Range Editor

* chore: tune CodeRabbit config to limit review scope and disable for drafts

- Add tone_instructions to focus only on newly introduced issues
- Add global path_instructions entry to ignore pre-existing issues in moved/reformatted code
- Disable draft PR reviews (drafts: false) and add WIP title keywords
- Disable ruff tool to prevent linter-based outside-diff-range comments

Addresses feedback from maintainers about CodeRabbit flagging pre-existing
issues in code that was merely moved or de-indented (e.g., PR #12557),
which can discourage community contributions and cause scope creep.

Amp-Thread-ID: https://ampcode.com/threads/T-019c82de-0481-7253-ad42-20cb595bb1ba

* chore: add 'DO NOT MERGE' to ignore_title_keywords

Amp-Thread-ID: https://ampcode.com/threads/T-019c82de-0481-7253-ad42-20cb595bb1ba

.coderabbit.yaml

@@ -0,0 +1,127 @@
+# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json
+language: "en-US"
+early_access: false
+tone_instructions: "Only comment on issues introduced by this PR's changes. Do not flag pre-existing problems in moved, re-indented, or reformatted code."
+
+reviews:
+  profile: "chill"
+  request_changes_workflow: false
+  high_level_summary: false
+  poem: false
+  review_status: false
+  review_details: false
+  commit_status: true
+  collapse_walkthrough: true
+  changed_files_summary: false
+  sequence_diagrams: false
+  estimate_code_review_effort: false
+  assess_linked_issues: false
+  related_issues: false
+  related_prs: false
+  suggested_labels: false
+  auto_apply_labels: false
+  suggested_reviewers: false
+  auto_assign_reviewers: false
+  in_progress_fortune: false
+  enable_prompt_for_ai_agents: true
+
+  path_filters:
+    - "!comfy_api_nodes/apis/**"
+    - "!**/generated/*.pyi"
+    - "!.ci/**"
+    - "!script_examples/**"
+    - "!**/__pycache__/**"
+    - "!**/*.ipynb"
+    - "!**/*.png"
+    - "!**/*.bat"
+
+  path_instructions:
+    - path: "**"
+      instructions: |
+        IMPORTANT: Only comment on issues directly introduced by this PR's code changes.
+        Do NOT flag pre-existing issues in code that was merely moved, re-indented,
+        de-indented, or reformatted without logic changes. If code appears in the diff
+        only due to whitespace or structural reformatting (e.g., removing a `with:` block),
+        treat it as unchanged. Contributors should not feel obligated to address
+        pre-existing issues outside the scope of their contribution.
+    - path: "comfy/**"
+      instructions: |
+        Core ML/diffusion engine. Focus on:
+        - Backward compatibility (breaking changes affect all custom nodes)
+        - Memory management and GPU resource handling
+        - Performance implications in hot paths
+        - Thread safety for concurrent execution
+    - path: "comfy_api_nodes/**"
+      instructions: |
+        Third-party API integration nodes. Focus on:
+        - No hardcoded API keys or secrets
+        - Proper error handling for API failures (timeouts, rate limits, auth errors)
+        - Correct Pydantic model usage
+        - Security of user data passed to external APIs
+    - path: "comfy_extras/**"
+      instructions: |
+        Community-contributed extra nodes. Focus on:
+        - Consistency with node patterns (INPUT_TYPES, RETURN_TYPES, FUNCTION, CATEGORY)
+        - No breaking changes to existing node interfaces
+    - path: "comfy_execution/**"
+      instructions: |
+        Execution engine (graph execution, caching, jobs). Focus on:
+        - Caching correctness
+        - Concurrent execution safety
+        - Graph validation edge cases
+    - path: "nodes.py"
+      instructions: |
+        Core node definitions (2500+ lines). Focus on:
+        - Backward compatibility of NODE_CLASS_MAPPINGS
+        - Consistency of INPUT_TYPES return format
+    - path: "alembic_db/**"
+      instructions: |
+        Database migrations. Focus on:
+        - Migration safety and rollback support
+        - Data preservation during schema changes
+
+  auto_review:
+    enabled: true
+    auto_incremental_review: true
+    drafts: false
+    ignore_title_keywords:
+      - "WIP"
+      - "DO NOT REVIEW"
+      - "DO NOT MERGE"
+
+  finishing_touches:
+    docstrings:
+      enabled: false
+    unit_tests:
+      enabled: false
+
+  tools:
+    ruff:
+      enabled: false
+    pylint:
+      enabled: false
+    flake8:
+      enabled: false
+    gitleaks:
+      enabled: true
+    shellcheck:
+      enabled: false
+    markdownlint:
+      enabled: false
+    yamllint:
+      enabled: false
+    languagetool:
+      enabled: false
+    github-checks:
+      enabled: true
+      timeout_ms: 90000
+    ast-grep:
+      essential_rules: true
+
+chat:
+  auto_reply: true
+
+knowledge_base:
+  opt_out: false
+  learnings:
+    scope: "auto"

README.md

@@ -229,9 +229,9 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins
 
 ```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1```
 
-This is the command to install the nightly with ROCm 7.1 which might have some performance improvements:
+This is the command to install the nightly with ROCm 7.2 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.1```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.2```
 
 
 ### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.

app/subgraph_manager.py

@@ -53,7 +53,7 @@ class SubgraphManager:
         return entry_id, entry
 
     async def load_entry_data(self, entry: SubgraphEntry):
-        with open(entry['path'], 'r') as f:
+        with open(entry['path'], 'r', encoding='utf-8') as f:
             entry['data'] = f.read()
         return entry
 

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/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"}, "category": "Image Tools/Color adjust"}]}}

blueprints/Prompt Enhance.json

@@ -0,0 +1 @@
+{"revision": 0, "last_node_id": 15, "last_link_id": 0, "nodes": [{"id": 15, "type": "24d8bbfd-39d4-4774-bff0-3de40cc7a471", "pos": [-1490, 2040], "size": [400, 260], "flags": {}, "order": 0, "mode": 0, "inputs": [{"name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": null}, {"label": "reference images", "name": "images", "type": "IMAGE", "link": null}], "outputs": [{"name": "STRING", "type": "STRING", "links": null}], "title": "Prompt Enhance", "properties": {"proxyWidgets": [["-1", "prompt"]], "cnr_id": "comfy-core", "ver": "0.14.1"}, "widgets_values": [""]}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "24d8bbfd-39d4-4774-bff0-3de40cc7a471", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 15, "lastLinkId": 14, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Prompt Enhance", "inputNode": {"id": -10, "bounding": [-2170, 2110, 138.876953125, 80]}, "outputNode": {"id": -20, "bounding": [-640, 2110, 120, 60]}, "inputs": [{"id": "aeab7216-00e0-4528-a09b-bba50845c5a6", "name": "prompt", "type": "STRING", "linkIds": [11], "pos": [-2051.123046875, 2130]}, {"id": "7b73fd36-aa31-4771-9066-f6c83879994b", "name": "images", "type": "IMAGE", "linkIds": [14], "label": "reference images", "pos": [-2051.123046875, 2150]}], "outputs": [{"id": "c7b0d930-68a1-48d1-b496-0519e5837064", "name": "STRING", "type": "STRING", "linkIds": [13], "pos": [-620, 2130]}], "widgets": [], "nodes": [{"id": 11, "type": "GeminiNode", "pos": [-1560, 1990], "size": [470, 470], "flags": {}, "order": 0, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "shape": 7, "type": "IMAGE", "link": 14}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": null}, {"localized_name": "video", "name": "video", "shape": 7, "type": "VIDEO", "link": null}, {"localized_name": "files", "name": "files", "shape": 7, "type": "GEMINI_INPUT_FILES", "link": null}, {"localized_name": "prompt", "name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": 11}, {"localized_name": "model", "name": "model", "type": "COMBO", "widget": {"name": "model"}, "link": null}, {"localized_name": "seed", "name": "seed", "type": "INT", "widget": {"name": "seed"}, "link": null}, {"localized_name": "system_prompt", "name": "system_prompt", "shape": 7, "type": "STRING", "widget": {"name": "system_prompt"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": [13]}], "properties": {"cnr_id": "comfy-core", "ver": "0.14.1", "Node name for S&R": "GeminiNode"}, "widgets_values": ["", "gemini-3-pro-preview", 42, "randomize", "You are an expert in prompt writing.