Merge branch 'master' into feature/implement-morphology-with-max-pool

.gitignore

@@ -21,6 +21,5 @@ venv*/
 *.log
 web_custom_versions/
 .DS_Store
-openapi.yaml
 filtered-openapi.yaml
 uv.lock

blueprints/.glsl/Glow_30.frag

@@ -2,7 +2,6 @@
 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
@@ -75,7 +74,7 @@ void main() {
     float t0 = threshold - 0.15;
     float t1 = threshold + 0.15;
     
-    vec2 texelSize = 1.0 / u_resolution;
+    vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));
     float radius2 = radius * radius;
     
     float sampleScale = clamp(radius * 0.75, 0.35, 1.0);

blueprints/.glsl/Image_Blur_1.frag

@@ -12,7 +12,6 @@ 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)
@@ -25,7 +24,7 @@ float gaussian(float x, float sigma) {
 }
 
 void main() {
-    vec2 texelSize = 1.0 / u_resolution;
+    vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));
     float radius = max(u_float0, 0.0);
 
     // Radial (angular) blur - single pass, doesn't use separable

blueprints/.glsl/Sharpen_23.frag

@@ -2,14 +2,13 @@
 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;
+    vec2 texel = 1.0 / vec2(textureSize(u_image0, 0));
     
     // Sample center and neighbors
     vec4 center = texture(u_image0, v_texCoord);

blueprints/.glsl/Unsharp_Mask_26.frag

@@ -2,7 +2,6 @@
 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
@@ -19,7 +18,7 @@ float getLuminance(vec3 color) {
 }
 
 void main() {
-    vec2 texel = 1.0 / u_resolution;
+    vec2 texel = 1.0 / vec2(textureSize(u_image0, 0));
     float radius = max(u_float1, 0.5);
     float amount = u_float0;
     float threshold = u_float2;

blueprints/Depth to Image (Z-Image-Turbo).json

@@ -160,7 +160,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Depth to Image (Z-Image-Turbo)",
+        "name": "Depth to Image (Z-Image-Turbo)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -2482,4 +2482,4 @@
     "VHS_KeepIntermediate": true
   },
   "version": 0.4
-}
+}

blueprints/Depth to Video (ltx 2.0).json

@@ -261,7 +261,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Depth to Video (LTX 2.0)",
+        "name": "Depth to Video (LTX 2.0)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -5208,4 +5208,4 @@
     "workflowRendererVersion": "LG"
   },
   "version": 0.4
-}
+}

blueprints/Glow.json

@@ -268,7 +268,7 @@
               "Node name for S&R": "GLSLShader"
             },
             "widgets_values": [
-              "#version 300 es\nprecision mediump float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0;      // Blend mode\nuniform int u_int1;      // Color tint\nuniform float u_float0;  // Intensity\nuniform float u_float1;  // Radius\nuniform float u_float2;  // Threshold\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int BLEND_ADD      = 0;\nconst int BLEND_SCREEN   = 1;\nconst int BLEND_SOFT     = 2;\nconst int BLEND_OVERLAY  = 3;\nconst int BLEND_LIGHTEN  = 4;\n\nconst float GOLDEN_ANGLE = 2.39996323;\nconst int MAX_SAMPLES = 48;\nconst vec3 LUMA = vec3(0.299, 0.587, 0.114);\n\nfloat hash(vec2 p) {\n    p = fract(p * vec2(123.34, 456.21));\n    p += dot(p, p + 45.32);\n    return fract(p.x * p.y);\n}\n\nvec3 hexToRgb(int h) {\n    return vec3(\n        float((h >> 16) & 255),\n        float((h >> 8) & 255),\n        float(h & 255)\n    ) * (1.0 / 255.0);\n}\n\nvec3 blend(vec3 base, vec3 glow, int mode) {\n    if (mode == BLEND_SCREEN) {\n        return 1.0 - (1.0 - base) * (1.0 - glow);\n    }\n    if (mode == BLEND_SOFT) {\n        return mix(\n            base - (1.0 - 2.0 * glow) * base * (1.0 - base),\n            base + (2.0 * glow - 1.0) * (sqrt(base) - base),\n            step(0.5, glow)\n        );\n    }\n    if (mode == BLEND_OVERLAY) {\n        return mix(\n            2.0 * base * glow,\n            1.0 - 2.0 * (1.0 - base) * (1.0 - glow),\n            step(0.5, base)\n        );\n    }\n    if (mode == BLEND_LIGHTEN) {\n        return max(base, glow);\n    }\n    return base + glow;\n}\n\nvoid main() {\n    vec4 original = texture(u_image0, v_texCoord);\n    \n    float intensity = u_float0 * 0.05;\n    float radius = u_float1 * u_float1 * 0.012;\n    \n    if (intensity < 0.001 || radius < 0.1) {\n        fragColor = original;\n        return;\n    }\n    \n    float threshold = 1.0 - u_float2 * 0.01;\n    float t0 = threshold - 0.15;\n    float t1 = threshold + 0.15;\n    \n    vec2 texelSize = 1.0 / u_resolution;\n    float radius2 = radius * radius;\n    \n    float sampleScale = clamp(radius * 0.75, 0.35, 1.0);\n    int samples = int(float(MAX_SAMPLES) * sampleScale);\n    \n    float noise = hash(gl_FragCoord.xy);\n    float angleOffset = noise * GOLDEN_ANGLE;\n    float radiusJitter = 0.85 + noise * 0.3;\n    \n    float ca = cos(GOLDEN_ANGLE);\n    float sa = sin(GOLDEN_ANGLE);\n    vec2 dir = vec2(cos(angleOffset), sin(angleOffset));\n    \n    vec3 glow = vec3(0.0);\n    float totalWeight = 0.0;\n    \n    // Center tap\n    float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA));\n    glow += original.rgb * centerMask * 2.0;\n    totalWeight += 2.0;\n    \n    for (int i = 1; i < MAX_SAMPLES; i++) {\n        if (i >= samples) break;\n        \n        float fi = float(i);\n        float dist = sqrt(fi / float(samples)) * radius * radiusJitter;\n        \n        vec2 offset = dir * dist * texelSize;\n        vec3 c = texture(u_image0, v_texCoord + offset).rgb;\n        float mask = smoothstep(t0, t1, dot(c, LUMA));\n        \n        float w = 1.0 - (dist * dist) / (radius2 * 1.5);\n        w = max(w, 0.0);\n        w *= w;\n        \n        glow += c * mask * w;\n        totalWeight += w;\n        \n        dir = vec2(\n            dir.x * ca - dir.y * sa,\n            dir.x * sa + dir.y * ca\n        );\n    }\n    \n    glow *= intensity / max(totalWeight, 0.001);\n    \n    if (u_int1 > 0) {\n        glow *= hexToRgb(u_int1);\n    }\n    \n    vec3 result = blend(original.rgb, glow, u_int0);\n    result += (noise - 0.5) * (1.0 / 255.0);\n    \n    fragColor = vec4(clamp(result, 0.0, 1.0), original.a);\n}",
+              "#version 300 es\nprecision mediump float;\n\nuniform sampler2D u_image0;\nuniform int u_int0;      // Blend mode\nuniform int u_int1;      // Color tint\nuniform float u_float0;  // Intensity\nuniform float u_float1;  // Radius\nuniform float u_float2;  // Threshold\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int BLEND_ADD      = 0;\nconst int BLEND_SCREEN   = 1;\nconst int BLEND_SOFT     = 2;\nconst int BLEND_OVERLAY  = 3;\nconst int BLEND_LIGHTEN  = 4;\n\nconst float GOLDEN_ANGLE = 2.39996323;\nconst int MAX_SAMPLES = 48;\nconst vec3 LUMA = vec3(0.299, 0.587, 0.114);\n\nfloat hash(vec2 p) {\n    p = fract(p * vec2(123.34, 456.21));\n    p += dot(p, p + 45.32);\n    return fract(p.x * p.y);\n}\n\nvec3 hexToRgb(int h) {\n    return vec3(\n        float((h >> 16) & 255),\n        float((h >> 8) & 255),\n        float(h & 255)\n    ) * (1.0 / 255.0);\n}\n\nvec3 blend(vec3 base, vec3 glow, int mode) {\n    if (mode == BLEND_SCREEN) {\n        return 1.0 - (1.0 - base) * (1.0 - glow);\n    }\n    if (mode == BLEND_SOFT) {\n        return mix(\n            base - (1.0 - 2.0 * glow) * base * (1.0 - base),\n            base + (2.0 * glow - 1.0) * (sqrt(base) - base),\n            step(0.5, glow)\n        );\n    }\n    if (mode == BLEND_OVERLAY) {\n        return mix(\n            2.0 * base * glow,\n            1.0 - 2.0 * (1.0 - base) * (1.0 - glow),\n            step(0.5, base)\n        );\n    }\n    if (mode == BLEND_LIGHTEN) {\n        return max(base, glow);\n    }\n    return base + glow;\n}\n\nvoid main() {\n    vec4 original = texture(u_image0, v_texCoord);\n    \n    float intensity = u_float0 * 0.05;\n    float radius = u_float1 * u_float1 * 0.012;\n    \n    if (intensity < 0.001 || radius < 0.1) {\n        fragColor = original;\n        return;\n    }\n    \n    float threshold = 1.0 - u_float2 * 0.01;\n    float t0 = threshold - 0.15;\n    float t1 = threshold + 0.15;\n    \n    vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));\n    float radius2 = radius * radius;\n    \n    float sampleScale = clamp(radius * 0.75, 0.35, 1.0);\n    int samples = int(float(MAX_SAMPLES) * sampleScale);\n    \n    float noise = hash(gl_FragCoord.xy);\n    float angleOffset = noise * GOLDEN_ANGLE;\n    float radiusJitter = 0.85 + noise * 0.3;\n    \n    float ca = cos(GOLDEN_ANGLE);\n    float sa = sin(GOLDEN_ANGLE);\n    vec2 dir = vec2(cos(angleOffset), sin(angleOffset));\n    \n    vec3 glow = vec3(0.0);\n    float totalWeight = 0.0;\n    \n    // Center tap\n    float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA));\n    glow += original.rgb * centerMask * 2.0;\n    totalWeight += 2.0;\n    \n    for (int i = 1; i < MAX_SAMPLES; i++) {\n        if (i >= samples) break;\n        \n        float fi = float(i);\n        float dist = sqrt(fi / float(samples)) * radius * radiusJitter;\n        \n        vec2 offset = dir * dist * texelSize;\n        vec3 c = texture(u_image0, v_texCoord + offset).rgb;\n        float mask = smoothstep(t0, t1, dot(c, LUMA));\n        \n        float w = 1.0 - (dist * dist) / (radius2 * 1.5);\n        w = max(w, 0.0);\n        w *= w;\n        \n        glow += c * mask * w;\n        totalWeight += w;\n        \n        dir = vec2(\n            dir.x * ca - dir.y * sa,\n            dir.x * sa + dir.y * ca\n        );\n    }\n    \n    glow *= intensity / max(totalWeight, 0.001);\n    \n    if (u_int1 > 0) {\n        glow *= hexToRgb(u_int1);\n    }\n    \n    vec3 result = blend(original.rgb, glow, u_int0);\n    result += (noise - 0.5) * (1.0 / 255.0);\n    \n    fragColor = vec4(clamp(result, 0.0, 1.0), original.a);\n}",
               "from_input"
             ]
           },

