wip add AI generated descriptions to all nodes

* lora: add weight shape calculations.

This lets the loader know if a lora will change the shape of a weight
so it can take appropriate action.

* MPDynamic: force load flux img_in weight

This weight is a bit special, in that the lora changes its geometry.
This is rather unique, not handled by existing estimate and doesn't
work for either offloading or dynamic_vram.

Fix for dynamic_vram as a special case. Ideally we can fully precalculate
these lora geometry changes at load time, but just get these models
working first.

app/node_replace_manager.py

@@ -0,0 +1,105 @@
+from __future__ import annotations
+
+from aiohttp import web
+
+from typing import TYPE_CHECKING, TypedDict
+if TYPE_CHECKING:
+    from comfy_api.latest._io_public import NodeReplace
+
+from comfy_execution.graph_utils import is_link
+import nodes
+
+class NodeStruct(TypedDict):
+    inputs: dict[str, str | int | float | bool | tuple[str, int]]
+    class_type: str
+    _meta: dict[str, str]
+
+def copy_node_struct(node_struct: NodeStruct, empty_inputs: bool = False) -> NodeStruct:
+    new_node_struct = node_struct.copy()
+    if empty_inputs:
+        new_node_struct["inputs"] = {}
+    else:
+        new_node_struct["inputs"] = node_struct["inputs"].copy()
+    new_node_struct["_meta"] = node_struct["_meta"].copy()
+    return new_node_struct
+
+
+class NodeReplaceManager:
+    """Manages node replacement registrations."""
+
+    def __init__(self):
+        self._replacements: dict[str, list[NodeReplace]] = {}
+
+    def register(self, node_replace: NodeReplace):
+        """Register a node replacement mapping."""
+        self._replacements.setdefault(node_replace.old_node_id, []).append(node_replace)
+
+    def get_replacement(self, old_node_id: str) -> list[NodeReplace] | None:
+        """Get replacements for an old node ID."""
+        return self._replacements.get(old_node_id)
+
+    def has_replacement(self, old_node_id: str) -> bool:
+        """Check if a replacement exists for an old node ID."""
+        return old_node_id in self._replacements
+
+    def apply_replacements(self, prompt: dict[str, NodeStruct]):
+        connections: dict[str, list[tuple[str, str, int]]] = {}
+        need_replacement: set[str] = set()
+        for node_number, node_struct in prompt.items():
+            class_type = node_struct["class_type"]
+            # need replacement if not in NODE_CLASS_MAPPINGS and has replacement
+            if class_type not in nodes.NODE_CLASS_MAPPINGS.keys() and self.has_replacement(class_type):
+                need_replacement.add(node_number)
+            # keep track of connections
+            for input_id, input_value in node_struct["inputs"].items():
+                if is_link(input_value):
+                    conn_number = input_value[0]
+                    connections.setdefault(conn_number, []).append((node_number, input_id, input_value[1]))
+        for node_number in need_replacement:
+            node_struct = prompt[node_number]
+            class_type = node_struct["class_type"]
+            replacements = self.get_replacement(class_type)
+            if replacements is None:
+                continue
+            # just use the first replacement
+            replacement = replacements[0]
+            new_node_id = replacement.new_node_id
+            # if replacement is not a valid node, skip trying to replace it as will only cause confusion
+            if new_node_id not in nodes.NODE_CLASS_MAPPINGS.keys():
+                continue
+            # first, replace node id (class_type)
+            new_node_struct = copy_node_struct(node_struct, empty_inputs=True)
+            new_node_struct["class_type"] = new_node_id
+            # TODO: consider replacing display_name in _meta as well for error reporting purposes; would need to query node schema
+            # second, replace inputs
+            if replacement.input_mapping is not None:
+                for input_map in replacement.input_mapping:
+                    if "set_value" in input_map:
+                        new_node_struct["inputs"][input_map["new_id"]] = input_map["set_value"]
+                    elif "old_id" in input_map:
+                        new_node_struct["inputs"][input_map["new_id"]] = node_struct["inputs"][input_map["old_id"]]
+            # finalize input replacement
+            prompt[node_number] = new_node_struct
+            # third, replace outputs
+            if replacement.output_mapping is not None:
+                # re-mapping outputs requires changing the input values of nodes that receive connections from this one
+                if node_number in connections:
+                    for conns in connections[node_number]:
+                        conn_node_number, conn_input_id, old_output_idx = conns
+                        for output_map in replacement.output_mapping:
+                            if output_map["old_idx"] == old_output_idx:
+                                new_output_idx = output_map["new_idx"]
+                                previous_input = prompt[conn_node_number]["inputs"][conn_input_id]
+                                previous_input[1] = new_output_idx
+
+    def as_dict(self):
+        """Serialize all replacements to dict."""
+        return {
+            k: [v.as_dict() for v in v_list]
+            for k, v_list in self._replacements.items()
+        }
+
+    def add_routes(self, routes):
+        @routes.get("/node_replacements")
+        async def get_node_replacements(request):
+            return web.json_response(self.as_dict())

blueprints/.glsl/Brightness_and_Contrast_1.frag

@@ -1,44 +0,0 @@
-#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

@@ -1,72 +0,0 @@
-#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

@@ -1,78 +0,0 @@
-#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

@@ -1,94 +0,0 @@
-#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

@@ -1,124 +0,0 @@
-#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

@@ -1,133 +0,0 @@
-#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

@@ -1,222 +0,0 @@
-#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(abs(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

@@ -1,111 +0,0 @@
-#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

@@ -1,19 +0,0 @@
-#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

@@ -1,71 +0,0 @@
-#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

@@ -1,28 +0,0 @@
-# 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

@@ -1,28 +0,0 @@
-#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

@@ -1,61 +0,0 @@
-#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

@@ -1,159 +0,0 @@
-#!/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

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

blueprints/Sharpen.json

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

comfy/ldm/modules/diffusionmodules/model.py

@@ -102,19 +102,7 @@ class VideoConv3d(nn.Module):
         return self.conv(x)
 
 def interpolate_up(x, scale_factor):
-    try:
-        return torch.nn.functional.interpolate(x, scale_factor=scale_factor, mode="nearest")
-    except: #operation not implemented for bf16
-        orig_shape = list(x.shape)
-        out_shape = orig_shape[:2]
-        for i in range(len(orig_shape) - 2):
-            out_shape.append(round(orig_shape[i + 2] * scale_factor[i]))
-        out = torch.empty(out_shape, dtype=x.dtype, layout=x.layout, device=x.device)
-        split = 8
-        l = out.shape[1] // split
-        for i in range(0, out.shape[1], l):
-            out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=scale_factor, mode="nearest").to(x.dtype)
-        return out
+    return torch.nn.functional.interpolate(x, scale_factor=scale_factor, mode="nearest")
 
 class Upsample(nn.Module):
     def __init__(self, in_channels, with_conv, conv_op=ops.Conv2d, scale_factor=2.0):

comfy/lora.py

@@ -374,6 +374,31 @@ def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Ten
 
     return padded_tensor
 
+def calculate_shape(patches, weight, key, original_weights=None):
+    current_shape = weight.shape
+
+    for p in patches:
+        v = p[1]
+        offset = p[3]
+
+        # Offsets restore the old shape; lists force a diff without metadata
+        if offset is not None or isinstance(v, list):
+            continue
+
+        if isinstance(v, weight_adapter.WeightAdapterBase):
+            adapter_shape = v.calculate_shape(key)
+            if adapter_shape is not None:
+                current_shape = adapter_shape
+            continue
+
+        # Standard diff logic with padding
+        if len(v) == 2:
+            patch_type, patch_data = v[0], v[1]
+            if patch_type == "diff" and len(patch_data) > 1 and patch_data[1]['pad_weight']:
+                current_shape = patch_data[0].shape
+
+    return current_shape
+
 def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32, original_weights=None):
     for p in patches:
         strength = p[0]

comfy/model_patcher.py

@@ -1514,8 +1514,10 @@ class ModelPatcherDynamic(ModelPatcher):
 
                     weight, _, _ = get_key_weight(self.model, key)
                     if weight is None:
-                        return 0
+                        return (False, 0)
                     if key in self.patches:
+                        if comfy.lora.calculate_shape(self.patches[key], weight, key) != weight.shape:
+                            return (True, 0)
                         setattr(m, param_key + "_lowvram_function", LowVramPatch(key, self.patches))
                         num_patches += 1
                     else:
@@ -1529,7 +1531,13 @@ class ModelPatcherDynamic(ModelPatcher):
                         model_dtype = getattr(m, param_key + "_comfy_model_dtype", None) or weight.dtype
                         weight._model_dtype = model_dtype
                         geometry = comfy.memory_management.TensorGeometry(shape=weight.shape, dtype=model_dtype)
-                    return comfy.memory_management.vram_aligned_size(geometry)
+                    return (False, comfy.memory_management.vram_aligned_size(geometry))
+
+                def force_load_param(self, param_key, device_to):
+                    key = key_param_name_to_key(n, param_key)
+                    if key in self.backup:
+                        comfy.utils.set_attr_param(self.model, key, self.backup[key].weight)
+                    self.patch_weight_to_device(key, device_to=device_to)
 
                 if hasattr(m, "comfy_cast_weights"):
                     m.comfy_cast_weights = True
@@ -1537,13 +1545,19 @@ class ModelPatcherDynamic(ModelPatcher):
                     m.seed_key = n
                     set_dirty(m, dirty)
 
-                    v_weight_size = 0
-                    v_weight_size += setup_param(self, m, n, "weight")
-                    v_weight_size += setup_param(self, m, n, "bias")
+                    force_load, v_weight_size = setup_param(self, m, n, "weight")
+                    force_load_bias, v_weight_bias = setup_param(self, m, n, "bias")
+                    force_load = force_load or force_load_bias
+                    v_weight_size += v_weight_bias
 
-                    if vbar is not None and not hasattr(m, "_v"):
-                        m._v = vbar.alloc(v_weight_size)
-                    allocated_size += v_weight_size
+                    if force_load:
+                        logging.info(f"Module {n} has resizing Lora - force loading")
+                        force_load_param(self, "weight", device_to)
+                        force_load_param(self, "bias", device_to)
+                    else:
+                        if vbar is not None and not hasattr(m, "_v"):
+                            m._v = vbar.alloc(v_weight_size)
+                        allocated_size += v_weight_size
 
                 else:
                     for param in params:
@@ -1606,6 +1620,11 @@ class ModelPatcherDynamic(ModelPatcher):
             for m in self.model.modules():
                 move_weight_functions(m, device_to)
 
+            keys = list(self.backup.keys())
+            for k in keys:
+                bk = self.backup[k]
+                comfy.utils.set_attr_param(self.model, k, bk.weight)
+
     def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):
         assert not force_patch_weights #See above
         with self.use_ejected(skip_and_inject_on_exit_only=True):

comfy/weight_adapter/base.py

@@ -49,6 +49,12 @@ class WeightAdapterBase:
         """
         raise NotImplementedError
 
+    def calculate_shape(
+        self,
+        key
+    ):
+        return None
+
     def calculate_weight(
         self,
         weight,

comfy/weight_adapter/lora.py

@@ -214,6 +214,13 @@ class LoRAAdapter(WeightAdapterBase):
         else:
             return None
 
+    def calculate_shape(
+        self,
+        key
+    ):
+        reshape = self.weights[5]
+        return tuple(reshape) if reshape is not None else None
+
     def calculate_weight(
         self,
         weight,

comfy_api/feature_flags.py

@@ -14,6 +14,7 @@ SERVER_FEATURE_FLAGS: dict[str, Any] = {
     "supports_preview_metadata": True,
     "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes
     "extension": {"manager": {"supports_v4": True}},
+    "node_replacements": True,
 }
 
 

comfy_api/latest/__init__.py

@@ -21,6 +21,17 @@ class ComfyAPI_latest(ComfyAPIBase):
     VERSION = "latest"
     STABLE = False
 
+    def __init__(self):
+        super().__init__()
+        self.node_replacement = self.NodeReplacement()
+        self.execution = self.Execution()
+
+    class NodeReplacement(ProxiedSingleton):
+        async def register(self, node_replace: io.NodeReplace) -> None:
+            """Register a node replacement mapping."""
+            from server import PromptServer
+            PromptServer.instance.node_replace_manager.register(node_replace)
+
     class Execution(ProxiedSingleton):
         async def set_progress(
             self,
@@ -73,8 +84,6 @@ class ComfyAPI_latest(ComfyAPIBase):
                 image=to_display,
             )
 
-    execution: Execution
-
 class ComfyExtension(ABC):
     async def on_load(self) -> None:
         """

comfy_api/latest/_io.py

@@ -1300,6 +1300,7 @@ class NodeInfoV1:
     name: str=None
     display_name: str=None
     description: str=None
+    short_description: str=None
     python_module: Any=None
     category: str=None
     output_node: bool=None
@@ -1390,6 +1391,8 @@ class Schema:
     hidden: list[Hidden] = field(default_factory=list)
     description: str=""
     """Node description, shown as a tooltip when hovering over the node."""
+    short_description: str=""
+    """Short node description, shown in the node list/search."""
     search_aliases: list[str] = field(default_factory=list)
     """Alternative names for search. Useful for synonyms, abbreviations, or old names after renaming."""
     is_input_list: bool = False
@@ -1528,6 +1531,7 @@ class Schema:
             display_name=self.display_name,
             category=self.category,
             description=self.description,
+            short_description=self.short_description,
             output_node=self.is_output_node,
             deprecated=self.is_deprecated,
             experimental=self.is_experimental,
@@ -1771,6 +1775,14 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
             cls.GET_SCHEMA()
         return cls._DESCRIPTION
 
+    _SHORT_DESCRIPTION = None
+    @final
+    @classproperty
+    def SHORT_DESCRIPTION(cls):  # noqa
+        if cls._SHORT_DESCRIPTION is None:
+            cls.GET_SCHEMA()
+        return cls._SHORT_DESCRIPTION
+
     _CATEGORY = None
     @final
     @classproperty
@@ -1899,6 +1911,8 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
         schema.validate()
         if cls._DESCRIPTION is None:
             cls._DESCRIPTION = schema.description
+        if cls._SHORT_DESCRIPTION is None:
+            cls._SHORT_DESCRIPTION = schema.short_description
         if cls._CATEGORY is None:
             cls._CATEGORY = schema.category
         if cls._EXPERIMENTAL is None:
@@ -2030,6 +2044,68 @@ class _UIOutput(ABC):
         ...
 
 
+class InputMapOldId(TypedDict):
+    """Map an old node input to a new node input by ID."""
+    new_id: str
+    old_id: str
+
+class InputMapSetValue(TypedDict):
+    """Set a specific value for a new node input."""
+    new_id: str
+    set_value: Any
+
+InputMap = InputMapOldId | InputMapSetValue
+"""
+Input mapping for node replacement. Type is inferred by dictionary keys:
+- {"new_id": str, "old_id": str} - maps old input to new input
+- {"new_id": str, "set_value": Any} - sets a specific value for new input
+"""
+
+class OutputMap(TypedDict):
+    """Map outputs of node replacement via indexes."""
+    new_idx: int
+    old_idx: int
+
+class NodeReplace:
+    """
+    Defines a possible node replacement, mapping inputs and outputs of the old node to the new node.
+
+    Also supports assigning specific values to the input widgets of the new node.
+
+    Args:
+        new_node_id: The class name of the new replacement node.
+        old_node_id: The class name of the deprecated node.
+        old_widget_ids: Ordered list of input IDs for widgets that may not have an input slot
+            connected. The workflow JSON stores widget values by their relative position index,
+            not by ID. This list maps those positional indexes to input IDs, enabling the
+            replacement system to correctly identify widget values during node migration.
+        input_mapping: List of input mappings from old node to new node.
+        output_mapping: List of output mappings from old node to new node.
+    """
+    def __init__(self,
+        new_node_id: str,
+        old_node_id: str,
+        old_widget_ids: list[str] | None=None,
+        input_mapping: list[InputMap] | None=None,
+        output_mapping: list[OutputMap] | None=None,
+    ):
+        self.new_node_id = new_node_id
+        self.old_node_id = old_node_id
+        self.old_widget_ids = old_widget_ids
+        self.input_mapping = input_mapping
+        self.output_mapping = output_mapping
+
+    def as_dict(self):
+        """Create serializable representation of the node replacement."""
+        return {
+            "new_node_id": self.new_node_id,
+            "old_node_id": self.old_node_id,
+            "old_widget_ids": self.old_widget_ids,
+            "input_mapping": list(self.input_mapping) if self.input_mapping else None,
+            "output_mapping": list(self.output_mapping) if self.output_mapping else None,
+        }
+
+
 __all__ = [
     "FolderType",
     "UploadType",
@@ -2121,4 +2197,5 @@ __all__ = [
     "ImageCompare",
     "PriceBadgeDepends",
     "PriceBadge",
+    "NodeReplace",
 ]

comfy_api_nodes/apis/bria.py

@@ -45,17 +45,55 @@ class BriaEditImageRequest(BaseModel):
     )
 
 
+class BriaRemoveBackgroundRequest(BaseModel):
+    image: str = Field(...)
+    sync: bool = Field(False)
+    visual_input_content_moderation: bool = Field(
+        False, description="If true, returns 422 on input image moderation failure."
+    )
+    visual_output_content_moderation: bool = Field(
+        False, description="If true, returns 422 on visual output moderation failure."
+    )
+    seed: int = Field(...)
+
+
 class BriaStatusResponse(BaseModel):
     request_id: str = Field(...)
     status_url: str = Field(...)
     warning: str | None = Field(None)
 
 
-class BriaResult(BaseModel):
+class BriaRemoveBackgroundResult(BaseModel):
+    image_url: str = Field(...)
+
+
+class BriaRemoveBackgroundResponse(BaseModel):
+    status: str = Field(...)
+    result: BriaRemoveBackgroundResult | None = Field(None)
+
+
+class BriaImageEditResult(BaseModel):
     structured_prompt: str = Field(...)
     image_url: str = Field(...)
 
 
-class BriaResponse(BaseModel):
+class BriaImageEditResponse(BaseModel):
     status: str = Field(...)
-    result: BriaResult | None = Field(None)
+    result: BriaImageEditResult | None = Field(None)
+
+
+class BriaRemoveVideoBackgroundRequest(BaseModel):
+    video: str = Field(...)
+    background_color: str = Field(default="transparent", description="Background color for the output video.")
+    output_container_and_codec: str = Field(...)
+    preserve_audio: bool = Field(True)
+    seed: int = Field(...)
+
+
+class BriaRemoveVideoBackgroundResult(BaseModel):
+    video_url: str = Field(...)
+
+
+class BriaRemoveVideoBackgroundResponse(BaseModel):
+    status: str = Field(...)
+    result: BriaRemoveVideoBackgroundResult | None = Field(None)

comfy_api_nodes/apis/hunyuan3d.py

@@ -64,3 +64,23 @@ class To3DProTaskResultResponse(BaseModel):
 
 class To3DProTaskQueryRequest(BaseModel):
     JobId: str = Field(...)
+
+
+class To3DUVFileInput(BaseModel):
+    Type: str = Field(..., description="File type: GLB, OBJ, or FBX")
+    Url: str = Field(...)
+
+
+class To3DUVTaskRequest(BaseModel):
+    File: To3DUVFileInput = Field(...)
+
+
+class TextureEditImageInfo(BaseModel):
+    Url: str = Field(...)
+
+
+class TextureEditTaskRequest(BaseModel):
+    File3D: To3DUVFileInput = Field(...)
+    Image: TextureEditImageInfo | None = Field(None)
+    Prompt: str | None = Field(None)
+    EnablePBR: bool | None = Field(None)

comfy_api_nodes/nodes_bfl.py

@@ -44,6 +44,7 @@ class FluxProUltraImageNode(IO.ComfyNode):
             display_name="Flux 1.1 [pro] Ultra Image",
             category="api node/image/BFL",
             description="Generates images using Flux Pro 1.1 Ultra via api based on prompt and resolution.",
+            short_description="Generate images with Flux Pro 1.1 Ultra API.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -154,13 +155,17 @@ class FluxProUltraImageNode(IO.ComfyNode):
 
 class FluxKontextProImageNode(IO.ComfyNode):
 
+    DESCRIPTION = "Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio."
+    SHORT_DESCRIPTION = "Edit images with Flux.1 Kontext [pro] API."
+
     @classmethod
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id=cls.NODE_ID,
             display_name=cls.DISPLAY_NAME,
             category="api node/image/BFL",
-            description="Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio.",
+            description=cls.DESCRIPTION,
+            short_description=cls.SHORT_DESCRIPTION,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -268,6 +273,7 @@ class FluxKontextProImageNode(IO.ComfyNode):
 class FluxKontextMaxImageNode(FluxKontextProImageNode):
 
     DESCRIPTION = "Edits images using Flux.1 Kontext [max] via api based on prompt and aspect ratio."
+    SHORT_DESCRIPTION = "Edit images with Flux.1 Kontext [max] API."
     BFL_PATH = "/proxy/bfl/flux-kontext-max/generate"
     NODE_ID = "FluxKontextMaxImageNode"
     DISPLAY_NAME = "Flux.1 Kontext [max] Image"
@@ -282,6 +288,7 @@ class FluxProExpandNode(IO.ComfyNode):
             display_name="Flux.1 Expand Image",
             category="api node/image/BFL",
             description="Outpaints image based on prompt.",
+            short_description=None,
             inputs=[
                 IO.Image.Input("image"),
                 IO.String.Input(
@@ -418,6 +425,7 @@ class FluxProFillNode(IO.ComfyNode):
             display_name="Flux.1 Fill Image",
             category="api node/image/BFL",
             description="Inpaints image based on mask and prompt.",
+            short_description=None,
             inputs=[
                 IO.Image.Input("image"),
                 IO.Mask.Input("mask"),
@@ -543,6 +551,7 @@ class Flux2ProImageNode(IO.ComfyNode):
             display_name=cls.DISPLAY_NAME,
             category="api node/image/BFL",
             description="Generates images synchronously based on prompt and resolution.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",

comfy_api_nodes/nodes_bria.py

@@ -3,7 +3,11 @@ from typing_extensions import override
 from comfy_api.latest import IO, ComfyExtension, Input
 from comfy_api_nodes.apis.bria import (
     BriaEditImageRequest,
-    BriaResponse,
+    BriaRemoveBackgroundRequest,
+    BriaRemoveBackgroundResponse,
+    BriaRemoveVideoBackgroundRequest,
+    BriaRemoveVideoBackgroundResponse,
+    BriaImageEditResponse,
     BriaStatusResponse,
     InputModerationSettings,
 )
@@ -11,10 +15,12 @@ from comfy_api_nodes.util import (
     ApiEndpoint,
     convert_mask_to_image,
     download_url_to_image_tensor,
-    get_number_of_images,
+    download_url_to_video_output,
     poll_op,
     sync_op,
-    upload_images_to_comfyapi,
+    upload_image_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_video_duration,
 )
 
