHelper.save_images(
+            output_batch,
+            filename_prefix="GLSLShader_output",
+            folder_type=io.FolderType.temp,
+            cls=None,
+            compress_level=1,
+        )
+
+        return {"input_images": input_images_ui, "images": output_images_ui}
+
+
+class GLSLExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [GLSLShader]
+
+
+async def comfy_entrypoint() -> GLSLExtension:
+    return GLSLExtension()

comfy_extras/nodes_hooks.py

@@ -233,8 +233,8 @@ class SetClipHooks:
         return {
             "required": {
                 "clip": ("CLIP",),
-                "apply_to_conds": ("BOOLEAN", {"default": True}),
-                "schedule_clip": ("BOOLEAN", {"default": False})
+                "apply_to_conds": ("BOOLEAN", {"default": True, "advanced": True}),
+                "schedule_clip": ("BOOLEAN", {"default": False, "advanced": True})
             },
             "optional": {
                 "hooks": ("HOOKS",)
@@ -512,7 +512,7 @@ class CreateHookKeyframesInterpolated:
                 "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                 "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                 "keyframes_count": ("INT", {"default": 5, "min": 2, "max": 100, "step": 1}),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
+                "print_keyframes": ("BOOLEAN", {"default": False, "advanced": True}),
             },
             "optional": {
                 "prev_hook_kf": ("HOOK_KEYFRAMES",),
@@ -557,7 +557,7 @@ class CreateHookKeyframesFromFloats:
                 "floats_strength": ("FLOATS", {"default": -1, "min": -1, "step": 0.001, "forceInput": True}),
                 "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                 "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
+                "print_keyframes": ("BOOLEAN", {"default": False, "advanced": True}),
             },
             "optional": {
                 "prev_hook_kf": ("HOOK_KEYFRAMES",),

comfy_extras/nodes_hunyuan.py

@@ -138,7 +138,7 @@ class HunyuanVideo15SuperResolution(io.ComfyNode):
                 io.Image.Input("start_image", optional=True),
                 io.ClipVisionOutput.Input("clip_vision_output", optional=True),
                 io.Latent.Input("latent"),
-                io.Float.Input("noise_augmentation", default=0.70, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("noise_augmentation", default=0.70, min=0.0, max=1.0, step=0.01, advanced=True),
 
             ],
             outputs=[
@@ -285,6 +285,7 @@ class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
                     min=1,
                     max=512,
                     tooltip="How much the image influences things vs the text prompt. Higher number means more influence from the text prompt.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -313,7 +314,7 @@ class HunyuanImageToVideo(io.ComfyNode):
                 io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("length", default=53, min=1, max=nodes.MAX_RESOLUTION, step=4),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
-                io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"]),
+                io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"], advanced=True),
                 io.Image.Input("start_image", optional=True),
             ],
             outputs=[
@@ -384,7 +385,7 @@ class HunyuanRefinerLatent(io.ComfyNode):
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
                 io.Latent.Input("latent"),
-                io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01, advanced=True),
 
             ],
             outputs=[

comfy_extras/nodes_hunyuan3d.py

@@ -106,8 +106,8 @@ class VAEDecodeHunyuan3D(IO.ComfyNode):
             inputs=[
                 IO.Latent.Input("samples"),
                 IO.Vae.Input("vae"),
-                IO.Int.Input("num_chunks", default=8000, min=1000, max=500000),
-                IO.Int.Input("octree_resolution", default=256, min=16, max=512),
+                IO.Int.Input("num_chunks", default=8000, min=1000, max=500000, advanced=True),
+                IO.Int.Input("octree_resolution", default=256, min=16, max=512, advanced=True),
             ],
             outputs=[
                 IO.Voxel.Output(),
@@ -456,7 +456,7 @@ class VoxelToMesh(IO.ComfyNode):
             category="3d",
             inputs=[
                 IO.Voxel.Input("voxel"),
-                IO.Combo.Input("algorithm", options=["surface net", "basic"]),
+                IO.Combo.Input("algorithm", options=["surface net", "basic"], advanced=True),
                 IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01),
             ],
             outputs=[
@@ -621,6 +621,7 @@ class SaveGLB(IO.ComfyNode):
             display_name="Save 3D Model",
             search_aliases=["export 3d model", "save mesh"],
             category="3d",
+            essentials_category="Basics",
             is_output_node=True,
             inputs=[
                 IO.MultiType.Input(