blueprints/Image Blur.json

@@ -331,7 +331,7 @@
               "Node name for S&R": "GLSLShader"
             },
             "widgets_values": [
-              "#version 300 es\n#pragma passes 2\nprecision highp float;\n\n// Blur type constants\nconst int BLUR_GAUSSIAN = 0;\nconst int BLUR_BOX = 1;\nconst int BLUR_RADIAL = 2;\n\n// Radial blur config\nconst int RADIAL_SAMPLES = 12;\nconst float RADIAL_STRENGTH = 0.0003;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0;      // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)\nuniform float u_float0;  // Blur radius/amount\nuniform int u_pass;      // Pass index (0 = horizontal, 1 = vertical)\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n    return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nvoid main() {\n    vec2 texelSize = 1.0 / u_resolution;\n    float radius = max(u_float0, 0.0);\n\n    // Radial (angular) blur - single pass, doesn't use separable\n    if (u_int0 == BLUR_RADIAL) {\n        // Only execute on first pass\n        if (u_pass > 0) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec2 center = vec2(0.5);\n        vec2 dir = v_texCoord - center;\n        float dist = length(dir);\n\n        if (dist < 1e-4) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec4 sum = vec4(0.0);\n        float totalWeight = 0.0;\n        float angleStep = radius * RADIAL_STRENGTH;\n\n        dir /= dist;\n\n        float cosStep = cos(angleStep);\n        float sinStep = sin(angleStep);\n\n        float negAngle = -float(RADIAL_SAMPLES) * angleStep;\n        vec2 rotDir = vec2(\n            dir.x * cos(negAngle) - dir.y * sin(negAngle),\n            dir.x * sin(negAngle) + dir.y * cos(negAngle)\n        );\n\n        for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\n            vec2 uv = center + rotDir * dist;\n            float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\n            sum += texture(u_image0, uv) * w;\n            totalWeight += w;\n\n            rotDir = vec2(\n                rotDir.x * cosStep - rotDir.y * sinStep,\n                rotDir.x * sinStep + rotDir.y * cosStep\n            );\n        }\n\n        fragColor0 = sum / max(totalWeight, 0.001);\n        return;\n    }\n\n    // Separable Gaussian / Box blur\n    int samples = int(ceil(radius));\n\n    if (samples == 0) {\n        fragColor0 = texture(u_image0, v_texCoord);\n        return;\n    }\n\n    // Direction: pass 0 = horizontal, pass 1 = vertical\n    vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\n    vec4 color = vec4(0.0);\n    float totalWeight = 0.0;\n    float sigma = radius / 2.0;\n\n    for (int i = -samples; i <= samples; i++) {\n        vec2 offset = dir * float(i) * texelSize;\n        vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n        float weight;\n        if (u_int0 == BLUR_GAUSSIAN) {\n            weight = gaussian(float(i), sigma);\n        } else {\n            // BLUR_BOX\n            weight = 1.0;\n        }\n\n        color += sample_color * weight;\n        totalWeight += weight;\n    }\n\n    fragColor0 = color / totalWeight;\n}\n",
+              "#version 300 es\n#pragma passes 2\nprecision highp float;\n\n// Blur type constants\nconst int BLUR_GAUSSIAN = 0;\nconst int BLUR_BOX = 1;\nconst int BLUR_RADIAL = 2;\n\n// Radial blur config\nconst int RADIAL_SAMPLES = 12;\nconst float RADIAL_STRENGTH = 0.0003;\n\nuniform sampler2D u_image0;\nuniform int u_int0;      // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)\nuniform float u_float0;  // Blur radius/amount\nuniform int u_pass;      // Pass index (0 = horizontal, 1 = vertical)\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n    return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nvoid main() {\n    vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));\n    float radius = max(u_float0, 0.0);\n\n    // Radial (angular) blur - single pass, doesn't use separable\n    if (u_int0 == BLUR_RADIAL) {\n        // Only execute on first pass\n        if (u_pass > 0) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec2 center = vec2(0.5);\n        vec2 dir = v_texCoord - center;\n        float dist = length(dir);\n\n        if (dist < 1e-4) {\n            fragColor0 = texture(u_image0, v_texCoord);\n            return;\n        }\n\n        vec4 sum = vec4(0.0);\n        float totalWeight = 0.0;\n        float angleStep = radius * RADIAL_STRENGTH;\n\n        dir /= dist;\n\n        float cosStep = cos(angleStep);\n        float sinStep = sin(angleStep);\n\n        float negAngle = -float(RADIAL_SAMPLES) * angleStep;\n        vec2 rotDir = vec2(\n            dir.x * cos(negAngle) - dir.y * sin(negAngle),\n            dir.x * sin(negAngle) + dir.y * cos(negAngle)\n        );\n\n        for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\n            vec2 uv = center + rotDir * dist;\n            float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\n            sum += texture(u_image0, uv) * w;\n            totalWeight += w;\n\n            rotDir = vec2(\n                rotDir.x * cosStep - rotDir.y * sinStep,\n                rotDir.x * sinStep + rotDir.y * cosStep\n            );\n        }\n\n        fragColor0 = sum / max(totalWeight, 0.001);\n        return;\n    }\n\n    // Separable Gaussian / Box blur\n    int samples = int(ceil(radius));\n\n    if (samples == 0) {\n        fragColor0 = texture(u_image0, v_texCoord);\n        return;\n    }\n\n    // Direction: pass 0 = horizontal, pass 1 = vertical\n    vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\n    vec4 color = vec4(0.0);\n    float totalWeight = 0.0;\n    float sigma = radius / 2.0;\n\n    for (int i = -samples; i <= samples; i++) {\n        vec2 offset = dir * float(i) * texelSize;\n        vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n        float weight;\n        if (u_int0 == BLUR_GAUSSIAN) {\n            weight = gaussian(float(i), sigma);\n        } else {\n            // BLUR_BOX\n            weight = 1.0;\n        }\n\n        color += sample_color * weight;\n        totalWeight += weight;\n    }\n\n    fragColor0 = color / totalWeight;\n}\n",
               "from_input"
             ]
           }

blueprints/Image Edit (Flux.2 Klein 4B).json

@@ -128,7 +128,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Image Edit (Flux.2 Klein 4B)",
+        "name": "Image Edit (Flux.2 Klein 4B)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -1837,4 +1837,4 @@
     }
   },
   "version": 0.4
-}
+}

blueprints/Image Inpainting (Qwen-image).json

@@ -124,7 +124,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Image Inpainting (Qwen-image)",
+        "name": "Image Inpainting (Qwen-image)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -1923,4 +1923,4 @@
     "workflowRendererVersion": "LG"
   },
   "version": 0.4
-}
+}

blueprints/Image Outpainting (Qwen-Image).json

@@ -204,7 +204,7 @@
         },
         "revision": 0,
         "config": {},
-        "name": "local-Image Outpainting (Qwen-Image)",
+        "name": "Image Outpainting (Qwen-Image)",
         "inputNode": {
           "id": -10,
           "bounding": [
@@ -2749,4 +2749,4 @@
     }
   },
   "version": 0.4
-}
+}

