Add OpenAPI 3.1 specification for ComfyUI API (#13397)

* Add OpenAPI 3.1 specification for ComfyUI API

Adds a comprehensive OpenAPI 3.1 spec documenting all HTTP endpoints
exposed by ComfyUI's server, including prompt execution, queue management,
file uploads, userdata, settings, system stats, object info, assets,
and internal routes.

The spec was validated against the source code with adversarial review
from multiple models, and passes Spectral linting with zero errors.

Also removes openapi.yaml from .gitignore so the spec is tracked.

* Mark /api/history endpoints as deprecated

Address Jacob's review feedback on PR #13397 by explicitly marking the
three /api/history operations as deprecated in the OpenAPI spec:

  * GET  /api/history              -> superseded by GET /api/jobs
  * POST /api/history              -> superseded by /api/jobs management
  * GET  /api/history/{prompt_id}  -> superseded by GET /api/jobs/{job_id}

Each operation gains deprecated: true plus a description that names the
replacement. A formal sunset timeline (RFC 8594 Deprecation and RFC 8553
Sunset headers, minimum-runway policy) is being defined separately and
will be applied as a follow-up.

* Address Spectral lint findings in openapi.yaml

- Add operation descriptions to 52 endpoints (prompt, queue, upload,
  view, models, userdata, settings, assets, internal, etc.)
- Add schema descriptions to 22 component schemas
- Add parameter descriptions to 8 path parameters that were missing them
- Remove 6 unused component schemas: TaskOutput, EmbeddingsResponse,
  ExtensionsResponse, LogRawResponse, UserInfo, UserDataFullInfo

No wire/shape changes. Reduces Spectral findings from 92 to 4. The
remaining 4 are real issues (WebSocket 101 on /ws, loose error schema,
and two snake_case warnings on real wire field names) and are worth
addressing separately.

* fix(openapi): address jtreminio oneOf review on /api/userdata

Restructure the UserData response schemas to address the review feedback
on the `oneOf` without a discriminator, and fix two accuracy bugs found
while doing it.

Changes
- GET /api/userdata response: extract the inline `oneOf` to a named
  schema (`ListUserdataResponse`) and add the missing third variant
  returned when `split=true` and `full_info=false` (array of
  `[relative_path, ...path_components]`). Previously only two of the
  three actual server response shapes were described.
- UserDataResponse (POST endpoints): correct the description — this
  schema is a single item, not a list — and point at the canonical
  `GetUserDataResponseFullFile` schema instead of the duplicate
  `UserDataResponseFull`. Also removes the malformed blank line in
  `UserDataResponseShort`.
- Delete the now-unused `UserDataResponseFull` and
  `UserDataResponseShort` schemas (replaced by reuse of
  `GetUserDataResponseFullFile` and an inline string variant).
- Add an `x-variant-selector` vendor extension to both `oneOf` sites
  documenting which query-parameter combination selects which branch,
  since a true OpenAPI `discriminator` is not applicable (the variants
  are type-disjoint and the selector lives in the request, not the
  response body).

This keeps the shapes the server actually emits (no wire-breaking
change) while making the selection rule explicit for SDK generators
and readers.

---------

Co-authored-by: guill <jacob.e.segal@gmail.com>

.gitignore

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

README.md

@@ -195,7 +195,9 @@ The portable above currently comes with python 3.13 and pytorch cuda 13.0. Updat
 
 #### Alternative Downloads:
 
-[Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
+[Portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
+
+[Experimental portable for Intel GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_intel.7z)
 
 [Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).
 

api_server/routes/internal/internal_routes.py

@@ -67,7 +67,7 @@ class InternalRoutes:
                 (entry for entry in os.scandir(directory) if is_visible_file(entry)),
                 key=lambda entry: -entry.stat().st_mtime
             )
-            return web.json_response([entry.name for entry in sorted_files], status=200)
+            return web.json_response([f"{entry.name} [{directory_type}]" for entry in sorted_files], status=200)
 
 
     def get_app(self):

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/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/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/ernie/model.py

@@ -118,8 +118,6 @@ class ErnieImageAttention(nn.Module):
             query = apply_rotary_emb(query, image_rotary_emb)
             key = apply_rotary_emb(key, image_rotary_emb)
 
-        query, key = query.to(x.dtype), key.to(x.dtype)
-
         q_flat = query.reshape(B, S, -1)
         k_flat = key.reshape(B, S, -1)
 