comfy_extras/nodes_hypertile.py

@@ -30,10 +30,10 @@ class HyperTile(io.ComfyNode):
             category="model_patches/unet",
             inputs=[
                 io.Model.Input("model"),
-                io.Int.Input("tile_size", default=256, min=1, max=2048),
-                io.Int.Input("swap_size", default=2, min=1, max=128),
-                io.Int.Input("max_depth", default=0, min=0, max=10),
-                io.Boolean.Input("scale_depth", default=False),
+                io.Int.Input("tile_size", default=256, min=1, max=2048, advanced=True),
+                io.Int.Input("swap_size", default=2, min=1, max=128, advanced=True),
+                io.Int.Input("max_depth", default=0, min=0, max=10, advanced=True),
+                io.Boolean.Input("scale_depth", default=False, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),

comfy_extras/nodes_images.py

@@ -26,6 +26,7 @@ class ImageCrop(IO.ComfyNode):
             display_name="Image Crop (Deprecated)",
             category="image/transform",
             is_deprecated=True,
+            essentials_category="Image Tools",
             inputs=[
                 IO.Image.Input("image"),
                 IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
@@ -227,7 +228,7 @@ class SaveAnimatedPNG(IO.ComfyNode):
                 IO.Image.Input("images"),
                 IO.String.Input("filename_prefix", default="ComfyUI"),
                 IO.Float.Input("fps", default=6.0, min=0.01, max=1000.0, step=0.01),
-                IO.Int.Input("compress_level", default=4, min=0, max=9),
+                IO.Int.Input("compress_level", default=4, min=0, max=9, advanced=True),
             ],
             hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
             is_output_node=True,
@@ -264,8 +265,8 @@ class ImageStitch(IO.ComfyNode):
                 IO.Image.Input("image1"),
                 IO.Combo.Input("direction", options=["right", "down", "left", "up"], default="right"),
                 IO.Boolean.Input("match_image_size", default=True),
-                IO.Int.Input("spacing_width", default=0, min=0, max=1024, step=2),
-                IO.Combo.Input("spacing_color", options=["white", "black", "red", "green", "blue"], default="white"),
+                IO.Int.Input("spacing_width", default=0, min=0, max=1024, step=2, advanced=True),
+                IO.Combo.Input("spacing_color", options=["white", "black", "red", "green", "blue"], default="white", advanced=True),
                 IO.Image.Input("image2", optional=True),
             ],
             outputs=[IO.Image.Output()],
@@ -435,8 +436,8 @@ class ResizeAndPadImage(IO.ComfyNode):
                 IO.Image.Input("image"),
                 IO.Int.Input("target_width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
                 IO.Int.Input("target_height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
-                IO.Combo.Input("padding_color", options=["white", "black"]),
-                IO.Combo.Input("interpolation", options=["area", "bicubic", "nearest-exact", "bilinear", "lanczos"]),
+                IO.Combo.Input("padding_color", options=["white", "black"], advanced=True),
+                IO.Combo.Input("interpolation", options=["area", "bicubic", "nearest-exact", "bilinear", "lanczos"], advanced=True),
             ],
             outputs=[IO.Image.Output()],
         )
@@ -589,6 +590,7 @@ class ImageRotate(IO.ComfyNode):
             node_id="ImageRotate",
             search_aliases=["turn", "flip orientation"],
             category="image/transform",
+            essentials_category="Image Tools",
             inputs=[
                 IO.Image.Input("image"),
                 IO.Combo.Input("rotation", options=["none", "90 degrees", "180 degrees", "270 degrees"]),

comfy_extras/nodes_latent.py

@@ -413,9 +413,9 @@ class LatentOperationSharpen(io.ComfyNode):
             category="latent/advanced/operations",
             is_experimental=True,
             inputs=[
-                io.Int.Input("sharpen_radius", default=9, min=1, max=31, step=1),
-                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1),
-                io.Float.Input("alpha", default=0.1, min=0.0, max=5.0, step=0.01),
+                io.Int.Input("sharpen_radius", default=9, min=1, max=31, step=1, advanced=True),
+                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1, advanced=True),
+                io.Float.Input("alpha", default=0.1, min=0.0, max=5.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.LatentOperation.Output(),

comfy_extras/nodes_load_3d.py

@@ -31,6 +31,7 @@ class Load3D(IO.ComfyNode):
             node_id="Load3D",
             display_name="Load 3D & Animation",
             category="3d",
+            essentials_category="Basics",
             is_experimental=True,
             inputs=[
                 IO.Combo.Input("model_file", options=sorted(files), upload=IO.UploadType.model),
@@ -97,8 +98,8 @@ class Preview3D(IO.ComfyNode):
                     ],
                     tooltip="3D model file or path string",
                 ),
-                IO.Load3DCamera.Input("camera_info", optional=True),
-                IO.Image.Input("bg_image", optional=True),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Image.Input("bg_image", optional=True, advanced=True),
             ],
             outputs=[],
         )