blueprints/Image to Layers(Qwen-Image-Layered).json

@@ -1,15 +1,14 @@
 {
-  "id": "1a761372-7c82-4016-b9bf-fa285967e1e9",
   "revision": 0,
-  "last_node_id": 83,
+  "last_node_id": 176,
   "last_link_id": 0,
   "nodes": [
     {
-      "id": 83,
+      "id": 176,
-      "type": "f754a936-daaf-4b6e-9658-41fdc54d301d",
+      "type": "2d2e3c8e-53b3-4618-be52-6d1d99382f0e",
       "pos": [
-        61.999827823554256,
+        -1150,
-        153.3332507624185
+        200
       ],
       "size": [
         400,
@@ -56,6 +55,38 @@
             "name": "layers"
           },
           "link": null
+        },
+        {
+          "name": "seed",
+          "type": "INT",
+          "widget": {
+            "name": "seed"
+          },
+          "link": null
+        },
+        {
+          "name": "unet_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "unet_name"
+          },
+          "link": null
+        },
+        {
+          "name": "clip_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "clip_name"
+          },
+          "link": null
+        },
+        {
+          "name": "vae_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "vae_name"
+          },
+          "link": null
         }
       ],
       "outputs": [
@@ -66,28 +97,41 @@
           "links": []
         }
       ],
+      "title": "Image to Layers (Qwen-Image-Layered)",
       "properties": {
         "proxyWidgets": [
           [
-            "-1",
+            "6",
             "text"
           ],
           [
-            "-1",
+            "3",
             "steps"
           ],
           [
-            "-1",
+            "3",
             "cfg"
           ],
           [
-            "-1",
+            "83",
             "layers"
           ],
           [
             "3",
             "seed"
           ],
+          [
+            "37",
+            "unet_name"
+          ],
+          [
+            "38",
+            "clip_name"
+          ],
+          [
+            "39",
+            "vae_name"
+          ],
           [
             "3",
             "control_after_generate"
@@ -95,6 +139,11 @@
         ],
         "cnr_id": "comfy-core",
         "ver": "0.5.1",
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "input_ue_unconnectable": {},
+          "version": "7.7"
+        },
         "enableTabs": false,
         "tabWidth": 65,
         "tabXOffset": 10,
@@ -103,25 +152,20 @@
         "secondTabOffset": 80,
         "secondTabWidth": 65
       },
-      "widgets_values": [
+      "widgets_values": []
-        "",
-        20,
-        2.5,
-        2
-      ]
     }
   ],
   "links": [],
-  "groups": [],
+  "version": 0.4,
   "definitions": {
     "subgraphs": [
       {
-        "id": "f754a936-daaf-4b6e-9658-41fdc54d301d",
+        "id": "2d2e3c8e-53b3-4618-be52-6d1d99382f0e",
         "version": 1,
         "state": {
-          "lastGroupId": 3,
+          "lastGroupId": 8,
-          "lastNodeId": 83,
+          "lastNodeId": 176,
-          "lastLinkId": 159,
+          "lastLinkId": 380,
           "lastRerouteId": 0
         },
         "revision": 0,
@@ -130,10 +174,10 @@
         "inputNode": {
           "id": -10,
           "bounding": [
-            -510,
+            -720,
-            523,
+            720,
             120,
-            140
+            220
           ]
         },
         "outputNode": {
@@ -156,8 +200,8 @@
             ],
             "localized_name": "image",
             "pos": [
-              -410,
+              -620,
-              543
+              740
             ]
           },
           {
@@ -168,8 +212,8 @@
               150
             ],
             "pos": [
-              -410,
+              -620,
-              563
+              760
             ]
           },
           {
@@ -180,8 +224,8 @@
               153
             ],
             "pos": [
-              -410,
+              -620,
-              583
+              780
             ]
           },
           {
@@ -192,8 +236,8 @@
               154
             ],
             "pos": [
-              -410,
+              -620,
-              603
+              800
             ]
           },
           {
@@ -204,8 +248,56 @@
               159
             ],
             "pos": [
-              -410,
+              -620,
-              623
+              820
+            ]
+          },
+          {
+            "id": "9f76338b-f4ca-4bb3-b61a-57b3f233061e",
+            "name": "seed",
+            "type": "INT",
+            "linkIds": [
+              377
+            ],
+            "pos": [
+              -620,
+              840
+            ]
+          },
+          {
+            "id": "8d0422d5-5eee-4f7e-9817-dc613cc62eca",
+            "name": "unet_name",
+            "type": "COMBO",
+            "linkIds": [
+              378
+            ],
+            "pos": [
+              -620,
+              860
+            ]
+          },
+          {
+            "id": "552eece2-a735-4d00-ae78-ded454622bc1",
+            "name": "clip_name",
+            "type": "COMBO",
+            "linkIds": [
+              379
+            ],
+            "pos": [
+              -620,
+              880
+            ]
+          },
+          {
+            "id": "1e6d141c-d0f9-4a2b-895c-b6780e57cfa0",
+            "name": "vae_name",
+            "type": "COMBO",
+            "linkIds": [
+              380
+            ],
+            "pos": [
+              -620,
+              900
             ]
           }
         ],
@@ -231,14 +323,14 @@
             "type": "CLIPLoader",
             "pos": [
               -320,
-              310
+              360
             ],
             "size": [
-              346.7470703125,
+              350,
-              106
+              150
             ],
             "flags": {},
-            "order": 0,
+            "order": 5,
             "mode": 0,
             "inputs": [
               {
@@ -248,7 +340,7 @@
                 "widget": {
                   "name": "clip_name"
                 },
-                "link": null
+                "link": 379
               },
               {
                 "localized_name": "type",
@@ -283,9 +375,14 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "CLIPLoader",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "CLIPLoader",
               "models": [
                 {
                   "name": "qwen_2.5_vl_7b_fp8_scaled.safetensors",
@@ -312,14 +409,14 @@
             "type": "VAELoader",
             "pos": [
               -320,
-              460
+              580
             ],
             "size": [
-              346.7470703125,
+              350,
-              58
+              110
             ],
             "flags": {},
-            "order": 1,
+            "order": 6,
             "mode": 0,
             "inputs": [
               {
@@ -329,7 +426,7 @@
                 "widget": {
                   "name": "vae_name"
                 },
-                "link": null
+                "link": 380
               }
             ],
             "outputs": [
@@ -345,9 +442,14 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "VAELoader",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "VAELoader",
               "models": [
                 {
                   "name": "qwen_image_layered_vae.safetensors",
@@ -375,11 +477,11 @@
               420
             ],
             "size": [
-              425.27801513671875,
+              430,
-              180.6060791015625
+              190
             ],
             "flags": {},
-            "order": 3,
+            "order": 2,
             "mode": 0,
             "inputs": [
               {
@@ -411,9 +513,14 @@
             ],
             "title": "CLIP Text Encode (Negative Prompt)",
             "properties": {
-              "Node name for S&R": "CLIPTextEncode",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "CLIPTextEncode",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -432,12 +539,12 @@
             "id": 70,
             "type": "ReferenceLatent",
             "pos": [
-              330,
+              140,
-              670
+              700
             ],
             "size": [
-              204.1666717529297,
+              210,
-              46
+              50
             ],
             "flags": {
               "collapsed": true
@@ -470,9 +577,14 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "ReferenceLatent",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "ReferenceLatent",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -480,19 +592,18 @@
               "secondTabText": "Send Back",
               "secondTabOffset": 80,
               "secondTabWidth": 65
-            },
+            }
-            "widgets_values": []
           },
           {
             "id": 69,
             "type": "ReferenceLatent",
             "pos": [
-              330,
+              160,
-              710
+              820
             ],
             "size": [
-              204.1666717529297,
+              210,
-              46
+              50
             ],
             "flags": {
               "collapsed": true
@@ -525,9 +636,14 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "ReferenceLatent",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "ReferenceLatent",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -535,8 +651,7 @@
               "secondTabText": "Send Back",
               "secondTabOffset": 80,
               "secondTabWidth": 65
-            },
+            }
-            "widgets_values": []
           },
           {
             "id": 66,
@@ -547,10 +662,10 @@
             ],
             "size": [
               270,
-              58
+              110
             ],
             "flags": {},
-            "order": 4,
+            "order": 7,
             "mode": 0,
             "inputs": [
               {
@@ -580,9 +695,14 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "ModelSamplingAuraFlow",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "ModelSamplingAuraFlow",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -600,11 +720,11 @@
             "type": "LatentCutToBatch",
             "pos": [
               830,
-              160
+              140
             ],
             "size": [
               270,
-              82
+              140
             ],
             "flags": {},
             "order": 11,
@@ -646,9 +766,14 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "LatentCutToBatch",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "LatentCutToBatch",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -666,12 +791,12 @@
             "id": 71,
             "type": "VAEEncode",
             "pos": [
-              100,
+              -280,
-              690
+              780
             ],
             "size": [
-              140,
+              230,
-              46
+              100
             ],
             "flags": {
               "collapsed": false
@@ -704,9 +829,14 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "VAEEncode",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "VAEEncode",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -714,24 +844,23 @@
               "secondTabText": "Send Back",
               "secondTabOffset": 80,
               "secondTabWidth": 65
-            },
+            }
-            "widgets_values": []
           },
           {
             "id": 8,
             "type": "VAEDecode",
             "pos": [
               850,
-              310
+              370
             ],
             "size": [
               210,
-              46
+              50
             ],
             "flags": {
               "collapsed": true
             },
-            "order": 7,
+            "order": 3,
             "mode": 0,
             "inputs": [
               {
@@ -759,9 +888,14 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "VAEDecode",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "VAEDecode",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -769,8 +903,7 @@
               "secondTabText": "Send Back",
               "secondTabOffset": 80,
               "secondTabWidth": 65
-            },
+            }
-            "widgets_values": []
           },
           {
             "id": 6,
@@ -780,11 +913,11 @@
               180
             ],
             "size": [
-              422.84503173828125,
+              430,
-              164.31304931640625
+              170
             ],
             "flags": {},
-            "order": 6,
+            "order": 1,
             "mode": 0,
             "inputs": [
               {
@@ -816,9 +949,14 @@
             ],
             "title": "CLIP Text Encode (Positive Prompt)",
             "properties": {
-              "Node name for S&R": "CLIPTextEncode",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "CLIPTextEncode",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -838,14 +976,14 @@
             "type": "KSampler",
             "pos": [
               530,
-              280
+              340
             ],
             "size": [
               270,
               400
             ],
             "flags": {},
-            "order": 5,
+            "order": 0,
             "mode": 0,
             "inputs": [
               {
@@ -879,7 +1017,7 @@
                 "widget": {
                   "name": "seed"
                 },
-                "link": null
+                "link": 377
               },
               {
                 "localized_name": "steps",
@@ -939,9 +1077,14 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "KSampler",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "KSampler",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -964,12 +1107,12 @@
             "id": 78,
             "type": "GetImageSize",
             "pos": [
-              80,
+              -280,
-              790
+              930
             ],
             "size": [
-              210,
+              230,
-              136
+              140
             ],
             "flags": {},
             "order": 12,
@@ -1007,9 +1150,14 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "GetImageSize",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "GetImageSize",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -1017,23 +1165,23 @@
               "secondTabText": "Send Back",
               "secondTabOffset": 80,
               "secondTabWidth": 65
-            },
+            }
-            "widgets_values": []
           },
           {
             "id": 83,
             "type": "EmptyQwenImageLayeredLatentImage",
             "pos": [
-              320,
+              -280,
-              790
+              1120
             ],
             "size": [
-              330.9341796875,
+              340,
-              130
+              200
             ],
             "flags": {},
             "order": 13,
             "mode": 0,
+            "showAdvanced": true,
             "inputs": [
               {
                 "localized_name": "width",
@@ -1083,9 +1231,14 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "EmptyQwenImageLayeredLatentImage",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "EmptyQwenImageLayeredLatentImage",
               "enableTabs": false,
               "tabWidth": 65,
               "tabXOffset": 10,
@@ -1109,11 +1262,11 @@
               180
             ],
             "size": [
-              346.7470703125,
+              350,
-              82
+              110
             ],
             "flags": {},
-            "order": 2,
+            "order": 4,
             "mode": 0,
             "inputs": [
               {
@@ -1123,7 +1276,7 @@
                 "widget": {
                   "name": "unet_name"
                 },
-                "link": null
+                "link": 378
               },
               {
                 "localized_name": "weight_dtype",
@@ -1147,9 +1300,14 @@
               }
             ],
             "properties": {
-              "Node name for S&R": "UNETLoader",
               "cnr_id": "comfy-core",
               "ver": "0.5.1",
+              "ue_properties": {
+                "widget_ue_connectable": {},
+                "input_ue_unconnectable": {},
+                "version": "7.7"
+              },
+              "Node name for S&R": "UNETLoader",
               "models": [
                 {
                   "name": "qwen_image_layered_bf16.safetensors",
@@ -1191,8 +1349,8 @@
             "bounding": [
               -330,
               110,
-              366.7470703125,
+              370,
-              421.6
+              610
             ],
             "color": "#3f789e",
             "font_size": 24,
@@ -1391,6 +1549,38 @@
             "target_id": 83,
             "target_slot": 2,
             "type": "INT"
+          },
+          {
+            "id": 377,
+            "origin_id": -10,
+            "origin_slot": 5,
+            "target_id": 3,
+            "target_slot": 4,
+            "type": "INT"
+          },
+          {
+            "id": 378,
+            "origin_id": -10,
+            "origin_slot": 6,
+            "target_id": 37,
+            "target_slot": 0,
+            "type": "COMBO"
+          },
+          {
+            "id": 379,
+            "origin_id": -10,
+            "origin_slot": 7,
+            "target_id": 38,
+            "target_slot": 0,
+            "type": "COMBO"
+          },
+          {
+            "id": 380,
+            "origin_id": -10,
+            "origin_slot": 8,
+            "target_id": 39,
+            "target_slot": 0,
+            "type": "COMBO"
           }
         ],
         "extra": {
@@ -1400,7 +1590,6 @@
       }
     ]
   },
-  "config": {},
   "extra": {
     "ds": {
       "scale": 1.14,
@@ -1409,7 +1598,6 @@
         6.855893974423647
       ]
     },
-    "workflowRendererVersion": "LG"
+    "ue_links": []
-  },
+  }
-  "version": 0.4
+}
-}