@@ -161,16 +159,16 @@ class ErnieImageSharedAdaLNBlock(nn.Module):
 
         residual = x
         x_norm = self.adaLN_sa_ln(x)
-        x_norm = (x_norm.float() * (1 + scale_msa.float()) + shift_msa.float()).to(x.dtype)
+        x_norm = x_norm * (1 + scale_msa) + shift_msa
 
         attn_out = self.self_attention(x_norm, attention_mask=attention_mask, image_rotary_emb=rotary_pos_emb)
-        x = residual + (gate_msa.float() * attn_out.float()).to(x.dtype)
+        x = residual + gate_msa * attn_out
 
         residual = x
         x_norm = self.adaLN_mlp_ln(x)
-        x_norm = (x_norm.float() * (1 + scale_mlp.float()) + shift_mlp.float()).to(x.dtype)
+        x_norm = x_norm * (1 + scale_mlp) + shift_mlp
 
-        return residual + (gate_mlp.float() * self.mlp(x_norm).float()).to(x.dtype)
+        return residual + gate_mlp * self.mlp(x_norm)
 
 class ErnieImageAdaLNContinuous(nn.Module):
     def __init__(self, hidden_size: int, eps: float = 1e-6, operations=None, device=None, dtype=None):
@@ -183,7 +181,7 @@ class ErnieImageAdaLNContinuous(nn.Module):
     def forward(self, x: torch.Tensor, conditioning: torch.Tensor) -> torch.Tensor:
         scale, shift = self.linear(conditioning).chunk(2, dim=-1)
         x = self.norm(x)
-        x = x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+        x = torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1))
         return x
 
 class ErnieImageModel(nn.Module):

comfy/ldm/lightricks/vae/audio_vae.py

@@ -4,9 +4,6 @@ import math
 import torch
 import torchaudio
 
-import comfy.model_management
-import comfy.model_patcher
-import comfy.utils as utils
 from comfy.ldm.mmaudio.vae.distributions import DiagonalGaussianDistribution
 from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
 from comfy.ldm.lightricks.vae.causal_audio_autoencoder import (
@@ -43,30 +40,6 @@ class AudioVAEComponentConfig:
 
         return cls(autoencoder=audio_config, vocoder=vocoder_config)
 
-
-class ModelDeviceManager:
-    """Manages device placement and GPU residency for the composed model."""
-
-    def __init__(self, module: torch.nn.Module):
-        load_device = comfy.model_management.get_torch_device()
-        offload_device = comfy.model_management.vae_offload_device()
-        self.patcher = comfy.model_patcher.ModelPatcher(module, load_device, offload_device)
-
-    def ensure_model_loaded(self) -> None:
-        comfy.model_management.free_memory(
-            self.patcher.model_size(),
-            self.patcher.load_device,
-        )
-        comfy.model_management.load_model_gpu(self.patcher)
-
-    def move_to_load_device(self, tensor: torch.Tensor) -> torch.Tensor:
-        return tensor.to(self.patcher.load_device)
-
-    @property
-    def load_device(self):
-        return self.patcher.load_device
-
-
 class AudioLatentNormalizer:
     """Applies per-channel statistics in patch space and restores original layout."""
 
@@ -132,23 +105,17 @@ class AudioPreprocessor:
 class AudioVAE(torch.nn.Module):
     """High-level Audio VAE wrapper exposing encode and decode entry points."""
 
-    def __init__(self, state_dict: dict, metadata: dict):
+    def __init__(self, metadata: dict):
         super().__init__()
 
         component_config = AudioVAEComponentConfig.from_metadata(metadata)
 
-        vae_sd = utils.state_dict_prefix_replace(state_dict, {"audio_vae.": ""}, filter_keys=True)
-        vocoder_sd = utils.state_dict_prefix_replace(state_dict, {"vocoder.": ""}, filter_keys=True)
-
         self.autoencoder = CausalAudioAutoencoder(config=component_config.autoencoder)
         if "bwe" in component_config.vocoder:
             self.vocoder = VocoderWithBWE(config=component_config.vocoder)
         else:
             self.vocoder = Vocoder(config=component_config.vocoder)
 
-        self.autoencoder.load_state_dict(vae_sd, strict=False)
-        self.vocoder.load_state_dict(vocoder_sd, strict=False)
-
         autoencoder_config = self.autoencoder.get_config()
         self.normalizer = AudioLatentNormalizer(
             AudioPatchifier(
@@ -168,18 +135,12 @@ class AudioVAE(torch.nn.Module):
             n_fft=autoencoder_config["n_fft"],
         )
 
-        self.device_manager = ModelDeviceManager(self)
-
-    def encode(self, audio: dict) -> torch.Tensor:
+    def encode(self, audio, sample_rate=44100) -> torch.Tensor:
         """Encode a waveform dictionary into normalized latent tensors."""
 