comfy_extras/nodes_lora_extract.py

@@ -94,9 +94,9 @@ class LoraSave(io.ComfyNode):
             category="_for_testing",
             inputs=[
                 io.String.Input("filename_prefix", default="loras/ComfyUI_extracted_lora"),
-                io.Int.Input("rank", default=8, min=1, max=4096, step=1),
-                io.Combo.Input("lora_type", options=tuple(LORA_TYPES.keys())),
-                io.Boolean.Input("bias_diff", default=True),
+                io.Int.Input("rank", default=8, min=1, max=4096, step=1, advanced=True),
+                io.Combo.Input("lora_type", options=tuple(LORA_TYPES.keys()), advanced=True),
+                io.Boolean.Input("bias_diff", default=True, advanced=True),
                 io.Model.Input(
                     "model_diff",
                     tooltip="The ModelSubtract output to be converted to a lora.",

comfy_extras/nodes_lt.py

@@ -450,6 +450,7 @@ class LTXVScheduler(io.ComfyNode):
                     id="stretch",
                     default=True,
                     tooltip="Stretch the sigmas to be in the range [terminal, 1].",
+                    advanced=True,
                 ),
                 io.Float.Input(
                     id="terminal",
@@ -458,6 +459,7 @@ class LTXVScheduler(io.ComfyNode):
                     max=0.99,
                     step=0.01,
                     tooltip="The terminal value of the sigmas after stretching.",
+                    advanced=True,
                 ),
                 io.Latent.Input("latent", optional=True),
             ],

comfy_extras/nodes_lt_audio.py

@@ -189,6 +189,7 @@ class LTXAVTextEncoderLoader(io.ComfyNode):
                 io.Combo.Input(
                     "device",
                     options=["default", "cpu"],
+                    advanced=True,
                 )
             ],
             outputs=[io.Clip.Output()],

comfy_extras/nodes_lumina2.py

@@ -12,8 +12,8 @@ class RenormCFG(io.ComfyNode):
             category="advanced/model",
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01),
-                io.Float.Input("renorm_cfg", default=1.0, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01, advanced=True),
+                io.Float.Input("renorm_cfg", default=1.0, min=0.0, max=100.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),

comfy_extras/nodes_mask.py

@@ -348,7 +348,7 @@ class GrowMask(IO.ComfyNode):
             inputs=[
                 IO.Mask.Input("mask"),
                 IO.Int.Input("expand", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1),
-                IO.Boolean.Input("tapered_corners", default=True),
+                IO.Boolean.Input("tapered_corners", default=True, advanced=True),
             ],
             outputs=[IO.Mask.Output()],
         )

comfy_extras/nodes_model_advanced.py

@@ -53,7 +53,7 @@ class ModelSamplingDiscrete:
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "sampling": (["eps", "v_prediction", "lcm", "x0", "img_to_img"],),
-                              "zsnr": ("BOOLEAN", {"default": False}),
+                              "zsnr": ("BOOLEAN", {"default": False, "advanced": True}),
                               }}
 
     RETURN_TYPES = ("MODEL",)
@@ -153,8 +153,8 @@ class ModelSamplingFlux:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01}),
-                              "base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01}),
+                              "max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01, "advanced": True}),
+                              "base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "advanced": True}),
                               "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               }}
@@ -190,8 +190,8 @@ class ModelSamplingContinuousEDM:
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "sampling": (["v_prediction", "edm", "edm_playground_v2.5", "eps", "cosmos_rflow"],),
-                              "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
-                              "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
+                              "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
                               }}
 
     RETURN_TYPES = ("MODEL",)
@@ -235,8 +235,8 @@ class ModelSamplingContinuousV:
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "sampling": (["v_prediction"],),
-                              "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
-                              "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
+                              "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
                               }}
 
     RETURN_TYPES = ("MODEL",)
@@ -303,7 +303,7 @@ class ModelComputeDtype:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "dtype": (["default", "fp32", "fp16", "bf16"],),
+                              "dtype": (["default", "fp32", "fp16", "bf16"], {"advanced": True}),
                               }}
 