blueprints/Sharpen.json

@@ -267,7 +267,7 @@
               "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}",
+              "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\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 / vec2(textureSize(u_image0, 0));\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"
             ]
           }

blueprints/Unsharp Mask.json

@@ -383,7 +383,7 @@
               "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;  // amount    [0.0 - 3.0]  typical: 0.5-1.5\nuniform float u_float1;  // radius    [0.5 - 10.0] blur radius in pixels\nuniform float u_float2;  // threshold [0.0 - 0.1]  min difference to sharpen\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n    return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nfloat getLuminance(vec3 color) {\n    return dot(color, vec3(0.2126, 0.7152, 0.0722));\n}\n\nvoid main() {\n    vec2 texel = 1.0 / u_resolution;\n    float radius = max(u_float1, 0.5);\n    float amount = u_float0;\n    float threshold = u_float2;\n\n    vec4 original = texture(u_image0, v_texCoord);\n\n    // Gaussian blur for the \"unsharp\" mask\n    int samples = int(ceil(radius));\n    float sigma = radius / 2.0;\n\n    vec4 blurred = vec4(0.0);\n    float totalWeight = 0.0;\n\n    for (int x = -samples; x <= samples; x++) {\n        for (int y = -samples; y <= samples; y++) {\n            vec2 offset = vec2(float(x), float(y)) * texel;\n            vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n            float dist = length(vec2(float(x), float(y)));\n            float weight = gaussian(dist, sigma);\n            blurred += sample_color * weight;\n            totalWeight += weight;\n        }\n    }\n    blurred /= totalWeight;\n\n    // Unsharp mask = original - blurred\n    vec3 mask = original.rgb - blurred.rgb;\n\n    // Luminance-based threshold with smooth falloff\n    float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb));\n    float thresholdScale = smoothstep(0.0, threshold, lumaDelta);\n    mask *= thresholdScale;\n\n    // Sharpen: original + mask * amount\n    vec3 sharpened = original.rgb + mask * amount;\n\n    fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a);\n}\n",
+              "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0;  // amount    [0.0 - 3.0]  typical: 0.5-1.5\nuniform float u_float1;  // radius    [0.5 - 10.0] blur radius in pixels\nuniform float u_float2;  // threshold [0.0 - 0.1]  min difference to sharpen\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n    return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nfloat getLuminance(vec3 color) {\n    return dot(color, vec3(0.2126, 0.7152, 0.0722));\n}\n\nvoid main() {\n    vec2 texel = 1.0 / vec2(textureSize(u_image0, 0));\n    float radius = max(u_float1, 0.5);\n    float amount = u_float0;\n    float threshold = u_float2;\n\n    vec4 original = texture(u_image0, v_texCoord);\n\n    // Gaussian blur for the \"unsharp\" mask\n    int samples = int(ceil(radius));\n    float sigma = radius / 2.0;\n\n    vec4 blurred = vec4(0.0);\n    float totalWeight = 0.0;\n\n    for (int x = -samples; x <= samples; x++) {\n        for (int y = -samples; y <= samples; y++) {\n            vec2 offset = vec2(float(x), float(y)) * texel;\n            vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n            float dist = length(vec2(float(x), float(y)));\n            float weight = gaussian(dist, sigma);\n            blurred += sample_color * weight;\n            totalWeight += weight;\n        }\n    }\n    blurred /= totalWeight;\n\n    // Unsharp mask = original - blurred\n    vec3 mask = original.rgb - blurred.rgb;\n\n    // Luminance-based threshold with smooth falloff\n    float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb));\n    float thresholdScale = smoothstep(0.0, threshold, lumaDelta);\n    mask *= thresholdScale;\n\n    // Sharpen: original + mask * amount\n    vec3 sharpened = original.rgb + mask * amount;\n\n    fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a);\n}\n",
               "from_input"
             ]
           }

comfy/ldm/sam3/detector.py

@@ -54,7 +54,7 @@ class SplitMHA(nn.Module):
         if mask is not None and mask.ndim == 2:
             mask = mask[:, None, None, :]  # [B, T] -> [B, 1, 1, T] for SDPA broadcast
         dtype = q.dtype  # manual_cast may produce mixed dtypes
-        out = optimized_attention(q, k.to(dtype), v.to(dtype), self.num_heads, mask=mask)
+        out = optimized_attention(q, k.to(dtype), v.to(dtype), self.num_heads, mask=mask, low_precision_attention=False)
         return self.out_proj(out)
 
 

comfy/ldm/sam3/sam.py

@@ -40,7 +40,7 @@ class SAMAttention(nn.Module):
         q = self.q_proj(q)
         k = self.k_proj(k)
         v = self.v_proj(v)
-        return self.out_proj(optimized_attention(q, k, v, self.num_heads))
+        return self.out_proj(optimized_attention(q, k, v, self.num_heads, low_precision_attention=False))
 
 
 class TwoWayAttentionBlock(nn.Module):
@@ -179,7 +179,7 @@ class Attention(nn.Module):
         q, k, v = qkv.permute(2, 0, 3, 1, 4).unbind(dim=0)
         if self.use_rope and freqs_cis is not None:
             q, k = apply_rope(q, k, freqs_cis)
-        return self.proj(optimized_attention(q, k, v, self.num_heads, skip_reshape=True))
+        return self.proj(optimized_attention(q, k, v, self.num_heads, skip_reshape=True, low_precision_attention=False))
 
 
 class Block(nn.Module):

comfy/ldm/sam3/tracker.py

@@ -364,7 +364,7 @@ class SplitAttn(nn.Module):
         v = self.v_proj(v)
         if rope is not None:
             q, k = apply_rope_memory(q, k, rope, self.num_heads, num_k_exclude_rope)
-        out = optimized_attention(q, k, v, self.num_heads)
+        out = optimized_attention(q, k, v, self.num_heads, low_precision_attention=False)
         return self.out_proj(out)
 
 
@@ -657,7 +657,7 @@ class DecoupledMemoryAttnLayer(nn.Module):
         v = self.self_attn_v_proj(normed)
         if rope is not None:
             q, k = apply_rope_memory(q, k, rope, self.num_heads, 0)
-        x = x + self.self_attn_out_proj(optimized_attention(q, k, v, self.num_heads))
+        x = x + self.self_attn_out_proj(optimized_attention(q, k, v, self.num_heads, low_precision_attention=False))
 
         # Decoupled cross-attention: fuse image and memory projections
         normed = self.norm2(x)
@@ -668,7 +668,7 @@ class DecoupledMemoryAttnLayer(nn.Module):
         v = self.cross_attn_v_proj(memory)
         if rope is not None:
             q, k = apply_rope_memory(q, k, rope, self.num_heads, num_k_exclude_rope)
-        x = x + self.cross_attn_out_proj(optimized_attention(q, k, v, self.num_heads))
+        x = x + self.cross_attn_out_proj(optimized_attention(q, k, v, self.num_heads, low_precision_attention=False))
 