\nBased on the input, rewrite the user's input into a detailed prompt.\nincluding camera settings, lighting, composition, and style.\nReturn the prompt only"], "color": "#432", "bgcolor": "#653"}], "groups": [], "links": [{"id": 11, "origin_id": -10, "origin_slot": 0, "target_id": 11, "target_slot": 4, "type": "STRING"}, {"id": 13, "origin_id": 11, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "STRING"}, {"id": 14, "origin_id": -10, "origin_slot": 1, "target_id": 11, "target_slot": 0, "type": "IMAGE"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Text generation/Prompt enhance"}]}, "extra": {}}

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"}, "category": "Image Tools/Sharpen"}]}}

blueprints/Video Upscale(GAN x4).json

@@ -0,0 +1 @@
+{"revision": 0, "last_node_id": 13, "last_link_id": 0, "nodes": [{"id": 13, "type": "cf95b747-3e17-46cb-8097-cac60ff9b2e1", "pos": [1120, 330], "size": [240, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"localized_name": "video", "name": "video", "type": "VIDEO", "link": null}, {"name": "model_name", "type": "COMBO", "widget": {"name": "model_name"}, "link": null}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": []}], "title": "Video Upscale(GAN x4)", "properties": {"proxyWidgets": [["-1", "model_name"]], "cnr_id": "comfy-core", "ver": "0.14.1"}, "widgets_values": ["RealESRGAN_x4plus.safetensors"]}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "cf95b747-3e17-46cb-8097-cac60ff9b2e1", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 13, "lastLinkId": 19, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Video Upscale(GAN x4)", "inputNode": {"id": -10, "bounding": [550, 460, 120, 80]}, "outputNode": {"id": -20, "bounding": [1490, 460, 120, 60]}, "inputs": [{"id": "666d633e-93e7-42dc-8d11-2b7b99b0f2a6", "name": "video", "type": "VIDEO", "linkIds": [10], "localized_name": "video", "pos": [650, 480]}, {"id": "2e23a087-caa8-4d65-99e6-662761aa905a", "name": "model_name", "type": "COMBO", "linkIds": [19], "pos": [650, 500]}], "outputs": [{"id": "0c1768ea-3ec2-412f-9af6-8e0fa36dae70", "name": "VIDEO", "type": "VIDEO", "linkIds": [15], "localized_name": "VIDEO", "pos": [1510, 480]}], "widgets": [], "nodes": [{"id": 2, "type": "ImageUpscaleWithModel", "pos": [1110, 450], "size": [320, 46], "flags": {}, "order": 1, "mode": 0, "inputs": [{"localized_name": "upscale_model", "name": "upscale_model", "type": "UPSCALE_MODEL", "link": 1}, {"localized_name": "image", "name": "image", "type": "IMAGE", "link": 14}], "outputs": [{"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "links": [13]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "ImageUpscaleWithModel"}}, {"id": 11, "type": "CreateVideo", "pos": [1110, 550], "size": [320, 78], "flags": {}, "order": 3, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "link": 13}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": 16}, {"localized_name": "fps", "name": "fps", "type": "FLOAT", "widget": {"name": "fps"}, "link": 12}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": [15]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "CreateVideo"}, "widgets_values": [30]}, {"id": 10, "type": "GetVideoComponents", "pos": [1110, 330], "size": [320, 70], "flags": {}, "order": 2, "mode": 0, "inputs": [{"localized_name": "video", "name": "video", "type": "VIDEO", "link": 10}], "outputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "links": [14]}, {"localized_name": "audio", "name": "audio", "type": "AUDIO", "links": [16]}, {"localized_name": "fps", "name": "fps", "type": "FLOAT", "links": [12]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "GetVideoComponents"}}, {"id": 1, "type": "UpscaleModelLoader", "pos": [750, 450], "size": [280, 60], "flags": {}, "order": 0, "mode": 0, "inputs": [{"localized_name": "model_name", "name": "model_name", "type": "COMBO", "widget": {"name": "model_name"}, "link": 19}], "outputs": [{"localized_name": "UPSCALE_MODEL", "name": "UPSCALE_MODEL", "type": "UPSCALE_MODEL", "links": [1]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "UpscaleModelLoader", "models": [{"name": "RealESRGAN_x4plus.safetensors", "url": "https://huggingface.co/Comfy-Org/Real-ESRGAN_repackaged/resolve/main/RealESRGAN_x4plus.safetensors", "directory": "upscale_models"}]}, "widgets_values": ["RealESRGAN_x4plus.safetensors"]}], "groups": [], "links": [{"id": 1, "origin_id": 1, "origin_slot": 0, "target_id": 2, "target_slot": 0, "type": "UPSCALE_MODEL"}, {"id": 14, "origin_id": 10, "origin_slot": 0, "target_id": 2, "target_slot": 1, "type": "IMAGE"}, {"id": 13, "origin_id": 2, "origin_slot": 0, "target_id": 11, "target_slot": 0, "type": "IMAGE"}, {"id": 16, "origin_id": 10, "origin_slot": 1, "target_id": 11, "target_slot": 1, "type": "AUDIO"}, {"id": 12, "origin_id": 10, "origin_slot": 2, "target_id": 11, "target_slot": 2, "type": "FLOAT"}, {"id": 10, "origin_id": -10, "origin_slot": 0, "target_id": 10, "target_slot": 0, "type": "VIDEO"}, {"id": 15, "origin_id": 11, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "VIDEO"}, {"id": 19, "origin_id": -10, "origin_slot": 1, "target_id": 1, "target_slot": 0, "type": "COMBO"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Video generation and editing/Enhance video"}]}, "extra": {}}

comfy/comfy_types/node_typing.py

@@ -176,6 +176,8 @@ class InputTypeOptions(TypedDict):
     """COMBO type only. Specifies the configuration for a multi-select widget.
     Available after ComfyUI frontend v1.13.4
     https://github.com/Comfy-Org/ComfyUI_frontend/pull/2987"""
+    gradient_stops: NotRequired[list[list[float]]]
+    """Gradient color stops for gradientslider display mode. Each stop is [offset, r, g, b] (``FLOAT``)."""
 
 
 class HiddenInputTypeDict(TypedDict):

comfy/ldm/lightricks/av_model.py

@@ -9,6 +9,7 @@ from comfy.ldm.lightricks.model import (
     LTXVModel,
 )
 from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
+from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
 import comfy.ldm.common_dit
 
 class CompressedTimestep:
@@ -450,6 +451,29 @@ class LTXAVModel(LTXVModel):
             operations=self.operations,
         )
 
+        self.audio_embeddings_connector = Embeddings1DConnector(
+            split_rope=True,
+            double_precision_rope=True,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+
+        self.video_embeddings_connector = Embeddings1DConnector(
+            split_rope=True,
+            double_precision_rope=True,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+
+    def preprocess_text_embeds(self, context):
+        if context.shape[-1] == self.caption_channels * 2:
+            return context
+        out_vid = self.video_embeddings_connector(context)[0]
+        out_audio = self.audio_embeddings_connector(context)[0]
+        return torch.concat((out_vid, out_audio), dim=-1)
+
     def _init_transformer_blocks(self, device, dtype, **kwargs):
         """Initialize transformer blocks for LTXAV."""