-        waveform = audio["waveform"]
-        waveform_sample_rate = audio["sample_rate"]
+        waveform = audio
+        waveform_sample_rate = sample_rate
         input_device = waveform.device
-        # Ensure that Audio VAE is loaded on the correct device.
-        self.device_manager.ensure_model_loaded()
-
-        waveform = self.device_manager.move_to_load_device(waveform)
         expected_channels = self.autoencoder.encoder.in_channels
         if waveform.shape[1] != expected_channels:
             if waveform.shape[1] == 1:
@@ -190,7 +151,7 @@ class AudioVAE(torch.nn.Module):
                 )
 
         mel_spec = self.preprocessor.waveform_to_mel(
-            waveform, waveform_sample_rate, device=self.device_manager.load_device
+            waveform, waveform_sample_rate, device=waveform.device
         )
 
         latents = self.autoencoder.encode(mel_spec)
@@ -204,17 +165,13 @@ class AudioVAE(torch.nn.Module):
         """Decode normalized latent tensors into an audio waveform."""
         original_shape = latents.shape
 
-        # Ensure that Audio VAE is loaded on the correct device.
-        self.device_manager.ensure_model_loaded()
-
-        latents = self.device_manager.move_to_load_device(latents)
         latents = self.normalizer.denormalize(latents)
 
         target_shape = self.target_shape_from_latents(original_shape)
         mel_spec = self.autoencoder.decode(latents, target_shape=target_shape)
 
         waveform = self.run_vocoder(mel_spec)
-        return self.device_manager.move_to_load_device(waveform)
+        return waveform
 
     def target_shape_from_latents(self, latents_shape):
         batch, _, time, _ = latents_shape

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -34,6 +34,16 @@ class TimestepBlock(nn.Module):
 #This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index"
 def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None):
     for layer in ts:
+        if "patches" in transformer_options and "forward_timestep_embed_patch" in transformer_options["patches"]:
+            found_patched = False
+            for class_type, handler in transformer_options["patches"]["forward_timestep_embed_patch"]:
+                if isinstance(layer, class_type):
+                    x = handler(layer, x, emb, context, transformer_options, output_shape, time_context, num_video_frames, image_only_indicator)
+                    found_patched = True
+                    break
+            if found_patched:
+                continue
+
         if isinstance(layer, VideoResBlock):
             x = layer(x, emb, num_video_frames, image_only_indicator)
         elif isinstance(layer, TimestepBlock):
@@ -49,15 +59,6 @@ def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, out
         elif isinstance(layer, Upsample):
             x = layer(x, output_shape=output_shape)
         else:
-            if "patches" in transformer_options and "forward_timestep_embed_patch" in transformer_options["patches"]:
-                found_patched = False
-                for class_type, handler in transformer_options["patches"]["forward_timestep_embed_patch"]:
-                    if isinstance(layer, class_type):
-                        x = handler(layer, x, emb, context, transformer_options, output_shape, time_context, num_video_frames, image_only_indicator)
-                        found_patched = True
-                        break
-                if found_patched:
-                    continue
             x = layer(x)
     return x
 
@@ -894,6 +895,12 @@ class UNetModel(nn.Module):
             h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
         h = apply_control(h, control, 'middle')
 
+        if "middle_block_after_patch" in transformer_patches:
+            patch = transformer_patches["middle_block_after_patch"]
+            for p in patch:
+                out = p({"h": h, "x": x, "emb": emb, "context": context, "y": y,
+                         "timesteps": timesteps, "transformer_options": transformer_options})
+                h = out["h"]
 
         for id, module in enumerate(self.output_blocks):
             transformer_options["block"] = ("output", id)
@@ -905,8 +912,9 @@ class UNetModel(nn.Module):
                 for p in patch:
                     h, hsp = p(h, hsp, transformer_options)
 
-            h = th.cat([h, hsp], dim=1)
-            del hsp
+            if hsp is not None:
+                h = th.cat([h, hsp], dim=1)
+                del hsp
             if len(hs) > 0:
                 output_shape = hs[-1].shape
             else:

comfy/ldm/sam3/detector.py

@@ -0,0 +1,596 @@
+# SAM3 detector: transformer encoder-decoder, segmentation head, geometry encoder, scoring.
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision.ops import roi_align
+
+from comfy.ldm.modules.attention import optimized_attention
+from comfy.ldm.sam3.tracker import SAM3Tracker, SAM31Tracker
+from comfy.ldm.sam3.sam import SAM3VisionBackbone  # noqa: used in __init__
+from comfy.ldm.sam3.sam import MLP, PositionEmbeddingSine
+
+TRACKER_CLASSES = {"SAM3": SAM3Tracker, "SAM31": SAM31Tracker}
+from comfy.ops import cast_to_input
+
+
+def box_cxcywh_to_xyxy(x):
+    cx, cy, w, h = x.unbind(-1)
+    return torch.stack([cx - 0.5 * w, cy - 0.5 * h, cx + 0.5 * w, cy + 0.5 * h], dim=-1)
+
+
+def gen_sineembed_for_position(pos_tensor, num_feats=256):
+    """Per-coordinate sinusoidal embedding: (..., N) -> (..., N * num_feats)."""
+    assert num_feats % 2 == 0
+    hdim = num_feats // 2
+    freqs = 10000.0 ** (2 * (torch.arange(hdim, dtype=torch.float32, device=pos_tensor.device) // 2) / hdim)
+    embeds = []
+    for c in range(pos_tensor.shape[-1]):
+        raw = (pos_tensor[..., c].float() * 2 * math.pi).unsqueeze(-1) / freqs
+        embeds.append(torch.stack([raw[..., 0::2].sin(), raw[..., 1::2].cos()], dim=-1).flatten(-2))
+    return torch.cat(embeds, dim=-1).to(pos_tensor.dtype)
+
+
+class SplitMHA(nn.Module):
+    """Multi-head attention with separate Q/K/V projections (split from fused in_proj_weight)."""
+    def __init__(self, d_model, num_heads=8, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.num_heads = num_heads
+        self.q_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
+        self.k_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
+        self.v_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
+        self.out_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
+
+    def forward(self, q_input, k_input=None, v_input=None, mask=None):
+        q = self.q_proj(q_input)
+        if k_input is None:
+            k = self.k_proj(q_input)
+            v = self.v_proj(q_input)
+        else:
+            k = self.k_proj(k_input)
+            v = self.v_proj(v_input if v_input is not None else k_input)
+        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, low_precision_attention=False)
+        return self.out_proj(out)
+
+
+class MLPWithNorm(nn.Module):
+    """MLP with residual connection and output LayerNorm."""
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, residual=True, device=None, dtype=None, operations=None):
+        super().__init__()
+        dims = [input_dim] + [hidden_dim] * (num_layers - 1) + [output_dim]
+        self.layers = nn.ModuleList([
+            operations.Linear(dims[i], dims[i + 1], device=device, dtype=dtype)
+            for i in range(num_layers)
+        ])
+        self.out_norm = operations.LayerNorm(output_dim, device=device, dtype=dtype)
+        self.residual = residual and (input_dim == output_dim)
+
+    def forward(self, x):
+        orig = x
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+            if i < len(self.layers) - 1:
+                x = F.relu(x)
+        if self.residual:
+            x = x + orig
+        return self.out_norm(x)
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.self_attn = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
+        self.cross_attn_image = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
+        self.linear1 = operations.Linear(d_model, dim_ff, device=device, dtype=dtype)
+        self.linear2 = operations.Linear(dim_ff, d_model, device=device, dtype=dtype)
+        self.norm1 = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.norm2 = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.norm3 = operations.LayerNorm(d_model, device=device, dtype=dtype)
+
+    def forward(self, x, pos, text_memory=None, text_mask=None):
+        normed = self.norm1(x)
+        q_k = normed + pos
+        x = x + self.self_attn(q_k, q_k, normed)
+        if text_memory is not None:
+            normed = self.norm2(x)
+            x = x + self.cross_attn_image(normed, text_memory, text_memory, mask=text_mask)
+        normed = self.norm3(x)
+        x = x + self.linear2(F.relu(self.linear1(normed)))
+        return x
+
+
+class TransformerEncoder(nn.Module):
+    """Checkpoint: transformer.encoder.layers.N.*"""
+    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, num_layers=6, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.layers = nn.ModuleList([
+            EncoderLayer(d_model, num_heads, dim_ff, device=device, dtype=dtype, operations=operations)