         # FFN
         x = x + self.linear2(F.gelu(self.linear1(self.norm3(x))))

comfy_api/input/__init__.py

@@ -9,6 +9,7 @@ from comfy_api.latest._input import (
     CurveInput,
     MonotoneCubicCurve,
     LinearCurve,
+    RangeInput,
 )
 
 __all__ = [
@@ -21,4 +22,5 @@ __all__ = [
     "CurveInput",
     "MonotoneCubicCurve",
     "LinearCurve",
+    "RangeInput",
 ]

comfy_api/latest/_input/__init__.py

@@ -1,5 +1,6 @@
 from .basic_types import ImageInput, AudioInput, MaskInput, LatentInput
 from .curve_types import CurvePoint, CurveInput, MonotoneCubicCurve, LinearCurve
+from .range_types import RangeInput
 from .video_types import VideoInput
 
 __all__ = [
@@ -12,4 +13,5 @@ __all__ = [
     "CurveInput",
     "MonotoneCubicCurve",
     "LinearCurve",
+    "RangeInput",
 ]

comfy_api/latest/_input/range_types.py

@@ -0,0 +1,70 @@
+from __future__ import annotations
+
+import logging
+import math
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+class RangeInput:
+    """Represents a levels/range adjustment: input range [min, max] with
+    optional midpoint (gamma control).
+
+    Generates a 1D LUT identical to GIMP's levels mapping:
+        1. Normalize input to [0, 1] using [min, max]
+        2. Apply gamma correction: pow(value, 1/gamma)
+        3. Clamp to [0, 1]
+
+    The midpoint field is a position in [0, 1] representing where the
+    midtone falls within [min, max]. It maps to gamma via:
+        gamma = -log2(midpoint)
+    So midpoint=0.5 → gamma=1.0 (linear).
+    """
+
+    def __init__(self, min_val: float, max_val: float, midpoint: float | None = None):
+        self.min_val = min_val
+        self.max_val = max_val
+        self.midpoint = midpoint
+
+    @staticmethod
+    def from_raw(data) -> RangeInput:
+        if isinstance(data, RangeInput):
+            return data
+        if isinstance(data, dict):
+            return RangeInput(
+                min_val=float(data.get("min", 0.0)),
+                max_val=float(data.get("max", 1.0)),
+                midpoint=float(data["midpoint"]) if data.get("midpoint") is not None else None,
+            )
+        raise TypeError(f"Cannot convert {type(data)} to RangeInput")
+
+    def to_lut(self, size: int = 256) -> np.ndarray:
+        """Generate a float64 lookup table mapping [0, 1] input through this
+        levels adjustment.
+
+        The LUT maps normalized input values (0..1) to output values (0..1),
+        matching the GIMP levels formula.
+        """
+        xs = np.linspace(0.0, 1.0, size, dtype=np.float64)
+
+        in_range = self.max_val - self.min_val
+        if abs(in_range) < 1e-10:
+            return np.where(xs >= self.min_val, 1.0, 0.0).astype(np.float64)
+
+        # Normalize: map [min, max] → [0, 1]
+        result = (xs - self.min_val) / in_range
+        result = np.clip(result, 0.0, 1.0)
+
+        # Gamma correction from midpoint
+        if self.midpoint is not None and self.midpoint > 0 and self.midpoint != 0.5:
+            gamma = max(-math.log2(self.midpoint), 0.001)
+            inv_gamma = 1.0 / gamma
+            mask = result > 0
+            result[mask] = np.power(result[mask], inv_gamma)
+
+        return result
+
+    def __repr__(self) -> str:
+        mid = f", midpoint={self.midpoint}" if self.midpoint is not None else ""
+        return f"RangeInput(min={self.min_val}, max={self.max_val}{mid})"

comfy_api/latest/_input_impl/video_types.py

@@ -240,19 +240,34 @@ class VideoFromFile(VideoInput):
             start_time = self.__start_time
         # Get video frames
         frames = []
+        alphas = None
         start_pts = int(start_time / video_stream.time_base)
         end_pts = int((start_time + self.__duration) / video_stream.time_base)
         container.seek(start_pts, stream=video_stream)
+        image_format = 'gbrpf32le'
         for frame in container.decode(video_stream):
+            if alphas is None:
+                for comp in frame.format.components:
+                    if comp.is_alpha:
+                        alphas = []
+                        image_format = 'gbrapf32le'
+                        break
+
             if frame.pts < start_pts:
                 continue
             if self.__duration and frame.pts >= end_pts:
                 break
-            img = frame.to_ndarray(format='rgb24')  # shape: (H, W, 3)
-            img = torch.from_numpy(img) / 255.0  # shape: (H, W, 3)
-            frames.append(img)
 
-        images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 3, 0, 0)
+            img = frame.to_ndarray(format=image_format)  # shape: (H, W, 4)
+            if alphas is None:
+                frames.append(torch.from_numpy(img))
+            else:
+                frames.append(torch.from_numpy(img[..., :-1]))
+                alphas.append(torch.from_numpy(img[..., -1:]))
+
+        images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 0, 0, 3)
+        if alphas is not None:
+            alphas = torch.stack(alphas) if len(alphas) > 0 else torch.zeros(0, 0, 0, 1)
 
         # Get frame rate
         frame_rate = Fraction(video_stream.average_rate) if video_stream.average_rate else Fraction(1)
@@ -295,7 +310,7 @@ class VideoFromFile(VideoInput):
                 })
 
         metadata = container.metadata
-        return VideoComponents(images=images, audio=audio, frame_rate=frame_rate, metadata=metadata)
+        return VideoComponents(images=images, alpha=alphas, audio=audio, frame_rate=frame_rate, metadata=metadata)
 
     def get_components(self) -> VideoComponents:
         if isinstance(self.__file, io.BytesIO):

comfy_api/latest/_io.py

@@ -1266,6 +1266,43 @@ class Histogram(ComfyTypeIO):
     Type = list[int]
 
 
+@comfytype(io_type="RANGE")
+class Range(ComfyTypeIO):
+    from comfy_api.input import RangeInput
+    if TYPE_CHECKING:
+        Type = RangeInput
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: dict=None,
+                     display: str=None,
+                     gradient_stops: list=None,
+                     show_midpoint: bool=None,
+                     midpoint_scale: str=None,
+                     value_min: float=None,
+                     value_max: float=None,
+                     advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            if default is None:
+                self.default = {"min": 0.0, "max": 1.0}
+            self.display = display
+            self.gradient_stops = gradient_stops
+            self.show_midpoint = show_midpoint
+            self.midpoint_scale = midpoint_scale
+            self.value_min = value_min
+            self.value_max = value_max
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "display": self.display,
+                "gradient_stops": self.gradient_stops,
+                "show_midpoint": self.show_midpoint,
+                "midpoint_scale": self.midpoint_scale,
+                "value_min": self.value_min,
+                "value_max": self.value_max,
+            })
+
+
 DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
 def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
     DYNAMIC_INPUT_LOOKUP[io_type] = func
@@ -2276,5 +2313,6 @@ __all__ = [
     "BoundingBox",
     "Curve",
     "Histogram",
+    "Range",
     "NodeReplace",
 ]

comfy_api/latest/_util/video_types.py

@@ -3,7 +3,7 @@ from dataclasses import dataclass
 from enum import Enum
 from fractions import Fraction
 from typing import Optional
-from .._input import ImageInput, AudioInput
+from .._input import ImageInput, AudioInput, MaskInput
 
 class VideoCodec(str, Enum):
     AUTO = "auto"
@@ -48,5 +48,4 @@ class VideoComponents:
     frame_rate: Fraction
     audio: Optional[AudioInput] = None
     metadata: Optional[dict] = None
-
+    alpha: Optional[MaskInput] = None
-

comfy_api_nodes/apis/wan.py

@@ -118,7 +118,7 @@ class Wan27ReferenceVideoInputField(BaseModel):
 class Wan27ReferenceVideoParametersField(BaseModel):
     resolution: str = Field(...)
     ratio: str | None = Field(None)
-    duration: int = Field(5, ge=2, le=10)
+    duration: int = Field(5, ge=2, le=15)
     watermark: bool = Field(False)
     seed: int = Field(..., ge=0, le=2147483647)
 