 
@@ -27,6 +33,7 @@ class BriaImageEditNode(IO.ComfyNode):
             display_name="Bria FIBO Image Edit",
             category="api node/image/Bria",
             description="Edit images using Bria latest model",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["FIBO"]),
                 IO.Image.Input("image"),
@@ -73,21 +80,15 @@ class BriaImageEditNode(IO.ComfyNode):
                 IO.DynamicCombo.Input(
                     "moderation",
                     options=[
+                        IO.DynamicCombo.Option("false", []),
                         IO.DynamicCombo.Option(
                             "true",
                             [
-                                IO.Boolean.Input(
-                                    "prompt_content_moderation", default=False
-                                ),
-                                IO.Boolean.Input(
-                                    "visual_input_moderation", default=False
-                                ),
-                                IO.Boolean.Input(
-                                    "visual_output_moderation", default=True
-                                ),
+                                IO.Boolean.Input("prompt_content_moderation", default=False),
+                                IO.Boolean.Input("visual_input_moderation", default=False),
+                                IO.Boolean.Input("visual_output_moderation", default=True),
                             ],
                         ),
-                        IO.DynamicCombo.Option("false", []),
                     ],
                     tooltip="Moderation settings",
                 ),
@@ -127,50 +128,26 @@ class BriaImageEditNode(IO.ComfyNode):
         mask: Input.Image | None = None,
     ) -> IO.NodeOutput:
         if not prompt and not structured_prompt:
-            raise ValueError(
-                "One of prompt or structured_prompt is required to be non-empty."
-            )
-        if get_number_of_images(image) != 1:
-            raise ValueError("Exactly one input image is required.")
+            raise ValueError("One of prompt or structured_prompt is required to be non-empty.")
         mask_url = None
         if mask is not None:
-            mask_url = (
-                await upload_images_to_comfyapi(
-                    cls,
-                    convert_mask_to_image(mask),
-                    max_images=1,
-                    mime_type="image/png",
-                    wait_label="Uploading mask",
-                )
-            )[0]
+            mask_url = await upload_image_to_comfyapi(cls, convert_mask_to_image(mask), wait_label="Uploading mask")
         response = await sync_op(
             cls,
             ApiEndpoint(path="proxy/bria/v2/image/edit", method="POST"),
             data=BriaEditImageRequest(
                 instruction=prompt if prompt else None,
                 structured_instruction=structured_prompt if structured_prompt else None,
-                images=await upload_images_to_comfyapi(
-                    cls,
-                    image,
-                    max_images=1,
-                    mime_type="image/png",
-                    wait_label="Uploading image",
-                ),
+                images=[await upload_image_to_comfyapi(cls, image, wait_label="Uploading image")],
                 mask=mask_url,
                 negative_prompt=negative_prompt if negative_prompt else None,
                 guidance_scale=guidance_scale,
                 seed=seed,
                 model_version=model,
                 steps_num=steps,
-                prompt_content_moderation=moderation.get(
-                    "prompt_content_moderation", False
-                ),
-                visual_input_content_moderation=moderation.get(
-                    "visual_input_moderation", False
-                ),
-                visual_output_content_moderation=moderation.get(
-                    "visual_output_moderation", False
-                ),
+                prompt_content_moderation=moderation.get("prompt_content_moderation", False),
+                visual_input_content_moderation=moderation.get("visual_input_moderation", False),
+                visual_output_content_moderation=moderation.get("visual_output_moderation", False),
             ),
             response_model=BriaStatusResponse,
         )
@@ -178,7 +155,7 @@ class BriaImageEditNode(IO.ComfyNode):
             cls,
             ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
             status_extractor=lambda r: r.status,
-            response_model=BriaResponse,
+            response_model=BriaImageEditResponse,
         )
         return IO.NodeOutput(
             await download_url_to_image_tensor(response.result.image_url),
@@ -186,11 +163,167 @@ class BriaImageEditNode(IO.ComfyNode):
         )
 
 
+class BriaRemoveImageBackground(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaRemoveImageBackground",
+            display_name="Bria Remove Image Background",
+            category="api node/image/Bria",
+            description="Remove the background from an image using Bria RMBG 2.0.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.DynamicCombo.Input(
+                    "moderation",
+                    options=[
+                        IO.DynamicCombo.Option("false", []),
+                        IO.DynamicCombo.Option(
+                            "true",
+                            [
+                                IO.Boolean.Input("visual_input_moderation", default=False),
+                                IO.Boolean.Input("visual_output_moderation", default=True),
+                            ],
+                        ),
+                    ],
+                    tooltip="Moderation settings",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Image.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":0.018}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        moderation: dict,
+        seed: int,
+    ) -> IO.NodeOutput:
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/image/edit/remove_background", method="POST"),
+            data=BriaRemoveBackgroundRequest(
+                image=await upload_image_to_comfyapi(cls, image, wait_label="Uploading image"),
+                sync=False,
+                visual_input_content_moderation=moderation.get("visual_input_moderation", False),
+                visual_output_content_moderation=moderation.get("visual_output_moderation", False),
+                seed=seed,
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaRemoveBackgroundResponse,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result.image_url))
+
+
+class BriaRemoveVideoBackground(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaRemoveVideoBackground",
+            display_name="Bria Remove Video Background",
+            category="api node/video/Bria",
+            description="Remove the background from a video using Bria. ",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Combo.Input(
+                    "background_color",
+                    options=[
+                        "Black",
+                        "White",
+                        "Gray",
+                        "Red",
+                        "Green",
+                        "Blue",
+                        "Yellow",
+                        "Cyan",
+                        "Magenta",
+                        "Orange",
+                    ],
+                    tooltip="Background color for the output video.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Video.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":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        background_color: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_video_duration(video, max_duration=60.0)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/video/edit/remove_background", method="POST"),
+            data=BriaRemoveVideoBackgroundRequest(
+                video=await upload_video_to_comfyapi(cls, video),
+                background_color=background_color,
+                output_container_and_codec="mp4_h264",
+                seed=seed,
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaRemoveVideoBackgroundResponse,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.result.video_url))
+
+
 class BriaExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
             BriaImageEditNode,
+            BriaRemoveImageBackground,
+            BriaRemoveVideoBackground,
         ]
 
 

comfy_api_nodes/nodes_bytedance.py

@@ -60,6 +60,7 @@ class ByteDanceImageNode(IO.ComfyNode):
             display_name="ByteDance Image",
             category="api node/image/ByteDance",
             description="Generate images using ByteDance models via api based on prompt",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["seedream-3-0-t2i-250415"]),
                 IO.String.Input(
@@ -182,6 +183,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
             display_name="ByteDance Seedream 4.5",
             category="api node/image/ByteDance",
             description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
+            short_description="Text-to-image generation and editing up to 4K.",
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -380,6 +382,7 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
             display_name="ByteDance Text to Video",
             category="api node/video/ByteDance",
             description="Generate video using ByteDance models via api based on prompt",
+            short_description=None,
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -505,6 +508,7 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
             display_name="ByteDance Image to Video",
             category="api node/video/ByteDance",
             description="Generate video using ByteDance models via api based on image and prompt",
+            short_description="Generate video from image and prompt via ByteDance API.",
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -639,6 +643,7 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
             display_name="ByteDance First-Last-Frame to Video",
             category="api node/video/ByteDance",
             description="Generate video using prompt and first and last frames.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -784,6 +789,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
             display_name="ByteDance Reference Images to Video",
             category="api node/video/ByteDance",
             description="Generate video using prompt and reference images.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input(
                     "model",

comfy_api_nodes/nodes_gemini.py

@@ -254,6 +254,7 @@ class GeminiNode(IO.ComfyNode):
             description="Generate text responses with Google's Gemini AI model. "
             "You can provide multiple types of inputs (text, images, audio, video) "
             "as context for generating more relevant and meaningful responses.",
+            short_description="Generate text responses with Google's Gemini AI.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -480,6 +481,7 @@ class GeminiInputFiles(IO.ComfyNode):
             "The files will be read by the Gemini model when generating a response. "
             "The contents of the text file count toward the token limit. "
             "🛈 TIP: Can be chained together with other Gemini Input File nodes.",
+            short_description="Load and prepare input files for Gemini LLM nodes.",
             inputs=[
                 IO.Combo.Input(
                     "file",
@@ -534,6 +536,7 @@ class GeminiImage(IO.ComfyNode):
             display_name="Nano Banana (Google Gemini Image)",
             category="api node/image/Gemini",
             description="Edit images synchronously via Google API.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -665,6 +668,7 @@ class GeminiImage2(IO.ComfyNode):
             display_name="Nano Banana Pro (Google Gemini Image)",
             category="api node/image/Gemini",
             description="Generate or edit images synchronously via Google Vertex API.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",

comfy_api_nodes/nodes_grok.py

@@ -36,6 +36,7 @@ class GrokImageNode(IO.ComfyNode):
             display_name="Grok Image",
             category="api node/image/Grok",
             description="Generate images using Grok based on a text prompt",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["grok-imagine-image-beta"]),
                 IO.String.Input(
@@ -137,6 +138,7 @@ class GrokImageEditNode(IO.ComfyNode):
             display_name="Grok Image Edit",
             category="api node/image/Grok",
             description="Modify an existing image based on a text prompt",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["grok-imagine-image-beta"]),
                 IO.Image.Input("image"),
@@ -226,6 +228,7 @@ class GrokVideoNode(IO.ComfyNode):
             display_name="Grok Video",
             category="api node/video/Grok",
             description="Generate video from a prompt or an image",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["grok-imagine-video-beta"]),
                 IO.String.Input(
@@ -334,6 +337,7 @@ class GrokVideoEditNode(IO.ComfyNode):
             display_name="Grok Video Edit",
             category="api node/video/Grok",
             description="Edit an existing video based on a text prompt.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["grok-imagine-video-beta"]),
                 IO.String.Input(

comfy_api_nodes/nodes_hitpaw.py

@@ -74,6 +74,7 @@ class HitPawGeneralImageEnhance(IO.ComfyNode):
             category="api node/image/HitPaw",
             description="Upscale low-resolution images to super-resolution, eliminate artifacts and noise. "
             f"Maximum output: {MAX_MP_GENERATIVE} megapixels.",
+            short_description="Upscale images to super-resolution, removing artifacts and noise.",
             inputs=[
                 IO.Combo.Input("model", options=["generative_portrait", "generative"]),
                 IO.Image.Input("image"),
@@ -205,6 +206,7 @@ class HitPawVideoEnhance(IO.ComfyNode):
             category="api node/video/HitPaw",
             description="Upscale low-resolution videos to high resolution, eliminate artifacts and noise. "
             "Prices shown are per second of video.",
+            short_description="Upscale videos to high resolution, removing artifacts and noise.",
             inputs=[
                 IO.DynamicCombo.Input("model", options=model_options),
                 IO.Video.Input("video"),

comfy_api_nodes/nodes_hunyuan3d.py

@@ -1,31 +1,48 @@
 from typing_extensions import override
 
-from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api.latest import IO, ComfyExtension, Input, Types
 from comfy_api_nodes.apis.hunyuan3d import (
     Hunyuan3DViewImage,
     InputGenerateType,
     ResultFile3D,
+    TextureEditTaskRequest,
     To3DProTaskCreateResponse,
     To3DProTaskQueryRequest,
     To3DProTaskRequest,
     To3DProTaskResultResponse,
+    To3DUVFileInput,
+    To3DUVTaskRequest,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
     download_url_to_file_3d,
+    download_url_to_image_tensor,
     downscale_image_tensor_by_max_side,
     poll_op,
     sync_op,
+    upload_3d_model_to_comfyapi,
     upload_image_to_comfyapi,
     validate_image_dimensions,
     validate_string,
 )
 
 
-def get_file_from_response(response_objs: list[ResultFile3D], file_type: str) -> ResultFile3D | None:
+def _is_tencent_rate_limited(status: int, body: object) -> bool:
+    return (
+        status == 400
+        and isinstance(body, dict)
+        and "RequestLimitExceeded" in str(body.get("Response", {}).get("Error", {}).get("Code", ""))
+    )
+
+
+def get_file_from_response(
+    response_objs: list[ResultFile3D], file_type: str, raise_if_not_found: bool = True
+) -> ResultFile3D | None:
     for i in response_objs:
         if i.Type.lower() == file_type.lower():
             return i
+    if raise_if_not_found:
+        raise ValueError(f"'{file_type}' file type is not found in the response.")
     return None
 
 
@@ -35,8 +52,10 @@ class TencentTextToModelNode(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="TencentTextToModelNode",
-            display_name="Hunyuan3D: Text to Model (Pro)",
+            display_name="Hunyuan3D: Text to Model",
             category="api node/3d/Tencent",
+            description="Generate 3D models from text prompts using Hunyuan3D Pro with configurable face count and geometry options.",
+            short_description="Generate 3D models from text using Hunyuan3D Pro.",
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -120,6 +139,7 @@ class TencentTextToModelNode(IO.ComfyNode):
                 EnablePBR=generate_type.get("pbr", None),
                 PolygonType=generate_type.get("polygon_type", None),
             ),
+            is_rate_limited=_is_tencent_rate_limited,
         )
         if response.Error:
             raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
@@ -131,11 +151,14 @@ class TencentTextToModelNode(IO.ComfyNode):
             response_model=To3DProTaskResultResponse,
             status_extractor=lambda r: r.Status,
         )
-        glb_result = get_file_from_response(result.ResultFile3Ds, "glb")
-        obj_result = get_file_from_response(result.ResultFile3Ds, "obj")
-        file_glb = await download_url_to_file_3d(glb_result.Url, "glb", task_id=task_id) if glb_result else None
         return IO.NodeOutput(
-            file_glb, file_glb, await download_url_to_file_3d(obj_result.Url, "obj", task_id=task_id) if obj_result else None
+            f"{task_id}.glb",
+            await download_url_to_file_3d(
+                get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
+            ),
+            await download_url_to_file_3d(
+                get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj", task_id=task_id
+            ),
         )
 
 
@@ -145,8 +168,10 @@ class TencentImageToModelNode(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="TencentImageToModelNode",
-            display_name="Hunyuan3D: Image(s) to Model (Pro)",
+            display_name="Hunyuan3D: Image(s) to Model",
             category="api node/3d/Tencent",
+            description="Generate 3D models from images using Hunyuan3D Pro with optional multi-view inputs and configurable geometry.",
+            short_description="Generate 3D models from images using Hunyuan3D Pro.",
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -268,6 +293,7 @@ class TencentImageToModelNode(IO.ComfyNode):
                 EnablePBR=generate_type.get("pbr", None),
                 PolygonType=generate_type.get("polygon_type", None),
             ),
+            is_rate_limited=_is_tencent_rate_limited,
         )
         if response.Error:
             raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
@@ -279,11 +305,257 @@ class TencentImageToModelNode(IO.ComfyNode):
             response_model=To3DProTaskResultResponse,
             status_extractor=lambda r: r.Status,
         )
-        glb_result = get_file_from_response(result.ResultFile3Ds, "glb")
-        obj_result = get_file_from_response(result.ResultFile3Ds, "obj")
-        file_glb = await download_url_to_file_3d(glb_result.Url, "glb", task_id=task_id) if glb_result else None
         return IO.NodeOutput(
-            file_glb, file_glb, await download_url_to_file_3d(obj_result.Url, "obj", task_id=task_id) if obj_result else None
+            f"{task_id}.glb",
+            await download_url_to_file_3d(
+                get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
+            ),
+            await download_url_to_file_3d(
+                get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj", task_id=task_id
+            ),
+        )
+
+
+class TencentModelTo3DUVNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TencentModelTo3DUVNode",
+            display_name="Hunyuan3D: Model to UV",
+            category="api node/3d/Tencent",
+            description="Perform UV unfolding on a 3D model to generate UV texture. "
+            "Input model must have less than 30000 faces.",
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DGLB, IO.File3DOBJ, IO.File3DFBX, IO.File3DAny],
+                    tooltip="Input 3D model (GLB, OBJ, or FBX)",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.File3DFBX.Output(display_name="FBX"),
+                IO.Image.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":0.2}'),
+        )
+
+    SUPPORTED_FORMATS = {"glb", "obj", "fbx"}
+
+    @classmethod
+    async def execute(
+        cls,
+        model_3d: Types.File3D,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        file_format = model_3d.format.lower()
+        if file_format not in cls.SUPPORTED_FORMATS:
+            raise ValueError(
+                f"Unsupported file format: '{file_format}'. "
+                f"Supported formats: {', '.join(sorted(cls.SUPPORTED_FORMATS))}."
+            )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-uv", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=To3DUVTaskRequest(
+                File=To3DUVFileInput(
+                    Type=file_format.upper(),
+                    Url=await upload_3d_model_to_comfyapi(cls, model_3d, file_format),
+                )
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-uv/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        return IO.NodeOutput(
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
+            await download_url_to_image_tensor(get_file_from_response(result.ResultFile3Ds, "image").Url),
+        )
+
+
+class Tencent3DTextureEditNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Tencent3DTextureEditNode",
+            display_name="Hunyuan3D: 3D Texture Edit",
+            category="api node/3d/Tencent",
+            description="After inputting the 3D model, perform 3D model texture redrawing.",
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DFBX, IO.File3DAny],
+                    tooltip="3D model in FBX format. Model should have less than 100000 faces.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Describes texture editing. Supports up to 1024 UTF-8 characters.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DFBX.Output(display_name="FBX"),
+            ],
+            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": 0.6}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_3d: Types.File3D,
+        prompt: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        file_format = model_3d.format.lower()
+        if file_format != "fbx":
+            raise ValueError(f"Unsupported file format: '{file_format}'. Only FBX format is supported.")
+        validate_string(prompt, field_name="prompt", min_length=1, max_length=1024)
+        model_url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-texture-edit", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=TextureEditTaskRequest(
+                File3D=To3DUVFileInput(Type=file_format.upper(), Url=model_url),
+                Prompt=prompt,
+                EnablePBR=True,
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-texture-edit/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        return IO.NodeOutput(
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb"),
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
+        )
+
+
+class Tencent3DPartNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Tencent3DPartNode",
+            display_name="Hunyuan3D: 3D Part",
+            category="api node/3d/Tencent",
+            description="Automatically perform component identification and generation based on the model structure.",
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DFBX, IO.File3DAny],
+                    tooltip="3D model in FBX format. Model should have less than 30000 faces.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.File3DFBX.Output(display_name="FBX"),
+            ],
+            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":0.6}'),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_3d: Types.File3D,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        file_format = model_3d.format.lower()
+        if file_format != "fbx":
+            raise ValueError(f"Unsupported file format: '{file_format}'. Only FBX format is supported.")
+        model_url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-part", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=To3DUVTaskRequest(
+                File=To3DUVFileInput(Type=file_format.upper(), Url=model_url),
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-part/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        return IO.NodeOutput(
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
         )
 
 
@@ -293,6 +565,9 @@ class TencentHunyuan3DExtension(ComfyExtension):
         return [
             TencentTextToModelNode,
             TencentImageToModelNode,
+            # TencentModelTo3DUVNode,
+            # Tencent3DTextureEditNode,
+            Tencent3DPartNode,
         ]
 
 

comfy_api_nodes/nodes_ideogram.py

@@ -236,6 +236,7 @@ class IdeogramV1(IO.ComfyNode):
             display_name="Ideogram V1",
             category="api node/image/Ideogram",
             description="Generates images using the Ideogram V1 model.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -361,6 +362,7 @@ class IdeogramV2(IO.ComfyNode):
             display_name="Ideogram V2",
             category="api node/image/Ideogram",
             description="Generates images using the Ideogram V2 model.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -526,6 +528,7 @@ class IdeogramV3(IO.ComfyNode):
             category="api node/image/Ideogram",
             description="Generates images using the Ideogram V3 model. "
                         "Supports both regular image generation from text prompts and image editing with mask.",
+            short_description="Generate and edit images with Ideogram V3.",
             inputs=[
                 IO.String.Input(
                     "prompt",

comfy_api_nodes/nodes_kling.py

@@ -642,6 +642,7 @@ class KlingCameraControls(IO.ComfyNode):
             display_name="Kling Camera Controls",
             category="api node/video/Kling",
             description="Allows specifying configuration options for Kling Camera Controls and motion control effects.",
+            short_description="Configure Kling camera controls and motion effects.",
             inputs=[
                 IO.Combo.Input("camera_control_type", options=KlingCameraControlType),
                 IO.Float.Input(
@@ -762,6 +763,7 @@ class KlingTextToVideoNode(IO.ComfyNode):
             display_name="Kling Text to Video",
             category="api node/video/Kling",
             description="Kling Text to Video Node",
+            short_description=None,
             inputs=[
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
                 IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
@@ -849,6 +851,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
             display_name="Kling 3.0 Omni Text to Video",
             category="api node/video/Kling",
             description="Use text prompts to generate videos with the latest Kling model.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
                 IO.String.Input(
@@ -989,6 +992,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
             display_name="Kling 3.0 Omni First-Last-Frame to Video",
             category="api node/video/Kling",
             description="Use a start frame, an optional end frame, or reference images with the latest Kling model.",
+            short_description="Generate video from start/end frames or reference images.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
                 IO.String.Input(
@@ -1187,6 +1191,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
             display_name="Kling 3.0 Omni Image to Video",
             category="api node/video/Kling",
             description="Use up to 7 reference images to generate a video with the latest Kling model.",
+            short_description="Generate video from up to 7 reference images.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
                 IO.String.Input(
@@ -1347,6 +1352,7 @@ class OmniProVideoToVideoNode(IO.ComfyNode):
             display_name="Kling 3.0 Omni Video to Video",
             category="api node/video/Kling",
             description="Use a video and up to 4 reference images to generate a video with the latest Kling model.",
+            short_description="Generate video from a video and reference images.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
                 IO.String.Input(
@@ -1458,6 +1464,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
             display_name="Kling 3.0 Omni Edit Video",
             category="api node/video/Kling",
             description="Edit an existing video with the latest model from Kling.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
                 IO.String.Input(
@@ -1565,6 +1572,7 @@ class OmniProImageNode(IO.ComfyNode):
             display_name="Kling 3.0 Omni Image",
             category="api node/image/Kling",
             description="Create or edit images with the latest model from Kling.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-image-o1"]),
                 IO.String.Input(
@@ -1693,6 +1701,7 @@ class KlingCameraControlT2VNode(IO.ComfyNode):
             display_name="Kling Text to Video (Camera Control)",
             category="api node/video/Kling",
             description="Transform text into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original text.",
+            short_description="Generate videos from text with camera movement controls.",
             inputs=[
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
                 IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
@@ -1754,6 +1763,8 @@ class KlingImage2VideoNode(IO.ComfyNode):
             node_id="KlingImage2VideoNode",
             display_name="Kling Image(First Frame) to Video",
             category="api node/video/Kling",
+            description="Generate a video from a first-frame image with configurable model, mode, aspect ratio, and duration settings.",
+            short_description="Generate video from a first-frame reference image.",
             inputs=[
                 IO.Image.Input("start_frame", tooltip="The reference image used to generate the video."),
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
@@ -1854,6 +1865,7 @@ class KlingCameraControlI2VNode(IO.ComfyNode):
             display_name="Kling Image to Video (Camera Control)",
             category="api node/video/Kling",
             description="Transform still images into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original image.",
+            short_description="Generate videos from images with camera movement controls.",
             inputs=[
                 IO.Image.Input(
                     "start_frame",
@@ -1925,6 +1937,7 @@ class KlingStartEndFrameNode(IO.ComfyNode):
             display_name="Kling Start-End Frame to Video",
             category="api node/video/Kling",
             description="Generate a video sequence that transitions between your provided start and end images. The node creates all frames in between, producing a smooth transformation from the first frame to the last.",
+            short_description="Generate video transitioning between start and end frame images.",
             inputs=[
                 IO.Image.Input(
                     "start_frame",
@@ -2019,6 +2032,7 @@ class KlingVideoExtendNode(IO.ComfyNode):
             display_name="Kling Video Extend",
             category="api node/video/Kling",
             description="Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes.",
+            short_description="Extend videos generated by other Kling nodes.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -2100,6 +2114,7 @@ class KlingDualCharacterVideoEffectNode(IO.ComfyNode):
             display_name="Kling Dual Character Video Effects",
             category="api node/video/Kling",
             description="Achieve different special effects when generating a video based on the effect_scene. First image will be positioned on left side, second on right side of the composite.",
+            short_description="Apply dual-character video effects from two images.",
             inputs=[
                 IO.Image.Input("image_left", tooltip="Left side image"),
                 IO.Image.Input("image_right", tooltip="Right side image"),
@@ -2190,6 +2205,7 @@ class KlingSingleImageVideoEffectNode(IO.ComfyNode):
             display_name="Kling Video Effects",
             category="api node/video/Kling",
             description="Achieve different special effects when generating a video based on the effect_scene.",
+            short_description="Apply special video effects to a single image.",
             inputs=[
                 IO.Image.Input(
                     "image",
@@ -2263,6 +2279,7 @@ class KlingLipSyncAudioToVideoNode(IO.ComfyNode):
             display_name="Kling Lip Sync Video with Audio",
             category="api node/video/Kling",
             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.",
+            short_description="Sync video mouth movements to audio content.",
             inputs=[
                 IO.Video.Input("video"),
                 IO.Audio.Input("audio"),
@@ -2314,6 +2331,7 @@ class KlingLipSyncTextToVideoNode(IO.ComfyNode):
             display_name="Kling Lip Sync Video with Text",
             category="api node/video/Kling",
             description="Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt. 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.",
+            short_description="Sync video mouth movements to a text prompt.",
             inputs=[
                 IO.Video.Input("video"),
                 IO.String.Input(
@@ -2381,6 +2399,7 @@ class KlingVirtualTryOnNode(IO.ComfyNode):
             display_name="Kling Virtual Try On",
             category="api node/image/Kling",
             description="Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human. You can merge multiple clothing item pictures into one image with a white background.",
+            short_description="Virtually try clothing onto a human image.",
             inputs=[
                 IO.Image.Input("human_image"),
                 IO.Image.Input("cloth_image"),
@@ -2448,6 +2467,7 @@ class KlingImageGenerationNode(IO.ComfyNode):
             display_name="Kling 3.0 Image",
             category="api node/image/Kling",
             description="Kling Image Generation Node. Generate an image from a text prompt with an optional reference image.",
+            short_description="Generate images from text with optional reference image.",
             inputs=[
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
                 IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
@@ -2581,6 +2601,8 @@ class TextToVideoWithAudio(IO.ComfyNode):
             node_id="KlingTextToVideoWithAudio",
             display_name="Kling 2.6 Text to Video with Audio",
             category="api node/video/Kling",
+            description="Generate a video with synchronized audio from a text prompt using the Kling v2-6 model.",
+            short_description="Generate video with audio from text using Kling v2-6.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v2-6"]),
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt."),
@@ -2649,6 +2671,8 @@ class ImageToVideoWithAudio(IO.ComfyNode):
             node_id="KlingImageToVideoWithAudio",
             display_name="Kling 2.6 Image(First Frame) to Video with Audio",
             category="api node/video/Kling",
+            description="Generate a video with synchronized audio from a first-frame image and text prompt using the Kling v2-6 model.",
+            short_description="Generate video with audio from an image using Kling v2-6.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v2-6"]),
                 IO.Image.Input("start_frame"),
@@ -2719,6 +2743,8 @@ class MotionControl(IO.ComfyNode):
             node_id="KlingMotionControl",
             display_name="Kling Motion Control",
             category="api node/video/Kling",
+            description="Drive character movement and expression in video using a reference image and motion reference video.",
+            short_description="Control video character motion using reference image and video.",
             inputs=[
                 IO.String.Input("prompt", multiline=True),
                 IO.Image.Input("reference_image"),
@@ -2815,6 +2841,7 @@ class KlingVideoNode(IO.ComfyNode):
             category="api node/video/Kling",
             description="Generate videos with Kling V3. "
             "Supports text-to-video and image-to-video with optional storyboard multi-prompt and audio generation.",
+            short_description="Generate videos with Kling V3 from text or images.",
             inputs=[
                 IO.DynamicCombo.Input(
                     "multi_shot",