         self.transformer_blocks = nn.ModuleList(

comfy/ldm/lightricks/embeddings_connector.py

@@ -234,7 +234,7 @@ class Embeddings1DConnector(nn.Module):
 
         return indices
 
-    def precompute_freqs_cis(self, indices_grid, spacing="exp"):
+    def precompute_freqs_cis(self, indices_grid, spacing="exp", out_dtype=None):
         dim = self.inner_dim
         n_elem = 2  # 2 because of cos and sin
         freqs = self.precompute_freqs(indices_grid, spacing)
@@ -247,7 +247,7 @@ class Embeddings1DConnector(nn.Module):
             )
         else:
             cos_freq, sin_freq = interleaved_freqs_cis(freqs, dim % n_elem)
-        return cos_freq.to(self.dtype), sin_freq.to(self.dtype), self.split_rope
+        return cos_freq.to(dtype=out_dtype), sin_freq.to(dtype=out_dtype), self.split_rope
 
     def forward(
         self,
@@ -288,7 +288,7 @@ class Embeddings1DConnector(nn.Module):
             hidden_states.shape[1], dtype=torch.float32, device=hidden_states.device
         )
         indices_grid = indices_grid[None, None, :]
-        freqs_cis = self.precompute_freqs_cis(indices_grid)
+        freqs_cis = self.precompute_freqs_cis(indices_grid, out_dtype=hidden_states.dtype)
 
         # 2. Blocks
         for block_idx, block in enumerate(self.transformer_1d_blocks):

comfy/memory_management.py

@@ -78,4 +78,4 @@ def interpret_gathered_like(tensors, gathered):
 
     return dest_views
 
-aimdo_allocator = None
+aimdo_enabled = False

comfy/model_base.py

@@ -988,10 +988,14 @@ class LTXAV(BaseModel):
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
         attention_mask = kwargs.get("attention_mask", None)
+        device = kwargs["device"]
+
         if attention_mask is not None:
             out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
+            if hasattr(self.diffusion_model, "preprocess_text_embeds"):
+                cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype_inference()))
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
 
         out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25))

comfy/model_management.py

@@ -836,7 +836,7 @@ def unet_inital_load_device(parameters, dtype):
 
     mem_dev = get_free_memory(torch_dev)
     mem_cpu = get_free_memory(cpu_dev)
-    if mem_dev > mem_cpu and model_size < mem_dev and comfy.memory_management.aimdo_allocator is None:
+    if mem_dev > mem_cpu and model_size < mem_dev and comfy.memory_management.aimdo_enabled:
         return torch_dev
     else:
         return cpu_dev
@@ -1121,7 +1121,6 @@ def get_cast_buffer(offload_stream, device, size, ref):
             synchronize()
             del STREAM_CAST_BUFFERS[offload_stream]
             del cast_buffer
-            #FIXME: This doesn't work in Aimdo because mempool cant clear cache
             soft_empty_cache()
         with wf_context:
             cast_buffer = torch.empty((size), dtype=torch.int8, device=device)

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

@@ -3,9 +3,9 @@ import os
 from transformers import T5TokenizerFast
 from .spiece_tokenizer import SPieceTokenizer
 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 +22,79 @@ 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={}):
@@ -69,22 +108,6 @@ class LTXAVTEModel(torch.nn.Module):
         operations = self.gemma3_12b.operations # TODO
         self.text_embedding_projection = operations.Linear(3840 * 49, 3840, bias=False, dtype=dtype, device=device)
 
-        self.audio_embeddings_connector = Embeddings1DConnector(
-            split_rope=True,
-            double_precision_rope=True,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-
-        self.video_embeddings_connector = Embeddings1DConnector(
-            split_rope=True,
-            double_precision_rope=True,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-
     def set_clip_options(self, options):
         self.execution_device = options.get("execution_device", self.execution_device)
         self.gemma3_12b.set_clip_options(options)
@@ -106,24 +129,23 @@ class LTXAVTEModel(torch.nn.Module):
         out = out.reshape((out.shape[0], out.shape[1], -1))
         out = self.text_embedding_projection(out)
         out = out.float()
-        out_vid = self.video_embeddings_connector(out)[0]
-        out_audio = self.audio_embeddings_connector(out)[0]
-        out = torch.concat((out_vid, out_audio), dim=-1)
-
         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)
         else:
-            sdo = comfy.utils.state_dict_prefix_replace(sd, {"text_embedding_projection.aggregate_embed.weight": "text_embedding_projection.weight", "model.diffusion_model.video_embeddings_connector.": "video_embeddings_connector.", "model.diffusion_model.audio_embeddings_connector.": "audio_embeddings_connector."}, filter_keys=True)
+            sdo = comfy.utils.state_dict_prefix_replace(sd, {"text_embedding_projection.aggregate_embed.weight": "text_embedding_projection.weight"}, filter_keys=True)
             if len(sdo) == 0:
                 sdo = sd
 
             missing_all = []
             unexpected_all = []
 
-            for prefix, component in [("text_embedding_projection.", self.text_embedding_projection), ("video_embeddings_connector.", self.video_embeddings_connector), ("audio_embeddings_connector.", self.audio_embeddings_connector)]:
+            for prefix, component in [("text_embedding_projection.", self.text_embedding_projection)]:
                 component_sd = {k.replace(prefix, ""): v for k, v in sdo.items() if k.startswith(prefix)}
                 if component_sd:
                     missing, unexpected = component.load_state_dict(component_sd, strict=False, assign=getattr(self, "can_assign_sd", False))
@@ -152,3 +174,14 @@ 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

@@ -1154,7 +1154,7 @@ def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_am
     return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=output_device, pbar=pbar)
 
 def model_trange(*args, **kwargs):
-    if comfy.memory_management.aimdo_allocator is None:
+    if not comfy.memory_management.aimdo_enabled:
         return trange(*args, **kwargs)
 
     pbar = trange(*args, **kwargs, smoothing=1.0)
@@ -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/_input_impl/video_types.py

@@ -444,7 +444,7 @@ class VideoFromComponents(VideoInput):
             output.mux(packet)
 
             if audio_stream and self.__components.audio:
-                frame = av.AudioFrame.from_ndarray(waveform.float().cpu().numpy(), format='fltp', layout=layout)
+                frame = av.AudioFrame.from_ndarray(waveform.float().cpu().contiguous().numpy(), format='fltp', layout=layout)
                 frame.sample_rate = audio_sample_rate
                 frame.pts = 0
                 output.mux(audio_stream.encode(frame))

comfy_api/latest/_io.py

@@ -73,6 +73,7 @@ class RemoteOptions:
 class NumberDisplay(str, Enum):
     number = "number"
     slider = "slider"
+    gradient_slider = "gradientslider"
 
 
 class ControlAfterGenerate(str, Enum):
@@ -296,13 +297,15 @@ class Float(ComfyTypeIO):
         '''Float input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: float=None, min: float=None, max: float=None, step: float=None, round: float=None,
-                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+                    display_mode: NumberDisplay=None, gradient_stops: list[list[float]]=None,
+                    socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
             super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link, advanced)
             self.min = min
             self.max = max
             self.step = step
             self.round = round
             self.display_mode = display_mode
+            self.gradient_stops = gradient_stops
             self.default: float
 
         def as_dict(self):
@@ -312,6 +315,7 @@ class Float(ComfyTypeIO):
                 "step": self.step,
                 "round": self.round,
                 "display": self.display_mode,
+                "gradient_stops": self.gradient_stops,
             })
 
 @comfytype(io_type="STRING")
@@ -1233,6 +1237,82 @@ class BoundingBox(ComfyTypeIO):
             return d
 
 
+@comfytype(io_type="CURVE")
+class Curve(ComfyTypeIO):
+    Type = list
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: list=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            if default is None:
+                self.default = [[0, 0], [1, 1]]
+
+        def as_dict(self):
+            return super().as_dict()
+
+
+@comfytype(io_type="RANGE")
+class Range(ComfyTypeIO):
+    Type = dict  # {"min": float, "max": float, "midpoint"?: float}
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: dict=None,
+                     display: str=None,
+                     gradient_stops: list=None,
+                     show_midpoint: bool=None,
+                     midpoint_scale: str=None,
+                     value_min: float=None,
+                     value_max: float=None,
+                     advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            if default is None:
+                self.default = {"min": 0.0, "max": 1.0}
+            self.display = display
+            self.gradient_stops = gradient_stops
+            self.show_midpoint = show_midpoint
+            self.midpoint_scale = midpoint_scale
+            self.value_min = value_min
+            self.value_max = value_max
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "display": self.display,
+                "gradient_stops": self.gradient_stops,
+                "show_midpoint": self.show_midpoint,
+                "midpoint_scale": self.midpoint_scale,
+                "value_min": self.value_min,
+                "value_max": self.value_max,
+            })
+
+
+@comfytype(io_type="COLOR_CURVES")
+class ColorCurves(ComfyTypeIO):
+    class ColorCurvesDict(TypedDict):
+        rgb: list[list[float]]
+        red: list[list[float]]
+        green: list[list[float]]
+        blue: list[list[float]]
+
+    Type = ColorCurvesDict
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: dict=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            if default is None:
+                self.default = {
+                    "rgb": [[0, 0], [1, 1]],
+                    "red": [[0, 0], [1, 1]],
+                    "green": [[0, 0], [1, 1]],
+                    "blue": [[0, 0], [1, 1]]
+                }
+
+        def as_dict(self):
+            return super().as_dict()
+
+
 DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
 def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
     DYNAMIC_INPUT_LOOKUP[io_type] = func
@@ -1339,6 +1419,7 @@ class NodeInfoV1:
     api_node: bool=None
     price_badge: dict | None = None
     search_aliases: list[str]=None
+    essentials_category: str=None
 
 
 @dataclass
@@ -1460,6 +1541,8 @@ 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 +1649,7 @@ 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
 
@@ -2215,5 +2299,7 @@ __all__ = [
     "PriceBadgeDepends",
     "PriceBadge",
     "BoundingBox",
+    "Curve",
+    "ColorCurves",
     "NodeReplace",
 ]

comfy_api_nodes/apis/elevenlabs.py

@@ -0,0 +1,88 @@
+from pydantic import BaseModel, Field
+
+
+class SpeechToTextRequest(BaseModel):
+    model_id: str = Field(...)