@@ -157,7 +157,7 @@ class Wan27VideoEditInputField(BaseModel):
 class Wan27VideoEditParametersField(BaseModel):
     resolution: str = Field(...)
     ratio: str | None = Field(None)
-    duration: int = Field(0)
+    duration: int | None = Field(0)
     audio_setting: str = Field("auto")
     watermark: bool = Field(False)
     seed: int = Field(..., ge=0, le=2147483647)

comfy_api_nodes/nodes_kling.py

@@ -276,6 +276,7 @@ async def finish_omni_video_task(cls: type[IO.ComfyNode], response: TaskStatusRe
         cls,
         ApiEndpoint(path=f"/proxy/kling/v1/videos/omni-video/{response.data.task_id}"),
         response_model=TaskStatusResponse,
+        max_poll_attempts=280,
         status_extractor=lambda r: (r.data.task_status if r.data else None),
     )
     return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
@@ -862,7 +863,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
                 ),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
                 IO.Int.Input("duration", default=5, min=3, max=15, display_mode=IO.NumberDisplay.slider),
-                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
+                IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p", optional=True),
                 IO.DynamicCombo.Input(
                     "storyboards",
                     options=[
@@ -904,12 +905,13 @@ class OmniProTextToVideoNode(IO.ComfyNode):
                 depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution", "model_name", "generate_audio"]),
                 expr="""
                 (
-                  $mode := (widgets.resolution = "720p") ? "std" : "pro";
+                  $res := widgets.resolution;
+                  $mode := $res = "4k" ? "4k" : ($res = "720p" ? "std" : "pro");
                   $isV3 := $contains(widgets.model_name, "v3");
                   $audio := $isV3 and widgets.generate_audio;
                   $rates := $audio
-                    ? {"std": 0.112, "pro": 0.14}
+                    ? {"std": 0.112, "pro": 0.14, "4k": 0.42}
-                    : {"std": 0.084, "pro": 0.112};
+                    : {"std": 0.084, "pro": 0.112, "4k": 0.42};
                   {"type":"usd","usd": $lookup($rates, $mode) * widgets.duration}
                 )
                 """,
@@ -934,6 +936,8 @@ class OmniProTextToVideoNode(IO.ComfyNode):
                 raise ValueError("kling-video-o1 only supports durations of 5 or 10 seconds.")
             if generate_audio:
                 raise ValueError("kling-video-o1 does not support audio generation.")
+            if resolution == "4k":
+                raise ValueError("kling-video-o1 does not support 4k resolution.")
         stories_enabled = storyboards is not None and storyboards["storyboards"] != "disabled"
         if stories_enabled and model_name == "kling-video-o1":
             raise ValueError("kling-video-o1 does not support storyboards.")
@@ -963,6 +967,12 @@ class OmniProTextToVideoNode(IO.ComfyNode):
                     f"must equal the global duration ({duration}s)."
                 )
 
+        if resolution == "4k":
+            mode = "4k"
+        elif resolution == "1080p":
+            mode = "pro"
+        else:
+            mode = "std"
         response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
@@ -972,7 +982,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
                 prompt=prompt,
                 aspect_ratio=aspect_ratio,
                 duration=str(duration),
-                mode="pro" if resolution == "1080p" else "std",
+                mode=mode,
                 multi_shot=multi_shot,
                 multi_prompt=multi_prompt_list,
                 shot_type="customize" if multi_shot else None,
@@ -1014,7 +1024,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
                     optional=True,
                     tooltip="Up to 6 additional reference images.",
                 ),
-                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
+                IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p", optional=True),
                 IO.DynamicCombo.Input(
                     "storyboards",
                     options=[
@@ -1061,12 +1071,13 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
                 depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution", "model_name", "generate_audio"]),
                 expr="""
                 (
-                  $mode := (widgets.resolution = "720p") ? "std" : "pro";
+                  $res := widgets.resolution;
+                  $mode := $res = "4k" ? "4k" : ($res = "720p" ? "std" : "pro");
                   $isV3 := $contains(widgets.model_name, "v3");
                   $audio := $isV3 and widgets.generate_audio;
                   $rates := $audio
-                    ? {"std": 0.112, "pro": 0.14}
+                    ? {"std": 0.112, "pro": 0.14, "4k": 0.42}
-                    : {"std": 0.084, "pro": 0.112};
+                    : {"std": 0.084, "pro": 0.112, "4k": 0.42};
                   {"type":"usd","usd": $lookup($rates, $mode) * widgets.duration}
                 )
                 """,
@@ -1093,6 +1104,8 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
                 raise ValueError("kling-video-o1 does not support durations greater than 10 seconds.")
             if generate_audio:
                 raise ValueError("kling-video-o1 does not support audio generation.")
+            if resolution == "4k":
+                raise ValueError("kling-video-o1 does not support 4k resolution.")
         stories_enabled = storyboards is not None and storyboards["storyboards"] != "disabled"
         if stories_enabled and model_name == "kling-video-o1":
             raise ValueError("kling-video-o1 does not support storyboards.")
@@ -1161,6 +1174,12 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
                 validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
             for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference frame(s)"):
                 image_list.append(OmniParamImage(image_url=i))
+        if resolution == "4k":
+            mode = "4k"
+        elif resolution == "1080p":
+            mode = "pro"
+        else:
+            mode = "std"
         response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
@@ -1170,7 +1189,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
                 prompt=prompt,
                 duration=str(duration),
                 image_list=image_list,
-                mode="pro" if resolution == "1080p" else "std",
+                mode=mode,
                 sound="on" if generate_audio else "off",
                 multi_shot=multi_shot,
                 multi_prompt=multi_prompt_list,
@@ -1204,7 +1223,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
                     "reference_images",
                     tooltip="Up to 7 reference images.",
                 ),
-                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
+                IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p", optional=True),
                 IO.DynamicCombo.Input(
                     "storyboards",
                     options=[
@@ -1251,12 +1270,13 @@ class OmniProImageToVideoNode(IO.ComfyNode):
                 depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution", "model_name", "generate_audio"]),
                 expr="""
                 (
-                  $mode := (widgets.resolution = "720p") ? "std" : "pro";
+                  $res := widgets.resolution;
+                  $mode := $res = "4k" ? "4k" : ($res = "720p" ? "std" : "pro");
                   $isV3 := $contains(widgets.model_name, "v3");
                   $audio := $isV3 and widgets.generate_audio;
                   $rates := $audio
-                    ? {"std": 0.112, "pro": 0.14}
+                    ? {"std": 0.112, "pro": 0.14, "4k": 0.42}
-                    : {"std": 0.084, "pro": 0.112};
+                    : {"std": 0.084, "pro": 0.112, "4k": 0.42};
                   {"type":"usd","usd": $lookup($rates, $mode) * widgets.duration}
                 )
                 """,
@@ -1282,6 +1302,8 @@ class OmniProImageToVideoNode(IO.ComfyNode):
                 raise ValueError("kling-video-o1 does not support durations greater than 10 seconds.")
             if generate_audio:
                 raise ValueError("kling-video-o1 does not support audio generation.")
+            if resolution == "4k":
+                raise ValueError("kling-video-o1 does not support 4k resolution.")
         stories_enabled = storyboards is not None and storyboards["storyboards"] != "disabled"
         if stories_enabled and model_name == "kling-video-o1":
             raise ValueError("kling-video-o1 does not support storyboards.")
@@ -1320,6 +1342,12 @@ class OmniProImageToVideoNode(IO.ComfyNode):
         image_list: list[OmniParamImage] = []
         for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"):
             image_list.append(OmniParamImage(image_url=i))
+        if resolution == "4k":
+            mode = "4k"
+        elif resolution == "1080p":
+            mode = "pro"
+        else:
+            mode = "std"
         response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
@@ -1330,7 +1358,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
                 aspect_ratio=aspect_ratio,
                 duration=str(duration),
                 image_list=image_list,
-                mode="pro" if resolution == "1080p" else "std",
+                mode=mode,
                 sound="on" if generate_audio else "off",
                 multi_shot=multi_shot,
                 multi_prompt=multi_prompt_list,
@@ -2860,7 +2888,7 @@ class KlingVideoNode(IO.ComfyNode):
                         IO.DynamicCombo.Option(
                             "kling-v3",
                             [
-                                IO.Combo.Input("resolution", options=["1080p", "720p"]),
+                                IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p"),
                                 IO.Combo.Input(
                                     "aspect_ratio",
                                     options=["16:9", "9:16", "1:1"],
@@ -2913,7 +2941,11 @@ class KlingVideoNode(IO.ComfyNode):
                 ),
                 expr="""
                 (
-                  $rates := {"1080p": {"off": 0.112, "on": 0.168}, "720p": {"off": 0.084, "on": 0.126}};
+                  $rates := {
+                    "4k": {"off": 0.42, "on": 0.42},
+                    "1080p": {"off": 0.112, "on": 0.168},
+                    "720p": {"off": 0.084, "on": 0.126}
+                  };
                   $res := $lookup(widgets, "model.resolution");
                   $audio := widgets.generate_audio ? "on" : "off";
                   $rate := $lookup($lookup($rates, $res), $audio);
@@ -2943,7 +2975,12 @@ class KlingVideoNode(IO.ComfyNode):
         start_frame: Input.Image | None = None,
     ) -> IO.NodeOutput:
         _ = seed
-        mode = "pro" if model["resolution"] == "1080p" else "std"
+        if model["resolution"] == "4k":
+            mode = "4k"
+        elif model["resolution"] == "1080p":
+            mode = "pro"
+        else:
+            mode = "std"
         custom_multi_shot = False
         if multi_shot["multi_shot"] == "disabled":
             shot_type = None
@@ -3025,6 +3062,7 @@ class KlingVideoNode(IO.ComfyNode):
             cls,
             ApiEndpoint(path=poll_path),
             response_model=TaskStatusResponse,
+            max_poll_attempts=280,
             status_extractor=lambda r: (r.data.task_status if r.data else None),
         )
         return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
@@ -3057,7 +3095,7 @@ class KlingFirstLastFrameNode(IO.ComfyNode):
                         IO.DynamicCombo.Option(
                             "kling-v3",
                             [
-                                IO.Combo.Input("resolution", options=["1080p", "720p"]),
+                                IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p"),
                             ],
                         ),
                     ],
@@ -3089,7 +3127,11 @@ class KlingFirstLastFrameNode(IO.ComfyNode):
                 ),
                 expr="""
                 (
-                  $rates := {"1080p": {"off": 0.112, "on": 0.168}, "720p": {"off": 0.084, "on": 0.126}};
+                  $rates := {
+                    "4k": {"off": 0.42, "on": 0.42},
+                    "1080p": {"off": 0.112, "on": 0.168},
+                    "720p": {"off": 0.084, "on": 0.126}
+                  };
                   $res := $lookup(widgets, "model.resolution");
                   $audio := widgets.generate_audio ? "on" : "off";
                   $rate := $lookup($lookup($rates, $res), $audio);
@@ -3118,6 +3160,12 @@ class KlingFirstLastFrameNode(IO.ComfyNode):
         validate_image_aspect_ratio(end_frame, (1, 2.5), (2.5, 1))
         image_url = await upload_image_to_comfyapi(cls, first_frame, wait_label="Uploading first frame")
         image_tail_url = await upload_image_to_comfyapi(cls, end_frame, wait_label="Uploading end frame")
+        if model["resolution"] == "4k":
+            mode = "4k"
+        elif model["resolution"] == "1080p":
+            mode = "pro"
+        else:
+            mode = "std"
         response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/kling/v1/videos/image2video", method="POST"),
@@ -3127,7 +3175,7 @@ class KlingFirstLastFrameNode(IO.ComfyNode):
                 image=image_url,
                 image_tail=image_tail_url,
                 prompt=prompt,
-                mode="pro" if model["resolution"] == "1080p" else "std",
+                mode=mode,
                 duration=str(duration),
                 sound="on" if generate_audio else "off",
             ),
@@ -3140,6 +3188,7 @@ class KlingFirstLastFrameNode(IO.ComfyNode):
             cls,
             ApiEndpoint(path=f"/proxy/kling/v1/videos/image2video/{response.data.task_id}"),
             response_model=TaskStatusResponse,
+            max_poll_attempts=280,
             status_extractor=lambda r: (r.data.task_status if r.data else None),
         )
         return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))

comfy_api_nodes/nodes_openai.py

@@ -357,6 +357,10 @@ def calculate_tokens_price_image_1_5(response: OpenAIImageGenerationResponse) ->
     return ((response.usage.input_tokens * 8.0) + (response.usage.output_tokens * 32.0)) / 1_000_000.0
 