comfy_api_nodes/nodes_ltxv.py

@@ -52,6 +52,7 @@ class TextToVideoNode(IO.ComfyNode):
             display_name="LTXV Text To Video",
             category="api node/video/LTXV",
             description="Professional-quality videos with customizable duration and resolution.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=list(MODELS_MAP.keys())),
                 IO.String.Input(
@@ -128,6 +129,7 @@ class ImageToVideoNode(IO.ComfyNode):
             display_name="LTXV Image To Video",
             category="api node/video/LTXV",
             description="Professional-quality videos with customizable duration and resolution based on start image.",
+            short_description=None,
             inputs=[
                 IO.Image.Input("image", tooltip="First frame to be used for the video."),
                 IO.Combo.Input("model", options=list(MODELS_MAP.keys())),

comfy_api_nodes/nodes_luma.py

@@ -46,6 +46,7 @@ class LumaReferenceNode(IO.ComfyNode):
             display_name="Luma Reference",
             category="api node/image/Luma",
             description="Holds an image and weight for use with Luma Generate Image node.",
+            short_description="Image and weight input for Luma generation.",
             inputs=[
                 IO.Image.Input(
                     "image",
@@ -85,6 +86,7 @@ class LumaConceptsNode(IO.ComfyNode):
             display_name="Luma Concepts",
             category="api node/video/Luma",
             description="Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes.",
+            short_description="Camera concepts for Luma video generation nodes.",
             inputs=[
                 IO.Combo.Input(
                     "concept1",
@@ -134,6 +136,7 @@ class LumaImageGenerationNode(IO.ComfyNode):
             display_name="Luma Text to Image",
             category="api node/image/Luma",
             description="Generates images synchronously based on prompt and aspect ratio.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -278,6 +281,7 @@ class LumaImageModifyNode(IO.ComfyNode):
             display_name="Luma Image to Image",
             category="api node/image/Luma",
             description="Modifies images synchronously based on prompt and aspect ratio.",
+            short_description=None,
             inputs=[
                 IO.Image.Input(
                     "image",
@@ -371,6 +375,7 @@ class LumaTextToVideoGenerationNode(IO.ComfyNode):
             display_name="Luma Text to Video",
             category="api node/video/Luma",
             description="Generates videos synchronously based on prompt and output_size.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -472,6 +477,7 @@ class LumaImageToVideoGenerationNode(IO.ComfyNode):
             display_name="Luma Image to Video",
             category="api node/video/Luma",
             description="Generates videos synchronously based on prompt, input images, and output_size.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",

comfy_api_nodes/nodes_magnific.py

@@ -242,6 +242,7 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
             category="api node/image/Magnific",
             description="High-fidelity upscaling with fine control over sharpness, grain, and detail. "
             "Maximum output: 10060×10060 pixels.",
+            short_description="High-fidelity upscaling with sharpness, grain, and detail control.",
             inputs=[
                 IO.Image.Input("image"),
                 IO.Combo.Input("scale_factor", options=["2x", "4x", "8x", "16x"]),
@@ -401,6 +402,7 @@ class MagnificImageStyleTransferNode(IO.ComfyNode):
             display_name="Magnific Image Style Transfer",
             category="api node/image/Magnific",
             description="Transfer the style from a reference image to your input image.",
+            short_description=None,
             inputs=[
                 IO.Image.Input("image", tooltip="The image to apply style transfer to."),
                 IO.Image.Input("reference_image", tooltip="The reference image to extract style from."),
@@ -549,6 +551,7 @@ class MagnificImageRelightNode(IO.ComfyNode):
             display_name="Magnific Image Relight",
             category="api node/image/Magnific",
             description="Relight an image with lighting adjustments and optional reference-based light transfer.",
+            short_description=None,
             inputs=[
                 IO.Image.Input("image", tooltip="The image to relight."),
                 IO.String.Input(
@@ -787,6 +790,7 @@ class MagnificImageSkinEnhancerNode(IO.ComfyNode):
             display_name="Magnific Image Skin Enhancer",
             category="api node/image/Magnific",
             description="Skin enhancement for portraits with multiple processing modes.",
+            short_description=None,
             inputs=[
                 IO.Image.Input("image", tooltip="The portrait image to enhance."),
                 IO.Int.Input(

comfy_api_nodes/nodes_meshy.py

@@ -34,6 +34,8 @@ class MeshyTextToModelNode(IO.ComfyNode):
             node_id="MeshyTextToModelNode",
             display_name="Meshy: Text to Model",
             category="api node/3d/Meshy",
+            description="Generate a 3D model from a text prompt using the Meshy API.",
+            short_description="Generate a 3D model from a text prompt.",
             inputs=[
                 IO.Combo.Input("model", options=["latest"]),
                 IO.String.Input("prompt", multiline=True, default=""),
@@ -146,6 +148,7 @@ class MeshyRefineNode(IO.ComfyNode):
             display_name="Meshy: Refine Draft Model",
             category="api node/3d/Meshy",
             description="Refine a previously created draft model.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["latest"]),
                 IO.Custom("MESHY_TASK_ID").Input("meshy_task_id"),
@@ -239,6 +242,8 @@ class MeshyImageToModelNode(IO.ComfyNode):
             node_id="MeshyImageToModelNode",
             display_name="Meshy: Image to Model",
             category="api node/3d/Meshy",
+            description="Generate a 3D model from a single image using the Meshy API.",
+            short_description="Generate a 3D model from an image.",
             inputs=[
                 IO.Combo.Input("model", options=["latest"]),
                 IO.Image.Input("image"),
@@ -403,6 +408,7 @@ class MeshyMultiImageToModelNode(IO.ComfyNode):
             node_id="MeshyMultiImageToModelNode",
             display_name="Meshy: Multi-Image to Model",
             category="api node/3d/Meshy",
+            description="Generate a 3D model from multiple images using the Meshy API.",
             inputs=[
                 IO.Combo.Input("model", options=["latest"]),
                 IO.Autogrow.Input(
@@ -575,6 +581,7 @@ class MeshyRigModelNode(IO.ComfyNode):
             description="Provides a rigged character in standard formats. "
             "Auto-rigging is currently not suitable for untextured meshes, non-humanoid assets, "
             "or humanoid assets with unclear limb and body structure.",
+            short_description="Rig a character model for animation.",
             inputs=[
                 IO.Custom("MESHY_TASK_ID").Input("meshy_task_id"),
                 IO.Float.Input(
@@ -654,6 +661,7 @@ class MeshyAnimateModelNode(IO.ComfyNode):
             display_name="Meshy: Animate Model",
             category="api node/3d/Meshy",
             description="Apply a specific animation action to a previously rigged character.",
+            short_description=None,
             inputs=[
                 IO.Custom("MESHY_RIGGED_TASK_ID").Input("rig_task_id"),
                 IO.Int.Input(
@@ -719,6 +727,7 @@ class MeshyTextureNode(IO.ComfyNode):
             node_id="MeshyTextureNode",
             display_name="Meshy: Texture Model",
             category="api node/3d/Meshy",
+            description="Apply textures to an existing 3D model using the Meshy API.",
             inputs=[
                 IO.Combo.Input("model", options=["latest"]),
                 IO.Custom("MESHY_TASK_ID").Input("meshy_task_id"),

comfy_api_nodes/nodes_minimax.py

@@ -103,6 +103,7 @@ class MinimaxTextToVideoNode(IO.ComfyNode):
             display_name="MiniMax Text to Video",
             category="api node/video/MiniMax",
             description="Generates videos synchronously based on a prompt, and optional parameters.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt_text",
@@ -165,6 +166,7 @@ class MinimaxImageToVideoNode(IO.ComfyNode):
             display_name="MiniMax Image to Video",
             category="api node/video/MiniMax",
             description="Generates videos synchronously based on an image and prompt, and optional parameters.",
+            short_description="Generate videos from an image, prompt, and optional parameters.",
             inputs=[
                 IO.Image.Input(
                     "image",
@@ -232,6 +234,7 @@ class MinimaxSubjectToVideoNode(IO.ComfyNode):
             display_name="MiniMax Subject to Video",
             category="api node/video/MiniMax",
             description="Generates videos synchronously based on an image and prompt, and optional parameters.",
+            short_description="Subject-driven video generation from image and prompt.",
             inputs=[
                 IO.Image.Input(
                     "subject",
@@ -296,6 +299,7 @@ class MinimaxHailuoVideoNode(IO.ComfyNode):
             display_name="MiniMax Hailuo Video",
             category="api node/video/MiniMax",
             description="Generates videos from prompt, with optional start frame using the new MiniMax Hailuo-02 model.",
+            short_description="Generate videos with optional start frame using Hailuo-02.",
             inputs=[
                 IO.String.Input(
                     "prompt_text",

comfy_api_nodes/nodes_moonvalley.py

@@ -166,6 +166,7 @@ class MoonvalleyImg2VideoNode(IO.ComfyNode):
             display_name="Moonvalley Marey Image to Video",
             category="api node/video/Moonvalley Marey",
             description="Moonvalley Marey Image to Video Node",
+            short_description=None,
             inputs=[
                 IO.Image.Input(
                     "image",
@@ -290,7 +291,8 @@ class MoonvalleyVideo2VideoNode(IO.ComfyNode):
             node_id="MoonvalleyVideo2VideoNode",
             display_name="Moonvalley Marey Video to Video",
             category="api node/video/Moonvalley Marey",
-            description="",
+            description="Transform an input video into a new video using a text prompt and motion or pose control.",
+            short_description="Transform video using text prompt with motion or pose control.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -415,7 +417,8 @@ class MoonvalleyTxt2VideoNode(IO.ComfyNode):
             node_id="MoonvalleyTxt2VideoNode",
             display_name="Moonvalley Marey Text to Video",
             category="api node/video/Moonvalley Marey",
-            description="",
+            description="Generate a video from a text prompt using the Moonvalley Marey model.",
+            short_description="Generate video from a text prompt using Moonvalley Marey.",
             inputs=[
                 IO.String.Input(
                     "prompt",

comfy_api_nodes/nodes_openai.py

@@ -43,7 +43,6 @@ class SupportedOpenAIModel(str, Enum):
     o1 = "o1"
     o3 = "o3"
     o1_pro = "o1-pro"
-    gpt_4o = "gpt-4o"
     gpt_4_1 = "gpt-4.1"
     gpt_4_1_mini = "gpt-4.1-mini"
     gpt_4_1_nano = "gpt-4.1-nano"
@@ -99,6 +98,7 @@ class OpenAIDalle2(IO.ComfyNode):
             display_name="OpenAI DALL·E 2",
             category="api node/image/OpenAI",
             description="Generates images synchronously via OpenAI's DALL·E 2 endpoint.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -249,6 +249,7 @@ class OpenAIDalle3(IO.ComfyNode):
             display_name="OpenAI DALL·E 3",
             category="api node/image/OpenAI",
             description="Generates images synchronously via OpenAI's DALL·E 3 endpoint.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -367,6 +368,7 @@ class OpenAIGPTImage1(IO.ComfyNode):
             display_name="OpenAI GPT Image 1.5",
             category="api node/image/OpenAI",
             description="Generates images synchronously via OpenAI's GPT Image endpoint.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -577,6 +579,7 @@ class OpenAIChatNode(IO.ComfyNode):
             display_name="OpenAI ChatGPT",
             category="api node/text/OpenAI",
             description="Generate text responses from an OpenAI model.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -649,11 +652,6 @@ class OpenAIChatNode(IO.ComfyNode):
                     "usd": [0.01, 0.04],
                     "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
                   }
-                  : $contains($m, "gpt-4o") ? {
-                    "type": "list_usd",
-                    "usd": [0.0025, 0.01],
-                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
-                  }
                   : $contains($m, "gpt-4.1-nano") ? {
                     "type": "list_usd",
                     "usd": [0.0001, 0.0004],
@@ -809,6 +807,7 @@ class OpenAIInputFiles(IO.ComfyNode):
             display_name="OpenAI ChatGPT Input Files",
             category="api node/text/OpenAI",
             description="Loads and prepares input files (text, pdf, etc.) to include as inputs for the OpenAI Chat Node. The files will be read by the OpenAI model when generating a response. 🛈 TIP: Can be chained together with other OpenAI Input File nodes.",
+            short_description="Load and prepare input files for OpenAI Chat.",
             inputs=[
                 IO.Combo.Input(
                     "file",
@@ -856,6 +855,7 @@ class OpenAIChatConfig(IO.ComfyNode):
             display_name="OpenAI ChatGPT Advanced Options",
             category="api node/text/OpenAI",
             description="Allows specifying advanced configuration options for the OpenAI Chat Nodes.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input(
                     "truncation",

comfy_api_nodes/nodes_pixverse.py

@@ -54,6 +54,8 @@ class PixverseTemplateNode(IO.ComfyNode):
             node_id="PixverseTemplateNode",
             display_name="PixVerse Template",
             category="api node/video/PixVerse",
+            description="Select a style template for PixVerse video generation.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("template", options=list(pixverse_templates.keys())),
             ],
@@ -76,6 +78,7 @@ class PixverseTextToVideoNode(IO.ComfyNode):
             display_name="PixVerse Text to Video",
             category="api node/video/PixVerse",
             description="Generates videos based on prompt and output_size.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -194,6 +197,7 @@ class PixverseImageToVideoNode(IO.ComfyNode):
             display_name="PixVerse Image to Video",
             category="api node/video/PixVerse",
             description="Generates videos based on prompt and output_size.",
+            short_description=None,
             inputs=[
                 IO.Image.Input("image"),
                 IO.String.Input(
@@ -312,6 +316,7 @@ class PixverseTransitionVideoNode(IO.ComfyNode):
             display_name="PixVerse Transition Video",
             category="api node/video/PixVerse",
             description="Generates videos based on prompt and output_size.",
+            short_description=None,
             inputs=[
                 IO.Image.Input("first_frame"),
                 IO.Image.Input("last_frame"),

comfy_api_nodes/nodes_recraft.py

@@ -180,6 +180,7 @@ class RecraftColorRGBNode(IO.ComfyNode):
             display_name="Recraft Color RGB",
             category="api node/image/Recraft",
             description="Create Recraft Color by choosing specific RGB values.",
+            short_description=None,
             inputs=[
                 IO.Int.Input("r", default=0, min=0, max=255, tooltip="Red value of color."),
                 IO.Int.Input("g", default=0, min=0, max=255, tooltip="Green value of color."),
@@ -206,6 +207,7 @@ class RecraftControlsNode(IO.ComfyNode):
             display_name="Recraft Controls",
             category="api node/image/Recraft",
             description="Create Recraft Controls for customizing Recraft generation.",
+            short_description=None,
             inputs=[
                 IO.Custom(RecraftIO.COLOR).Input("colors", optional=True),
                 IO.Custom(RecraftIO.COLOR).Input("background_color", optional=True),
@@ -230,6 +232,7 @@ class RecraftStyleV3RealisticImageNode(IO.ComfyNode):
             display_name="Recraft Style - Realistic Image",
             category="api node/image/Recraft",
             description="Select realistic_image style and optional substyle.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)),
             ],
@@ -254,7 +257,8 @@ class RecraftStyleV3DigitalIllustrationNode(RecraftStyleV3RealisticImageNode):
             node_id="RecraftStyleV3DigitalIllustration",
             display_name="Recraft Style - Digital Illustration",
             category="api node/image/Recraft",
-            description="Select realistic_image style and optional substyle.",
+            description="Select digital_illustration style and optional substyle.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)),
             ],
@@ -271,9 +275,10 @@ class RecraftStyleV3VectorIllustrationNode(RecraftStyleV3RealisticImageNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="RecraftStyleV3VectorIllustrationNode",
-            display_name="Recraft Style - Realistic Image",
+            display_name="Recraft Style - Vector Illustration",
             category="api node/image/Recraft",
-            description="Select realistic_image style and optional substyle.",
+            description="Select vector_illustration style and optional substyle.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)),
             ],
@@ -292,7 +297,8 @@ class RecraftStyleV3LogoRasterNode(RecraftStyleV3RealisticImageNode):
             node_id="RecraftStyleV3LogoRaster",
             display_name="Recraft Style - Logo Raster",
             category="api node/image/Recraft",
-            description="Select realistic_image style and optional substyle.",
+            description="Select logo_raster style and optional substyle.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE, include_none=False)),
             ],
@@ -310,6 +316,7 @@ class RecraftStyleInfiniteStyleLibrary(IO.ComfyNode):
             display_name="Recraft Style - Infinite Style Library",
             category="api node/image/Recraft",
             description="Select style based on preexisting UUID from Recraft's Infinite Style Library.",
+            short_description=None,
             inputs=[
                 IO.String.Input("style_id", default="", tooltip="UUID of style from Infinite Style Library."),
             ],
@@ -335,6 +342,7 @@ class RecraftCreateStyleNode(IO.ComfyNode):
             description="Create a custom style from reference images. "
             "Upload 1-5 images to use as style references. "
             "Total size of all images is limited to 5 MB.",
+            short_description="Create a custom style from 1-5 reference images.",
             inputs=[
                 IO.Combo.Input(
                     "style",
@@ -402,6 +410,7 @@ class RecraftTextToImageNode(IO.ComfyNode):
             display_name="Recraft Text to Image",
             category="api node/image/Recraft",
             description="Generates images synchronously based on prompt and resolution.",
+            short_description=None,
             inputs=[
                 IO.String.Input("prompt", multiline=True, default="", tooltip="Prompt for the image generation."),
                 IO.Combo.Input(
@@ -514,6 +523,7 @@ class RecraftImageToImageNode(IO.ComfyNode):
             display_name="Recraft Image to Image",
             category="api node/image/Recraft",
             description="Modify image based on prompt and strength.",
+            short_description=None,
             inputs=[
                 IO.Image.Input("image"),
                 IO.String.Input("prompt", multiline=True, default="", tooltip="Prompt for the image generation."),
@@ -632,6 +642,7 @@ class RecraftImageInpaintingNode(IO.ComfyNode):
             display_name="Recraft Image Inpainting",
             category="api node/image/Recraft",
             description="Modify image based on prompt and mask.",
+            short_description=None,
             inputs=[
                 IO.Image.Input("image"),
                 IO.Mask.Input("mask"),
@@ -734,6 +745,7 @@ class RecraftTextToVectorNode(IO.ComfyNode):
             display_name="Recraft Text to Vector",
             category="api node/image/Recraft",
             description="Generates SVG synchronously based on prompt and resolution.",
+            short_description=None,
             inputs=[
                 IO.String.Input("prompt", default="", tooltip="Prompt for the image generation.", multiline=True),
                 IO.Combo.Input("substyle", options=get_v3_substyles(RecraftStyleV3.vector_illustration)),
@@ -834,6 +846,7 @@ class RecraftVectorizeImageNode(IO.ComfyNode):
             display_name="Recraft Vectorize Image",
             category="api node/image/Recraft",
             description="Generates SVG synchronously from an input image.",
+            short_description=None,
             inputs=[
                 IO.Image.Input("image"),
             ],
@@ -877,6 +890,7 @@ class RecraftReplaceBackgroundNode(IO.ComfyNode):
             display_name="Recraft Replace Background",
             category="api node/image/Recraft",
             description="Replace background on image, based on provided prompt.",
+            short_description=None,
             inputs=[
                 IO.Image.Input("image"),
                 IO.String.Input("prompt", tooltip="Prompt for the image generation.", default="", multiline=True),
@@ -964,6 +978,7 @@ class RecraftRemoveBackgroundNode(IO.ComfyNode):
             display_name="Recraft Remove Background",
             category="api node/image/Recraft",
             description="Remove background from image, and return processed image and mask.",
+            short_description=None,
             inputs=[
                 IO.Image.Input("image"),
             ],
@@ -1012,8 +1027,9 @@ class RecraftCrispUpscaleNode(IO.ComfyNode):
             display_name="Recraft Crisp Upscale Image",
             category="api node/image/Recraft",
             description="Upscale image synchronously.\n"
-            "Enhances a given raster image using ‘crisp upscale’ tool, "
+            "Enhances a given raster image using 'crisp upscale' tool, "
             "increasing image resolution, making the image sharper and cleaner.",
+            short_description="Crisp upscale to sharpen and increase image resolution.",
             inputs=[
                 IO.Image.Input("image"),
             ],
@@ -1058,8 +1074,9 @@ class RecraftCreativeUpscaleNode(RecraftCrispUpscaleNode):
             display_name="Recraft Creative Upscale Image",
             category="api node/image/Recraft",
             description="Upscale image synchronously.\n"
-            "Enhances a given raster image using ‘creative upscale’ tool, "
+            "Enhances a given raster image using 'creative upscale' tool, "
             "boosting resolution with a focus on refining small details and faces.",
+            short_description="Creative upscale focusing on small details and faces.",
             inputs=[
                 IO.Image.Input("image"),
             ],

comfy_api_nodes/nodes_rodin.py

@@ -238,6 +238,7 @@ class Rodin3D_Regular(IO.ComfyNode):
             display_name="Rodin 3D Generate - Regular Generate",
             category="api node/3d/Rodin",
             description=cleandoc(cls.__doc__ or ""),
+            short_description=None,
             inputs=[
                 IO.Image.Input("Images"),
                 *COMMON_PARAMETERS,
@@ -297,6 +298,7 @@ class Rodin3D_Detail(IO.ComfyNode):
             display_name="Rodin 3D Generate - Detail Generate",
             category="api node/3d/Rodin",
             description=cleandoc(cls.__doc__ or ""),
+            short_description=None,
             inputs=[
                 IO.Image.Input("Images"),
                 *COMMON_PARAMETERS,
@@ -356,6 +358,7 @@ class Rodin3D_Smooth(IO.ComfyNode):
             display_name="Rodin 3D Generate - Smooth Generate",
             category="api node/3d/Rodin",
             description=cleandoc(cls.__doc__ or ""),
+            short_description=None,
             inputs=[
                 IO.Image.Input("Images"),
                 *COMMON_PARAMETERS,
@@ -414,6 +417,7 @@ class Rodin3D_Sketch(IO.ComfyNode):
             display_name="Rodin 3D Generate - Sketch Generate",
             category="api node/3d/Rodin",
             description=cleandoc(cls.__doc__ or ""),
+            short_description=None,
             inputs=[
                 IO.Image.Input("Images"),
                 IO.Int.Input(
@@ -476,6 +480,7 @@ class Rodin3D_Gen2(IO.ComfyNode):
             display_name="Rodin 3D Generate - Gen-2 Generate",
             category="api node/3d/Rodin",
             description=cleandoc(cls.__doc__ or ""),
+            short_description=None,
             inputs=[
                 IO.Image.Input("Images"),
                 IO.Int.Input(