+    cloud_storage_url: str = Field(...)
+    language_code: str | None = Field(None, description="ISO-639-1 or ISO-639-3 language code")
+    tag_audio_events: bool | None = Field(None, description="Annotate sounds like (laughter) in transcript")
+    num_speakers: int | None = Field(None, description="Max speakers predicted")
+    timestamps_granularity: str = Field(default="word", description="Timing precision: none, word, or character")
+    diarize: bool | None = Field(None, description="Annotate which speaker is talking")
+    diarization_threshold: float | None = Field(None, description="Speaker separation sensitivity")
+    temperature: float | None = Field(None, description="Randomness control")
+    seed: int = Field(..., description="Seed for deterministic sampling")
+
+
+class SpeechToTextWord(BaseModel):
+    text: str = Field(..., description="The word text")
+    type: str = Field(default="word", description="Type of text element (word, spacing, etc.)")
+    start: float | None = Field(None, description="Start time in seconds (when timestamps enabled)")
+    end: float | None = Field(None, description="End time in seconds (when timestamps enabled)")
+    speaker_id: str | None = Field(None, description="Speaker identifier when diarization is enabled")
+    logprob: float | None = Field(None, description="Log probability of the word")
+
+
+class SpeechToTextResponse(BaseModel):
+    language_code: str = Field(..., description="Detected or specified language code")
+    language_probability: float | None = Field(None, description="Confidence of language detection")
+    text: str = Field(..., description="Full transcript text")
+    words: list[SpeechToTextWord] | None = Field(None, description="Word-level timing information")
+
+
+class TextToSpeechVoiceSettings(BaseModel):
+    stability: float | None = Field(None, description="Voice stability")
+    similarity_boost: float | None = Field(None, description="Similarity boost")
+    style: float | None = Field(None, description="Style exaggeration")
+    use_speaker_boost: bool | None = Field(None, description="Boost similarity to original speaker")
+    speed: float | None = Field(None, description="Speech speed")
+
+
+class TextToSpeechRequest(BaseModel):
+    text: str = Field(..., description="Text to convert to speech")
+    model_id: str = Field(..., description="Model ID for TTS")
+    language_code: str | None = Field(None, description="ISO-639-1 or ISO-639-3 language code")
+    voice_settings: TextToSpeechVoiceSettings | None = Field(None, description="Voice settings")
+    seed: int = Field(..., description="Seed for deterministic sampling")
+    apply_text_normalization: str | None = Field(None, description="Text normalization mode: auto, on, off")
+
+
+class TextToSoundEffectsRequest(BaseModel):
+    text: str = Field(..., description="Text prompt to convert into a sound effect")
+    duration_seconds: float = Field(..., description="Duration of generated sound in seconds")
+    prompt_influence: float = Field(..., description="How closely generation follows the prompt")
+    loop: bool | None = Field(None, description="Whether to create a smoothly looping sound effect")
+
+
+class AddVoiceRequest(BaseModel):
+    name: str = Field(..., description="Name that identifies the voice")
+    remove_background_noise: bool = Field(..., description="Remove background noise from voice samples")
+
+
+class AddVoiceResponse(BaseModel):
+    voice_id: str = Field(..., description="The newly created voice's unique identifier")
+
+
+class SpeechToSpeechRequest(BaseModel):
+    model_id: str = Field(..., description="Model ID for speech-to-speech")
+    voice_settings: str = Field(..., description="JSON string of voice settings")
+    seed: int = Field(..., description="Seed for deterministic sampling")
+    remove_background_noise: bool = Field(..., description="Remove background noise from input audio")
+
+
+class DialogueInput(BaseModel):
+    text: str = Field(..., description="Text content to convert to speech")
+    voice_id: str = Field(..., description="Voice identifier for this dialogue segment")
+
+
+class DialogueSettings(BaseModel):
+    stability: float | None = Field(None, description="Voice stability (0-1)")
+
+
+class TextToDialogueRequest(BaseModel):
+    inputs: list[DialogueInput] = Field(..., description="List of dialogue segments")
+    model_id: str = Field(..., description="Model ID for dialogue generation")
+    language_code: str | None = Field(None, description="ISO-639-1 language code")
+    settings: DialogueSettings | None = Field(None, description="Voice settings")
+    seed: int | None = Field(None, description="Seed for deterministic sampling")
+    apply_text_normalization: str | None = Field(None, description="Text normalization mode: auto, on, off")

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_elevenlabs.py

@@ -0,0 +1,924 @@
+import json
+import uuid
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.elevenlabs import (
+    AddVoiceRequest,
+    AddVoiceResponse,
+    DialogueInput,
+    DialogueSettings,
+    SpeechToSpeechRequest,
+    SpeechToTextRequest,
+    SpeechToTextResponse,
+    TextToDialogueRequest,
+    TextToSoundEffectsRequest,
+    TextToSpeechRequest,
+    TextToSpeechVoiceSettings,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    audio_bytes_to_audio_input,
+    audio_ndarray_to_bytesio,
+    audio_tensor_to_contiguous_ndarray,
+    sync_op,
+    sync_op_raw,
+    upload_audio_to_comfyapi,
+    validate_string,
+)
+
+ELEVENLABS_MUSIC_SECTIONS = "ELEVENLABS_MUSIC_SECTIONS"  # Custom type for music sections
+ELEVENLABS_COMPOSITION_PLAN = "ELEVENLABS_COMPOSITION_PLAN"  # Custom type for composition plan
+ELEVENLABS_VOICE = "ELEVENLABS_VOICE"  # Custom type for voice selection
+
+# Predefined ElevenLabs voices: (voice_id, display_name, gender, accent)
+ELEVENLABS_VOICES = [
+    ("CwhRBWXzGAHq8TQ4Fs17", "Roger", "male", "american"),
+    ("EXAVITQu4vr4xnSDxMaL", "Sarah", "female", "american"),
+    ("FGY2WhTYpPnrIDTdsKH5", "Laura", "female", "american"),
+    ("IKne3meq5aSn9XLyUdCD", "Charlie", "male", "australian"),
+    ("JBFqnCBsd6RMkjVDRZzb", "George", "male", "british"),
+    ("N2lVS1w4EtoT3dr4eOWO", "Callum", "male", "american"),
+    ("SAz9YHcvj6GT2YYXdXww", "River", "neutral", "american"),
+    ("SOYHLrjzK2X1ezoPC6cr", "Harry", "male", "american"),
+    ("TX3LPaxmHKxFdv7VOQHJ", "Liam", "male", "american"),
+    ("Xb7hH8MSUJpSbSDYk0k2", "Alice", "female", "british"),
+    ("XrExE9yKIg1WjnnlVkGX", "Matilda", "female", "american"),
+    ("bIHbv24MWmeRgasZH58o", "Will", "male", "american"),
+    ("cgSgspJ2msm6clMCkdW9", "Jessica", "female", "american"),
+    ("cjVigY5qzO86Huf0OWal", "Eric", "male", "american"),
+    ("hpp4J3VqNfWAUOO0d1Us", "Bella", "female", "american"),
+    ("iP95p4xoKVk53GoZ742B", "Chris", "male", "american"),
+    ("nPczCjzI2devNBz1zQrb", "Brian", "male", "american"),
+    ("onwK4e9ZLuTAKqWW03F9", "Daniel", "male", "british"),
+    ("pFZP5JQG7iQjIQuC4Bku", "Lily", "female", "british"),
+    ("pNInz6obpgDQGcFmaJgB", "Adam", "male", "american"),
+    ("pqHfZKP75CvOlQylNhV4", "Bill", "male", "american"),
+]
+
+ELEVENLABS_VOICE_OPTIONS = [f"{name} ({gender}, {accent})" for _, name, gender, accent in ELEVENLABS_VOICES]
+ELEVENLABS_VOICE_MAP = {
+    f"{name} ({gender}, {accent})": voice_id for voice_id, name, gender, accent in ELEVENLABS_VOICES
+}
+
+
+class ElevenLabsSpeechToText(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsSpeechToText",
+            display_name="ElevenLabs Speech to Text",
+            category="api node/audio/ElevenLabs",
+            description="Transcribe audio to text. "
+            "Supports automatic language detection, speaker diarization, and audio event tagging.",
+            inputs=[
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Audio to transcribe.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "scribe_v2",
+                            [
+                                IO.Boolean.Input(
+                                    "tag_audio_events",
+                                    default=False,
+                                    tooltip="Annotate sounds like (laughter), (music), etc. in transcript.",
+                                ),
+                                IO.Boolean.Input(
+                                    "diarize",
+                                    default=False,
+                                    tooltip="Annotate which speaker is talking.",
+                                ),
+                                IO.Float.Input(
+                                    "diarization_threshold",
+                                    default=0.22,
+                                    min=0.1,
+                                    max=0.4,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Speaker separation sensitivity. "
+                                    "Lower values are more sensitive to speaker changes.",