 
+def calculate_tokens_price_image_2_0(response: OpenAIImageGenerationResponse) -> float | None:
+    return ((response.usage.input_tokens * 8.0) + (response.usage.output_tokens * 30.0)) / 1_000_000.0
+
+
 class OpenAIGPTImage1(IO.ComfyNode):
 
     @classmethod
@@ -401,7 +405,17 @@ class OpenAIGPTImage1(IO.ComfyNode):
                 IO.Combo.Input(
                     "size",
                     default="auto",
-                    options=["auto", "1024x1024", "1024x1536", "1536x1024"],
+                    options=[
+                        "auto",
+                        "1024x1024",
+                        "1024x1536",
+                        "1536x1024",
+                        "2048x2048",
+                        "2048x1152",
+                        "1152x2048",
+                        "3840x2160",
+                        "2160x3840",
+                    ],
                     tooltip="Image size",
                     optional=True,
                 ),
@@ -427,7 +441,7 @@ class OpenAIGPTImage1(IO.ComfyNode):
                 ),
                 IO.Combo.Input(
                     "model",
-                    options=["gpt-image-1", "gpt-image-1.5", 'gpt-image-2'],
+                    options=["gpt-image-1", "gpt-image-1.5", "gpt-image-2"],
                     default="gpt-image-2",
                     optional=True,
                 ),
@@ -442,23 +456,36 @@ class OpenAIGPTImage1(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["quality", "n"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["quality", "n", "model"]),
                 expr="""
                 (
                   $ranges := {
-                    "low":    [0.011, 0.02],
+                    "gpt-image-1": {
-                    "medium": [0.046, 0.07],
+                      "low":    [0.011, 0.02],
-                    "high":   [0.167, 0.3]
+                      "medium": [0.042, 0.07],
+                      "high":   [0.167, 0.25]
+                    },
+                    "gpt-image-1.5": {
+                      "low":    [0.009, 0.02],
+                      "medium": [0.034, 0.062],
+                      "high":   [0.133, 0.22]
+                    },
+                    "gpt-image-2": {
+                      "low":    [0.0048, 0.012],
+                      "medium": [0.041, 0.112],
+                      "high":   [0.165, 0.43]
+                    }
                   };
-                  $range := $lookup($ranges, widgets.quality);
+                  $range := $lookup($lookup($ranges, widgets.model), widgets.quality);
-                  $n := widgets.n;
+                  $nRaw := widgets.n;
+                  $n := ($nRaw != null and $nRaw != 0) ? $nRaw : 1;
                   ($n = 1)
-                    ? {"type":"range_usd","min_usd": $range[0], "max_usd": $range[1]}
+                    ? {"type":"range_usd","min_usd": $range[0], "max_usd": $range[1], "format": {"approximate": true}}
                     : {
                         "type":"range_usd",
-                        "min_usd": $range[0],
+                        "min_usd": $range[0] * $n,
-                        "max_usd": $range[1],
+                        "max_usd": $range[1] * $n,
-                        "format": { "suffix": " x " & $string($n) & "/Run" }
+                        "format": { "suffix": "/Run", "approximate": true }
                       }
                 )
                 """,
@@ -483,12 +510,18 @@ class OpenAIGPTImage1(IO.ComfyNode):
         if mask is not None and image is None:
             raise ValueError("Cannot use a mask without an input image")
 
+        if model in ("gpt-image-1", "gpt-image-1.5"):
+            if size not in ("auto", "1024x1024", "1024x1536", "1536x1024"):
+                raise ValueError(f"Resolution {size} is only supported by GPT Image 2 model")
+
         if model == "gpt-image-1":
             price_extractor = calculate_tokens_price_image_1
         elif model == "gpt-image-1.5":
             price_extractor = calculate_tokens_price_image_1_5
         elif model == "gpt-image-2":
-            price_extractor = calculate_tokens_price_image_1_5
+            price_extractor = calculate_tokens_price_image_2_0
+            if background == "transparent":
+                raise ValueError("Transparent background is not supported for GPT Image 2 model")
         else:
             raise ValueError(f"Unknown model: {model}")
 

comfy_api_nodes/nodes_sora.py

@@ -33,9 +33,13 @@ class OpenAIVideoSora2(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="OpenAIVideoSora2",
-            display_name="OpenAI Sora - Video",
+            display_name="OpenAI Sora - Video (Deprecated)",
             category="api node/video/Sora",
-            description="OpenAI video and audio generation.",
+            description=(
+                "OpenAI video and audio generation.\n\n"
+                "DEPRECATION NOTICE: OpenAI will stop serving the Sora v2 API in September 2026. "
+                "This node will be removed from ComfyUI at that time."
+            ),
             inputs=[
                 IO.Combo.Input(
                     "model",

comfy_api_nodes/nodes_wan.py

@@ -1646,6 +1646,557 @@ class Wan2ReferenceVideoApi(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
 