comfy_api_nodes/nodes_runway.py

@@ -145,6 +145,7 @@ class RunwayImageToVideoNodeGen3a(IO.ComfyNode):
             "Before diving in, review these best practices to ensure that "
             "your input selections will set your generation up for success: "
             "https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo.",
+            short_description="Generate video from a starting frame using Gen3a Turbo.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -239,6 +240,7 @@ class RunwayImageToVideoNodeGen4(IO.ComfyNode):
             "Before diving in, review these best practices to ensure that "
             "your input selections will set your generation up for success: "
             "https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video.",
+            short_description="Generate video from a starting frame using Gen4 Turbo.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -337,6 +339,7 @@ class RunwayFirstLastFrameNode(IO.ComfyNode):
             "Before diving in, review these best practices to ensure that your input selections "
             "will set your generation up for success: "
             "https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3.",
+            short_description="Generate video from first and last keyframes with a prompt.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -443,6 +446,7 @@ class RunwayTextToImageNode(IO.ComfyNode):
             category="api node/image/Runway",
             description="Generate an image from a text prompt using Runway's Gen 4 model. "
             "You can also include reference image to guide the generation.",
+            short_description="Generate an image from text using Runway Gen 4.",
             inputs=[
                 IO.String.Input(
                     "prompt",

comfy_api_nodes/nodes_sora.py

@@ -36,6 +36,7 @@ class OpenAIVideoSora2(IO.ComfyNode):
             display_name="OpenAI Sora - Video",
             category="api node/video/Sora",
             description="OpenAI video and audio generation.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input(
                     "model",

comfy_api_nodes/nodes_stability.py

@@ -64,6 +64,7 @@ class StabilityStableImageUltraNode(IO.ComfyNode):
             display_name="Stability AI Stable Image Ultra",
             category="api node/image/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -197,6 +198,7 @@ class StabilityStableImageSD_3_5Node(IO.ComfyNode):
             display_name="Stability AI Stable Diffusion 3.5 Image",
             category="api node/image/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -352,6 +354,7 @@ class StabilityUpscaleConservativeNode(IO.ComfyNode):
             display_name="Stability AI Upscale Conservative",
             category="api node/image/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
+            short_description=None,
             inputs=[
                 IO.Image.Input("image"),
                 IO.String.Input(
@@ -454,6 +457,7 @@ class StabilityUpscaleCreativeNode(IO.ComfyNode):
             display_name="Stability AI Upscale Creative",
             category="api node/image/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
+            short_description=None,
             inputs=[
                 IO.Image.Input("image"),
                 IO.String.Input(
@@ -573,6 +577,7 @@ class StabilityUpscaleFastNode(IO.ComfyNode):
             display_name="Stability AI Upscale Fast",
             category="api node/image/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
+            short_description="Quickly upscale an image to 4x its original size.",
             inputs=[
                 IO.Image.Input("image"),
             ],
@@ -625,6 +630,7 @@ class StabilityTextToAudio(IO.ComfyNode):
             display_name="Stability AI Text To Audio",
             category="api node/audio/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
+            short_description=None,
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -701,6 +707,7 @@ class StabilityAudioToAudio(IO.ComfyNode):
             display_name="Stability AI Audio To Audio",
             category="api node/audio/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
+            short_description=None,
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -794,6 +801,7 @@ class StabilityAudioInpaint(IO.ComfyNode):
             display_name="Stability AI Audio Inpaint",
             category="api node/audio/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
+            short_description=None,
             inputs=[
                 IO.Combo.Input(
                     "model",

comfy_api_nodes/nodes_topaz.py

@@ -49,6 +49,7 @@ class TopazImageEnhance(IO.ComfyNode):
             display_name="Topaz Image Enhance",
             category="api node/image/Topaz",
             description="Industry-standard upscaling and image enhancement.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["Reimagine"]),
                 IO.Image.Input("image"),
@@ -223,6 +224,7 @@ class TopazVideoEnhance(IO.ComfyNode):
             display_name="Topaz Video Enhance",
             category="api node/video/Topaz",
             description="Breathe new life into video with powerful upscaling and recovery technology.",
+            short_description=None,
             inputs=[
                 IO.Video.Input("video"),
                 IO.Boolean.Input("upscaler_enabled", default=True),

comfy_api_nodes/nodes_tripo.py

@@ -80,6 +80,7 @@ class TripoTextToModelNode(IO.ComfyNode):
             node_id="TripoTextToModelNode",
             display_name="Tripo: Text to Model",
             category="api node/3d/Tripo",
+            description="Generate a 3D model from a text prompt using Tripo's API.",
             inputs=[
                 IO.String.Input("prompt", multiline=True),
                 IO.String.Input("negative_prompt", multiline=True, optional=True),
@@ -199,6 +200,7 @@ class TripoImageToModelNode(IO.ComfyNode):
             node_id="TripoImageToModelNode",
             display_name="Tripo: Image to Model",
             category="api node/3d/Tripo",
+            description="Generate a 3D model from a single image using Tripo's API.",
             inputs=[
                 IO.Image.Input("image"),
                 IO.Combo.Input(
@@ -331,6 +333,7 @@ class TripoMultiviewToModelNode(IO.ComfyNode):
             node_id="TripoMultiviewToModelNode",
             display_name="Tripo: Multiview to Model",
             category="api node/3d/Tripo",
+            description="Generate a 3D model from multiple view images using Tripo's API.",
             inputs=[
                 IO.Image.Input("image"),
                 IO.Image.Input("image_left", optional=True),
@@ -470,6 +473,7 @@ class TripoTextureNode(IO.ComfyNode):
             node_id="TripoTextureNode",
             display_name="Tripo: Texture model",
             category="api node/3d/Tripo",
+            description="Apply textures to an existing 3D model using Tripo's API.",
             inputs=[
                 IO.Custom("MODEL_TASK_ID").Input("model_task_id"),
                 IO.Boolean.Input("texture", default=True, optional=True),
@@ -538,6 +542,7 @@ class TripoRefineNode(IO.ComfyNode):
             display_name="Tripo: Refine Draft model",
             category="api node/3d/Tripo",
             description="Refine a draft model created by v1.4 Tripo models only.",
+            short_description=None,
             inputs=[
                 IO.Custom("MODEL_TASK_ID").Input("model_task_id", tooltip="Must be a v1.4 Tripo model"),
             ],
@@ -577,6 +582,8 @@ class TripoRigNode(IO.ComfyNode):
             node_id="TripoRigNode",
             display_name="Tripo: Rig model",
             category="api node/3d/Tripo",
+            description="Add a skeleton rig to an existing 3D model using Tripo's API.",
+            short_description="Add a skeleton rig to a 3D model.",
             inputs=[IO.Custom("MODEL_TASK_ID").Input("original_model_task_id")],
             outputs=[
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
@@ -614,6 +621,8 @@ class TripoRetargetNode(IO.ComfyNode):
             node_id="TripoRetargetNode",
             display_name="Tripo: Retarget rigged model",
             category="api node/3d/Tripo",
+            description="Apply a preset animation to a rigged 3D model using Tripo's API.",
+            short_description="Apply a preset animation to a rigged model.",
             inputs=[
                 IO.Custom("RIG_TASK_ID").Input("original_model_task_id"),
                 IO.Combo.Input(
@@ -679,6 +688,8 @@ class TripoConversionNode(IO.ComfyNode):
             node_id="TripoConversionNode",
             display_name="Tripo: Convert model",
             category="api node/3d/Tripo",
+            description="Convert a 3D model to different formats with optional post-processing using Tripo's API.",
+            short_description="Convert a 3D model to different formats.",
             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"]),

comfy_api_nodes/nodes_veo2.py

@@ -46,6 +46,7 @@ class VeoVideoGenerationNode(IO.ComfyNode):
             display_name="Google Veo 2 Video Generation",
             category="api node/video/Veo",
             description="Generates videos from text prompts using Google's Veo 2 API",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -264,6 +265,7 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
             display_name="Google Veo 3 Video Generation",
             category="api node/video/Veo",
             description="Generates videos from text prompts using Google's Veo 3 API",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -377,6 +379,7 @@ class Veo3FirstLastFrameNode(IO.ComfyNode):
             display_name="Google Veo 3 First-Last-Frame to Video",
             category="api node/video/Veo",
             description="Generate video using prompt and first and last frames.",
+            short_description=None,
             inputs=[
                 IO.String.Input(
                     "prompt",

comfy_api_nodes/nodes_vidu.py

@@ -72,6 +72,7 @@ class ViduTextToVideoNode(IO.ComfyNode):
             display_name="Vidu Text To Video Generation",
             category="api node/video/Vidu",
             description="Generate video from a text prompt",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"),
                 IO.String.Input(
@@ -168,6 +169,7 @@ class ViduImageToVideoNode(IO.ComfyNode):
             display_name="Vidu Image To Video Generation",
             category="api node/video/Vidu",
             description="Generate video from image and optional prompt",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"),
                 IO.Image.Input(
@@ -270,6 +272,7 @@ class ViduReferenceVideoNode(IO.ComfyNode):
             display_name="Vidu Reference To Video Generation",
             category="api node/video/Vidu",
             description="Generate video from multiple images and a prompt",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"),
                 IO.Image.Input(
@@ -383,6 +386,7 @@ class ViduStartEndToVideoNode(IO.ComfyNode):
             display_name="Vidu Start End To Video Generation",
             category="api node/video/Vidu",
             description="Generate a video from start and end frames and a prompt",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"),
                 IO.Image.Input(
@@ -485,6 +489,7 @@ class Vidu2TextToVideoNode(IO.ComfyNode):
             display_name="Vidu2 Text-to-Video Generation",
             category="api node/video/Vidu",
             description="Generate video from a text prompt",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["viduq2"]),
                 IO.String.Input(
@@ -576,6 +581,7 @@ class Vidu2ImageToVideoNode(IO.ComfyNode):
             display_name="Vidu2 Image-to-Video Generation",
             category="api node/video/Vidu",
             description="Generate a video from an image and an optional prompt.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["viduq2-pro-fast", "viduq2-pro", "viduq2-turbo"]),
                 IO.Image.Input(
@@ -704,6 +710,7 @@ class Vidu2ReferenceVideoNode(IO.ComfyNode):
             display_name="Vidu2 Reference-to-Video Generation",
             category="api node/video/Vidu",
             description="Generate a video from multiple reference images and a prompt.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["viduq2"]),
                 IO.Autogrow.Input(
@@ -837,6 +844,7 @@ class Vidu2StartEndToVideoNode(IO.ComfyNode):
             display_name="Vidu2 Start/End Frame-to-Video Generation",
             category="api node/video/Vidu",
             description="Generate a video from a start frame, an end frame, and a prompt.",
+            short_description="Generate video from start frame, end frame, and prompt.",
             inputs=[
                 IO.Combo.Input("model", options=["viduq2-pro-fast", "viduq2-pro", "viduq2-turbo"]),
                 IO.Image.Input("first_frame"),
@@ -956,6 +964,7 @@ class ViduExtendVideoNode(IO.ComfyNode):
             display_name="Vidu Video Extension",
             category="api node/video/Vidu",
             description="Extend an existing video by generating additional frames.",
+            short_description=None,
             inputs=[
                 IO.DynamicCombo.Input(
                     "model",
@@ -1126,6 +1135,7 @@ class ViduMultiFrameVideoNode(IO.ComfyNode):
             display_name="Vidu Multi-Frame Video Generation",
             category="api node/video/Vidu",
             description="Generate a video with multiple keyframe transitions.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input("model", options=["viduq2-pro", "viduq2-turbo"]),
                 IO.Image.Input(
@@ -1272,6 +1282,7 @@ class Vidu3TextToVideoNode(IO.ComfyNode):
             display_name="Vidu Q3 Text-to-Video Generation",
             category="api node/video/Vidu",
             description="Generate video from a text prompt.",
+            short_description=None,
             inputs=[
                 IO.DynamicCombo.Input(
                     "model",
@@ -1380,6 +1391,7 @@ class Vidu3ImageToVideoNode(IO.ComfyNode):
             display_name="Vidu Q3 Image-to-Video Generation",
             category="api node/video/Vidu",
             description="Generate a video from an image and an optional prompt.",
+            short_description=None,
             inputs=[
                 IO.DynamicCombo.Input(
                     "model",

comfy_api_nodes/nodes_wan.py

@@ -175,6 +175,7 @@ class WanTextToImageApi(IO.ComfyNode):
             display_name="Wan Text to Image",
             category="api node/image/Wan",
             description="Generates an image based on a text prompt.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -298,6 +299,7 @@ class WanImageToImageApi(IO.ComfyNode):
             category="api node/image/Wan",
             description="Generates an image from one or two input images and a text prompt. "
             "The output image is currently fixed at 1.6 MP, and its aspect ratio matches the input image(s).",
+            short_description="Generate an image from input images and a text prompt.",
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -424,6 +426,7 @@ class WanTextToVideoApi(IO.ComfyNode):
             display_name="Wan Text to Video",
             category="api node/video/Wan",
             description="Generates a video based on a text prompt.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -603,6 +606,7 @@ class WanImageToVideoApi(IO.ComfyNode):
             display_name="Wan Image to Video",
             category="api node/video/Wan",
             description="Generates a video from the first frame and a text prompt.",
+            short_description=None,
             inputs=[
                 IO.Combo.Input(
                     "model",
@@ -779,6 +783,7 @@ class WanReferenceVideoApi(IO.ComfyNode):
             category="api node/video/Wan",
             description="Use the character and voice from input videos, combined with a prompt, "
             "to generate a new video that maintains character consistency.",
+            short_description="Generate character-consistent video from reference videos and prompt.",
             inputs=[
                 IO.Combo.Input("model", options=["wan2.6-r2v"]),
                 IO.String.Input(

comfy_api_nodes/nodes_wavespeed.py

@@ -30,6 +30,7 @@ class WavespeedFlashVSRNode(IO.ComfyNode):
             category="api node/video/WaveSpeed",
             description="Fast, high-quality video upscaler that "
             "boosts resolution and restores clarity for low-resolution or blurry footage.",
+            short_description="Fast video upscaler that boosts resolution and restores clarity.",
             inputs=[
                 IO.Video.Input("video"),
                 IO.Combo.Input("target_resolution", options=["720p", "1080p", "2K", "4K"]),
@@ -101,6 +102,7 @@ class WavespeedImageUpscaleNode(IO.ComfyNode):
             display_name="WaveSpeed Image Upscale",
             category="api node/image/WaveSpeed",
             description="Boost image resolution and quality, upscaling photos to 4K or 8K for sharp, detailed results.",
+            short_description="Upscale images to 4K or 8K with enhanced quality.",
             inputs=[
                 IO.Combo.Input("model", options=["SeedVR2", "Ultimate"]),
                 IO.Image.Input("image"),

comfy_api_nodes/util/__init__.py

@@ -33,6 +33,7 @@ from .download_helpers import (
     download_url_to_video_output,
 )
 from .upload_helpers import (
+    upload_3d_model_to_comfyapi,
     upload_audio_to_comfyapi,
     upload_file_to_comfyapi,
     upload_image_to_comfyapi,
@@ -62,6 +63,7 @@ __all__ = [
     "sync_op",
     "sync_op_raw",
     # Upload helpers
+    "upload_3d_model_to_comfyapi",
     "upload_audio_to_comfyapi",
     "upload_file_to_comfyapi",
     "upload_image_to_comfyapi",

comfy_api_nodes/util/conversions.py

@@ -57,7 +57,7 @@ def tensor_to_bytesio(
     image: torch.Tensor,
     *,
     total_pixels: int | None = 2048 * 2048,
-    mime_type: str = "image/png",
+    mime_type: str | None = "image/png",
 ) -> BytesIO:
     """Converts a torch.Tensor image to a named BytesIO object.
 

comfy_api_nodes/util/upload_helpers.py

@@ -164,6 +164,27 @@ async def upload_video_to_comfyapi(
     return await upload_file_to_comfyapi(cls, video_bytes_io, filename, upload_mime_type, wait_label)
 
 
+_3D_MIME_TYPES = {
+    "glb": "model/gltf-binary",
+    "obj": "model/obj",
+    "fbx": "application/octet-stream",
+}
+
+
+async def upload_3d_model_to_comfyapi(
+    cls: type[IO.ComfyNode],
+    model_3d: Types.File3D,
+    file_format: str,
+) -> str:
+    """Uploads a 3D model file to ComfyUI API and returns its download URL."""
+    return await upload_file_to_comfyapi(
+        cls,
+        model_3d.get_data(),
+        f"{uuid.uuid4()}.{file_format}",
+        _3D_MIME_TYPES.get(file_format, "application/octet-stream"),
+    )
+
+
 async def upload_file_to_comfyapi(
     cls: type[IO.ComfyNode],
     file_bytes_io: BytesIO,

comfy_extras/nodes_ace.py

@@ -12,6 +12,8 @@ class TextEncodeAceStepAudio(io.ComfyNode):
         return io.Schema(
             node_id="TextEncodeAceStepAudio",
             category="conditioning",
+            description="Encodes tags and lyrics into conditioning for ACE-Step 1.0 audio generation with adjustable lyrics strength.",
+            short_description="Encodes tags and lyrics for ACE-Step 1.0 audio.",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("tags", multiline=True, dynamic_prompts=True),
@@ -34,6 +36,8 @@ class TextEncodeAceStepAudio15(io.ComfyNode):
         return io.Schema(
             node_id="TextEncodeAceStepAudio1.5",
             category="conditioning",
+            description="Encodes tags, lyrics, and music parameters like BPM, key, and language into conditioning for ACE-Step 1.5 audio generation.",
+            short_description="Encodes text and music parameters for ACE-Step 1.5.",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("tags", multiline=True, dynamic_prompts=True),
@@ -68,6 +72,8 @@ class EmptyAceStepLatentAudio(io.ComfyNode):
             node_id="EmptyAceStepLatentAudio",
             display_name="Empty Ace Step 1.0 Latent Audio",
             category="latent/audio",
+            description="Creates an empty latent audio tensor for ACE-Step 1.0 with a specified duration and batch size.",
+            short_description="Creates an empty ACE-Step 1.0 audio latent.",
             inputs=[
                 io.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1),
                 io.Int.Input(
@@ -91,6 +97,8 @@ class EmptyAceStep15LatentAudio(io.ComfyNode):
             node_id="EmptyAceStep1.5LatentAudio",
             display_name="Empty Ace Step 1.5 Latent Audio",
             category="latent/audio",
+            description="Creates an empty latent audio tensor for ACE-Step 1.5 with a specified duration and batch size.",
+            short_description="Creates an empty ACE-Step 1.5 audio latent.",
             inputs=[
                 io.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.01),
                 io.Int.Input(
@@ -115,6 +123,7 @@ class ReferenceAudio(io.ComfyNode):
             category="advanced/conditioning/audio",
             is_experimental=True,
             description="This node sets the reference audio for ace step 1.5",
+            short_description=None,
             inputs=[
                 io.Conditioning.Input("conditioning"),
                 io.Latent.Input("latent", optional=True),

comfy_extras/nodes_advanced_samplers.py

@@ -46,6 +46,8 @@ class SamplerLCMUpscale(io.ComfyNode):
         return io.Schema(
             node_id="SamplerLCMUpscale",
             category="sampling/custom_sampling/samplers",
+            description="Sampler that progressively upscales the latent during LCM sampling steps, combining denoising with gradual resolution increase.",
+            short_description="LCM sampler with progressive latent upscaling.",
             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),
@@ -93,6 +95,8 @@ class SamplerEulerCFGpp(io.ComfyNode):
             node_id="SamplerEulerCFGpp",
             display_name="SamplerEulerCFG++",
             category="_for_testing",  # "sampling/custom_sampling/samplers"
+            description="Euler sampler variant using the CFG++ formulation, which modifies the denoising direction using unconditional predictions for improved guidance.",
+            short_description="Euler sampler using CFG++ guidance formulation.",
             inputs=[
                 io.Combo.Input("version", options=["regular", "alternative"]),
             ],

comfy_extras/nodes_align_your_steps.py

@@ -30,6 +30,8 @@ class AlignYourStepsScheduler(io.ComfyNode):
             node_id="AlignYourStepsScheduler",
             search_aliases=["AYS scheduler"],
             category="sampling/custom_sampling/schedulers",
+            description="Generates an optimized noise schedule using the Align Your Steps method with log-linear interpolation.",
+            short_description="Optimized noise schedule using Align Your Steps.",
             inputs=[
                 io.Combo.Input("model_type", options=["SD1", "SDXL", "SVD"]),
                 io.Int.Input("steps", default=10, min=1, max=10000),

comfy_extras/nodes_apg.py

@@ -17,6 +17,8 @@ class APG(io.ComfyNode):
             node_id="APG",
             display_name="Adaptive Projected Guidance",
             category="sampling/custom_sampling",
+            description="Applies Adaptive Projected Guidance to a model, decomposing CFG guidance into parallel and orthogonal components with optional momentum and norm thresholding for improved sampling quality.",
+            short_description="Decomposes CFG guidance with projection and normalization.",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input(

comfy_extras/nodes_attention_multiply.py

@@ -26,6 +26,8 @@ class UNetSelfAttentionMultiply(io.ComfyNode):
         return io.Schema(
             node_id="UNetSelfAttentionMultiply",
             category="_for_testing/attention_experiments",
+            description="Scales the query, key, value, and output weights of UNet self-attention layers by specified multipliers to experiment with attention behavior.",
+            short_description="Scale UNet self-attention Q/K/V/Out weights.",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
@@ -49,6 +51,8 @@ class UNetCrossAttentionMultiply(io.ComfyNode):
         return io.Schema(
             node_id="UNetCrossAttentionMultiply",
             category="_for_testing/attention_experiments",
+            description="Scales the query, key, value, and output weights of UNet cross-attention layers by specified multipliers to experiment with text-to-image attention.",
+            short_description="Scale UNet cross-attention Q/K/V/Out weights.",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
@@ -73,6 +77,8 @@ class CLIPAttentionMultiply(io.ComfyNode):
             node_id="CLIPAttentionMultiply",
             search_aliases=["clip attention scale", "text encoder attention"],
             category="_for_testing/attention_experiments",
+            description="Scales the query, key, value, and output projection weights of CLIP text encoder self-attention layers by specified multipliers.",
+            short_description="Scale CLIP text encoder attention weights.",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
@@ -107,6 +113,8 @@ class UNetTemporalAttentionMultiply(io.ComfyNode):
         return io.Schema(
             node_id="UNetTemporalAttentionMultiply",
             category="_for_testing/attention_experiments",
+            description="Scales the output weights of UNet temporal and structural attention layers independently, allowing fine-grained control over video model attention behavior.",
+            short_description="Scale UNet temporal and structural attention weights.",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01),