+                                ),
+                                IO.Float.Input(
+                                    "temperature",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=2.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Randomness control. "
+                                    "0.0 uses model default. Higher values increase randomness.",
+                                ),
+                                IO.Combo.Input(
+                                    "timestamps_granularity",
+                                    options=["word", "character", "none"],
+                                    default="word",
+                                    tooltip="Timing precision for transcript words.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for transcription.",
+                ),
+                IO.String.Input(
+                    "language_code",
+                    default="",
+                    tooltip="ISO-639-1 or ISO-639-3 language code (e.g., 'en', 'es', 'fra'). "
+                    "Leave empty for automatic detection.",
+                ),
+                IO.Int.Input(
+                    "num_speakers",
+                    default=0,
+                    min=0,
+                    max=32,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Maximum number of speakers to predict. Set to 0 for automatic detection.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    tooltip="Seed for reproducibility (determinism not guaranteed).",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="text"),
+                IO.String.Output(display_name="language_code"),
+                IO.String.Output(display_name="words_json"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.0073,"format":{"approximate":true,"suffix":"/minute"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        audio: Input.Audio,
+        model: dict,
+        language_code: str,
+        num_speakers: int,
+        seed: int,
+    ) -> IO.NodeOutput:
+        if model["diarize"] and num_speakers:
+            raise ValueError(
+                "Number of speakers cannot be specified when diarization is enabled. "
+                "Either disable diarization or set num_speakers to 0."
+            )
+        request = SpeechToTextRequest(
+            model_id=model["model"],
+            cloud_storage_url=await upload_audio_to_comfyapi(
+                cls, audio, container_format="mp4", codec_name="aac", mime_type="audio/mp4"
+            ),
+            language_code=language_code if language_code.strip() else None,
+            tag_audio_events=model["tag_audio_events"],
+            num_speakers=num_speakers if num_speakers > 0 else None,
+            timestamps_granularity=model["timestamps_granularity"],
+            diarize=model["diarize"],
+            diarization_threshold=model["diarization_threshold"] if model["diarize"] else None,
+            seed=seed,
+            temperature=model["temperature"],
+        )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/elevenlabs/v1/speech-to-text", method="POST"),
+            response_model=SpeechToTextResponse,
+            data=request,
+            content_type="multipart/form-data",
+        )
+        words_json = json.dumps(
+            [w.model_dump(exclude_none=True) for w in response.words] if response.words else [],
+            indent=2,
+        )
+        return IO.NodeOutput(response.text, response.language_code, words_json)
+
+
+class ElevenLabsVoiceSelector(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsVoiceSelector",
+            display_name="ElevenLabs Voice Selector",
+            category="api node/audio/ElevenLabs",
+            description="Select a predefined ElevenLabs voice for text-to-speech generation.",
+            inputs=[
+                IO.Combo.Input(
+                    "voice",
+                    options=ELEVENLABS_VOICE_OPTIONS,
+                    tooltip="Choose a voice from the predefined ElevenLabs voices.",
+                ),
+            ],
+            outputs=[
+                IO.Custom(ELEVENLABS_VOICE).Output(display_name="voice"),
+            ],
+            is_api_node=False,
+        )
+
+    @classmethod
+    def execute(cls, voice: str) -> IO.NodeOutput:
+        voice_id = ELEVENLABS_VOICE_MAP.get(voice)
+        if not voice_id:
+            raise ValueError(f"Unknown voice: {voice}")
+        return IO.NodeOutput(voice_id)
+
+
+class ElevenLabsTextToSpeech(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsTextToSpeech",
+            display_name="ElevenLabs Text to Speech",
+            category="api node/audio/ElevenLabs",
+            description="Convert text to speech.",
+            inputs=[
+                IO.Custom(ELEVENLABS_VOICE).Input(
+                    "voice",
+                    tooltip="Voice to use for speech synthesis. Connect from Voice Selector or Instant Voice Clone.",
+                ),
+                IO.String.Input(
+                    "text",
+                    multiline=True,
+                    default="",
+                    tooltip="The text to convert to speech.",
+                ),
+                IO.Float.Input(
+                    "stability",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Voice stability. Lower values give broader emotional range, "
+                    "higher values produce more consistent but potentially monotonous speech.",
+                ),
+                IO.Combo.Input(
+                    "apply_text_normalization",
+                    options=["auto", "on", "off"],
+                    tooltip="Text normalization mode. 'auto' lets the system decide, "
+                    "'on' always applies normalization, 'off' skips it.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "eleven_multilingual_v2",
+                            [
+                                IO.Float.Input(
+                                    "speed",
+                                    default=1.0,
+                                    min=0.7,
+                                    max=1.3,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Speech speed. 1.0 is normal, <1.0 slower, >1.0 faster.",
+                                ),
+                                IO.Float.Input(
+                                    "similarity_boost",
+                                    default=0.75,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Similarity boost. Higher values make the voice more similar to the original.",
+                                ),
+                                IO.Boolean.Input(
+                                    "use_speaker_boost",
+                                    default=False,
+                                    tooltip="Boost similarity to the original speaker voice.",
+                                ),
+                                IO.Float.Input(
+                                    "style",
+                                    default=0.0,
+                                    min=0.0,
+                                    max=0.2,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Style exaggeration. Higher values increase stylistic expression "
+                                    "but may reduce stability.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "eleven_v3",
+                            [
+                                IO.Float.Input(
+                                    "speed",
+                                    default=1.0,
+                                    min=0.7,
+                                    max=1.3,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Speech speed. 1.0 is normal, <1.0 slower, >1.0 faster.",
+                                ),
+                                IO.Float.Input(
+                                    "similarity_boost",
+                                    default=0.75,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Similarity boost. Higher values make the voice more similar to the original.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for text-to-speech.",
+                ),
+                IO.String.Input(
+                    "language_code",
+                    default="",
+                    tooltip="ISO-639-1 or ISO-639-3 language code (e.g., 'en', 'es', 'fra'). "
+                    "Leave empty for automatic detection.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    tooltip="Seed for reproducibility (determinism not guaranteed).",
+                ),
+                IO.Combo.Input(
+                    "output_format",
+                    options=["mp3_44100_192", "opus_48000_192"],
+                    tooltip="Audio output format.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.24,"format":{"approximate":true,"suffix":"/1K chars"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        voice: str,
+        text: str,
+        stability: float,
+        apply_text_normalization: str,