 
+class HappyHorseTextToVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="HappyHorseTextToVideoApi",
+            display_name="HappyHorse Text to Video",
+            category="api node/video/Wan",
+            description="Generates a video based on a text prompt using the HappyHorse model.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "happyhorse-1.0-t2v",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the elements and visual features. "
+                                    "Supports English and Chinese.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["16:9", "9:16", "1:1", "4:3", "3:4"],
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=3,
+                                    max=15,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            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(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $dur := $lookup(widgets, "model.duration");
+                  $ppsTable := { "720p": 0.14, "1080p": 0.24 };
+                  $pps := $lookup($ppsTable, $res);
+                  { "type": "usd", "usd": $pps * $dur }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        seed: int,
+        watermark: bool,
+    ):
+        validate_string(model["prompt"], strip_whitespace=False, min_length=1)
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(
+                path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+                method="POST",
+            ),
+            response_model=TaskCreationResponse,
+            data=Wan27Text2VideoTaskCreationRequest(
+                model=model["model"],
+                input=Text2VideoInputField(
+                    prompt=model["prompt"],
+                    negative_prompt=None,
+                ),
+                parameters=Wan27Text2VideoParametersField(
+                    resolution=model["resolution"],
+                    ratio=model["ratio"],
+                    duration=model["duration"],
+                    seed=seed,
+                    watermark=watermark,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=7,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class HappyHorseImageToVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="HappyHorseImageToVideoApi",
+            display_name="HappyHorse Image to Video",
+            category="api node/video/Wan",
+            description="Generate a video from a first-frame image using the HappyHorse model.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "happyhorse-1.0-i2v",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the elements and visual features. "
+                                    "Supports English and Chinese.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=3,
+                                    max=15,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Image.Input(
+                    "first_frame",
+                    tooltip="First frame image. The output aspect ratio is derived from this image.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            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(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $dur := $lookup(widgets, "model.duration");
+                  $ppsTable := { "720p": 0.14, "1080p": 0.24 };
+                  $pps := $lookup($ppsTable, $res);
+                  { "type": "usd", "usd": $pps * $dur }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        first_frame: Input.Image,
+        seed: int,
+        watermark: bool,
+    ):
+        media = [
+            Wan27MediaItem(
+                type="first_frame",
+                url=await upload_image_to_comfyapi(cls, image=first_frame),
+            )
+        ]
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(
+                path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+                method="POST",
+            ),
+            response_model=TaskCreationResponse,
+            data=Wan27ImageToVideoTaskCreationRequest(
+                model=model["model"],
+                input=Wan27ImageToVideoInputField(
+                    prompt=model["prompt"] or None,
+                    negative_prompt=None,
+                    media=media,
+                ),
+                parameters=Wan27ImageToVideoParametersField(
+                    resolution=model["resolution"],
+                    duration=model["duration"],
+                    seed=seed,
+                    watermark=watermark,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=7,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class HappyHorseVideoEditApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="HappyHorseVideoEditApi",
+            display_name="HappyHorse Video Edit",
+            category="api node/video/Wan",
+            description="Edit a video using text instructions or reference images with the HappyHorse model. "
+            "Output duration is 3-15s and matches the input video; inputs longer than 15s are truncated.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "happyhorse-1.0-video-edit",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Editing instructions or style transfer requirements.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["16:9", "9:16", "1:1", "4:3", "3:4"],
+                                    tooltip="Aspect ratio. If not changed, approximates the input video ratio.",
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("reference_image"),
+                                        names=[
+                                            "image1",
+                                            "image2",
+                                            "image3",
+                                            "image4",
+                                            "image5",
+                                        ],
+                                        min=0,
+                                    ),
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Video.Input(
+                    "video",
+                    tooltip="The video to edit.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            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(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $ppsTable := { "720p": 0.14, "1080p": 0.24 };
+                  $pps := $lookup($ppsTable, $res);
+                  { "type": "usd", "usd": $pps, "format": { "suffix": "/second" } }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        video: Input.Video,
+        seed: int,
+        watermark: bool,
+    ):
+        validate_string(model["prompt"], strip_whitespace=False, min_length=1)
+        validate_video_duration(video, min_duration=3, max_duration=60)
+        media = [Wan27MediaItem(type="video", url=await upload_video_to_comfyapi(cls, video))]
+        reference_images = model.get("reference_images", {})
+        for key in reference_images:
+            media.append(
+                Wan27MediaItem(
+                    type="reference_image", url=await upload_image_to_comfyapi(cls, image=reference_images[key])
+                )
+            )
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(
+                path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+                method="POST",
+            ),
+            response_model=TaskCreationResponse,
+            data=Wan27VideoEditTaskCreationRequest(
+                model=model["model"],
+                input=Wan27VideoEditInputField(prompt=model["prompt"], media=media),
+                parameters=Wan27VideoEditParametersField(
+                    resolution=model["resolution"],
+                    ratio=model["ratio"],
+                    duration=None,
+                    watermark=watermark,
+                    seed=seed,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=7,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class HappyHorseReferenceVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="HappyHorseReferenceVideoApi",
+            display_name="HappyHorse Reference to Video",
+            category="api node/video/Wan",
+            description="Generate a video featuring a person or object from reference materials with the HappyHorse "
+            "model. Supports single-character performances and multi-character interactions.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "happyhorse-1.0-r2v",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Prompt describing the video. Use identifiers such as 'character1' and "
+                                    "'character2' to refer to the reference characters.",
+                                ),
+                                IO.Combo.Input(
+                                    "resolution",
+                                    options=["720P", "1080P"],
+                                ),
+                                IO.Combo.Input(
+                                    "ratio",
+                                    options=["16:9", "9:16", "1:1", "4:3", "3:4"],
+                                ),
+                                IO.Int.Input(
+                                    "duration",
+                                    default=5,
+                                    min=3,
+                                    max=15,
+                                    step=1,
+                                    display_mode=IO.NumberDisplay.number,
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("reference_image"),
+                                        names=[
+                                            "image1",
+                                            "image2",
+                                            "image3",
+                                            "image4",
+                                            "image5",
+                                            "image6",
+                                            "image7",
+                                            "image8",
+                                            "image9",
+                                        ],
+                                        min=1,
+                                    ),
+                                ),
+                            ],
+                        ),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                    advanced=True,
+                ),
+            ],
+            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(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
+                expr="""
+                (
+                  $res := $lookup(widgets, "model.resolution");
+                  $dur := $lookup(widgets, "model.duration");
+                  $ppsTable := { "720p": 0.14, "1080p": 0.24 };
+                  $pps := $lookup($ppsTable, $res);
+                  { "type": "usd", "usd": $pps * $dur }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: dict,
+        seed: int,
+        watermark: bool,
+    ):
+        validate_string(model["prompt"], strip_whitespace=False, min_length=1)
+        media = []
+        reference_images = model.get("reference_images", {})
+        for key in reference_images:
+            media.append(
+                Wan27MediaItem(
+                    type="reference_image",
+                    url=await upload_image_to_comfyapi(cls, image=reference_images[key]),
+                )
+            )
+        if not media:
+            raise ValueError("At least one reference reference image must be provided.")
+
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(
+                path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+                method="POST",
+            ),
+            response_model=TaskCreationResponse,
+            data=Wan27ReferenceVideoTaskCreationRequest(
+                model=model["model"],
+                input=Wan27ReferenceVideoInputField(
+                    prompt=model["prompt"],
+                    negative_prompt=None,
+                    media=media,
+                ),
+                parameters=Wan27ReferenceVideoParametersField(
+                    resolution=model["resolution"],
+                    ratio=model["ratio"],
+                    duration=model["duration"],
+                    watermark=watermark,
+                    seed=seed,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=7,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
 class WanApiExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1660,6 +2211,10 @@ class WanApiExtension(ComfyExtension):
             Wan2VideoContinuationApi,
             Wan2VideoEditApi,
             Wan2ReferenceVideoApi,
+            HappyHorseTextToVideoApi,
+            HappyHorseImageToVideoApi,
+            HappyHorseVideoEditApi,
+            HappyHorseReferenceVideoApi,
         ]
 
 

comfy_extras/nodes_lt.py

@@ -1,6 +1,7 @@
 import nodes
 import node_helpers
 import torch
+import torchaudio
 import comfy.model_management
 import comfy.model_sampling
 import comfy.samplers
@@ -711,7 +712,14 @@ class LTXVReferenceAudio(io.ComfyNode):
     @classmethod
     def execute(cls, model, positive, negative, reference_audio, audio_vae, identity_guidance_scale, start_percent, end_percent) -> io.NodeOutput:
         # Encode reference audio to latents and patchify
-        audio_latents = audio_vae.encode(reference_audio)
+        sample_rate = reference_audio["sample_rate"]
+        vae_sample_rate = getattr(audio_vae, "audio_sample_rate", 44100)
+        if vae_sample_rate != sample_rate:
+            waveform = torchaudio.functional.resample(reference_audio["waveform"], sample_rate, vae_sample_rate)
+        else:
+            waveform = reference_audio["waveform"]
+
+        audio_latents = audio_vae.encode(waveform.movedim(1, -1))
         b, c, t, f = audio_latents.shape
         ref_tokens = audio_latents.permute(0, 2, 1, 3).reshape(b, t, c * f)
         ref_audio = {"tokens": ref_tokens}

comfy_extras/nodes_preview_any.py

@@ -1,5 +1,6 @@
 import json
 from comfy.comfy_types.node_typing import IO
+import torch
 
 # Preview Any - original implement from
 # https://github.com/rgthree/rgthree-comfy/blob/main/py/display_any.py
@@ -19,6 +20,7 @@ class PreviewAny():
     SEARCH_ALIASES = ["show output", "inspect", "debug", "print value", "show text"]
 
     def main(self, source=None):
+        torch.set_printoptions(edgeitems=6)
         value = 'None'
         if isinstance(source, str):
             value = source
@@ -33,6 +35,7 @@ class PreviewAny():
                 except Exception:
                     value = 'source exists, but could not be serialized.'
 
+        torch.set_printoptions()
         return {"ui": {"text": (value,)}, "result": (value,)}
 
 NODE_CLASS_MAPPINGS = {

comfy_extras/nodes_sd3.py

@@ -54,7 +54,7 @@ class EmptySD3LatentImage(io.ComfyNode):
 
     @classmethod
     def execute(cls, width, height, batch_size=1) -> io.NodeOutput:
-        latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
         return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 8})
 
     generate = execute  # TODO: remove

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.19.3"
+__version__ = "0.20.0"

execution.py

@@ -811,11 +811,30 @@ class PromptExecutor:
             self._notify_prompt_lifecycle("end", prompt_id)
 
 
-async def validate_inputs(prompt_id, prompt, item, validated):
+async def validate_inputs(prompt_id, prompt, item, validated, visiting=None):
+    if visiting is None:
+        visiting = []
+
     unique_id = item
     if unique_id in validated:
         return validated[unique_id]
 
+    if unique_id in visiting:
+        cycle_path_nodes = visiting[visiting.index(unique_id):] + [unique_id]
+        cycle_nodes = list(dict.fromkeys(cycle_path_nodes))
+        cycle_path = " -> ".join(f"{node_id} ({prompt[node_id]['class_type']})" for node_id in cycle_path_nodes)
+        for node_id in cycle_nodes:
+            validated[node_id] = (False, [{
+                "type": "dependency_cycle",
+                "message": "Dependency cycle detected",
+                "details": cycle_path,
+                "extra_info": {
+                    "node_id": node_id,
+                    "cycle_nodes": cycle_nodes,
+                }
+            }], node_id)
+        return validated[unique_id]
+
     inputs = prompt[unique_id]['inputs']
     class_type = prompt[unique_id]['class_type']
     obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]
@@ -899,7 +918,11 @@ async def validate_inputs(prompt_id, prompt, item, validated):
                 errors.append(error)
                 continue
             try:
-                r = await validate_inputs(prompt_id, prompt, o_id, validated)
+                visiting.append(unique_id)
+                try:
+                    r = await validate_inputs(prompt_id, prompt, o_id, validated, visiting)
+                finally:
+                    visiting.pop()
                 if r[0] is False:
                     # `r` will be set in `validated[o_id]` already
                     valid = False
@@ -1048,10 +1071,13 @@ async def validate_inputs(prompt_id, prompt, item, validated):
                     errors.append(error)
                     continue
 
-    if len(errors) > 0 or valid is not True:
+    ret = validated.get(unique_id, (True, [], unique_id))
-        ret = (False, errors, unique_id)
+    # Recursive cycle detection may have already populated an error on us. Join it.
-    else:
+    ret = (
-        ret = (True, [], unique_id)
+        ret[0] and valid is True and not errors,
+        ret[1] + [error for error in errors if error not in ret[1]],
+        unique_id,
+    )
 
     validated[unique_id] = ret
     return ret

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.19.3"
+version = "0.20.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,6 +1,6 @@
-comfyui-frontend-package==1.42.14
+comfyui-frontend-package==1.42.15
-comfyui-workflow-templates==0.9.59
+comfyui-workflow-templates==0.9.63
-comfyui-embedded-docs==0.4.3
+comfyui-embedded-docs==0.4.4
 torch
 torchsde
 torchvision
@@ -23,7 +23,7 @@ SQLAlchemy>=2.0
 filelock
 av>=14.2.0
 comfy-kitchen>=0.2.8
-comfy-aimdo>=0.2.12
+comfy-aimdo==0.2.14
 requests
 simpleeval>=1.0.0
 blake3