comfy_extras/nodes_audio.py

@@ -19,6 +19,8 @@ class EmptyLatentAudio(IO.ComfyNode):
             node_id="EmptyLatentAudio",
             display_name="Empty Latent Audio",
             category="latent/audio",
+            description="Creates an empty latent audio tensor with a specified duration and batch size for Stable Audio generation.",
+            short_description="Creates an empty latent audio tensor.",
             inputs=[
                 IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
                 IO.Int.Input(
@@ -43,6 +45,8 @@ class ConditioningStableAudio(IO.ComfyNode):
         return IO.Schema(
             node_id="ConditioningStableAudio",
             category="conditioning",
+            description="Sets the start time and total duration on Stable Audio positive and negative conditioning.",
+            short_description="Sets timing parameters on Stable Audio conditioning.",
             inputs=[
                 IO.Conditioning.Input("positive"),
                 IO.Conditioning.Input("negative"),
@@ -72,6 +76,8 @@ class VAEEncodeAudio(IO.ComfyNode):
             search_aliases=["audio to latent"],
             display_name="VAE Encode Audio",
             category="latent/audio",
+            description="Encodes an audio waveform into a latent representation using a VAE, resampling if needed.",
+            short_description="Encodes audio into latent via VAE.",
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.Vae.Input("vae"),
@@ -115,6 +121,8 @@ class VAEDecodeAudio(IO.ComfyNode):
             search_aliases=["latent to audio"],
             display_name="VAE Decode Audio",
             category="latent/audio",
+            description="Decodes a latent representation back into an audio waveform using a VAE.",
+            short_description="Decodes latent into audio via VAE.",
             inputs=[
                 IO.Latent.Input("samples"),
                 IO.Vae.Input("vae"),
@@ -137,6 +145,8 @@ class VAEDecodeAudioTiled(IO.ComfyNode):
             search_aliases=["latent to audio"],
             display_name="VAE Decode Audio (Tiled)",
             category="latent/audio",
+            description="Decodes a latent representation into audio using tiled VAE decoding to reduce memory usage.",
+            short_description="Tiled VAE decoding of latent into audio.",
             inputs=[
                 IO.Latent.Input("samples"),
                 IO.Vae.Input("vae"),
@@ -159,6 +169,8 @@ class SaveAudio(IO.ComfyNode):
             search_aliases=["export flac"],
             display_name="Save Audio (FLAC)",
             category="audio",
+            description="Saves audio to disk in FLAC format with a configurable filename prefix.",
+            short_description="Saves audio to disk in FLAC format.",
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.String.Input("filename_prefix", default="audio/ComfyUI"),
@@ -184,6 +196,8 @@ class SaveAudioMP3(IO.ComfyNode):
             search_aliases=["export mp3"],
             display_name="Save Audio (MP3)",
             category="audio",
+            description="Saves audio to disk in MP3 format with configurable quality and filename prefix.",
+            short_description="Saves audio to disk in MP3 format.",
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.String.Input("filename_prefix", default="audio/ComfyUI"),
@@ -212,6 +226,8 @@ class SaveAudioOpus(IO.ComfyNode):
             search_aliases=["export opus"],
             display_name="Save Audio (Opus)",
             category="audio",
+            description="Saves audio to disk in Opus format with configurable quality and filename prefix.",
+            short_description="Saves audio to disk in Opus format.",
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.String.Input("filename_prefix", default="audio/ComfyUI"),
@@ -240,6 +256,8 @@ class PreviewAudio(IO.ComfyNode):
             search_aliases=["play audio"],
             display_name="Preview Audio",
             category="audio",
+            description="Plays back audio in the UI for previewing.",
+            short_description=None,
             inputs=[
                 IO.Audio.Input("audio"),
             ],
@@ -300,6 +318,8 @@ class LoadAudio(IO.ComfyNode):
             search_aliases=["import audio", "open audio", "audio file"],
             display_name="Load Audio",
             category="audio",
+            description="Loads an audio or video file from disk and outputs the audio as a single Audio output.",
+            short_description="Loads an audio file from disk.",
             inputs=[
                 IO.Combo.Input("audio", upload=IO.UploadType.audio, options=sorted(files)),
             ],
@@ -338,6 +358,7 @@ class RecordAudio(IO.ComfyNode):
             search_aliases=["microphone input", "audio capture", "voice input"],
             display_name="Record Audio",
             category="audio",
+            description="Records audio from a microphone input and outputs the captured audio.",
             inputs=[
                 IO.Custom("AUDIO_RECORD").Input("audio"),
             ],
@@ -363,6 +384,7 @@ class TrimAudioDuration(IO.ComfyNode):
             search_aliases=["cut audio", "audio clip", "shorten audio"],
             display_name="Trim Audio Duration",
             description="Trim audio tensor into chosen time range.",
+            short_description=None,
             category="audio",
             inputs=[
                 IO.Audio.Input("audio"),
@@ -416,6 +438,7 @@ class SplitAudioChannels(IO.ComfyNode):
             search_aliases=["stereo to mono"],
             display_name="Split Audio Channels",
             description="Separates the audio into left and right channels.",
+            short_description=None,
             category="audio",
             inputs=[
                 IO.Audio.Input("audio"),
@@ -448,6 +471,7 @@ class JoinAudioChannels(IO.ComfyNode):
             node_id="JoinAudioChannels",
             display_name="Join Audio Channels",
             description="Joins left and right mono audio channels into a stereo audio.",
+            short_description=None,
             category="audio",
             inputs=[
                 IO.Audio.Input("audio_left"),
@@ -517,6 +541,7 @@ class AudioConcat(IO.ComfyNode):
             search_aliases=["join audio", "combine audio", "append audio"],
             display_name="Audio Concat",
             description="Concatenates the audio1 to audio2 in the specified direction.",
+            short_description=None,
             category="audio",
             inputs=[
                 IO.Audio.Input("audio1"),
@@ -565,6 +590,7 @@ class AudioMerge(IO.ComfyNode):
             search_aliases=["mix audio", "overlay audio", "layer audio"],
             display_name="Audio Merge",
             description="Combine two audio tracks by overlaying their waveforms.",
+            short_description=None,
             category="audio",
             inputs=[
                 IO.Audio.Input("audio1"),
@@ -626,6 +652,8 @@ class AudioAdjustVolume(IO.ComfyNode):
             search_aliases=["audio gain", "loudness", "audio level"],
             display_name="Audio Adjust Volume",
             category="audio",
+            description="Adjusts audio volume by a specified number of decibels.",
+            short_description=None,
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.Int.Input(
@@ -662,6 +690,8 @@ class EmptyAudio(IO.ComfyNode):
             search_aliases=["blank audio"],
             display_name="Empty Audio",
             category="audio",
+            description="Creates a silent audio clip with configurable duration, sample rate, and channel count.",
+            short_description="Creates a silent audio clip.",
             inputs=[
                 IO.Float.Input(
                     "duration",

comfy_extras/nodes_audio_encoder.py

@@ -11,6 +11,8 @@ class AudioEncoderLoader(io.ComfyNode):
         return io.Schema(
             node_id="AudioEncoderLoader",
             category="loaders",
+            description="Loads an audio encoder model from a checkpoint file for encoding audio into embeddings.",
+            short_description="Loads an audio encoder model from a checkpoint.",
             inputs=[
                 io.Combo.Input(
                     "audio_encoder_name",
@@ -36,6 +38,8 @@ class AudioEncoderEncode(io.ComfyNode):
         return io.Schema(
             node_id="AudioEncoderEncode",
             category="conditioning",
+            description="Encodes audio into embeddings using a loaded audio encoder model.",
+            short_description=None,
             inputs=[
                 io.AudioEncoder.Input("audio_encoder"),
                 io.Audio.Input("audio"),

comfy_extras/nodes_camera_trajectory.py

@@ -154,6 +154,8 @@ class WanCameraEmbedding(io.ComfyNode):
         return io.Schema(
             node_id="WanCameraEmbedding",
             category="camera",
+            description="Generates Plucker camera embeddings from a selected camera motion trajectory for Wan video generation.",
+            short_description="Generates camera embeddings for Wan video generation.",
             inputs=[
                 io.Combo.Input(
                     "camera_pose",

comfy_extras/nodes_canny.py

@@ -12,6 +12,8 @@ class Canny(io.ComfyNode):
             node_id="Canny",
             search_aliases=["edge detection", "outline", "contour detection", "line art"],
             category="image/preprocessors",
+            description="Detects edges in an image using the Canny edge detection algorithm with configurable low and high thresholds.",
+            short_description="Canny edge detection on images.",
             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_cfg.py

@@ -27,6 +27,8 @@ class CFGZeroStar(io.ComfyNode):
         return io.Schema(
             node_id="CFGZeroStar",
             category="advanced/guidance",
+            description="Applies CFG-Zero* post-CFG correction that computes an optimal scaling factor between conditional and unconditional predictions to reduce CFG artifacts.",
+            short_description="CFG-Zero* guidance correction to reduce artifacts.",
             inputs=[
                 io.Model.Input("model"),
             ],
@@ -54,6 +56,8 @@ class CFGNorm(io.ComfyNode):
         return io.Schema(
             node_id="CFGNorm",
             category="advanced/guidance",
+            description="Constrains the CFG-guided prediction norm to not exceed the conditional prediction norm, helping to prevent oversaturation at high CFG scales.",
+            short_description="Constrain CFG output norm to conditional prediction norm.",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01),

comfy_extras/nodes_chroma_radiance.py

@@ -14,6 +14,8 @@ class EmptyChromaRadianceLatentImage(io.ComfyNode):
         return io.Schema(
             node_id="EmptyChromaRadianceLatentImage",
             category="latent/chroma_radiance",
+            description="Creates an empty Chroma Radiance latent image tensor with the specified width, height, and batch size.",
+            short_description="Creates an empty Chroma Radiance latent image.",
             inputs=[
                 io.Int.Input(id="width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input(id="height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -35,6 +37,7 @@ class ChromaRadianceOptions(io.ComfyNode):
             node_id="ChromaRadianceOptions",
             category="model_patches/chroma_radiance",
             description="Allows setting advanced options for the Chroma Radiance model.",
+            short_description=None,
             inputs=[
                 io.Model.Input(id="model"),
                 io.Boolean.Input(

comfy_extras/nodes_clip_sdxl.py

@@ -10,6 +10,8 @@ class CLIPTextEncodeSDXLRefiner(io.ComfyNode):
         return io.Schema(
             node_id="CLIPTextEncodeSDXLRefiner",
             category="advanced/conditioning",
+            description="Encodes text for SDXL refiner models with aesthetic score and resolution conditioning parameters.",
+            short_description="Encodes text for SDXL refiner models.",
             inputs=[
                 io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01),
                 io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
@@ -31,6 +33,8 @@ class CLIPTextEncodeSDXL(io.ComfyNode):
         return io.Schema(
             node_id="CLIPTextEncodeSDXL",
             category="advanced/conditioning",
+            description="Encodes separate G and L text prompts for SDXL models with resolution and crop conditioning parameters.",
+            short_description="Encodes dual text prompts for SDXL models.",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),

comfy_extras/nodes_color.py

@@ -10,6 +10,7 @@ class ColorToRGBInt(io.ComfyNode):
             display_name="Color to RGB Int",
             category="utils",
             description="Convert a color to a RGB integer value.",
+            short_description=None,
             inputs=[
                 io.Color.Input("color"),
             ],

comfy_extras/nodes_compositing.py

@@ -112,6 +112,8 @@ class PorterDuffImageComposite(io.ComfyNode):
             search_aliases=["alpha composite", "blend modes", "layer blend", "transparency blend"],
             display_name="Porter-Duff Image Composite",
             category="mask/compositing",
+            description="Composites two images with alpha masks using Porter-Duff blend modes.",
+            short_description="",
             inputs=[
                 io.Image.Input("source"),
                 io.Mask.Input("source_alpha"),
@@ -169,6 +171,8 @@ class SplitImageWithAlpha(io.ComfyNode):
             search_aliases=["extract alpha", "separate transparency", "remove alpha"],
             display_name="Split Image with Alpha",
             category="mask/compositing",
+            description="Separates an RGBA image into its RGB color channels and an alpha transparency mask.",
+            short_description="Split RGBA image into RGB and alpha mask.",
             inputs=[
                 io.Image.Input("image"),
             ],
@@ -193,6 +197,8 @@ class JoinImageWithAlpha(io.ComfyNode):
             search_aliases=["add transparency", "apply alpha", "composite alpha", "RGBA"],
             display_name="Join Image with Alpha",
             category="mask/compositing",
+            description="Combines an RGB image with an alpha mask to produce an RGBA image with transparency.",
+            short_description="Combine RGB image and alpha into RGBA.",
             inputs=[
                 io.Image.Input("image"),
                 io.Mask.Input("alpha"),

comfy_extras/nodes_cond.py

@@ -9,6 +9,8 @@ class CLIPTextEncodeControlnet(io.ComfyNode):
         return io.Schema(
             node_id="CLIPTextEncodeControlnet",
             category="_for_testing/conditioning",
+            description="Encodes text with CLIP and attaches the result as cross-attention controlnet conditioning to existing conditioning data.",
+            short_description="CLIP text encode for controlnet cross-attention conditioning.",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Conditioning.Input("conditioning"),
@@ -36,6 +38,8 @@ class T5TokenizerOptions(io.ComfyNode):
         return io.Schema(
             node_id="T5TokenizerOptions",
             category="_for_testing/conditioning",
+            description="Configures minimum padding and length options for T5-family tokenizers used in CLIP text encoding.",
+            short_description="Set T5 tokenizer padding and length options.",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Int.Input("min_padding", default=0, min=0, max=10000, step=1),

comfy_extras/nodes_context_windows.py

@@ -12,6 +12,7 @@ class ContextWindowsManualNode(io.ComfyNode):
             display_name="Context Windows (Manual)",
             category="context",
             description="Manually set context windows.",
+            short_description=None,
             inputs=[
                 io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
                 io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window."),
@@ -65,6 +66,7 @@ class WanContextWindowsManualNode(ContextWindowsManualNode):
         schema.node_id = "WanContextWindowsManual"
         schema.display_name = "WAN Context Windows (Manual)"
         schema.description = "Manually set context windows for WAN-like models (dim=2)."
+        schema.short_description = None
         schema.inputs = [
             io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
                 io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window."),

comfy_extras/nodes_controlnet.py

@@ -10,6 +10,8 @@ class SetUnionControlNetType(io.ComfyNode):
         return io.Schema(
             node_id="SetUnionControlNetType",
             category="conditioning/controlnet",
+            description="Sets the control type for a Union ControlNet, selecting which conditioning mode to use.",
+            short_description="Select control mode for Union ControlNet.",
             inputs=[
                 io.ControlNet.Input("control_net"),
                 io.Combo.Input("type", options=["auto"] + list(UNION_CONTROLNET_TYPES.keys())),
@@ -40,6 +42,8 @@ class ControlNetInpaintingAliMamaApply(io.ComfyNode):
             node_id="ControlNetInpaintingAliMamaApply",
             search_aliases=["masked controlnet"],
             category="conditioning/controlnet",
+            description="Applies an AliMama inpainting ControlNet to positive and negative conditioning using an image and mask with VAE encoding.",
+            short_description="Applies AliMama inpainting ControlNet with mask.",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),

comfy_extras/nodes_cosmos.py

@@ -14,6 +14,7 @@ class EmptyCosmosLatentVideo(io.ComfyNode):
         return io.Schema(
             node_id="EmptyCosmosLatentVideo",
             category="latent/video",
+            description="Creates an empty latent tensor sized for Cosmos video generation.",
             inputs=[
                 io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -46,6 +47,8 @@ class CosmosImageToVideoLatent(io.ComfyNode):
         return io.Schema(
             node_id="CosmosImageToVideoLatent",
             category="conditioning/inpaint",
+            description="Creates an inpainting video latent for Cosmos by encoding optional start and end images with a noise mask.",
+            short_description="Cosmos inpainting video latent from start/end images.",
             inputs=[
                 io.Vae.Input("vae"),
                 io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -89,6 +92,8 @@ class CosmosPredict2ImageToVideoLatent(io.ComfyNode):
         return io.Schema(
             node_id="CosmosPredict2ImageToVideoLatent",
             category="conditioning/inpaint",
+            description="Creates an inpainting video latent for Cosmos Predict2 by encoding optional start and end images with Wan latent format processing.",
+            short_description="Cosmos Predict2 inpainting video latent from images.",
             inputs=[
                 io.Vae.Input("vae"),
                 io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),

comfy_extras/nodes_custom_sampler.py

@@ -18,6 +18,8 @@ class BasicScheduler(io.ComfyNode):
         return io.Schema(
             node_id="BasicScheduler",
             category="sampling/custom_sampling/schedulers",
+            description="Generates a sigma schedule from a model using a selected scheduler algorithm, step count, and denoise strength.",
+            short_description="Generate sigma schedule from model and scheduler.",
             inputs=[
                 io.Model.Input("model"),
                 io.Combo.Input("scheduler", options=comfy.samplers.SCHEDULER_NAMES),
@@ -48,6 +50,8 @@ class KarrasScheduler(io.ComfyNode):
         return io.Schema(
             node_id="KarrasScheduler",
             category="sampling/custom_sampling/schedulers",
+            description="Generates a sigma schedule using the Karras noise schedule with configurable sigma range and rho parameter.",
+            short_description="Generate sigmas using Karras noise schedule.",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
                 io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
@@ -70,6 +74,8 @@ class ExponentialScheduler(io.ComfyNode):
         return io.Schema(
             node_id="ExponentialScheduler",
             category="sampling/custom_sampling/schedulers",
+            description="Generates a sigma schedule using an exponential noise schedule with configurable sigma range.",
+            short_description="Generate sigmas using exponential noise schedule.",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
                 io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
@@ -91,6 +97,8 @@ class PolyexponentialScheduler(io.ComfyNode):
         return io.Schema(
             node_id="PolyexponentialScheduler",
             category="sampling/custom_sampling/schedulers",
+            description="Generates a sigma schedule using a polyexponential noise schedule with configurable sigma range and rho parameter.",
+            short_description="Generate sigmas using polyexponential noise schedule.",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
                 io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
@@ -113,6 +121,8 @@ class LaplaceScheduler(io.ComfyNode):
         return io.Schema(
             node_id="LaplaceScheduler",
             category="sampling/custom_sampling/schedulers",
+            description="Generates a sigma schedule using a Laplace distribution-based noise schedule with configurable mu and beta parameters.",
+            short_description="Generate sigmas using Laplace distribution schedule.",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
                 io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
@@ -137,6 +147,8 @@ class SDTurboScheduler(io.ComfyNode):
         return io.Schema(
             node_id="SDTurboScheduler",
             category="sampling/custom_sampling/schedulers",
+            description="Generates a sigma schedule optimized for SD Turbo models with very few steps and adjustable denoise strength.",
+            short_description="Generate sigma schedule for SD Turbo models.",
             inputs=[
                 io.Model.Input("model"),
                 io.Int.Input("steps", default=1, min=1, max=10),
@@ -161,6 +173,8 @@ class BetaSamplingScheduler(io.ComfyNode):
         return io.Schema(
             node_id="BetaSamplingScheduler",
             category="sampling/custom_sampling/schedulers",
+            description="Generates a sigma schedule using a beta distribution with configurable alpha and beta shape parameters.",
+            short_description="Generate sigmas using beta distribution schedule.",
             inputs=[
                 io.Model.Input("model"),
                 io.Int.Input("steps", default=20, min=1, max=10000),
@@ -183,6 +197,8 @@ class VPScheduler(io.ComfyNode):
         return io.Schema(
             node_id="VPScheduler",
             category="sampling/custom_sampling/schedulers",
+            description="Generates a sigma schedule using the Variance Preserving (VP) SDE formulation with configurable beta and epsilon parameters.",
+            short_description="Generate sigmas using VP SDE schedule.",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=10000),
                 io.Float.Input("beta_d", default=19.9, min=0.0, max=5000.0, step=0.01, round=False), #TODO: fix default values
@@ -205,6 +221,8 @@ class SplitSigmas(io.ComfyNode):
         return io.Schema(
             node_id="SplitSigmas",
             category="sampling/custom_sampling/sigmas",
+            description="Splits a sigma sequence into high and low portions at a specified step index for multi-pass sampling.",
+            short_description="Split sigmas into high and low at a step.",
             inputs=[
                 io.Sigmas.Input("sigmas"),
                 io.Int.Input("step", default=0, min=0, max=10000),
@@ -229,6 +247,8 @@ class SplitSigmasDenoise(io.ComfyNode):
         return io.Schema(
             node_id="SplitSigmasDenoise",
             category="sampling/custom_sampling/sigmas",
+            description="Splits a sigma sequence into high and low portions based on a denoise ratio for multi-pass sampling workflows.",
+            short_description="Split sigmas by denoise ratio.",
             inputs=[
                 io.Sigmas.Input("sigmas"),
                 io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
@@ -255,6 +275,8 @@ class FlipSigmas(io.ComfyNode):
         return io.Schema(
             node_id="FlipSigmas",
             category="sampling/custom_sampling/sigmas",
+            description="Reverses the order of a sigma sequence, useful for converting between ascending and descending noise schedules.",
+            short_description="Reverse the order of a sigma sequence.",
             inputs=[io.Sigmas.Input("sigmas")],
             outputs=[io.Sigmas.Output()]
         )
@@ -277,6 +299,8 @@ class SetFirstSigma(io.ComfyNode):
         return io.Schema(
             node_id="SetFirstSigma",
             category="sampling/custom_sampling/sigmas",
+            description="Overrides the first sigma value in a sequence with a custom value, allowing manual control of the initial noise level.",
+            short_description="Override the first sigma value in a sequence.",
             inputs=[
                 io.Sigmas.Input("sigmas"),
                 io.Float.Input("sigma", default=136.0, min=0.0, max=20000.0, step=0.001, round=False),
@@ -299,6 +323,8 @@ class ExtendIntermediateSigmas(io.ComfyNode):
             node_id="ExtendIntermediateSigmas",
             search_aliases=["interpolate sigmas"],
             category="sampling/custom_sampling/sigmas",
+            description="Interpolates additional intermediate sigma values between existing steps using selectable spacing within a specified sigma range.",
+            short_description="Interpolate additional sigma steps between existing values.",
             inputs=[
                 io.Sigmas.Input("sigmas"),
                 io.Int.Input("steps", default=2, min=1, max=100),
@@ -352,6 +378,8 @@ class SamplingPercentToSigma(io.ComfyNode):
         return io.Schema(
             node_id="SamplingPercentToSigma",
             category="sampling/custom_sampling/sigmas",
+            description="Converts a sampling percentage (0.0 to 1.0) to the corresponding sigma value using a model's noise schedule.",
+            short_description="Convert sampling percentage to sigma value.",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("sampling_percent", default=0.0, min=0.0, max=1.0, step=0.0001),
@@ -380,6 +408,8 @@ class KSamplerSelect(io.ComfyNode):
         return io.Schema(
             node_id="KSamplerSelect",
             category="sampling/custom_sampling/samplers",
+            description="Selects a sampler algorithm by name from the list of available samplers and outputs the sampler object.",
+            short_description="Select a sampler algorithm by name.",
             inputs=[io.Combo.Input("sampler_name", options=comfy.samplers.SAMPLER_NAMES)],
             outputs=[io.Sampler.Output()]
         )
@@ -397,6 +427,8 @@ class SamplerDPMPP_3M_SDE(io.ComfyNode):
         return io.Schema(
             node_id="SamplerDPMPP_3M_SDE",
             category="sampling/custom_sampling/samplers",
+            description="Creates a DPM++ 3M SDE sampler with configurable eta, noise scale, and GPU or CPU noise generation.",
+            short_description="Create a DPM++ 3M SDE sampler.",
             inputs=[
                 io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
                 io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
@@ -422,6 +454,8 @@ class SamplerDPMPP_2M_SDE(io.ComfyNode):
         return io.Schema(
             node_id="SamplerDPMPP_2M_SDE",
             category="sampling/custom_sampling/samplers",
+            description="Creates a DPM++ 2M SDE sampler with configurable solver type, eta, noise scale, and noise device.",
+            short_description="Create a DPM++ 2M SDE sampler.",
             inputs=[
                 io.Combo.Input("solver_type", options=['midpoint', 'heun']),
                 io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
@@ -449,6 +483,8 @@ class SamplerDPMPP_SDE(io.ComfyNode):
         return io.Schema(
             node_id="SamplerDPMPP_SDE",
             category="sampling/custom_sampling/samplers",
+            description="Creates a DPM++ SDE sampler with configurable eta, noise scale, r parameter, and noise device.",
+            short_description="Create a DPM++ SDE sampler.",
             inputs=[
                 io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
                 io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
@@ -475,6 +511,8 @@ class SamplerDPMPP_2S_Ancestral(io.ComfyNode):
         return io.Schema(
             node_id="SamplerDPMPP_2S_Ancestral",
             category="sampling/custom_sampling/samplers",
+            description="Creates a DPM++ 2S Ancestral sampler with configurable eta and noise scale parameters.",
+            short_description="Create a DPM++ 2S Ancestral sampler.",
             inputs=[
                 io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
                 io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
@@ -495,6 +533,8 @@ class SamplerEulerAncestral(io.ComfyNode):
         return io.Schema(
             node_id="SamplerEulerAncestral",
             category="sampling/custom_sampling/samplers",
+            description="Creates an Euler Ancestral sampler with configurable eta and noise scale for stochastic sampling.",
+            short_description="Create an Euler Ancestral stochastic sampler.",
             inputs=[
                 io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
                 io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
@@ -516,6 +556,8 @@ class SamplerEulerAncestralCFGPP(io.ComfyNode):
             node_id="SamplerEulerAncestralCFGPP",
             display_name="SamplerEulerAncestralCFG++",
             category="sampling/custom_sampling/samplers",
+            description="Creates an Euler Ancestral CFG++ sampler that applies classifier-free guidance with improved stability.",
+            short_description="Create an Euler Ancestral CFG++ sampler.",
             inputs=[
                 io.Float.Input("eta", default=1.0, min=0.0, max=1.0, step=0.01, round=False),
                 io.Float.Input("s_noise", default=1.0, min=0.0, max=10.0, step=0.01, round=False),
@@ -538,6 +580,8 @@ class SamplerLMS(io.ComfyNode):
         return io.Schema(
             node_id="SamplerLMS",
             category="sampling/custom_sampling/samplers",
+            description="Creates a Linear Multi-Step (LMS) sampler with a configurable order parameter.",
+            short_description="Create a Linear Multi-Step (LMS) sampler.",
             inputs=[io.Int.Input("order", default=4, min=1, max=100)],
             outputs=[io.Sampler.Output()]
         )
@@ -555,6 +599,8 @@ class SamplerDPMAdaptative(io.ComfyNode):
         return io.Schema(
             node_id="SamplerDPMAdaptative",
             category="sampling/custom_sampling/samplers",
+            description="Creates a DPM Adaptive sampler with configurable order, tolerances, PID coefficients, and stochastic noise parameters for adaptive step-size sampling.",
+            short_description="Create a DPM Adaptive step-size sampler.",
             inputs=[
                 io.Int.Input("order", default=3, min=2, max=3),
                 io.Float.Input("rtol", default=0.05, min=0.0, max=100.0, step=0.01, round=False),
@@ -586,6 +632,8 @@ class SamplerER_SDE(io.ComfyNode):
         return io.Schema(
             node_id="SamplerER_SDE",
             category="sampling/custom_sampling/samplers",
+            description="Creates an ER-SDE sampler supporting ER-SDE, reverse-time SDE, and ODE solver types with configurable stochastic strength and staging.",
+            short_description="Create an ER-SDE, reverse-time SDE, or ODE sampler.",
             inputs=[
                 io.Combo.Input("solver_type", options=["ER-SDE", "Reverse-time SDE", "ODE"]),
                 io.Int.Input("max_stage", default=3, min=1, max=3),
@@ -624,6 +672,8 @@ class SamplerSASolver(io.ComfyNode):
             node_id="SamplerSASolver",
             search_aliases=["sde"],
             category="sampling/custom_sampling/samplers",
+            description="Creates an SA-Solver sampler with configurable predictor/corrector orders, SDE region, and PECE mode for high-order diffusion sampling.",
+            short_description="Create an SA-Solver high-order diffusion sampler.",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False),
@@ -684,7 +734,8 @@ class SamplerSEEDS2(io.ComfyNode):
                 "- solver_type=phi_2, r=1.0, eta=0.0\n\n"
                 "exp_heun_2_x0_sde\n"
                 "- solver_type=phi_2, r=1.0, eta=1.0, s_noise=1.0"
-            )
+            ),
+            short_description="SEEDS2 sampler with configurable solver and SDE settings.",
         )
 