     RETURN_TYPES = ("MODEL",)

comfy_extras/nodes_model_downscale.py

@@ -13,11 +13,11 @@ class PatchModelAddDownscale(io.ComfyNode):
             category="model_patches/unet",
             inputs=[
                 io.Model.Input("model"),
-                io.Int.Input("block_number", default=3, min=1, max=32, step=1),
+                io.Int.Input("block_number", default=3, min=1, max=32, step=1, advanced=True),
                 io.Float.Input("downscale_factor", default=2.0, min=0.1, max=9.0, step=0.001),
-                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=0.35, min=0.0, max=1.0, step=0.001),
-                io.Boolean.Input("downscale_after_skip", default=True),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=0.35, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Boolean.Input("downscale_after_skip", default=True, advanced=True),
                 io.Combo.Input("downscale_method", options=cls.UPSCALE_METHODS),
                 io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS),
             ],

comfy_extras/nodes_perpneg.py

@@ -29,7 +29,7 @@ class PerpNeg(io.ComfyNode):
             inputs=[
                 io.Model.Input("model"),
                 io.Conditioning.Input("empty_conditioning"),
-                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),
@@ -134,7 +134,7 @@ class PerpNegGuider(io.ComfyNode):
                 io.Conditioning.Input("negative"),
                 io.Conditioning.Input("empty_conditioning"),
                 io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
-                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.Guider.Output(),

comfy_extras/nodes_post_processing.py

@@ -77,6 +77,7 @@ class Blur(io.ComfyNode):
         return io.Schema(
             node_id="ImageBlur",
             category="image/postprocessing",
+            essentials_category="Image Tools",
             inputs=[
                 io.Image.Input("image"),
                 io.Int.Input("blur_radius", default=1, min=1, max=31, step=1),
@@ -179,9 +180,9 @@ class Sharpen(io.ComfyNode):
             category="image/postprocessing",
             inputs=[
                 io.Image.Input("image"),
-                io.Int.Input("sharpen_radius", default=1, min=1, max=31, step=1),
-                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.01),
-                io.Float.Input("alpha", default=1.0, min=0.0, max=5.0, step=0.01),
+                io.Int.Input("sharpen_radius", default=1, min=1, max=31, step=1, advanced=True),
+                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.01, advanced=True),
+                io.Float.Input("alpha", default=1.0, min=0.0, max=5.0, step=0.01, advanced=True),
             ],
             outputs=[
                 io.Image.Output(),
@@ -225,7 +226,7 @@ class ImageScaleToTotalPixels(io.ComfyNode):
                 io.Image.Input("image"),
                 io.Combo.Input("upscale_method", options=cls.upscale_methods),
                 io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01),
-                io.Int.Input("resolution_steps", default=1, min=1, max=256),
+                io.Int.Input("resolution_steps", default=1, min=1, max=256, advanced=True),
             ],
             outputs=[
                 io.Image.Output(),

comfy_extras/nodes_qwen.py

@@ -116,7 +116,7 @@ class EmptyQwenImageLayeredLatentImage(io.ComfyNode):
             inputs=[
                 io.Int.Input("width", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
-                io.Int.Input("layers", default=3, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                io.Int.Input("layers", default=3, min=0, max=nodes.MAX_RESOLUTION, step=1, advanced=True),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
             ],
             outputs=[

comfy_extras/nodes_rope.py

@@ -12,14 +12,14 @@ class ScaleROPE(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
-                io.Float.Input("scale_x", default=1.0, min=0.0, max=100.0, step=0.1),
-                io.Float.Input("shift_x", default=0.0, min=-256.0, max=256.0, step=0.1),
+                io.Float.Input("scale_x", default=1.0, min=0.0, max=100.0, step=0.1, advanced=True),
+                io.Float.Input("shift_x", default=0.0, min=-256.0, max=256.0, step=0.1, advanced=True),
 
-                io.Float.Input("scale_y", default=1.0, min=0.0, max=100.0, step=0.1),
-                io.Float.Input("shift_y", default=0.0, min=-256.0, max=256.0, step=0.1),
+                io.Float.Input("scale_y", default=1.0, min=0.0, max=100.0, step=0.1, advanced=True),
+                io.Float.Input("shift_y", default=0.0, min=-256.0, max=256.0, step=0.1, advanced=True),
 
-                io.Float.Input("scale_t", default=1.0, min=0.0, max=100.0, step=0.1),
-                io.Float.Input("shift_t", default=0.0, min=-256.0, max=256.0, step=0.1),
+                io.Float.Input("scale_t", default=1.0, min=0.0, max=100.0, step=0.1, advanced=True),
+                io.Float.Input("shift_t", default=0.0, min=-256.0, max=256.0, step=0.1, advanced=True),
 