+            for _ in range(num_layers)
+        ])
+
+    def forward(self, x, pos, text_memory=None, text_mask=None):
+        for layer in self.layers:
+            x = layer(x, pos, text_memory, text_mask)
+        return x
+
+
+class DecoderLayer(nn.Module):
+    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.self_attn = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
+        self.cross_attn = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
+        self.ca_text = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
+        self.norm1 = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.norm2 = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.norm3 = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.catext_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.linear1 = operations.Linear(d_model, dim_ff, device=device, dtype=dtype)
+        self.linear2 = operations.Linear(dim_ff, d_model, device=device, dtype=dtype)
+
+    def forward(self, x, memory, x_pos, memory_pos, text_memory=None, text_mask=None, cross_attn_bias=None):
+        q_k = x + x_pos
+        x = self.norm2(x + self.self_attn(q_k, q_k, x))
+        if text_memory is not None:
+            x = self.catext_norm(x + self.ca_text(x + x_pos, text_memory, text_memory, mask=text_mask))
+        x = self.norm1(x + self.cross_attn(x + x_pos, memory + memory_pos, memory, mask=cross_attn_bias))
+        x = self.norm3(x + self.linear2(F.relu(self.linear1(x))))
+        return x
+
+
+class TransformerDecoder(nn.Module):
+    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, num_layers=6,
+                 num_queries=200, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.d_model = d_model
+        self.num_queries = num_queries
+
+        self.layers = nn.ModuleList([
+            DecoderLayer(d_model, num_heads, dim_ff, device=device, dtype=dtype, operations=operations)
+            for _ in range(num_layers)
+        ])
+        self.norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.query_embed = operations.Embedding(num_queries, d_model, device=device, dtype=dtype)
+        self.reference_points = operations.Embedding(num_queries, 4, device=device, dtype=dtype) # Reference points: Embedding(num_queries, 4) — learned anchor boxes
+        self.ref_point_head = MLP(d_model * 2, d_model, d_model, 2, device=device, dtype=dtype, operations=operations) # ref_point_head input: 512 (4 coords * 128 sine features each)
+        self.bbox_embed = MLP(d_model, d_model, 4, 3, device=device, dtype=dtype, operations=operations)
+
+        self.boxRPB_embed_x = MLP(2, d_model, num_heads, 2, device=device, dtype=dtype, operations=operations)
+        self.boxRPB_embed_y = MLP(2, d_model, num_heads, 2, device=device, dtype=dtype, operations=operations)
+
+        self.presence_token = operations.Embedding(1, d_model, device=device, dtype=dtype)
+        self.presence_token_head = MLP(d_model, d_model, 1, 3, device=device, dtype=dtype, operations=operations)
+        self.presence_token_out_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
+
+    @staticmethod
+    def _inverse_sigmoid(x):
+        return torch.log(x / (1 - x + 1e-6) + 1e-6)
+
+    def _compute_box_rpb(self, ref_points, H, W):
+        """Box rotary position bias: (B, Q, 4) cxcywh -> (B, n_heads, Q+1, H*W) bias."""
+        boxes_xyxy = box_cxcywh_to_xyxy(ref_points)
+        B, Q, _ = boxes_xyxy.shape
+        coords_h = torch.arange(H, device=ref_points.device, dtype=torch.float32) / H
+        coords_w = torch.arange(W, device=ref_points.device, dtype=torch.float32) / W
+        deltas_x = coords_w.view(1, 1, -1, 1) - boxes_xyxy[:, :, None, 0:3:2]
+        deltas_y = coords_h.view(1, 1, -1, 1) - boxes_xyxy[:, :, None, 1:4:2]
+
+        log2_8 = float(math.log2(8))
+        def log_scale(d):
+            return torch.sign(d * 8) * torch.log2(torch.abs(d * 8) + 1.0) / log2_8
+
+        rpb_x = self.boxRPB_embed_x(log_scale(deltas_x).to(ref_points.dtype))
+        rpb_y = self.boxRPB_embed_y(log_scale(deltas_y).to(ref_points.dtype))
+
+        bias = (rpb_y.unsqueeze(3) + rpb_x.unsqueeze(2)).flatten(2, 3).permute(0, 3, 1, 2)
+        pres_bias = torch.zeros(B, bias.shape[1], 1, bias.shape[3], device=bias.device, dtype=bias.dtype)
+        return torch.cat([pres_bias, bias], dim=2)
+
+    def forward(self, memory, memory_pos, text_memory=None, text_mask=None, H=72, W=72):
+        B = memory.shape[0]
+        tgt = cast_to_input(self.query_embed.weight, memory).unsqueeze(0).expand(B, -1, -1)
+        presence_out = cast_to_input(self.presence_token.weight, memory)[None].expand(B, -1, -1)