     @classmethod
@@ -728,6 +779,8 @@ class SamplerCustom(io.ComfyNode):
         return io.Schema(
             node_id="SamplerCustom",
             category="sampling/custom_sampling",
+            description="Runs a complete custom sampling pass by combining a model, sampler, sigmas, and conditioning with optional noise injection.",
+            short_description="Run custom sampling with manual sampler and sigmas.",
             inputs=[
                 io.Model.Input("model"),
                 io.Boolean.Input("add_noise", default=True),
@@ -794,6 +847,8 @@ class BasicGuider(io.ComfyNode):
         return io.Schema(
             node_id="BasicGuider",
             category="sampling/custom_sampling/guiders",
+            description="Creates a basic guider that applies a single conditioning input to guide the diffusion model without classifier-free guidance.",
+            short_description="Create a single-conditioning guider without CFG.",
             inputs=[
                 io.Model.Input("model"),
                 io.Conditioning.Input("conditioning"),
@@ -815,6 +870,8 @@ class CFGGuider(io.ComfyNode):
         return io.Schema(
             node_id="CFGGuider",
             category="sampling/custom_sampling/guiders",
+            description="Creates a classifier-free guidance guider that combines positive and negative conditioning with an adjustable CFG scale.",
+            short_description="Create a CFG guider with positive/negative conditioning.",
             inputs=[
                 io.Model.Input("model"),
                 io.Conditioning.Input("positive"),
@@ -869,6 +926,8 @@ class DualCFGGuider(io.ComfyNode):
             node_id="DualCFGGuider",
             search_aliases=["dual prompt guidance"],
             category="sampling/custom_sampling/guiders",
+            description="Creates a dual classifier-free guidance guider that blends two conditioning inputs against a negative with independent CFG scales and regular or nested styles.",
+            short_description="Create a dual CFG guider with two conditionings.",
             inputs=[
                 io.Model.Input("model"),
                 io.Conditioning.Input("cond1"),
@@ -897,6 +956,8 @@ class DisableNoise(io.ComfyNode):
             node_id="DisableNoise",
             search_aliases=["zero noise"],
             category="sampling/custom_sampling/noise",
+            description="Produces a zero-noise source that disables noise injection, useful for deterministic sampling or img2img without added noise.",
+            short_description="Produce zero noise to disable noise injection.",
             inputs=[],
             outputs=[io.Noise.Output()]
         )
@@ -914,6 +975,8 @@ class RandomNoise(io.ComfyNode):
         return io.Schema(
             node_id="RandomNoise",
             category="sampling/custom_sampling/noise",
+            description="Produces a random noise source from a seed value for use in custom sampling workflows.",
+            short_description="Produce seeded random noise for sampling.",
             inputs=[io.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True)],
             outputs=[io.Noise.Output()]
         )
@@ -931,6 +994,8 @@ class SamplerCustomAdvanced(io.ComfyNode):
         return io.Schema(
             node_id="SamplerCustomAdvanced",
             category="sampling/custom_sampling",
+            description="Runs an advanced custom sampling pass using separate noise, guider, sampler, and sigmas inputs for maximum control over the diffusion process.",
+            short_description="Run advanced custom sampling with separate components.",
             inputs=[
                 io.Noise.Input("noise"),
                 io.Guider.Input("guider"),
@@ -985,6 +1050,8 @@ class AddNoise(io.ComfyNode):
         return io.Schema(
             node_id="AddNoise",
             category="_for_testing/custom_sampling/noise",
+            description="Adds scaled noise to a latent image using the model's noise schedule and sigma values for manual noise injection.",
+            short_description="Add scaled noise to a latent image.",
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
@@ -1035,6 +1102,8 @@ class ManualSigmas(io.ComfyNode):
             node_id="ManualSigmas",
             search_aliases=["custom noise schedule", "define sigmas"],
             category="_for_testing/custom_sampling",
+            description="Defines a custom sigma sequence by manually entering comma-separated numeric values as a text string.",
+            short_description="Define custom sigmas from comma-separated values.",
             is_experimental=True,
             inputs=[
                 io.String.Input("sigmas", default="1, 0.5", multiline=False)

comfy_extras/nodes_dataset.py

@@ -49,6 +49,8 @@ class LoadImageDataSetFromFolderNode(io.ComfyNode):
             node_id="LoadImageDataSetFromFolder",
             display_name="Load Image Dataset from Folder",
             category="dataset",
+            description="Loads all images from a selected input subfolder and outputs them as a list of image tensors.",
+            short_description="Loads images from a folder as a list.",
             is_experimental=True,
             inputs=[
                 io.Combo.Input(
@@ -86,6 +88,8 @@ class LoadImageTextDataSetFromFolderNode(io.ComfyNode):
             node_id="LoadImageTextDataSetFromFolder",
             display_name="Load Image and Text Dataset from Folder",
             category="dataset",
+            description="Loads paired images and text captions from a folder, matching each image with its corresponding text file.",
+            short_description="Loads paired images and text captions from folder.",
             is_experimental=True,
             inputs=[
                 io.Combo.Input(
@@ -208,6 +212,8 @@ class SaveImageDataSetToFolderNode(io.ComfyNode):
             node_id="SaveImageDataSetToFolder",
             display_name="Save Image Dataset to Folder",
             category="dataset",
+            description="Saves a list of images to a named folder in the output directory with configurable filename prefix.",
+            short_description="Saves image list to an output folder.",
             is_experimental=True,
             is_output_node=True,
             is_input_list=True,  # Receive images as list
@@ -247,6 +253,8 @@ class SaveImageTextDataSetToFolderNode(io.ComfyNode):
             node_id="SaveImageTextDataSetToFolder",
             display_name="Save Image and Text Dataset to Folder",
             category="dataset",
+            description="Saves paired images and text captions to a named folder in the output directory with configurable filename prefix.",
+            short_description="Saves paired images and text to output folder.",
             is_experimental=True,
             is_output_node=True,
             is_input_list=True,  # Receive both images and texts as lists
@@ -401,6 +409,8 @@ class ImageProcessingNode(io.ComfyNode):
         return io.Schema(
             node_id=cls.node_id,
             display_name=cls.display_name or cls.node_id,
+            description=getattr(cls, 'description', ''),
+            short_description=getattr(cls, 'short_description', ''),
             category="dataset/image",
             is_experimental=True,
             is_input_list=is_group,  # True for group, False for individual
@@ -550,6 +560,8 @@ class TextProcessingNode(io.ComfyNode):
         return io.Schema(
             node_id=cls.node_id,
             display_name=cls.display_name or cls.node_id,
+            description=getattr(cls, 'description', ''),
+            short_description=getattr(cls, 'short_description', ''),
             category="dataset/text",
             is_experimental=True,
             is_input_list=is_group,  # True for group, False for individual
@@ -627,6 +639,7 @@ class ResizeImagesByShorterEdgeNode(ImageProcessingNode):
     node_id = "ResizeImagesByShorterEdge"
     display_name = "Resize Images by Shorter Edge"
     description = "Resize images so that the shorter edge matches the specified length while preserving aspect ratio."
+    short_description = "Resizes images by shorter edge preserving aspect ratio."
     extra_inputs = [
         io.Int.Input(
             "shorter_edge",
@@ -655,6 +668,7 @@ class ResizeImagesByLongerEdgeNode(ImageProcessingNode):
     node_id = "ResizeImagesByLongerEdge"
     display_name = "Resize Images by Longer Edge"
     description = "Resize images so that the longer edge matches the specified length while preserving aspect ratio."
+    short_description = "Resizes images by longer edge preserving aspect ratio."
     extra_inputs = [
         io.Int.Input(
             "longer_edge",
@@ -686,6 +700,7 @@ class CenterCropImagesNode(ImageProcessingNode):
     node_id = "CenterCropImages"
     display_name = "Center Crop Images"
     description = "Center crop all images to the specified dimensions."
+    short_description = None
     extra_inputs = [
         io.Int.Input("width", default=512, min=1, max=8192, tooltip="Crop width."),
         io.Int.Input("height", default=512, min=1, max=8192, tooltip="Crop height."),
@@ -708,6 +723,7 @@ class RandomCropImagesNode(ImageProcessingNode):
     description = (
         "Randomly crop all images to the specified dimensions (for data augmentation)."
     )
+    short_description = None
     extra_inputs = [
         io.Int.Input("width", default=512, min=1, max=8192, tooltip="Crop width."),
         io.Int.Input("height", default=512, min=1, max=8192, tooltip="Crop height."),
@@ -734,6 +750,7 @@ class NormalizeImagesNode(ImageProcessingNode):
     node_id = "NormalizeImages"
     display_name = "Normalize Images"
     description = "Normalize images using mean and standard deviation."
+    short_description = None
     extra_inputs = [
         io.Float.Input(
             "mean",
@@ -760,6 +777,7 @@ class AdjustBrightnessNode(ImageProcessingNode):
     node_id = "AdjustBrightness"
     display_name = "Adjust Brightness"
     description = "Adjust brightness of all images."
+    short_description = None
     extra_inputs = [
         io.Float.Input(
             "factor",
@@ -779,6 +797,7 @@ class AdjustContrastNode(ImageProcessingNode):
     node_id = "AdjustContrast"
     display_name = "Adjust Contrast"
     description = "Adjust contrast of all images."
+    short_description = None
     extra_inputs = [
         io.Float.Input(
             "factor",
@@ -798,6 +817,7 @@ class ShuffleDatasetNode(ImageProcessingNode):
     node_id = "ShuffleDataset"
     display_name = "Shuffle Image Dataset"
     description = "Randomly shuffle the order of images in the dataset."
+    short_description = None
     is_group_process = True  # Requires full list to shuffle
     extra_inputs = [
         io.Int.Input(
@@ -821,6 +841,8 @@ class ShuffleImageTextDatasetNode(io.ComfyNode):
             node_id="ShuffleImageTextDataset",
             display_name="Shuffle Image-Text Dataset",
             category="dataset/image",
+            description="Randomly shuffles paired image and text lists together using a seed, preserving their correspondence.",
+            short_description="Shuffles paired image-text lists together.",
             is_experimental=True,
             is_input_list=True,
             inputs=[
@@ -863,6 +885,7 @@ class TextToLowercaseNode(TextProcessingNode):
     node_id = "TextToLowercase"
     display_name = "Text to Lowercase"
     description = "Convert all texts to lowercase."
+    short_description = None
 
     @classmethod
     def _process(cls, text):
@@ -873,6 +896,7 @@ class TextToUppercaseNode(TextProcessingNode):
     node_id = "TextToUppercase"
     display_name = "Text to Uppercase"
     description = "Convert all texts to uppercase."
+    short_description = None
 
     @classmethod
     def _process(cls, text):
@@ -883,6 +907,7 @@ class TruncateTextNode(TextProcessingNode):
     node_id = "TruncateText"
     display_name = "Truncate Text"
     description = "Truncate all texts to a maximum length."
+    short_description = None
     extra_inputs = [
         io.Int.Input(
             "max_length", default=77, min=1, max=10000, tooltip="Maximum text length."
@@ -898,6 +923,7 @@ class AddTextPrefixNode(TextProcessingNode):
     node_id = "AddTextPrefix"
     display_name = "Add Text Prefix"
     description = "Add a prefix to all texts."
+    short_description = None
     extra_inputs = [
         io.String.Input("prefix", default="", tooltip="Prefix to add."),
     ]
@@ -911,6 +937,7 @@ class AddTextSuffixNode(TextProcessingNode):
     node_id = "AddTextSuffix"
     display_name = "Add Text Suffix"
     description = "Add a suffix to all texts."
+    short_description = None
     extra_inputs = [
         io.String.Input("suffix", default="", tooltip="Suffix to add."),
     ]
@@ -924,6 +951,7 @@ class ReplaceTextNode(TextProcessingNode):
     node_id = "ReplaceText"
     display_name = "Replace Text"
     description = "Replace text in all texts."
+    short_description = None
     extra_inputs = [
         io.String.Input("find", default="", tooltip="Text to find."),
         io.String.Input("replace", default="", tooltip="Text to replace with."),
@@ -938,6 +966,7 @@ class StripWhitespaceNode(TextProcessingNode):
     node_id = "StripWhitespace"
     display_name = "Strip Whitespace"
     description = "Strip leading and trailing whitespace from all texts."
+    short_description = None
 
     @classmethod
     def _process(cls, text):
@@ -953,6 +982,7 @@ class ImageDeduplicationNode(ImageProcessingNode):
     node_id = "ImageDeduplication"
     display_name = "Image Deduplication"
     description = "Remove duplicate or very similar images from the dataset."
+    short_description = None
     is_group_process = True  # Requires full list to compare images
     extra_inputs = [
         io.Float.Input(
@@ -1023,6 +1053,7 @@ class ImageGridNode(ImageProcessingNode):
     node_id = "ImageGrid"
     display_name = "Image Grid"
     description = "Arrange multiple images into a grid layout."
+    short_description = None
     is_group_process = True  # Requires full list to create grid
     is_output_list = False  # Outputs single grid image
     extra_inputs = [
@@ -1097,6 +1128,7 @@ class MergeImageListsNode(ImageProcessingNode):
     node_id = "MergeImageLists"
     display_name = "Merge Image Lists"
     description = "Concatenate multiple image lists into one."
+    short_description = None
     is_group_process = True  # Receives images as list
 
     @classmethod
@@ -1114,6 +1146,7 @@ class MergeTextListsNode(TextProcessingNode):
     node_id = "MergeTextLists"
     display_name = "Merge Text Lists"
     description = "Concatenate multiple text lists into one."
+    short_description = None
     is_group_process = True  # Receives texts as list
 
     @classmethod
@@ -1137,6 +1170,8 @@ class ResolutionBucket(io.ComfyNode):
             node_id="ResolutionBucket",
             display_name="Resolution Bucket",
             category="dataset",
+            description="Groups latents and conditioning by resolution into batched buckets for efficient training with mixed aspect ratios.",
+            short_description="Groups latents by resolution into training buckets.",
             is_experimental=True,
             is_input_list=True,
             inputs=[
@@ -1230,6 +1265,8 @@ class MakeTrainingDataset(io.ComfyNode):
             search_aliases=["encode dataset"],
             display_name="Make Training Dataset",
             category="dataset",
+            description="Encodes images with a VAE and text captions with CLIP to create paired latent and conditioning training data.",
+            short_description="Encodes images and text into training data.",
             is_experimental=True,
             is_input_list=True,  # images and texts as lists
             inputs=[
@@ -1316,6 +1353,8 @@ class SaveTrainingDataset(io.ComfyNode):
             search_aliases=["export training data"],
             display_name="Save Training Dataset",
             category="dataset",
+            description="Saves encoded latent and conditioning training data to disk in sharded files with configurable shard size.",
+            short_description="Saves encoded training data to sharded files.",
             is_experimental=True,
             is_output_node=True,
             is_input_list=True,  # Receive lists
@@ -1417,6 +1456,8 @@ class LoadTrainingDataset(io.ComfyNode):
             search_aliases=["import dataset", "training data"],
             display_name="Load Training Dataset",
             category="dataset",
+            description="Loads a previously saved training dataset of latents and conditioning from sharded files on disk.",
+            short_description="Loads saved training dataset from disk.",
             is_experimental=True,
             inputs=[
                 io.String.Input(

comfy_extras/nodes_differential_diffusion.py

@@ -14,6 +14,8 @@ class DifferentialDiffusion(io.ComfyNode):
             search_aliases=["inpaint gradient", "variable denoise strength"],
             display_name="Differential Diffusion",
             category="_for_testing",
+            description="Enables per-pixel variable denoise strength using a mask, where mask intensity controls how much each region is denoised during sampling.",
+            short_description="Per-pixel variable denoise strength via mask.",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input(

comfy_extras/nodes_easycache.py

@@ -363,6 +363,7 @@ class EasyCacheNode(io.ComfyNode):
             node_id="EasyCache",
             display_name="EasyCache",
             description="Native EasyCache implementation.",
+            short_description=None,
             category="advanced/debug/model",
             is_experimental=True,
             inputs=[
@@ -496,6 +497,7 @@ class LazyCacheNode(io.ComfyNode):
             node_id="LazyCache",
             display_name="LazyCache",
             description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.",
+            short_description="Simpler EasyCache alternative with universal ComfyUI compatibility.",
             category="advanced/debug/model",
             is_experimental=True,
             inputs=[

comfy_extras/nodes_edit_model.py

@@ -10,6 +10,7 @@ class ReferenceLatent(io.ComfyNode):
             node_id="ReferenceLatent",
             category="advanced/conditioning/edit_models",
             description="This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images.",
+            short_description="Sets guiding latent for edit models with chaining support.",
             inputs=[
                 io.Conditioning.Input("conditioning"),
                 io.Latent.Input("latent", optional=True),

comfy_extras/nodes_eps.py

@@ -19,6 +19,8 @@ class EpsilonScaling(io.ComfyNode):
         return io.Schema(
             node_id="Epsilon Scaling",
             category="model_patches/unet",
+            description="Applies epsilon scaling to mitigate exposure bias in diffusion models by scaling the predicted noise after CFG, improving sample quality.",
+            short_description="Scale predicted noise to reduce exposure bias.",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input(
@@ -121,6 +123,7 @@ class TemporalScoreRescaling(io.ComfyNode):
                 "TSR - Temporal Score Rescaling (2510.01184)\n\n"
                 "Rescaling the model's score or noise to steer the sampling diversity.\n"
             ),
+            short_description="Rescales temporal scores to control sampling diversity.",
         )
 
     @classmethod

comfy_extras/nodes_flux.py

@@ -13,6 +13,8 @@ class CLIPTextEncodeFlux(io.ComfyNode):
         return io.Schema(
             node_id="CLIPTextEncodeFlux",
             category="advanced/conditioning/flux",
+            description="Encodes separate CLIP-L and T5-XXL text prompts with a guidance value into Flux conditioning.",
+            short_description="Encodes CLIP-L and T5-XXL prompts for Flux.",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
@@ -40,6 +42,8 @@ class EmptyFlux2LatentImage(io.ComfyNode):
             node_id="EmptyFlux2LatentImage",
             display_name="Empty Flux 2 Latent",
             category="latent",
+            description="Creates an empty Flux 2 latent image tensor with the specified width, height, and batch size.",
+            short_description="Creates an empty Flux 2 latent image tensor.",
             inputs=[
                 io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -61,6 +65,8 @@ class FluxGuidance(io.ComfyNode):
         return io.Schema(
             node_id="FluxGuidance",
             category="advanced/conditioning/flux",
+            description="Sets the guidance strength value on Flux conditioning to control how closely generation follows the prompt.",
+            short_description="Sets guidance strength on Flux conditioning.",
             inputs=[
                 io.Conditioning.Input("conditioning"),
                 io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1),
@@ -85,6 +91,7 @@ class FluxDisableGuidance(io.ComfyNode):
             node_id="FluxDisableGuidance",
             category="advanced/conditioning/flux",
             description="This node completely disables the guidance embed on Flux and Flux like models",
+            short_description="Disables guidance embed on Flux and Flux-like models.",
             inputs=[
                 io.Conditioning.Input("conditioning"),
             ],
@@ -129,6 +136,7 @@ class FluxKontextImageScale(io.ComfyNode):
             node_id="FluxKontextImageScale",
             category="advanced/conditioning/flux",
             description="This node resizes the image to one that is more optimal for flux kontext.",
+            short_description="Resizes images to optimal dimensions for Flux Kontext.",
             inputs=[
                 io.Image.Input("image"),
             ],
@@ -156,6 +164,8 @@ class FluxKontextMultiReferenceLatentMethod(io.ComfyNode):
             node_id="FluxKontextMultiReferenceLatentMethod",
             display_name="Edit Model Reference Method",
             category="advanced/conditioning/flux",
+            description="Selects the method used for handling multiple reference latents in Flux Kontext edit models.",
+            short_description="Selects reference latent method for Flux Kontext.",
             inputs=[
                 io.Conditioning.Input("conditioning"),
                 io.Combo.Input(
@@ -214,6 +224,8 @@ class Flux2Scheduler(io.ComfyNode):
         return io.Schema(
             node_id="Flux2Scheduler",
             category="sampling/custom_sampling/schedulers",
+            description="Generates a sigma schedule for Flux 2 sampling based on step count and image resolution.",
+            short_description="Generates a sigma schedule for Flux 2 sampling.",
             inputs=[
                 io.Int.Input("steps", default=20, min=1, max=4096),
                 io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=1),

comfy_extras/nodes_freelunch.py

@@ -30,6 +30,8 @@ class FreeU(IO.ComfyNode):
         return IO.Schema(
             node_id="FreeU",
             category="model_patches/unet",
+            description="Applies FreeU v1 to a UNet model, boosting backbone features and filtering skip connections using Fourier transforms for improved quality.",
+            short_description="Applies FreeU v1 backbone boost and skip filtering.",
             inputs=[
                 IO.Model.Input("model"),
                 IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01),
@@ -77,6 +79,8 @@ class FreeU_V2(IO.ComfyNode):
         return IO.Schema(
             node_id="FreeU_V2",
             category="model_patches/unet",
+            description="Applies FreeU v2 to a UNet model with adaptive backbone scaling based on hidden state magnitude and Fourier skip filtering.",
+            short_description="Applies FreeU v2 with adaptive scaling.",
             inputs=[
                 IO.Model.Input("model"),
                 IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01),

comfy_extras/nodes_fresca.py

@@ -62,6 +62,7 @@ class FreSca(io.ComfyNode):
             display_name="FreSca",
             category="_for_testing",
             description="Applies frequency-dependent scaling to the guidance",
+            short_description=None,
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("scale_low", default=1.0, min=0, max=10, step=0.01,

comfy_extras/nodes_gits.py

@@ -341,6 +341,8 @@ class GITSScheduler(io.ComfyNode):
         return io.Schema(
             node_id="GITSScheduler",
             category="sampling/custom_sampling/schedulers",
+            description="Generates a noise schedule using the GITS method with precomputed optimal sigma levels and configurable coefficient.",
+            short_description="Generates a GITS noise schedule with optimal sigma levels.",
             inputs=[
                 io.Float.Input("coeff", default=1.20, min=0.80, max=1.50, step=0.05),
                 io.Int.Input("steps", default=10, min=2, max=1000),