 
             ],

comfy_extras/nodes_sag.py

@@ -117,7 +117,7 @@ class SelfAttentionGuidance(io.ComfyNode):
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("scale", default=0.5, min=-2.0, max=5.0, step=0.01),
-                io.Float.Input("blur_sigma", default=2.0, min=0.0, max=10.0, step=0.1),
+                io.Float.Input("blur_sigma", default=2.0, min=0.0, max=10.0, step=0.1, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),

comfy_extras/nodes_sd3.py

@@ -72,7 +72,7 @@ class CLIPTextEncodeSD3(io.ComfyNode):
                 io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
                 io.String.Input("clip_g", multiline=True, dynamic_prompts=True),
                 io.String.Input("t5xxl", multiline=True, dynamic_prompts=True),
-                io.Combo.Input("empty_padding", options=["none", "empty_prompt"]),
+                io.Combo.Input("empty_padding", options=["none", "empty_prompt"], advanced=True),
             ],
             outputs=[
                 io.Conditioning.Output(),
@@ -179,10 +179,10 @@ class SkipLayerGuidanceSD3(io.ComfyNode):
             description="Generic version of SkipLayerGuidance node that can be used on every DiT model.",
             inputs=[
                 io.Model.Input("model"),
-                io.String.Input("layers", default="7, 8, 9", multiline=False),
+                io.String.Input("layers", default="7, 8, 9", multiline=False, advanced=True),
                 io.Float.Input("scale", default=3.0, min=0.0, max=10.0, step=0.1),
-                io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),

comfy_extras/nodes_sdupscale.py

@@ -15,7 +15,7 @@ class SD_4XUpscale_Conditioning(io.ComfyNode):
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
                 io.Float.Input("scale_ratio", default=4.0, min=0.0, max=10.0, step=0.01),
-                io.Float.Input("noise_augmentation", default=0.0, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("noise_augmentation", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
             outputs=[
                 io.Conditioning.Output(display_name="positive"),

comfy_extras/nodes_slg.py

@@ -21,11 +21,11 @@ class SkipLayerGuidanceDiT(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
-                io.String.Input("double_layers", default="7, 8, 9"),
-                io.String.Input("single_layers", default="7, 8, 9"),
+                io.String.Input("double_layers", default="7, 8, 9", advanced=True),
+                io.String.Input("single_layers", default="7, 8, 9", advanced=True),
                 io.Float.Input("scale", default=3.0, min=0.0, max=10.0, step=0.1),
-                io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001, advanced=True),
                 io.Float.Input("rescaling_scale", default=0.0, min=0.0, max=10.0, step=0.01),
             ],
             outputs=[
@@ -101,10 +101,10 @@ class SkipLayerGuidanceDiTSimple(io.ComfyNode):
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
-                io.String.Input("double_layers", default="7, 8, 9"),
-                io.String.Input("single_layers", default="7, 8, 9"),
-                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
-                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001),
+                io.String.Input("double_layers", default="7, 8, 9", advanced=True),
+                io.String.Input("single_layers", default="7, 8, 9", advanced=True),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
             outputs=[
                 io.Model.Output(),

comfy_extras/nodes_stable3d.py

@@ -75,8 +75,8 @@ class StableZero123_Conditioning_Batched(io.ComfyNode):
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
                 io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
                 io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
-                io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
-                io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
+                io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False, advanced=True),
+                io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False, advanced=True)
             ],
             outputs=[
                 io.Conditioning.Output(display_name="positive"),

comfy_extras/nodes_stable_cascade.py

@@ -33,7 +33,7 @@ class StableCascade_EmptyLatentImage(io.ComfyNode):
             inputs=[
                 io.Int.Input("width", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
                 io.Int.Input("height", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
-                io.Int.Input("compression", default=42, min=4, max=128, step=1),
+                io.Int.Input("compression", default=42, min=4, max=128, step=1, advanced=True),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
             ],
             outputs=[
@@ -62,7 +62,7 @@ class StableCascade_StageC_VAEEncode(io.ComfyNode):
             inputs=[
                 io.Image.Input("image"),
                 io.Vae.Input("vae"),
-                io.Int.Input("compression", default=42, min=4, max=128, step=1),
+                io.Int.Input("compression", default=42, min=4, max=128, step=1, advanced=True),
             ],
             outputs=[
                 io.Latent.Output(display_name="stage_c"),