+        ref_points = cast_to_input(self.reference_points.weight, memory).unsqueeze(0).expand(B, -1, -1).sigmoid()
+
+        for layer_idx, layer in enumerate(self.layers):
+            query_pos = self.ref_point_head(gen_sineembed_for_position(ref_points, self.d_model))
+            tgt_with_pres = torch.cat([presence_out, tgt], dim=1)
+            pos_with_pres = torch.cat([torch.zeros_like(presence_out), query_pos], dim=1)
+            tgt_with_pres = layer(tgt_with_pres, memory, pos_with_pres, memory_pos,
+                                  text_memory, text_mask, self._compute_box_rpb(ref_points, H, W))
+            presence_out, tgt = tgt_with_pres[:, :1], tgt_with_pres[:, 1:]
+            if layer_idx < len(self.layers) - 1:
+                ref_inv = self._inverse_sigmoid(ref_points)
+                ref_points = (ref_inv + self.bbox_embed(self.norm(tgt))).sigmoid().detach()
+
+        query_out = self.norm(tgt)
+        ref_inv = self._inverse_sigmoid(ref_points)
+        boxes = (ref_inv + self.bbox_embed(query_out)).sigmoid()
+        presence = self.presence_token_head(self.presence_token_out_norm(presence_out)).squeeze(-1)
+        return {"decoder_output": query_out, "pred_boxes": boxes, "presence": presence}
+
+
+class Transformer(nn.Module):
+    def __init__(self, d_model=256, num_heads=8, dim_ff=2048, enc_layers=6, dec_layers=6,
+                 num_queries=200, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.encoder = TransformerEncoder(d_model, num_heads, dim_ff, enc_layers, device=device, dtype=dtype, operations=operations)
+        self.decoder = TransformerDecoder(d_model, num_heads, dim_ff, dec_layers, num_queries, device=device, dtype=dtype, operations=operations)
+
+
+class GeometryEncoder(nn.Module):
+    def __init__(self, d_model=256, num_heads=8, num_layers=3, roi_size=7, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.d_model = d_model
+        self.roi_size = roi_size
+        self.pos_enc = PositionEmbeddingSine(num_pos_feats=d_model, normalize=True)
+        self.points_direct_project = operations.Linear(2, d_model, device=device, dtype=dtype)
+        self.points_pool_project = operations.Linear(d_model, d_model, device=device, dtype=dtype)
+        self.points_pos_enc_project = operations.Linear(d_model, d_model, device=device, dtype=dtype)
+        self.boxes_direct_project = operations.Linear(4, d_model, device=device, dtype=dtype)
+        self.boxes_pool_project = operations.Conv2d(d_model, d_model, kernel_size=roi_size, device=device, dtype=dtype)
+        self.boxes_pos_enc_project = operations.Linear(d_model + 2, d_model, device=device, dtype=dtype)
+        self.label_embed = operations.Embedding(2, d_model, device=device, dtype=dtype)
+        self.cls_embed = operations.Embedding(1, d_model, device=device, dtype=dtype)
+        self.norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.img_pre_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.encode = nn.ModuleList([
+            EncoderLayer(d_model, num_heads, 2048, device=device, dtype=dtype, operations=operations)
+            for _ in range(num_layers)
+        ])
+        self.encode_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.final_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
+
+    def _encode_points(self, coords, labels, img_feat_2d):
+        """Encode point prompts: direct + pool + pos_enc + label. coords: [B, N, 2] normalized."""
+        B, N, _ = coords.shape
+        embed = self.points_direct_project(coords)
+        # Pool features from backbone at point locations via grid_sample
+        grid = (coords * 2 - 1).unsqueeze(2)  # [B, N, 1, 2] in [-1, 1]
+        sampled = F.grid_sample(img_feat_2d, grid, align_corners=False)  # [B, C, N, 1]
+        embed = embed + self.points_pool_project(sampled.squeeze(-1).permute(0, 2, 1))  # [B, N, C]
+        # Positional encoding of coordinates
+        x, y = coords[:, :, 0], coords[:, :, 1]  # [B, N]
+        pos_x, pos_y = self.pos_enc._encode_xy(x.flatten(), y.flatten())
+        enc = torch.cat([pos_x, pos_y], dim=-1).view(B, N, -1)
+        embed = embed + self.points_pos_enc_project(cast_to_input(enc, embed))
+        embed = embed + cast_to_input(self.label_embed(labels.long()), embed)
+        return embed
+
+    def _encode_boxes(self, boxes, labels, img_feat_2d):
+        """Encode box prompts: direct + pool + pos_enc + label. boxes: [B, N, 4] normalized cxcywh."""
+        B, N, _ = boxes.shape
+        embed = self.boxes_direct_project(boxes)
+        # ROI align from backbone at box regions