+        model: dict,
+        language_code: str,
+        seed: int,
+        output_format: str,
+    ) -> IO.NodeOutput:
+        validate_string(text, min_length=1)
+        request = TextToSpeechRequest(
+            text=text,
+            model_id=model["model"],
+            language_code=language_code if language_code.strip() else None,
+            voice_settings=TextToSpeechVoiceSettings(
+                stability=stability,
+                similarity_boost=model["similarity_boost"],
+                speed=model["speed"],
+                use_speaker_boost=model.get("use_speaker_boost", None),
+                style=model.get("style", None),
+            ),
+            seed=seed,
+            apply_text_normalization=apply_text_normalization,
+        )
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path=f"/proxy/elevenlabs/v1/text-to-speech/{voice}",
+                method="POST",
+                query_params={"output_format": output_format},
+            ),
+            data=request,
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class ElevenLabsAudioIsolation(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsAudioIsolation",
+            display_name="ElevenLabs Voice Isolation",
+            category="api node/audio/ElevenLabs",
+            description="Remove background noise from audio, isolating vocals or speech.",
+            inputs=[
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Audio to process for background noise removal.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.24,"format":{"approximate":true,"suffix":"/minute"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        audio: Input.Audio,
+    ) -> IO.NodeOutput:
+        audio_data_np = audio_tensor_to_contiguous_ndarray(audio["waveform"])
+        audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, audio["sample_rate"], "mp4", "aac")
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(path="/proxy/elevenlabs/v1/audio-isolation", method="POST"),
+            files={"audio": ("audio.mp4", audio_bytes_io, "audio/mp4")},
+            content_type="multipart/form-data",
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class ElevenLabsTextToSoundEffects(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsTextToSoundEffects",
+            display_name="ElevenLabs Text to Sound Effects",
+            category="api node/audio/ElevenLabs",
+            description="Generate sound effects from text descriptions.",
+            inputs=[
+                IO.String.Input(
+                    "text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text description of the sound effect to generate.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "eleven_sfx_v2",
+                            [
+                                IO.Float.Input(
+                                    "duration",
+                                    default=5.0,
+                                    min=0.5,
+                                    max=30.0,
+                                    step=0.1,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="Duration of generated sound in seconds.",
+                                ),
+                                IO.Boolean.Input(
+                                    "loop",
+                                    default=False,
+                                    tooltip="Create a smoothly looping sound effect.",
+                                ),
+                                IO.Float.Input(
+                                    "prompt_influence",
+                                    default=0.3,
+                                    min=0.0,
+                                    max=1.0,
+                                    step=0.01,
+                                    display_mode=IO.NumberDisplay.slider,
+                                    tooltip="How closely generation follows the prompt. "
+                                    "Higher values make the sound follow the text more closely.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Model to use for sound effect generation.",
+                ),
+                IO.Combo.Input(
+                    "output_format",
+                    options=["mp3_44100_192", "opus_48000_192"],
+                    tooltip="Audio output format.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.14,"format":{"approximate":true,"suffix":"/minute"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        text: str,
+        model: dict,
+        output_format: str,
+    ) -> IO.NodeOutput:
+        validate_string(text, min_length=1)
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path="/proxy/elevenlabs/v1/sound-generation",
+                method="POST",
+                query_params={"output_format": output_format},
+            ),
+            data=TextToSoundEffectsRequest(
+                text=text,
+                duration_seconds=model["duration"],
+                prompt_influence=model["prompt_influence"],
+                loop=model.get("loop", None),
+            ),
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class ElevenLabsInstantVoiceClone(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsInstantVoiceClone",
+            display_name="ElevenLabs Instant Voice Clone",
+            category="api node/audio/ElevenLabs",
+            description="Create a cloned voice from audio samples. "
+            "Provide 1-8 audio recordings of the voice to clone.",
+            inputs=[
+                IO.Autogrow.Input(
+                    "files",
+                    template=IO.Autogrow.TemplatePrefix(
+                        IO.Audio.Input("audio"),
+                        prefix="audio",
+                        min=1,
+                        max=8,
+                    ),
+                    tooltip="Audio recordings for voice cloning.",
+                ),
+                IO.Boolean.Input(
+                    "remove_background_noise",
+                    default=False,
+                    tooltip="Remove background noise from voice samples using audio isolation.",
+                ),
+            ],
+            outputs=[
+                IO.Custom(ELEVENLABS_VOICE).Output(display_name="voice"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(expr="""{"type":"usd","usd":0.15}"""),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        files: IO.Autogrow.Type,
+        remove_background_noise: bool,
+    ) -> IO.NodeOutput:
+        file_tuples: list[tuple[str, tuple[str, bytes, str]]] = []
+        for key in files:
+            audio = files[key]
+            sample_rate: int = audio["sample_rate"]
+            waveform = audio["waveform"]
+            audio_data_np = audio_tensor_to_contiguous_ndarray(waveform)
+            audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, sample_rate, "mp4", "aac")
+            file_tuples.append(("files", (f"{key}.mp4", audio_bytes_io.getvalue(), "audio/mp4")))
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/elevenlabs/v1/voices/add", method="POST"),
+            response_model=AddVoiceResponse,
+            data=AddVoiceRequest(
+                name=str(uuid.uuid4()),
+                remove_background_noise=remove_background_noise,
+            ),
+            files=file_tuples,
+            content_type="multipart/form-data",
+        )
+        return IO.NodeOutput(response.voice_id)
+
+
+ELEVENLABS_STS_VOICE_SETTINGS = [
+    IO.Float.Input(
+        "speed",
+        default=1.0,
+        min=0.7,
+        max=1.3,
+        step=0.01,
+        display_mode=IO.NumberDisplay.slider,
+        tooltip="Speech speed. 1.0 is normal, <1.0 slower, >1.0 faster.",
+    ),
+    IO.Float.Input(
+        "similarity_boost",
+        default=0.75,
+        min=0.0,
+        max=1.0,
+        step=0.01,
+        display_mode=IO.NumberDisplay.slider,
+        tooltip="Similarity boost. Higher values make the voice more similar to the original.",
+    ),
+    IO.Boolean.Input(
+        "use_speaker_boost",
+        default=False,
+        tooltip="Boost similarity to the original speaker voice.",
+    ),
+    IO.Float.Input(
+        "style",
+        default=0.0,
+        min=0.0,
+        max=0.2,
+        step=0.01,
+        display_mode=IO.NumberDisplay.slider,
+        tooltip="Style exaggeration. Higher values increase stylistic expression but may reduce stability.",
+    ),
+]
+
+
+class ElevenLabsSpeechToSpeech(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsSpeechToSpeech",
+            display_name="ElevenLabs Speech to Speech",
+            category="api node/audio/ElevenLabs",
+            description="Transform speech from one voice to another while preserving the original content and emotion.",
+            inputs=[
+                IO.Custom(ELEVENLABS_VOICE).Input(
+                    "voice",
+                    tooltip="Target voice for the transformation. "
+                    "Connect from Voice Selector or Instant Voice Clone.",