comfy_extras/nodes_string.py

@@ -169,7 +169,7 @@ class StringContains(io.ComfyNode):
             inputs=[
                 io.String.Input("string", multiline=True),
                 io.String.Input("substring", multiline=True),
-                io.Boolean.Input("case_sensitive", default=True),
+                io.Boolean.Input("case_sensitive", default=True, advanced=True),
             ],
             outputs=[
                 io.Boolean.Output(display_name="contains"),
@@ -198,7 +198,7 @@ class StringCompare(io.ComfyNode):
                 io.String.Input("string_a", multiline=True),
                 io.String.Input("string_b", multiline=True),
                 io.Combo.Input("mode", options=["Starts With", "Ends With", "Equal"]),
-                io.Boolean.Input("case_sensitive", default=True),
+                io.Boolean.Input("case_sensitive", default=True, advanced=True),
             ],
             outputs=[
                 io.Boolean.Output(),
@@ -233,9 +233,9 @@ class RegexMatch(io.ComfyNode):
             inputs=[
                 io.String.Input("string", multiline=True),
                 io.String.Input("regex_pattern", multiline=True),
-                io.Boolean.Input("case_insensitive", default=True),
-                io.Boolean.Input("multiline", default=False),
-                io.Boolean.Input("dotall", default=False),
+                io.Boolean.Input("case_insensitive", default=True, advanced=True),
+                io.Boolean.Input("multiline", default=False, advanced=True),
+                io.Boolean.Input("dotall", default=False, advanced=True),
             ],
             outputs=[
                 io.Boolean.Output(display_name="matches"),
@@ -275,10 +275,10 @@ class RegexExtract(io.ComfyNode):
                 io.String.Input("string", multiline=True),
                 io.String.Input("regex_pattern", multiline=True),
                 io.Combo.Input("mode", options=["First Match", "All Matches", "First Group", "All Groups"]),
-                io.Boolean.Input("case_insensitive", default=True),
-                io.Boolean.Input("multiline", default=False),
-                io.Boolean.Input("dotall", default=False),
-                io.Int.Input("group_index", default=1, min=0, max=100),
+                io.Boolean.Input("case_insensitive", default=True, advanced=True),
+                io.Boolean.Input("multiline", default=False, advanced=True),
+                io.Boolean.Input("dotall", default=False, advanced=True),
+                io.Int.Input("group_index", default=1, min=0, max=100, advanced=True),
             ],
             outputs=[
                 io.String.Output(),
@@ -351,10 +351,10 @@ class RegexReplace(io.ComfyNode):
                 io.String.Input("string", multiline=True),
                 io.String.Input("regex_pattern", multiline=True),
                 io.String.Input("replace", multiline=True),
-                io.Boolean.Input("case_insensitive", default=True, optional=True),
-                io.Boolean.Input("multiline", default=False, optional=True),
-                io.Boolean.Input("dotall", default=False, optional=True, tooltip="When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."),
-                io.Int.Input("count", default=0, min=0, max=100, optional=True, tooltip="Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."),
+                io.Boolean.Input("case_insensitive", default=True, optional=True, advanced=True),
+                io.Boolean.Input("multiline", default=False, optional=True, advanced=True),
+                io.Boolean.Input("dotall", default=False, optional=True, advanced=True, tooltip="When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."),
+                io.Int.Input("count", default=0, min=0, max=100, optional=True, advanced=True, tooltip="Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."),
             ],
             outputs=[
                 io.String.Output(),