+        H, W = img_feat_2d.shape[-2:]
+        boxes_xyxy = box_cxcywh_to_xyxy(boxes)
+        scale = torch.tensor([W, H, W, H], dtype=boxes_xyxy.dtype, device=boxes_xyxy.device)
+        boxes_scaled = boxes_xyxy * scale
+        sampled = roi_align(img_feat_2d, boxes_scaled.view(-1, 4).split(N), self.roi_size)
+        proj = self.boxes_pool_project(sampled).view(B, N, -1)  # Conv2d(roi_size) -> [B*N, C, 1, 1] -> [B, N, C]
+        embed = embed + proj
+        # Positional encoding of box center + size
+        cx, cy, w, h = boxes[:, :, 0], boxes[:, :, 1], boxes[:, :, 2], boxes[:, :, 3]
+        enc = self.pos_enc.encode_boxes(cx.flatten(), cy.flatten(), w.flatten(), h.flatten())
+        enc = enc.view(B, N, -1)
+        embed = embed + self.boxes_pos_enc_project(cast_to_input(enc, embed))
+        embed = embed + cast_to_input(self.label_embed(labels.long()), embed)
+        return embed
+
+    def forward(self, points=None, boxes=None, image_features=None):
+        """Encode geometry prompts. image_features: [B, HW, C] flattened backbone features."""
+        # Prepare 2D image features for pooling
+        img_feat_2d = None
+        if image_features is not None:
+            B = image_features.shape[0]
+            HW, C = image_features.shape[1], image_features.shape[2]
+            hw = int(math.sqrt(HW))
+            img_normed = self.img_pre_norm(image_features)
+            img_feat_2d = img_normed.permute(0, 2, 1).view(B, C, hw, hw)
+
+        embeddings = []
+        if points is not None:
+            coords, labels = points
+            embeddings.append(self._encode_points(coords, labels, img_feat_2d))
+        if boxes is not None:
+            B = boxes.shape[0]
+            box_labels = torch.ones(B, boxes.shape[1], dtype=torch.long, device=boxes.device)
+            embeddings.append(self._encode_boxes(boxes, box_labels, img_feat_2d))
+        if not embeddings:
+            return None
+        geo = torch.cat(embeddings, dim=1)
+        geo = self.norm(geo)
+        if image_features is not None:
+            for layer in self.encode:
+                geo = layer(geo, torch.zeros_like(geo), image_features)
+        geo = self.encode_norm(geo)
+        return self.final_proj(geo)
+
+
+class PixelDecoder(nn.Module):
+    """Top-down FPN pixel decoder with GroupNorm + ReLU + nearest interpolation."""
+    def __init__(self, d_model=256, num_stages=3, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.conv_layers = nn.ModuleList([operations.Conv2d(d_model, d_model, kernel_size=3, padding=1, device=device, dtype=dtype) for _ in range(num_stages)])
+        self.norms = nn.ModuleList([operations.GroupNorm(8, d_model, device=device, dtype=dtype) for _ in range(num_stages)])
+
+    def forward(self, backbone_features):
+        prev = backbone_features[-1]
+        for i, feat in enumerate(backbone_features[:-1][::-1]):
+            prev = F.relu(self.norms[i](self.conv_layers[i](feat + F.interpolate(prev, size=feat.shape[-2:], mode="nearest"))))
+        return prev
+
+
+class MaskPredictor(nn.Module):
+    def __init__(self, d_model=256, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.mask_embed = MLP(d_model, d_model, d_model, 3, device=device, dtype=dtype, operations=operations)
+
+    def forward(self, query_embeddings, pixel_features):
+        mask_embed = self.mask_embed(query_embeddings)
+        return torch.einsum("bqc,bchw->bqhw", mask_embed, pixel_features)
+
+
+class SegmentationHead(nn.Module):
+    def __init__(self, d_model=256, num_heads=8, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.d_model = d_model
+        self.pixel_decoder = PixelDecoder(d_model, 3, device=device, dtype=dtype, operations=operations)
+        self.mask_predictor = MaskPredictor(d_model, device=device, dtype=dtype, operations=operations)
+        self.cross_attend_prompt = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations)
+        self.cross_attn_norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
+        self.instance_seg_head = operations.Conv2d(d_model, d_model, kernel_size=1, device=device, dtype=dtype)
+        self.semantic_seg_head = operations.Conv2d(d_model, 1, kernel_size=1, device=device, dtype=dtype)
+
+    def forward(self, query_embeddings, backbone_features, encoder_hidden_states=None, prompt=None, prompt_mask=None):
+        if encoder_hidden_states is not None and prompt is not None:
+            enc_normed = self.cross_attn_norm(encoder_hidden_states)
+            enc_cross = self.cross_attend_prompt(enc_normed, prompt, prompt, mask=prompt_mask)
+            encoder_hidden_states = enc_cross + encoder_hidden_states