+                ),
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Source audio to transform.",
+                ),
+                IO.Float.Input(
+                    "stability",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Voice stability. Lower values give broader emotional range, "
+                    "higher values produce more consistent but potentially monotonous speech.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "eleven_multilingual_sts_v2",
+                            ELEVENLABS_STS_VOICE_SETTINGS,
+                        ),
+                        IO.DynamicCombo.Option(
+                            "eleven_english_sts_v2",
+                            ELEVENLABS_STS_VOICE_SETTINGS,
+                        ),
+                    ],
+                    tooltip="Model to use for speech-to-speech transformation.",
+                ),
+                IO.Combo.Input(
+                    "output_format",
+                    options=["mp3_44100_192", "opus_48000_192"],
+                    tooltip="Audio output format.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967295,
+                    tooltip="Seed for reproducibility.",
+                ),
+                IO.Boolean.Input(
+                    "remove_background_noise",
+                    default=False,
+                    tooltip="Remove background noise from input audio using audio isolation.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.24,"format":{"approximate":true,"suffix":"/minute"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        voice: str,
+        audio: Input.Audio,
+        stability: float,
+        model: dict,
+        output_format: str,
+        seed: int,
+        remove_background_noise: bool,
+    ) -> IO.NodeOutput:
+        audio_data_np = audio_tensor_to_contiguous_ndarray(audio["waveform"])
+        audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, audio["sample_rate"], "mp4", "aac")
+        voice_settings = TextToSpeechVoiceSettings(
+            stability=stability,
+            similarity_boost=model["similarity_boost"],
+            style=model["style"],
+            use_speaker_boost=model["use_speaker_boost"],
+            speed=model["speed"],
+        )
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path=f"/proxy/elevenlabs/v1/speech-to-speech/{voice}",
+                method="POST",
+                query_params={"output_format": output_format},
+            ),
+            data=SpeechToSpeechRequest(
+                model_id=model["model"],
+                voice_settings=voice_settings.model_dump_json(exclude_none=True),
+                seed=seed,
+                remove_background_noise=remove_background_noise,
+            ),
+            files={"audio": ("audio.mp4", audio_bytes_io.getvalue(), "audio/mp4")},
+            content_type="multipart/form-data",
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+def _generate_dialogue_inputs(count: int) -> list:
+    """Generate input widgets for a given number of dialogue entries."""
+    inputs = []
+    for i in range(1, count + 1):
+        inputs.extend(
+            [
+                IO.String.Input(
+                    f"text{i}",
+                    multiline=True,
+                    default="",
+                    tooltip=f"Text content for dialogue entry {i}.",
+                ),
+                IO.Custom(ELEVENLABS_VOICE).Input(
+                    f"voice{i}",
+                    tooltip=f"Voice for dialogue entry {i}. Connect from Voice Selector or Instant Voice Clone.",
+                ),
+            ]
+        )
+    return inputs
+
+
+class ElevenLabsTextToDialogue(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ElevenLabsTextToDialogue",
+            display_name="ElevenLabs Text to Dialogue",
+            category="api node/audio/ElevenLabs",
+            description="Generate multi-speaker dialogue from text. Each dialogue entry has its own text and voice.",
+            inputs=[
+                IO.Float.Input(
+                    "stability",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.5,
+                    display_mode=IO.NumberDisplay.slider,
+                    tooltip="Voice stability. Lower values give broader emotional range, "
+                    "higher values produce more consistent but potentially monotonous speech.",
+                ),
+                IO.Combo.Input(
+                    "apply_text_normalization",
+                    options=["auto", "on", "off"],
+                    tooltip="Text normalization mode. 'auto' lets the system decide, "
+                    "'on' always applies normalization, 'off' skips it.",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["eleven_v3"],
+                    tooltip="Model to use for dialogue generation.",
+                ),
+                IO.DynamicCombo.Input(
+                    "inputs",
+                    options=[
+                        IO.DynamicCombo.Option("1", _generate_dialogue_inputs(1)),
+                        IO.DynamicCombo.Option("2", _generate_dialogue_inputs(2)),
+                        IO.DynamicCombo.Option("3", _generate_dialogue_inputs(3)),
+                        IO.DynamicCombo.Option("4", _generate_dialogue_inputs(4)),
+                        IO.DynamicCombo.Option("5", _generate_dialogue_inputs(5)),
+                        IO.DynamicCombo.Option("6", _generate_dialogue_inputs(6)),
+                        IO.DynamicCombo.Option("7", _generate_dialogue_inputs(7)),
+                        IO.DynamicCombo.Option("8", _generate_dialogue_inputs(8)),
+                        IO.DynamicCombo.Option("9", _generate_dialogue_inputs(9)),
+                        IO.DynamicCombo.Option("10", _generate_dialogue_inputs(10)),
+                    ],
+                    tooltip="Number of dialogue entries.",
+                ),
+                IO.String.Input(
+                    "language_code",
+                    default="",
+                    tooltip="ISO-639-1 or ISO-639-3 language code (e.g., 'en', 'es', 'fra'). "
+                    "Leave empty for automatic detection.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=4294967295,
+                    tooltip="Seed for reproducibility.",
+                ),
+                IO.Combo.Input(
+                    "output_format",
+                    options=["mp3_44100_192", "opus_48000_192"],
+                    tooltip="Audio output format.",
+                ),
+            ],
+            outputs=[
+                IO.Audio.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.24,"format":{"approximate":true,"suffix":"/1K chars"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        stability: float,
+        apply_text_normalization: str,
+        model: str,
+        inputs: dict,
+        language_code: str,
+        seed: int,
+        output_format: str,
+    ) -> IO.NodeOutput:
+        num_entries = int(inputs["inputs"])
+        dialogue_inputs: list[DialogueInput] = []
+        for i in range(1, num_entries + 1):
+            text = inputs[f"text{i}"]
+            voice_id = inputs[f"voice{i}"]
+            validate_string(text, min_length=1)
+            dialogue_inputs.append(DialogueInput(text=text, voice_id=voice_id))
+        request = TextToDialogueRequest(
+            inputs=dialogue_inputs,
+            model_id=model,
+            language_code=language_code if language_code.strip() else None,
+            settings=DialogueSettings(stability=stability),
+            seed=seed,
+            apply_text_normalization=apply_text_normalization,
+        )
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(
+                path="/proxy/elevenlabs/v1/text-to-dialogue",
+                method="POST",
+                query_params={"output_format": output_format},
+            ),
+            data=request,
+            as_binary=True,
+        )
+        return IO.NodeOutput(audio_bytes_to_audio_input(response))
+
+
+class ElevenLabsExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            ElevenLabsSpeechToText,
+            ElevenLabsVoiceSelector,
+            ElevenLabsTextToSpeech,
+            ElevenLabsAudioIsolation,
+            ElevenLabsTextToSoundEffects,
+            ElevenLabsInstantVoiceClone,
+            ElevenLabsSpeechToSpeech,
+            ElevenLabsTextToDialogue,
+        ]
+
+
+async def comfy_entrypoint() -> ElevenLabsExtension:
+    return ElevenLabsExtension()

comfy_api_nodes/nodes_gemini.py

@@ -6,6 +6,7 @@ See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/infer
 import base64
 import os
 from enum import Enum
+from fnmatch import fnmatch
 from io import BytesIO
 from typing import Literal
 
@@ -119,6 +120,13 @@ async def create_image_parts(
     return image_parts
 
 
+def _mime_matches(mime: GeminiMimeType | None, pattern: str) -> bool:
+    """Check if a MIME type matches a pattern. Supports fnmatch globs (e.g. 'image/*')."""