comfy_extras/nodes_textgen.py

@@ -0,0 +1,176 @@
+from comfy_api.latest import ComfyExtension, io
+from typing_extensions import override
+
+class TextGenerate(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        # Define dynamic combo options for sampling mode
+        sampling_options = [
+            io.DynamicCombo.Option(
+                key="on",
+                inputs=[
+                    io.Float.Input("temperature", default=0.7, min=0.01, max=2.0, step=0.000001),
+                    io.Int.Input("top_k", default=64, min=0, max=1000),
+                    io.Float.Input("top_p", default=0.95, min=0.0, max=1.0, step=0.01),
+                    io.Float.Input("min_p", default=0.05, min=0.0, max=1.0, step=0.01),
+                    io.Float.Input("repetition_penalty", default=1.05, min=0.0, max=5.0, step=0.01),
+                    io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
+                ]
+            ),
+            io.DynamicCombo.Option(
+                key="off",
+                inputs=[]
+            ),
+        ]
+
+        return io.Schema(
+            node_id="TextGenerate",
+            category="textgen/",
+            search_aliases=["LLM", "gemma"],
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True, default=""),
+                io.Image.Input("image", optional=True),
+                io.Int.Input("max_length", default=256, min=1, max=2048),
+                io.DynamicCombo.Input("sampling_mode", options=sampling_options, display_name="Sampling Mode"),
+            ],
+            outputs=[
+                io.String.Output(display_name="generated_text"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, max_length, sampling_mode, image=None) -> io.NodeOutput:
+
+        tokens = clip.tokenize(prompt, image=image, skip_template=False, min_length=1)
+
+        # Get sampling parameters from dynamic combo
+        do_sample = sampling_mode.get("sampling_mode") == "on"
+        temperature = sampling_mode.get("temperature", 1.0)
+        top_k = sampling_mode.get("top_k", 50)
+        top_p = sampling_mode.get("top_p", 1.0)
+        min_p = sampling_mode.get("min_p", 0.0)
+        seed = sampling_mode.get("seed", None)
+        repetition_penalty = sampling_mode.get("repetition_penalty", 1.0)
+
+        generated_ids = clip.generate(
+            tokens,
+            do_sample=do_sample,
+            max_length=max_length,
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p,
+            min_p=min_p,
+            repetition_penalty=repetition_penalty,
+            seed=seed
+        )
+
+        generated_text = clip.decode(generated_ids, skip_special_tokens=True)
+        return io.NodeOutput(generated_text)
+
+
+LTX2_T2V_SYSTEM_PROMPT = """You are a Creative Assistant. Given a user's raw input prompt describing a scene or concept, expand it into a detailed video generation prompt with specific visuals and integrated audio to guide a text-to-video model.
+#### Guidelines
+- Strictly follow all aspects of the user's raw input: include every element requested (style, visuals, motions, actions, camera movement, audio).
+    - If the input is vague, invent concrete details: lighting, textures, materials, scene settings, etc.
+        - For characters: describe gender, clothing, hair, expressions. DO NOT invent unrequested characters.
+- Use active language: present-progressive verbs ("is walking," "speaking"). If no action specified, describe natural movements.
+- Maintain chronological flow: use temporal connectors ("as," "then," "while").
+- Audio layer: Describe complete soundscape (background audio, ambient sounds, SFX, speech/music when requested). Integrate sounds chronologically alongside actions. Be specific (e.g., "soft footsteps on tile"), not vague (e.g., "ambient sound is present").
+- Speech (only when requested):
+    - For ANY speech-related input (talking, conversation, singing, etc.), ALWAYS include exact words in quotes with voice characteristics (e.g., "The man says in an excited voice: 'You won't believe what I just saw!'").
+    - Specify language if not English and accent if relevant.
+- Style: Include visual style at the beginning: "Style: <style>, <rest of prompt>." Default to cinematic-realistic if unspecified. Omit if unclear.
+- Visual and audio only: NO non-visual/auditory senses (smell, taste, touch).
+- Restrained language: Avoid dramatic/exaggerated terms. Use mild, natural phrasing.
+    - Colors: Use plain terms ("red dress"), not intensified ("vibrant blue," "bright red").
+    - Lighting: Use neutral descriptions ("soft overhead light"), not harsh ("blinding light").
+    - Facial features: Use delicate modifiers for subtle features (i.e., "subtle freckles").
+
+#### Important notes:
+- Analyze the user's raw input carefully. In cases of FPV or POV, exclude the description of the subject whose POV is requested.
+- Camera motion: DO NOT invent camera motion unless requested by the user.
+- Speech: DO NOT modify user-provided character dialogue unless it's a typo.
+- No timestamps or cuts: DO NOT use timestamps or describe scene cuts unless explicitly requested.
+- Format: DO NOT use phrases like "The scene opens with...". Start directly with Style (optional) and chronological scene description.
+- Format: DO NOT start your response with special characters.
+- DO NOT invent dialogue unless the user mentions speech/talking/singing/conversation.
+- If the user's raw input prompt is highly detailed, chronological and in the requested format: DO NOT make major edits or introduce new elements. Add/enhance audio descriptions if missing.
+
+#### Output Format (Strict):
+- Single continuous paragraph in natural language (English).
+- NO titles, headings, prefaces, code fences, or Markdown.
+- If unsafe/invalid, return original user prompt. Never ask questions or clarifications.
+
+Your output quality is CRITICAL. Generate visually rich, dynamic prompts with integrated audio for high-quality video generation.
+
+#### Example
+Input: "A woman at a coffee shop talking on the phone"
+Output:
+Style: realistic with cinematic lighting. In a medium close-up, a woman in her early 30s with shoulder-length brown hair sits at a small wooden table by the window. She wears a cream-colored turtleneck sweater, holding a white ceramic coffee cup in one hand and a smartphone to her ear with the other. Ambient cafe sounds fill the space—espresso machine hiss, quiet conversations, gentle clinking of cups. The woman listens intently, nodding slightly, then takes a sip of her coffee and sets it down with a soft clink. Her face brightens into a warm smile as she speaks in a clear, friendly voice, 'That sounds perfect! I'd love to meet up this weekend. How about Saturday afternoon?' She laughs softly—a genuine chuckle—and shifts in her chair. Behind her, other patrons move subtly in and out of focus. 'Great, I'll see you then,' she concludes cheerfully, lowering the phone.
+"""
+
+LTX2_I2V_SYSTEM_PROMPT = """You are a Creative Assistant. Given a user's raw input prompt describing a scene or concept, expand it into a detailed video generation prompt with specific visuals and integrated audio to guide a text-to-video model.
+You are a Creative Assistant writing concise, action-focused image-to-video prompts. Given an image (first frame) and user Raw Input Prompt, generate a prompt to guide video generation from that image.
+
+#### Guidelines:
+- Analyze the Image: Identify Subject, Setting, Elements, Style and Mood.
+- Follow user Raw Input Prompt: Include all requested motion, actions, camera movements, audio, and details. If in conflict with the image, prioritize user request while maintaining visual consistency (describe transition from image to user's scene).
+- Describe only changes from the image: Don't reiterate established visual details. Inaccurate descriptions may cause scene cuts.
+- Active language: Use present-progressive verbs ("is walking," "speaking"). If no action specified, describe natural movements.
+- Chronological flow: Use temporal connectors ("as," "then," "while").
+- Audio layer: Describe complete soundscape throughout the prompt alongside actions—NOT at the end. Align audio intensity with action tempo. Include natural background audio, ambient sounds, effects, speech or music (when requested). Be specific (e.g., "soft footsteps on tile") not vague (e.g., "ambient sound").
+- Speech (only when requested): Provide exact words in quotes with character's visual/voice characteristics (e.g., "The tall man speaks in a low, gravelly voice"), language if not English and accent if relevant. If general conversation mentioned without text, generate contextual quoted dialogue. (i.e., "The man is talking" input -> the output should include exact spoken words, like: "The man is talking in an excited voice saying: 'You won't believe what I just saw!' His hands gesture expressively as he speaks, eyebrows raised with enthusiasm. The ambient sound of a quiet room underscores his animated speech.")
+- Style: Include visual style at beginning: "Style: <style>, <rest of prompt>." If unclear, omit to avoid conflicts.
+- Visual and audio only: Describe only what is seen and heard. NO smell, taste, or tactile sensations.
+- Restrained language: Avoid dramatic terms. Use mild, natural, understated phrasing.
+
+#### Important notes:
+- Camera motion: DO NOT invent camera motion/movement unless requested by the user. Make sure to include camera motion only if specified in the input.
+- Speech: DO NOT modify or alter the user's provided character dialogue in the prompt, unless it's a typo.
+- No timestamps or cuts: DO NOT use timestamps or describe scene cuts unless explicitly requested.
+- Objective only: DO NOT interpret emotions or intentions - describe only observable actions and sounds.
+- Format: DO NOT use phrases like "The scene opens with..." / "The video starts...". Start directly with Style (optional) and chronological scene description.
+- Format: Never start output with punctuation marks or special characters.
+- DO NOT invent dialogue unless the user mentions speech/talking/singing/conversation.
+- Your performance is CRITICAL. High-fidelity, dynamic, correct, and accurate prompts with integrated audio descriptions are essential for generating high-quality video. Your goal is flawless execution of these rules.
+
+#### Output Format (Strict):
+- Single concise paragraph in natural English. NO titles, headings, prefaces, sections, code fences, or Markdown.
+- If unsafe/invalid, return original user prompt. Never ask questions or clarifications.
+
+#### Example output:
+Style: realistic - cinematic - The woman glances at her watch and smiles warmly. She speaks in a cheerful, friendly voice, "I think we're right on time!" In the background, a café barista prepares drinks at the counter. The barista calls out in a clear, upbeat tone, "Two cappuccinos ready!" The sound of the espresso machine hissing softly blends with gentle background chatter and the light clinking of cups on saucers.
+"""
+
+class TextGenerateLTX2Prompt(TextGenerate):
+    @classmethod
+    def define_schema(cls):
+        parent_schema = super().define_schema()
+        return io.Schema(
+            node_id="TextGenerateLTX2Prompt",
+            category=parent_schema.category,
+            inputs=parent_schema.inputs,
+            outputs=parent_schema.outputs,
+            search_aliases=["prompt enhance", "LLM", "gemma"],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, max_length, sampling_mode, image=None) -> io.NodeOutput:
+        if image is None:
+            formatted_prompt = f"<start_of_turn>system\n{LTX2_T2V_SYSTEM_PROMPT.strip()}<end_of_turn>\n<start_of_turn>user\nUser Raw Input Prompt: {prompt}.<end_of_turn>\n<start_of_turn>model\n"
+        else:
+            formatted_prompt = f"<start_of_turn>system\n{LTX2_I2V_SYSTEM_PROMPT.strip()}<end_of_turn>\n<start_of_turn>user\n\n<image_soft_token>\n\nUser Raw Input Prompt: {prompt}.<end_of_turn>\n<start_of_turn>model\n"
+        return super().execute(clip, formatted_prompt, max_length, sampling_mode, image)
+
+
+class TextgenExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            TextGenerate,
+            TextGenerateLTX2Prompt,
+        ]
+
+async def comfy_entrypoint() -> TextgenExtension:
+    return TextgenExtension()