comfy_extras/nodes_glsl.py

@@ -1,897 +0,0 @@
-import os
-import sys
-import re
-import logging
-import ctypes.util
-import importlib.util
-from typing import TypedDict
-
-import numpy as np
-import torch
-
-import nodes
-from comfy_api.latest import ComfyExtension, io, ui
-from typing_extensions import override
-from utils.install_util import get_missing_requirements_message
-
-logger = logging.getLogger(__name__)
-
-
-def _check_opengl_availability():
-    """Early check for OpenGL availability. Raises RuntimeError if unlikely to work."""
-    logger.debug("_check_opengl_availability: starting")
-    missing = []
-
-    # Check Python packages (using find_spec to avoid importing)
-    logger.debug("_check_opengl_availability: checking for glfw package")
-    if importlib.util.find_spec("glfw") is None:
-        missing.append("glfw")
-
-    logger.debug("_check_opengl_availability: checking for OpenGL package")
-    if importlib.util.find_spec("OpenGL") is None:
-        missing.append("PyOpenGL")
-
-    if missing:
-        raise RuntimeError(
-            f"OpenGL dependencies not available.\n{get_missing_requirements_message()}\n"
-        )
-
-    # On Linux without display, check if headless backends are available
-    logger.debug(f"_check_opengl_availability: platform={sys.platform}")
-    if sys.platform.startswith("linux"):
-        has_display = os.environ.get("DISPLAY") or os.environ.get("WAYLAND_DISPLAY")
-        logger.debug(f"_check_opengl_availability: has_display={bool(has_display)}")
-        if not has_display:
-            # Check for EGL or OSMesa libraries
-            logger.debug("_check_opengl_availability: checking for EGL library")
-            has_egl = ctypes.util.find_library("EGL")
-            logger.debug("_check_opengl_availability: checking for OSMesa library")
-            has_osmesa = ctypes.util.find_library("OSMesa")
-
-            # Error disabled for CI as it fails this check
-            # if not has_egl and not has_osmesa:
-            #     raise RuntimeError(
-            #         "GLSL Shader node: No display and no headless backend (EGL/OSMesa) found.\n"
-            #         "See error below for installation instructions."
-            #     )
-            logger.debug(f"Headless mode: EGL={'yes' if has_egl else 'no'}, OSMesa={'yes' if has_osmesa else 'no'}")
-
-    logger.debug("_check_opengl_availability: completed")
-
-
-# Run early check at import time
-logger.debug("nodes_glsl: running _check_opengl_availability at import time")
-_check_opengl_availability()
-
-# OpenGL modules - initialized lazily when context is created
-gl = None
-glfw = None
-EGL = None
-
-
-def _import_opengl():
-    """Import OpenGL module. Called after context is created."""
-    global gl
-    if gl is None:
-        logger.debug("_import_opengl: importing OpenGL.GL")
-        import OpenGL.GL as _gl
-        gl = _gl
-        logger.debug("_import_opengl: import completed")
-    return gl
-
-
-class SizeModeInput(TypedDict):
-    size_mode: str
-    width: int
-    height: int
-
-
-MAX_IMAGES = 5      # u_image0-4
-MAX_UNIFORMS = 5    # u_float0-4, u_int0-4
-MAX_OUTPUTS = 4     # fragColor0-3 (MRT)
-
-# Vertex shader using gl_VertexID trick - no VBO needed.
-# Draws a single triangle that covers the entire screen:
-#
-#     (-1,3)
-#       /|
-#      / |  <- visible area is the unit square from (-1,-1) to (1,1)
-#     /  |     parts outside get clipped away
-# (-1,-1)---(3,-1)
-#
-# v_texCoord is computed from clip space: * 0.5 + 0.5 maps (-1,1) -> (0,1)
-VERTEX_SHADER = """#version 330 core
-out vec2 v_texCoord;
-void main() {
-    vec2 verts[3] = vec2[](vec2(-1, -1), vec2(3, -1), vec2(-1, 3));
-    v_texCoord = verts[gl_VertexID] * 0.5 + 0.5;
-    gl_Position = vec4(verts[gl_VertexID], 0, 1);
-}
-"""
-
-DEFAULT_FRAGMENT_SHADER = """#version 300 es
-precision highp float;
-
-uniform sampler2D u_image0;
-uniform vec2 u_resolution;
-
-in vec2 v_texCoord;
-layout(location = 0) out vec4 fragColor0;
-
-void main() {
-    fragColor0 = texture(u_image0, v_texCoord);
-}
-"""
-
-
-def _convert_es_to_desktop(source: str) -> str:
-    """Convert GLSL ES (WebGL) shader source to desktop GLSL 330 core."""
-    # Remove any existing #version directive
-    source = re.sub(r"#version\s+\d+(\s+es)?\s*\n?", "", source, flags=re.IGNORECASE)
-    # Remove precision qualifiers (not needed in desktop GLSL)
-    source = re.sub(r"precision\s+(lowp|mediump|highp)\s+\w+\s*;\s*\n?", "", source)
-    # Prepend desktop GLSL version
-    return "#version 330 core\n" + source
-
-
-def _detect_output_count(source: str) -> int:
-    """Detect how many fragColor outputs are used in the shader.
-
-    Returns the count of outputs needed (1 to MAX_OUTPUTS).
-    """
-    matches = re.findall(r"fragColor(\d+)", source)
-    if not matches:
-        return 1  # Default to 1 output if none found
-    max_index = max(int(m) for m in matches)
-    return min(max_index + 1, MAX_OUTPUTS)
-
-
-def _detect_pass_count(source: str) -> int:
-    """Detect multi-pass rendering from #pragma passes N directive.
-
-    Returns the number of passes (1 if not specified).
-    """
-    match = re.search(r'#pragma\s+passes\s+(\d+)', source)
-    if match:
-        return max(1, int(match.group(1)))
-    return 1
-
-
-def _init_glfw():
-    """Initialize GLFW. Returns (window, glfw_module). Raises RuntimeError on failure."""
-    logger.debug("_init_glfw: starting")
-    # On macOS, glfw.init() must be called from main thread or it hangs forever
-    if sys.platform == "darwin":
-        logger.debug("_init_glfw: skipping on macOS")
-        raise RuntimeError("GLFW backend not supported on macOS")
-
-    logger.debug("_init_glfw: importing glfw module")
-    import glfw as _glfw
-
-    logger.debug("_init_glfw: calling glfw.init()")
-    if not _glfw.init():
-        raise RuntimeError("glfw.init() failed")
-
-    try:
-        logger.debug("_init_glfw: setting window hints")
-        _glfw.window_hint(_glfw.VISIBLE, _glfw.FALSE)
-        _glfw.window_hint(_glfw.CONTEXT_VERSION_MAJOR, 3)
-        _glfw.window_hint(_glfw.CONTEXT_VERSION_MINOR, 3)
-        _glfw.window_hint(_glfw.OPENGL_PROFILE, _glfw.OPENGL_CORE_PROFILE)
-
-        logger.debug("_init_glfw: calling create_window()")
-        window = _glfw.create_window(64, 64, "ComfyUI GLSL", None, None)
-        if not window:
-            raise RuntimeError("glfw.create_window() failed")
-
-        logger.debug("_init_glfw: calling make_context_current()")
-        _glfw.make_context_current(window)
-        logger.debug("_init_glfw: completed successfully")
-        return window, _glfw
-    except Exception:
-        logger.debug("_init_glfw: failed, terminating glfw")
-        _glfw.terminate()
-        raise
-
-
-def _init_egl():
-    """Initialize EGL for headless rendering. Returns (display, context, surface, EGL_module). Raises RuntimeError on failure."""
-    logger.debug("_init_egl: starting")
-    from OpenGL import EGL as _EGL
-    from OpenGL.EGL import (
-        eglGetDisplay, eglInitialize, eglChooseConfig, eglCreateContext,
-        eglMakeCurrent, eglCreatePbufferSurface, eglBindAPI,
-        eglTerminate, eglDestroyContext, eglDestroySurface,
-        EGL_DEFAULT_DISPLAY, EGL_NO_CONTEXT, EGL_NONE,
-        EGL_SURFACE_TYPE, EGL_PBUFFER_BIT, EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT,
-        EGL_RED_SIZE, EGL_GREEN_SIZE, EGL_BLUE_SIZE, EGL_ALPHA_SIZE, EGL_DEPTH_SIZE,
-        EGL_WIDTH, EGL_HEIGHT, EGL_OPENGL_API,
-    )
-    logger.debug("_init_egl: imports completed")
-
-    display = None
-    context = None
-    surface = None
-
-    try:
-        logger.debug("_init_egl: calling eglGetDisplay()")
-        display = eglGetDisplay(EGL_DEFAULT_DISPLAY)
-        if display == _EGL.EGL_NO_DISPLAY:
-            raise RuntimeError("eglGetDisplay() failed")
-
-        logger.debug("_init_egl: calling eglInitialize()")
-        major, minor = _EGL.EGLint(), _EGL.EGLint()
-        if not eglInitialize(display, major, minor):
-            display = None  # Not initialized, don't terminate
-            raise RuntimeError("eglInitialize() failed")
-        logger.debug(f"_init_egl: EGL version {major.value}.{minor.value}")
-
-        config_attribs = [
-            EGL_SURFACE_TYPE, EGL_PBUFFER_BIT,
-            EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT,
-            EGL_RED_SIZE, 8, EGL_GREEN_SIZE, 8, EGL_BLUE_SIZE, 8, EGL_ALPHA_SIZE, 8,
-            EGL_DEPTH_SIZE, 0, EGL_NONE
-        ]
-        configs = (_EGL.EGLConfig * 1)()
-        num_configs = _EGL.EGLint()
-        if not eglChooseConfig(display, config_attribs, configs, 1, num_configs) or num_configs.value == 0:
-            raise RuntimeError("eglChooseConfig() failed")
-        config = configs[0]
-        logger.debug(f"_init_egl: config chosen, num_configs={num_configs.value}")
-
-        if not eglBindAPI(EGL_OPENGL_API):
-            raise RuntimeError("eglBindAPI() failed")
-
-        logger.debug("_init_egl: calling eglCreateContext()")
-        context_attribs = [
-            _EGL.EGL_CONTEXT_MAJOR_VERSION, 3,
-            _EGL.EGL_CONTEXT_MINOR_VERSION, 3,
-            _EGL.EGL_CONTEXT_OPENGL_PROFILE_MASK, _EGL.EGL_CONTEXT_OPENGL_CORE_PROFILE_BIT,
-            EGL_NONE
-        ]
-        context = eglCreateContext(display, config, EGL_NO_CONTEXT, context_attribs)
-        if context == EGL_NO_CONTEXT:
-            raise RuntimeError("eglCreateContext() failed")
-
-        logger.debug("_init_egl: calling eglCreatePbufferSurface()")
-        pbuffer_attribs = [EGL_WIDTH, 64, EGL_HEIGHT, 64, EGL_NONE]
-        surface = eglCreatePbufferSurface(display, config, pbuffer_attribs)
-        if surface == _EGL.EGL_NO_SURFACE:
-            raise RuntimeError("eglCreatePbufferSurface() failed")
-
-        logger.debug("_init_egl: calling eglMakeCurrent()")
-        if not eglMakeCurrent(display, surface, surface, context):
-            raise RuntimeError("eglMakeCurrent() failed")
-
-        logger.debug("_init_egl: completed successfully")
-        return display, context, surface, _EGL
-
-    except Exception:
-        logger.debug("_init_egl: failed, cleaning up")
-        # Clean up any resources on failure
-        if surface is not None:
-            eglDestroySurface(display, surface)
-        if context is not None:
-            eglDestroyContext(display, context)
-        if display is not None:
-            eglTerminate(display)
-        raise
-
-
-def _init_osmesa():
-    """Initialize OSMesa for software rendering. Returns (context, buffer). Raises RuntimeError on failure."""
-    import ctypes
-
-    logger.debug("_init_osmesa: starting")
-    os.environ["PYOPENGL_PLATFORM"] = "osmesa"
-
-    logger.debug("_init_osmesa: importing OpenGL.osmesa")
-    from OpenGL import GL as _gl
-    from OpenGL.osmesa import (
-        OSMesaCreateContextExt, OSMesaMakeCurrent, OSMesaDestroyContext,
-        OSMESA_RGBA,
-    )
-    logger.debug("_init_osmesa: imports completed")
-
-    ctx = OSMesaCreateContextExt(OSMESA_RGBA, 24, 0, 0, None)
-    if not ctx:
-        raise RuntimeError("OSMesaCreateContextExt() failed")
-
-    width, height = 64, 64
-    buffer = (ctypes.c_ubyte * (width * height * 4))()
-
-    logger.debug("_init_osmesa: calling OSMesaMakeCurrent()")
-    if not OSMesaMakeCurrent(ctx, buffer, _gl.GL_UNSIGNED_BYTE, width, height):
-        OSMesaDestroyContext(ctx)
-        raise RuntimeError("OSMesaMakeCurrent() failed")
-
-    logger.debug("_init_osmesa: completed successfully")
-    return ctx, buffer
-
-
-class GLContext:
-    """Manages OpenGL context and resources for shader execution.
-
-    Tries backends in order: GLFW (desktop) → EGL (headless GPU) → OSMesa (software).
-    """
-
-    _instance = None
-    _initialized = False
-
-    def __new__(cls):
-        if cls._instance is None:
-            cls._instance = super().__new__(cls)
-        return cls._instance
-
-    def __init__(self):
-        if GLContext._initialized:
-            logger.debug("GLContext.__init__: already initialized, skipping")
-            return
-
-        logger.debug("GLContext.__init__: starting initialization")
-
-        global glfw, EGL
-
-        import time
-        start = time.perf_counter()
-
-        self._backend = None
-        self._window = None
-        self._egl_display = None
-        self._egl_context = None
-        self._egl_surface = None
-        self._osmesa_ctx = None
-        self._osmesa_buffer = None
-        self._vao = None
-
-        # Try backends in order: GLFW → EGL → OSMesa
-        errors = []
-
-        logger.debug("GLContext.__init__: trying GLFW backend")
-        try:
-            self._window, glfw = _init_glfw()
-            self._backend = "glfw"
-            logger.debug("GLContext.__init__: GLFW backend succeeded")
-        except Exception as e:
-            logger.debug(f"GLContext.__init__: GLFW backend failed: {e}")
-            errors.append(("GLFW", e))
-
-        if self._backend is None:
-            logger.debug("GLContext.__init__: trying EGL backend")
-            try:
-                self._egl_display, self._egl_context, self._egl_surface, EGL = _init_egl()
-                self._backend = "egl"
-                logger.debug("GLContext.__init__: EGL backend succeeded")
-            except Exception as e:
-                logger.debug(f"GLContext.__init__: EGL backend failed: {e}")
-                errors.append(("EGL", e))
-
-        if self._backend is None:
-            logger.debug("GLContext.__init__: trying OSMesa backend")
-            try:
-                self._osmesa_ctx, self._osmesa_buffer = _init_osmesa()
-                self._backend = "osmesa"
-                logger.debug("GLContext.__init__: OSMesa backend succeeded")
-            except Exception as e:
-                logger.debug(f"GLContext.__init__: OSMesa backend failed: {e}")
-                errors.append(("OSMesa", e))
-
-        if self._backend is None:
-            if sys.platform == "win32":
-                platform_help = (
-                    "Windows: Ensure GPU drivers are installed and display is available.\n"
-                    "         CPU-only/headless mode is not supported on Windows."
-                )
-            elif sys.platform == "darwin":
-                platform_help = (
-                    "macOS: GLFW is not supported.\n"
-                    "  Install OSMesa via Homebrew: brew install mesa\n"
-                    "  Then: pip install PyOpenGL PyOpenGL-accelerate"
-                )
-            else:
-                platform_help = (
-                    "Linux: Install one of these backends:\n"
-                    "  Desktop:           sudo apt install libgl1-mesa-glx libglfw3\n"
-                    "  Headless with GPU: sudo apt install libegl1-mesa libgl1-mesa-dri\n"
-                    "  Headless (CPU):    sudo apt install libosmesa6"
-                )
-
-            error_details = "\n".join(f"  {name}: {err}" for name, err in errors)
-            raise RuntimeError(
-                f"Failed to create OpenGL context.\n\n"
-                f"Backend errors:\n{error_details}\n\n"
-                f"{platform_help}"
-            )
-
-        # Now import OpenGL.GL (after context is current)
-        logger.debug("GLContext.__init__: importing OpenGL.GL")
-        _import_opengl()
-
-        # Create VAO (required for core profile, but OSMesa may use compat profile)
-        logger.debug("GLContext.__init__: creating VAO")
-        try:
-            vao = gl.glGenVertexArrays(1)
-            gl.glBindVertexArray(vao)
-            self._vao = vao  # Only store after successful bind
-            logger.debug("GLContext.__init__: VAO created successfully")
-        except Exception as e:
-            logger.debug(f"GLContext.__init__: VAO creation failed (may be expected for OSMesa): {e}")
-            # OSMesa with older Mesa may not support VAOs
-            # Clean up if we created but couldn't bind
-            if vao:
-                try:
-                    gl.glDeleteVertexArrays(1, [vao])
-                except Exception:
-                    pass
-
-        elapsed = (time.perf_counter() - start) * 1000
-
-        # Log device info
-        renderer = gl.glGetString(gl.GL_RENDERER)
-        vendor = gl.glGetString(gl.GL_VENDOR)
-        version = gl.glGetString(gl.GL_VERSION)
-        renderer = renderer.decode() if renderer else "Unknown"
-        vendor = vendor.decode() if vendor else "Unknown"
-        version = version.decode() if version else "Unknown"
-
-        GLContext._initialized = True
-        logger.info(f"GLSL context initialized in {elapsed:.1f}ms ({self._backend}) - {renderer} ({vendor}), GL {version}")
-
-    def make_current(self):
-        if self._backend == "glfw":
-            glfw.make_context_current(self._window)
-        elif self._backend == "egl":
-            from OpenGL.EGL import eglMakeCurrent
-            eglMakeCurrent(self._egl_display, self._egl_surface, self._egl_surface, self._egl_context)
-        elif self._backend == "osmesa":
-            from OpenGL.osmesa import OSMesaMakeCurrent
-            OSMesaMakeCurrent(self._osmesa_ctx, self._osmesa_buffer, gl.GL_UNSIGNED_BYTE, 64, 64)
-
-        if self._vao is not None:
-            gl.glBindVertexArray(self._vao)
-
-
-def _compile_shader(source: str, shader_type: int) -> int:
-    """Compile a shader and return its ID."""
-    shader = gl.glCreateShader(shader_type)
-    gl.glShaderSource(shader, source)
-    gl.glCompileShader(shader)
-
-    if gl.glGetShaderiv(shader, gl.GL_COMPILE_STATUS) != gl.GL_TRUE:
-        error = gl.glGetShaderInfoLog(shader).decode()
-        gl.glDeleteShader(shader)
-        raise RuntimeError(f"Shader compilation failed:\n{error}")
-
-    return shader
-
-
-def _create_program(vertex_source: str, fragment_source: str) -> int:
-    """Create and link a shader program."""
-    vertex_shader = _compile_shader(vertex_source, gl.GL_VERTEX_SHADER)
-    try:
-        fragment_shader = _compile_shader(fragment_source, gl.GL_FRAGMENT_SHADER)
-    except RuntimeError:
-        gl.glDeleteShader(vertex_shader)
-        raise
-
-    program = gl.glCreateProgram()
-    gl.glAttachShader(program, vertex_shader)
-    gl.glAttachShader(program, fragment_shader)
-    gl.glLinkProgram(program)
-
-    gl.glDeleteShader(vertex_shader)
-    gl.glDeleteShader(fragment_shader)
-
-    if gl.glGetProgramiv(program, gl.GL_LINK_STATUS) != gl.GL_TRUE:
-        error = gl.glGetProgramInfoLog(program).decode()
-        gl.glDeleteProgram(program)
-        raise RuntimeError(f"Program linking failed:\n{error}")
-
-    return program
-
-
-def _render_shader_batch(
-    fragment_code: str,
-    width: int,
-    height: int,
-    image_batches: list[list[np.ndarray]],
-    floats: list[float],
-    ints: list[int],
-) -> list[list[np.ndarray]]:
-    """
-    Render a fragment shader for multiple batches efficiently.
-
-    Compiles shader once, reuses framebuffer/textures across batches.
-    Supports multi-pass rendering via #pragma passes N directive.
-
-    Args:
-        fragment_code: User's fragment shader code
-        width: Output width
-        height: Output height
-        image_batches: List of batches, each batch is a list of input images (H, W, C) float32 [0,1]
-        floats: List of float uniforms
-        ints: List of int uniforms
-
-    Returns:
-        List of batch outputs, each is a list of output images (H, W, 4) float32 [0,1]
-    """
-    import time
-    start_time = time.perf_counter()
-
-    if not image_batches:
-        return []
-
-    ctx = GLContext()
-    ctx.make_current()
-
-    # Convert from GLSL ES to desktop GLSL 330
-    fragment_source = _convert_es_to_desktop(fragment_code)
-
-    # Detect how many outputs the shader actually uses
-    num_outputs = _detect_output_count(fragment_code)
-
-    # Detect multi-pass rendering
-    num_passes = _detect_pass_count(fragment_code)
-
-    # Track resources for cleanup
-    program = None
-    fbo = None
-    output_textures = []
-    input_textures = []
-    ping_pong_textures = []
-    ping_pong_fbos = []
-
-    num_inputs = len(image_batches[0])
-
-    try:
-        # Compile shaders (once for all batches)
-        try:
-            program = _create_program(VERTEX_SHADER, fragment_source)
-        except RuntimeError:
-            logger.error(f"Fragment shader:\n{fragment_source}")
-            raise
-
-        gl.glUseProgram(program)
-
-        # Create framebuffer with only the needed color attachments
-        fbo = gl.glGenFramebuffers(1)
-        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
-
-        draw_buffers = []
-        for i in range(num_outputs):
-            tex = gl.glGenTextures(1)
-            output_textures.append(tex)
-            gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
-            gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA32F, width, height, 0, gl.GL_RGBA, gl.GL_FLOAT, None)
-            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
-            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
-            gl.glFramebufferTexture2D(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0 + i, gl.GL_TEXTURE_2D, tex, 0)
-            draw_buffers.append(gl.GL_COLOR_ATTACHMENT0 + i)
-
-        gl.glDrawBuffers(num_outputs, draw_buffers)
-
-        if gl.glCheckFramebufferStatus(gl.GL_FRAMEBUFFER) != gl.GL_FRAMEBUFFER_COMPLETE:
-            raise RuntimeError("Framebuffer is not complete")
-
-        # Create ping-pong resources for multi-pass rendering
-        if num_passes > 1:
-            for _ in range(2):
-                pp_tex = gl.glGenTextures(1)
-                ping_pong_textures.append(pp_tex)
-                gl.glBindTexture(gl.GL_TEXTURE_2D, pp_tex)
-                gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA32F, width, height, 0, gl.GL_RGBA, gl.GL_FLOAT, None)
-                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
-                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
-                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE)
-                gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE)
-
-                pp_fbo = gl.glGenFramebuffers(1)
-                ping_pong_fbos.append(pp_fbo)
-                gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, pp_fbo)
-                gl.glFramebufferTexture2D(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0, gl.GL_TEXTURE_2D, pp_tex, 0)
-                gl.glDrawBuffers(1, [gl.GL_COLOR_ATTACHMENT0])
-
-                if gl.glCheckFramebufferStatus(gl.GL_FRAMEBUFFER) != gl.GL_FRAMEBUFFER_COMPLETE:
-                    raise RuntimeError("Ping-pong framebuffer is not complete")
-
-        # Create input textures (reused for all batches)
-        for i in range(num_inputs):
-            tex = gl.glGenTextures(1)
-            input_textures.append(tex)
-            gl.glActiveTexture(gl.GL_TEXTURE0 + i)
-            gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
-            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
-            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
-            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE)
-            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE)
-
-            loc = gl.glGetUniformLocation(program, f"u_image{i}")
-            if loc >= 0:
-                gl.glUniform1i(loc, i)
-
-        # Set static uniforms (once for all batches)
-        loc = gl.glGetUniformLocation(program, "u_resolution")
-        if loc >= 0:
-            gl.glUniform2f(loc, float(width), float(height))
-
-        for i, v in enumerate(floats):
-            loc = gl.glGetUniformLocation(program, f"u_float{i}")
-            if loc >= 0:
-                gl.glUniform1f(loc, v)
-
-        for i, v in enumerate(ints):
-            loc = gl.glGetUniformLocation(program, f"u_int{i}")
-            if loc >= 0:
-                gl.glUniform1i(loc, v)
-
-        # Get u_pass uniform location for multi-pass
-        pass_loc = gl.glGetUniformLocation(program, "u_pass")
-
-        gl.glViewport(0, 0, width, height)
-        gl.glDisable(gl.GL_BLEND)  # Ensure no alpha blending - write output directly
-
-        # Process each batch
-        all_batch_outputs = []
-        for images in image_batches:
-            # Update input textures with this batch's images
-            for i, img in enumerate(images):
-                gl.glActiveTexture(gl.GL_TEXTURE0 + i)
-                gl.glBindTexture(gl.GL_TEXTURE_2D, input_textures[i])
-
-                # Flip vertically for GL coordinates, ensure RGBA
-                h, w, c = img.shape
-                if c == 3:
-                    img_upload = np.empty((h, w, 4), dtype=np.float32)
-                    img_upload[:, :, :3] = img[::-1, :, :]
-                    img_upload[:, :, 3] = 1.0
-                else:
-                    img_upload = np.ascontiguousarray(img[::-1, :, :])
-
-                gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA32F, w, h, 0, gl.GL_RGBA, gl.GL_FLOAT, img_upload)
-
-            if num_passes == 1:
-                # Single pass - render directly to output FBO
-                gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
-                if pass_loc >= 0:
-                    gl.glUniform1i(pass_loc, 0)
-                gl.glClearColor(0, 0, 0, 0)
-                gl.glClear(gl.GL_COLOR_BUFFER_BIT)
-                gl.glDrawArrays(gl.GL_TRIANGLES, 0, 3)
-            else:
-                # Multi-pass rendering with ping-pong
-                for p in range(num_passes):
-                    is_last_pass = (p == num_passes - 1)
-
-                    # Set pass uniform
-                    if pass_loc >= 0:
-                        gl.glUniform1i(pass_loc, p)
-
-                    if is_last_pass:
-                        # Last pass renders to the main output FBO
-                        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
-                    else:
-                        # Intermediate passes render to ping-pong FBO
-                        target_fbo = ping_pong_fbos[p % 2]
-                        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, target_fbo)
-
-                    # Set input texture for this pass
-                    gl.glActiveTexture(gl.GL_TEXTURE0)
-                    if p == 0:
-                        # First pass reads from original input
-                        gl.glBindTexture(gl.GL_TEXTURE_2D, input_textures[0])
-                    else:
-                        # Subsequent passes read from previous pass output
-                        source_tex = ping_pong_textures[(p - 1) % 2]
-                        gl.glBindTexture(gl.GL_TEXTURE_2D, source_tex)
-
-                    gl.glClearColor(0, 0, 0, 0)
-                    gl.glClear(gl.GL_COLOR_BUFFER_BIT)
-                    gl.glDrawArrays(gl.GL_TRIANGLES, 0, 3)
-
-            # Read back outputs for this batch
-            # (glGetTexImage is synchronous, implicitly waits for rendering)
-            batch_outputs = []
-            for tex in output_textures:
-                gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
-                data = gl.glGetTexImage(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA, gl.GL_FLOAT)
-                img = np.frombuffer(data, dtype=np.float32).reshape(height, width, 4)
-                batch_outputs.append(np.ascontiguousarray(img[::-1, :, :]))
-
-            # Pad with black images for unused outputs
-            black_img = np.zeros((height, width, 4), dtype=np.float32)
-            for _ in range(num_outputs, MAX_OUTPUTS):
-                batch_outputs.append(black_img)
-
-            all_batch_outputs.append(batch_outputs)
-
-        elapsed = (time.perf_counter() - start_time) * 1000
-        num_batches = len(image_batches)
-        pass_info = f", {num_passes} passes" if num_passes > 1 else ""
-        logger.info(f"GLSL shader executed in {elapsed:.1f}ms ({num_batches} batch{'es' if num_batches != 1 else ''}, {width}x{height}{pass_info})")
-
-        return all_batch_outputs
-
-    finally:
-        # Unbind before deleting
-        gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, 0)
-        gl.glUseProgram(0)
-
-        if input_textures:
-            gl.glDeleteTextures(len(input_textures), input_textures)
-        if output_textures:
-            gl.glDeleteTextures(len(output_textures), output_textures)
-        if ping_pong_textures:
-            gl.glDeleteTextures(len(ping_pong_textures), ping_pong_textures)
-        if fbo is not None:
-            gl.glDeleteFramebuffers(1, [fbo])
-        for pp_fbo in ping_pong_fbos:
-            gl.glDeleteFramebuffers(1, [pp_fbo])
-        if program is not None:
-            gl.glDeleteProgram(program)
-
-class GLSLShader(io.ComfyNode):
-
-    @classmethod
-    def define_schema(cls) -> io.Schema:
-        image_template = io.Autogrow.TemplatePrefix(
-            io.Image.Input("image"),
-            prefix="image",
-            min=1,
-            max=MAX_IMAGES,
-        )
-
-        float_template = io.Autogrow.TemplatePrefix(
-            io.Float.Input("float", default=0.0),
-            prefix="u_float",
-            min=0,
-            max=MAX_UNIFORMS,
-        )
-
-        int_template = io.Autogrow.TemplatePrefix(
-            io.Int.Input("int", default=0),
-            prefix="u_int",
-            min=0,
-            max=MAX_UNIFORMS,
-        )
-
-        return io.Schema(
-            node_id="GLSLShader",
-            display_name="GLSL Shader",
-            category="image/shader",
-            description=(
-                f"Apply GLSL fragment shaders to images. "
-                f"Inputs: u_image0-{MAX_IMAGES-1} (sampler2D), u_resolution (vec2), "
-                f"u_float0-{MAX_UNIFORMS-1}, u_int0-{MAX_UNIFORMS-1}. "
-                f"Outputs: layout(location = 0-{MAX_OUTPUTS-1}) out vec4 fragColor0-{MAX_OUTPUTS-1}."
-            ),
-            inputs=[
-                io.String.Input(
-                    "fragment_shader",
-                    default=DEFAULT_FRAGMENT_SHADER,
-                    multiline=True,
-                    tooltip="GLSL fragment shader source code (GLSL ES 3.00 / WebGL 2.0 compatible)",
-                ),
-                io.DynamicCombo.Input(
-                    "size_mode",
-                    options=[
-                        io.DynamicCombo.Option("from_input", []),
-                        io.DynamicCombo.Option(
-                            "custom",
-                            [
-                                io.Int.Input(
-                                    "width",
-                                    default=512,
-                                    min=1,
-                                    max=nodes.MAX_RESOLUTION,
-                                ),
-                                io.Int.Input(
-                                    "height",
-                                    default=512,
-                                    min=1,
-                                    max=nodes.MAX_RESOLUTION,
-                                ),
-                            ],
-                        ),
-                    ],
-                    tooltip="Output size: 'from_input' uses first input image dimensions, 'custom' allows manual size",
-                ),
-                io.Autogrow.Input("images", template=image_template),
-                io.Autogrow.Input("floats", template=float_template),
-                io.Autogrow.Input("ints", template=int_template),
-            ],
-            outputs=[
-                io.Image.Output(display_name="IMAGE0"),
-                io.Image.Output(display_name="IMAGE1"),
-                io.Image.Output(display_name="IMAGE2"),
-                io.Image.Output(display_name="IMAGE3"),
-            ],
-        )
-
-    @classmethod
-    def execute(
-        cls,
-        fragment_shader: str,
-        size_mode: SizeModeInput,
-        images: io.Autogrow.Type,
-        floats: io.Autogrow.Type = None,
-        ints: io.Autogrow.Type = None,
-        **kwargs,
-    ) -> io.NodeOutput:
-        image_list = [v for v in images.values() if v is not None]
-        float_list = (
-            [v if v is not None else 0.0 for v in floats.values()] if floats else []
-        )
-        int_list = [v if v is not None else 0 for v in ints.values()] if ints else []
-
-        if not image_list:
-            raise ValueError("At least one input image is required")
-
-        # Determine output dimensions
-        if size_mode["size_mode"] == "custom":
-            out_width = size_mode["width"]
-            out_height = size_mode["height"]
-        else:
-            out_height, out_width = image_list[0].shape[1:3]
-
-        batch_size = image_list[0].shape[0]
-
-        # Prepare batches
-        image_batches = []
-        for batch_idx in range(batch_size):
-            batch_images = [img_tensor[batch_idx].cpu().numpy().astype(np.float32) for img_tensor in image_list]
-            image_batches.append(batch_images)
-
-        all_batch_outputs = _render_shader_batch(
-            fragment_shader,
-            out_width,
-            out_height,
-            image_batches,
-            float_list,
-            int_list,
-        )
-
-        # Collect outputs into tensors
-        all_outputs = [[] for _ in range(MAX_OUTPUTS)]
-        for batch_outputs in all_batch_outputs:
-            for i, out_img in enumerate(batch_outputs):
-                all_outputs[i].append(torch.from_numpy(out_img))
-
-        output_tensors = [torch.stack(all_outputs[i], dim=0) for i in range(MAX_OUTPUTS)]
-        return io.NodeOutput(
-            *output_tensors,
-            ui=cls._build_ui_output(image_list, output_tensors[0]),
-        )
-
-    @classmethod
-    def _build_ui_output(
-        cls, image_list: list[torch.Tensor], output_batch: torch.Tensor
-    ) -> dict[str, list]:
-        """Build UI output with input and output images for client-side shader execution."""
-        combined_inputs = torch.cat(image_list, dim=0)
-        input_images_ui = ui.ImageSaveHelper.save_images(
-            combined_inputs,
-            filename_prefix="GLSLShader_input",
-            folder_type=io.FolderType.temp,
-            cls=None,
-            compress_level=1,
-        )
-
-        output_images_ui = ui.ImageSaveHelper.save_images(
-            output_batch,
-            filename_prefix="GLSLShader_output",
-            folder_type=io.FolderType.temp,
-            cls=None,
-            compress_level=1,
-        )
-
-        return {"input_images": input_images_ui, "images": output_images_ui}
-
-
-class GLSLExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [GLSLShader]
-
-
-async def comfy_entrypoint() -> GLSLExtension:
-    return GLSLExtension()