+    if mime is None:
+        return False
+    return fnmatch(mime.value, pattern)
+
+
 def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Literal["text"] | str) -> list[GeminiPart]:
     """
     Filter response parts by their type.
@@ -151,9 +159,9 @@ def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Litera
         for part in candidate.content.parts:
             if part_type == "text" and part.text:
                 parts.append(part)
-            elif part.inlineData and part.inlineData.mimeType == part_type:
+            elif part.inlineData and _mime_matches(part.inlineData.mimeType, part_type):
                 parts.append(part)
-            elif part.fileData and part.fileData.mimeType == part_type:
+            elif part.fileData and _mime_matches(part.fileData.mimeType, part_type):
                 parts.append(part)
 
     if not parts and blocked_reasons:
@@ -178,7 +186,7 @@ def get_text_from_response(response: GeminiGenerateContentResponse) -> str:
 
 async def get_image_from_response(response: GeminiGenerateContentResponse) -> Input.Image:
     image_tensors: list[Input.Image] = []
-    parts = get_parts_by_type(response, "image/png")
+    parts = get_parts_by_type(response, "image/*")
     for part in parts:
         if part.inlineData:
             image_data = base64.b64decode(part.inlineData.data)
@@ -308,6 +316,7 @@ class GeminiNode(IO.ComfyNode):
                     default="",
                     optional=True,
                     tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
                 ),
             ],
             outputs=[
@@ -585,6 +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",
@@ -592,6 +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=[
@@ -626,7 +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))
@@ -646,7 +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,
             ),
@@ -706,6 +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",
@@ -725,6 +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
@@ -505,6 +505,9 @@ class Rodin3D_Gen2(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.4}""",
+            ),
         )
 
     @classmethod

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_api_nodes/util/__init__.py

@@ -9,6 +9,8 @@ from .client import (
 from .conversions import (
     audio_bytes_to_audio_input,
     audio_input_to_mp3,
+    audio_ndarray_to_bytesio,
+    audio_tensor_to_contiguous_ndarray,
     audio_to_base64_string,
     bytesio_to_image_tensor,
     convert_mask_to_image,
@@ -78,6 +80,8 @@ __all__ = [
     # Conversions
     "audio_bytes_to_audio_input",
     "audio_input_to_mp3",
+    "audio_ndarray_to_bytesio",
+    "audio_tensor_to_contiguous_ndarray",
     "audio_to_base64_string",
     "bytesio_to_image_tensor",
     "convert_mask_to_image",

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

@@ -10,8 +10,10 @@ class Canny(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Canny",
+            display_name="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_color_curves.py

@@ -0,0 +1,137 @@
+from typing_extensions import override
+import torch
+import numpy as np
+
+from comfy_api.latest import ComfyExtension, io, ui
+
+
+def _monotone_cubic_hermite(xs, ys, x_query):
+    """Evaluate monotone cubic Hermite interpolation at x_query points."""
+    n = len(xs)
+    if n == 0:
+        return np.zeros_like(x_query)
+    if n == 1:
+        return np.full_like(x_query, ys[0])
+
+    # Compute slopes
+    deltas = np.diff(ys) / np.maximum(np.diff(xs), 1e-10)
+
+    # Compute tangents (Fritsch-Carlson)
+    slopes = np.zeros(n)
+    slopes[0] = deltas[0]
+    slopes[-1] = deltas[-1]
+    for i in range(1, n - 1):
+        if deltas[i - 1] * deltas[i] <= 0:
+            slopes[i] = 0
+        else:
+            slopes[i] = (deltas[i - 1] + deltas[i]) / 2
+
+    # Enforce monotonicity
+    for i in range(n - 1):
+        if deltas[i] == 0:
+            slopes[i] = 0
+            slopes[i + 1] = 0
+        else:
+            alpha = slopes[i] / deltas[i]
+            beta = slopes[i + 1] / deltas[i]
+            s = alpha ** 2 + beta ** 2
+            if s > 9:
+                t = 3 / np.sqrt(s)
+                slopes[i] = t * alpha * deltas[i]
+                slopes[i + 1] = t * beta * deltas[i]
+
+    # Evaluate
+    result = np.zeros_like(x_query, dtype=np.float64)
+    indices = np.searchsorted(xs, x_query, side='right') - 1
+    indices = np.clip(indices, 0, n - 2)
+
+    for i in range(n - 1):
+        mask = indices == i
+        if not np.any(mask):
+            continue
+        dx = xs[i + 1] - xs[i]
+        if dx == 0:
+            result[mask] = ys[i]
+            continue
+        t = (x_query[mask] - xs[i]) / dx
+        t2 = t * t
+        t3 = t2 * t
+        h00 = 2 * t3 - 3 * t2 + 1
+        h10 = t3 - 2 * t2 + t
+        h01 = -2 * t3 + 3 * t2
+        h11 = t3 - t2
+        result[mask] = h00 * ys[i] + h10 * dx * slopes[i] + h01 * ys[i + 1] + h11 * dx * slopes[i + 1]
+
+    # Clamp edges
+    result[x_query <= xs[0]] = ys[0]
+    result[x_query >= xs[-1]] = ys[-1]
+
+    return result
+
+
+def _build_lut(points):
+    """Build a 256-entry LUT from curve control points in [0,1] space."""
+    if not points or len(points) < 2:
+        return np.arange(256, dtype=np.float64) / 255.0
+
+    pts = sorted(points, key=lambda p: p[0])
+    xs = np.array([p[0] for p in pts], dtype=np.float64)
+    ys = np.array([p[1] for p in pts], dtype=np.float64)
+
+    x_query = np.linspace(0, 1, 256)
+    lut = _monotone_cubic_hermite(xs, ys, x_query)
+    return np.clip(lut, 0, 1)
+
+
+class ColorCurvesNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ColorCurves",
+            display_name="Color Curves",
+            category="image/adjustment",
+            inputs=[
+                io.Image.Input("image"),
+                io.ColorCurves.Input("settings"),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image: torch.Tensor, settings: dict) -> io.NodeOutput:
+        rgb_pts = settings.get("rgb", [[0, 0], [1, 1]])
+        red_pts = settings.get("red", [[0, 0], [1, 1]])
+        green_pts = settings.get("green", [[0, 0], [1, 1]])
+        blue_pts = settings.get("blue", [[0, 0], [1, 1]])
+
+        rgb_lut = _build_lut(rgb_pts)
+        red_lut = _build_lut(red_pts)
+        green_lut = _build_lut(green_pts)
+        blue_lut = _build_lut(blue_pts)
+
+        # Convert to numpy for LUT application
+        img_np = image.cpu().numpy().copy()
+
+        # Apply per-channel curves then RGB master curve.
+        # Index with floor(val * 256) clamped to [0, 255] to match GPU NEAREST
+        # texture sampling on a 256-wide LUT texture.
+        for ch, ch_lut in enumerate([red_lut, green_lut, blue_lut]):
+            indices = np.clip((img_np[..., ch] * 256).astype(np.int32), 0, 255)
+            img_np[..., ch] = ch_lut[indices]
+            indices = np.clip((img_np[..., ch] * 256).astype(np.int32), 0, 255)
+            img_np[..., ch] = rgb_lut[indices]
+
+        result = torch.from_numpy(np.clip(img_np, 0, 1)).to(image.device, dtype=image.dtype)
+        return io.NodeOutput(result, ui=ui.PreviewImage(result))
+
+
+class ColorCurvesExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [ColorCurvesNode]
+
+
+async def comfy_entrypoint() -> ColorCurvesExtension:
+    return ColorCurvesExtension()