comfy_extras/nodes_hidream.py

@@ -13,6 +13,7 @@ class QuadrupleCLIPLoader(io.ComfyNode):
             node_id="QuadrupleCLIPLoader",
             category="advanced/loaders",
             description="[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct",
+            short_description=None,
             inputs=[
                 io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")),
                 io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")),
@@ -40,6 +41,8 @@ class CLIPTextEncodeHiDream(io.ComfyNode):
             node_id="CLIPTextEncodeHiDream",
             search_aliases=["hidream prompt"],
             category="advanced/conditioning",
+            description="Encodes separate CLIP-L, CLIP-G, T5-XXL, and Llama text prompts into HiDream conditioning.",
+            short_description="Encodes multi-encoder text prompts for HiDream.",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("clip_l", multiline=True, dynamic_prompts=True),

comfy_extras/nodes_hooks.py

@@ -38,6 +38,8 @@ class PairConditioningSetProperties:
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
     RETURN_NAMES = ("positive", "negative")
     CATEGORY = "advanced/hooks/cond pair"
+    DESCRIPTION = "Set properties like strength, mask, hooks, and timesteps on a positive/negative conditioning pair."
+    SHORT_DESCRIPTION = "Set properties on a positive/negative conditioning pair."
     FUNCTION = "set_properties"
 
     def set_properties(self, positive_NEW, negative_NEW,
@@ -73,6 +75,8 @@ class PairConditioningSetPropertiesAndCombine:
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
     RETURN_NAMES = ("positive", "negative")
     CATEGORY = "advanced/hooks/cond pair"
+    DESCRIPTION = "Set properties on new conditioning pair and combine with existing positive/negative conditioning."
+    SHORT_DESCRIPTION = "Set properties on new cond pair, combine with existing."
     FUNCTION = "set_properties"
 
     def set_properties(self, positive, negative, positive_NEW, negative_NEW,
@@ -104,6 +108,8 @@ class ConditioningSetProperties:
     EXPERIMENTAL = True
     RETURN_TYPES = ("CONDITIONING",)
     CATEGORY = "advanced/hooks/cond single"
+    DESCRIPTION = "Set properties like strength, mask, hooks, and timesteps on a single conditioning input."
+    SHORT_DESCRIPTION = "Set properties on a single conditioning input."
     FUNCTION = "set_properties"
 
     def set_properties(self, cond_NEW,
@@ -136,6 +142,8 @@ class ConditioningSetPropertiesAndCombine:
     EXPERIMENTAL = True
     RETURN_TYPES = ("CONDITIONING",)
     CATEGORY = "advanced/hooks/cond single"
+    DESCRIPTION = "Set properties on new conditioning and combine it with an existing conditioning input."
+    SHORT_DESCRIPTION = "Set properties on new conditioning, combine with existing."
     FUNCTION = "set_properties"
 
     def set_properties(self, cond, cond_NEW,
@@ -164,6 +172,8 @@ class PairConditioningCombine:
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
     RETURN_NAMES = ("positive", "negative")
     CATEGORY = "advanced/hooks/cond pair"
+    DESCRIPTION = "Combine two positive/negative conditioning pairs into a single pair."
+    SHORT_DESCRIPTION = None
     FUNCTION = "combine"
 
     def combine(self, positive_A, negative_A, positive_B, negative_B):
@@ -191,6 +201,8 @@ class PairConditioningSetDefaultAndCombine:
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
     RETURN_NAMES = ("positive", "negative")
     CATEGORY = "advanced/hooks/cond pair"
+    DESCRIPTION = "Set default conditioning pair and combine it with existing positive/negative conditioning and optional hooks."
+    SHORT_DESCRIPTION = "Set default cond pair and combine with existing."
     FUNCTION = "set_default_and_combine"
 
     def set_default_and_combine(self, positive, negative, positive_DEFAULT, negative_DEFAULT,
@@ -217,6 +229,8 @@ class ConditioningSetDefaultAndCombine:
     EXPERIMENTAL = True
     RETURN_TYPES = ("CONDITIONING",)
     CATEGORY = "advanced/hooks/cond single"
+    DESCRIPTION = "Set default conditioning and combine it with existing conditioning input and optional hooks."
+    SHORT_DESCRIPTION = "Set default conditioning and combine with existing."
     FUNCTION = "set_default_and_combine"
 
     def set_default_and_combine(self, cond, cond_DEFAULT,
@@ -244,6 +258,8 @@ class SetClipHooks:
     EXPERIMENTAL = True
     RETURN_TYPES = ("CLIP",)
     CATEGORY = "advanced/hooks/clip"
+    DESCRIPTION = "Apply hooks to a CLIP model, optionally propagating them to conditioning outputs and enabling CLIP scheduling."
+    SHORT_DESCRIPTION = "Apply hooks to a CLIP model with scheduling options."
     FUNCTION = "apply_hooks"
 
     def apply_hooks(self, clip: CLIP, schedule_clip: bool, apply_to_conds: bool, hooks: comfy.hooks.HookGroup=None):
@@ -275,6 +291,8 @@ class ConditioningTimestepsRange:
     RETURN_TYPES = ("TIMESTEPS_RANGE", "TIMESTEPS_RANGE", "TIMESTEPS_RANGE")
     RETURN_NAMES = ("TIMESTEPS_RANGE", "BEFORE_RANGE", "AFTER_RANGE")
     CATEGORY = "advanced/hooks"
+    DESCRIPTION = "Define a timestep percentage range and output the range plus its complement before and after segments."
+    SHORT_DESCRIPTION = "Define a timestep range with before/after complements."
     FUNCTION = "create_range"
 
     def create_range(self, start_percent: float, end_percent: float):
@@ -308,6 +326,8 @@ class CreateHookLora:
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/create"
+    DESCRIPTION = "Create a LoRA hook with separate model and CLIP strength that can be scheduled on conditioning."
+    SHORT_DESCRIPTION = "Create a LoRA hook with model and CLIP strength."
     FUNCTION = "create_hook"
 
     def create_hook(self, lora_name: str, strength_model: float, strength_clip: float, prev_hooks: comfy.hooks.HookGroup=None):
@@ -353,6 +373,8 @@ class CreateHookLoraModelOnly(CreateHookLora):
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/create"
+    DESCRIPTION = "Create a LoRA hook that only affects the model (not CLIP) for scheduling on conditioning."
+    SHORT_DESCRIPTION = "Create a model-only LoRA hook."
     FUNCTION = "create_hook_model_only"
 
     def create_hook_model_only(self, lora_name: str, strength_model: float, prev_hooks: comfy.hooks.HookGroup=None):
@@ -383,6 +405,8 @@ class CreateHookModelAsLora:
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/create"
+    DESCRIPTION = "Create a hook from a full checkpoint treated as a LoRA, with separate model and CLIP strength controls."
+    SHORT_DESCRIPTION = "Create a hook from a checkpoint treated as LoRA."
     FUNCTION = "create_hook"
 
     def create_hook(self, ckpt_name: str, strength_model: float, strength_clip: float,
@@ -431,6 +455,8 @@ class CreateHookModelAsLoraModelOnly(CreateHookModelAsLora):
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/create"
+    DESCRIPTION = "Create a model-only hook from a full checkpoint treated as a LoRA, without affecting CLIP."
+    SHORT_DESCRIPTION = "Create a model-only hook from a checkpoint as LoRA."
     FUNCTION = "create_hook_model_only"
 
     def create_hook_model_only(self, ckpt_name: str, strength_model: float,
@@ -460,6 +486,8 @@ class SetHookKeyframes:
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/scheduling"
+    DESCRIPTION = "Assign keyframe schedules to hooks for controlling their strength over time during sampling."
+    SHORT_DESCRIPTION = "Assign keyframe schedules to hooks over time."
     FUNCTION = "set_hook_keyframes"
 
     def set_hook_keyframes(self, hooks: comfy.hooks.HookGroup, hook_kf: comfy.hooks.HookKeyframeGroup=None):
@@ -488,6 +516,8 @@ class CreateHookKeyframe:
     RETURN_TYPES = ("HOOK_KEYFRAMES",)
     RETURN_NAMES = ("HOOK_KF",)
     CATEGORY = "advanced/hooks/scheduling"
+    DESCRIPTION = "Create a single hook keyframe with a strength multiplier at a specific timestep percentage."
+    SHORT_DESCRIPTION = "Create a hook keyframe at a specific timestep."
     FUNCTION = "create_hook_keyframe"
 
     def create_hook_keyframe(self, strength_mult: float, start_percent: float, prev_hook_kf: comfy.hooks.HookKeyframeGroup=None):
@@ -523,6 +553,8 @@ class CreateHookKeyframesInterpolated:
     RETURN_TYPES = ("HOOK_KEYFRAMES",)
     RETURN_NAMES = ("HOOK_KF",)
     CATEGORY = "advanced/hooks/scheduling"
+    DESCRIPTION = "Generate multiple interpolated hook keyframes between start and end strength values over a timestep range."
+    SHORT_DESCRIPTION = "Generate interpolated hook keyframes over a timestep range."
     FUNCTION = "create_hook_keyframes"
 
     def create_hook_keyframes(self, strength_start: float, strength_end: float, interpolation: str,
@@ -568,6 +600,8 @@ class CreateHookKeyframesFromFloats:
     RETURN_TYPES = ("HOOK_KEYFRAMES",)
     RETURN_NAMES = ("HOOK_KF",)
     CATEGORY = "advanced/hooks/scheduling"
+    DESCRIPTION = "Create hook keyframes from a list of float values distributed evenly across a timestep percentage range."
+    SHORT_DESCRIPTION = "Create hook keyframes from a list of float values."
     FUNCTION = "create_hook_keyframes"
 
     def create_hook_keyframes(self, floats_strength: Union[float, list[float]],
@@ -639,6 +673,8 @@ class CombineHooks:
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/combine"
+    DESCRIPTION = "Combine two hook groups into one."
+    SHORT_DESCRIPTION = None
     FUNCTION = "combine_hooks"
 
     def combine_hooks(self,
@@ -666,6 +702,8 @@ class CombineHooksFour:
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/combine"
+    DESCRIPTION = "Combine up to four hook groups into one."
+    SHORT_DESCRIPTION = None
     FUNCTION = "combine_hooks"
 
     def combine_hooks(self,
@@ -699,6 +737,8 @@ class CombineHooksEight:
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/combine"
+    DESCRIPTION = "Combine up to eight hook groups into one."
+    SHORT_DESCRIPTION = None
     FUNCTION = "combine_hooks"
 
     def combine_hooks(self,

comfy_extras/nodes_hunyuan.py

@@ -15,6 +15,8 @@ class CLIPTextEncodeHunyuanDiT(io.ComfyNode):
         return io.Schema(
             node_id="CLIPTextEncodeHunyuanDiT",
             category="advanced/conditioning",
+            description="Encodes text using both BERT and mT5-XL tokenizers for Hunyuan DiT conditioning.",
+            short_description="Dual-tokenizer text encoding for Hunyuan DiT.",
             inputs=[
                 io.Clip.Input("clip"),
                 io.String.Input("bert", multiline=True, dynamic_prompts=True),
@@ -42,6 +44,8 @@ class EmptyHunyuanLatentVideo(io.ComfyNode):
             node_id="EmptyHunyuanLatentVideo",
             display_name="Empty HunyuanVideo 1.0 Latent",
             category="latent/video",
+            description="Creates an empty latent tensor sized for HunyuanVideo 1.0 video generation.",
+            short_description="Empty latent for HunyuanVideo 1.0 generation.",
             inputs=[
                 io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
                 io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
@@ -67,6 +71,8 @@ class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo):
         schema = super().define_schema()
         schema.node_id = "EmptyHunyuanVideo15Latent"
         schema.display_name = "Empty HunyuanVideo 1.5 Latent"
+        schema.description = "Creates an empty latent tensor sized for HunyuanVideo 1.5 video generation with 16x spatial downscale."
+        schema.short_description = "Empty latent for HunyuanVideo 1.5 generation."
         return schema
 
     @classmethod
@@ -82,6 +88,8 @@ class HunyuanVideo15ImageToVideo(io.ComfyNode):
         return io.Schema(
             node_id="HunyuanVideo15ImageToVideo",
             category="conditioning/video_models",
+            description="Prepares conditioning and latent for HunyuanVideo 1.5 image-to-video generation with start image and CLIP vision support.",
+            short_description="HunyuanVideo 1.5 image-to-video conditioning setup.",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -131,6 +139,9 @@ class HunyuanVideo15SuperResolution(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="HunyuanVideo15SuperResolution",
+            category="conditioning/video_models",
+            description="Sets up conditioning for HunyuanVideo 1.5 super-resolution upscaling of a latent with noise augmentation and optional image guidance.",
+            short_description="HunyuanVideo 1.5 super-resolution latent conditioning.",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -177,6 +188,8 @@ class LatentUpscaleModelLoader(io.ComfyNode):
             node_id="LatentUpscaleModelLoader",
             display_name="Load Latent Upscale Model",
             category="loaders",
+            description="Loads a latent upscale model from disk, supporting HunyuanVideo 720p, 1080p, and other latent upsampler architectures.",
+            short_description="Load a latent upscale model from file.",
             inputs=[
                 io.Combo.Input("model_name", options=folder_paths.get_filename_list("latent_upscale_models")),
             ],
@@ -226,6 +239,8 @@ class HunyuanVideo15LatentUpscaleWithModel(io.ComfyNode):
             node_id="HunyuanVideo15LatentUpscaleWithModel",
             display_name="Hunyuan Video 15 Latent Upscale With Model",
             category="latent",
+            description="Upscales a video latent to a target resolution using a loaded latent upscale model and configurable upscale method.",
+            short_description="Upscale video latent using a latent upscale model.",
             inputs=[
                 io.LatentUpscaleModel.Input("model"),
                 io.Latent.Input("samples"),
@@ -275,6 +290,8 @@ class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
         return io.Schema(
             node_id="TextEncodeHunyuanVideo_ImageToVideo",
             category="advanced/conditioning",
+            description="Encodes text with CLIP vision image embeddings for HunyuanVideo image-to-video conditioning using an interleaved template.",
+            short_description="Text and image encoding for HunyuanVideo image-to-video.",
             inputs=[
                 io.Clip.Input("clip"),
                 io.ClipVisionOutput.Input("clip_vision_output"),
@@ -306,6 +323,8 @@ class HunyuanImageToVideo(io.ComfyNode):
         return io.Schema(
             node_id="HunyuanImageToVideo",
             category="conditioning/video_models",
+            description="Prepares conditioning and latent for Hunyuan image-to-video generation with selectable guidance type.",
+            short_description="Hunyuan image-to-video conditioning with guidance options.",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Vae.Input("vae"),
@@ -357,6 +376,8 @@ class EmptyHunyuanImageLatent(io.ComfyNode):
         return io.Schema(
             node_id="EmptyHunyuanImageLatent",
             category="latent",
+            description="Creates an empty latent tensor sized for Hunyuan image generation.",
+            short_description="Empty latent for Hunyuan image generation.",
             inputs=[
                 io.Int.Input("width", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
                 io.Int.Input("height", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
@@ -380,6 +401,9 @@ class HunyuanRefinerLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="HunyuanRefinerLatent",
+            category="conditioning/video_models",
+            description="Prepares conditioning for a Hunyuan refiner pass by concatenating the input latent with noise augmentation settings.",
+            short_description="Hunyuan refiner conditioning with noise augmentation.",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),