+
+        if encoder_hidden_states is not None:
+            B, H, W = encoder_hidden_states.shape[0], backbone_features[-1].shape[-2], backbone_features[-1].shape[-1]
+            encoder_visual = encoder_hidden_states[:, :H * W].permute(0, 2, 1).view(B, self.d_model, H, W)
+            backbone_features = list(backbone_features)
+            backbone_features[-1] = encoder_visual
+
+        pixel_features = self.pixel_decoder(backbone_features)
+        instance_features = self.instance_seg_head(pixel_features)
+        masks = self.mask_predictor(query_embeddings, instance_features)
+        return masks
+
+
+class DotProductScoring(nn.Module):
+    def __init__(self, d_model=256, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.hs_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
+        self.prompt_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype)
+        self.prompt_mlp = MLPWithNorm(d_model, 2048, d_model, 2, device=device, dtype=dtype, operations=operations)
+        self.scale = 1.0 / (d_model ** 0.5)
+
+    def forward(self, query_embeddings, prompt_embeddings, prompt_mask=None):
+        prompt = self.prompt_mlp(prompt_embeddings)
+        if prompt_mask is not None:
+            weight = prompt_mask.unsqueeze(-1).to(dtype=prompt.dtype)
+            pooled = (prompt * weight).sum(dim=1) / weight.sum(dim=1).clamp(min=1)
+        else:
+            pooled = prompt.mean(dim=1)
+        hs = self.hs_proj(query_embeddings)
+        pp = self.prompt_proj(pooled).unsqueeze(-1).to(hs.dtype)
+        scores = torch.matmul(hs, pp)
+        return (scores * self.scale).clamp(-12.0, 12.0).squeeze(-1)
+
+
+class SAM3Detector(nn.Module):
+    def __init__(self, d_model=256, embed_dim=1024, num_queries=200, device=None, dtype=None, operations=None, **kwargs):
+        super().__init__()
+        image_model = kwargs.pop("image_model", "SAM3")
+        for k in ("num_heads", "num_head_channels"):
+            kwargs.pop(k, None)
+        multiplex = image_model == "SAM31"
+        # SAM3: 4 FPN levels, drop last (scalp=1); SAM3.1: 3 levels, use all (scalp=0)
+        self.scalp = 0 if multiplex else 1
+        self.backbone = nn.ModuleDict({
+            "vision_backbone": SAM3VisionBackbone(embed_dim=embed_dim, d_model=d_model, multiplex=multiplex, device=device, dtype=dtype, operations=operations, **kwargs),
+            "language_backbone": nn.ModuleDict({"resizer": operations.Linear(embed_dim, d_model, device=device, dtype=dtype)}),
+        })
+        self.transformer = Transformer(d_model=d_model, num_queries=num_queries, device=device, dtype=dtype, operations=operations)
+        self.segmentation_head = SegmentationHead(d_model=d_model, device=device, dtype=dtype, operations=operations)
+        self.geometry_encoder = GeometryEncoder(d_model=d_model, device=device, dtype=dtype, operations=operations)
+        self.dot_prod_scoring = DotProductScoring(d_model=d_model, device=device, dtype=dtype, operations=operations)
+
+    def _get_backbone_features(self, images):
+        """Run backbone and return (detector_features, detector_positions, tracker_features, tracker_positions)."""
+        bb = self.backbone["vision_backbone"]
+        if bb.multiplex:
+            all_f, all_p, tf, tp = bb(images, tracker_mode="propagation")
+        else:
+            all_f, all_p, tf, tp = bb(images, need_tracker=True)
+        return all_f, all_p, tf, tp
+
+    @staticmethod
+    def _run_geo_layer(layer, x, memory, memory_pos):
+        x = x + layer.self_attn(layer.norm1(x))
+        x = x + layer.cross_attn_image(layer.norm2(x), memory + memory_pos, memory)
+        x = x + layer.linear2(F.relu(layer.linear1(layer.norm3(x))))
+        return x
+
+    def _detect(self, features, positions, text_embeddings=None, text_mask=None,
+                points=None, boxes=None):
+        """Shared detection: geometry encoding, transformer, scoring, segmentation."""
+        B = features[0].shape[0]
+        # Scalp for encoder (use top-level feature), but keep all levels for segmentation head
+        seg_features = features
+        if self.scalp > 0:
+            features = features[:-self.scalp]
+            positions = positions[:-self.scalp]
+        enc_feat, enc_pos = features[-1], positions[-1]
+        _, _, H, W = enc_feat.shape
+        img_flat = enc_feat.flatten(2).permute(0, 2, 1)
+        pos_flat = enc_pos.flatten(2).permute(0, 2, 1)
+
+        has_prompts = text_embeddings is not None or points is not None or boxes is not None
+        if has_prompts:
+            geo_enc = self.geometry_encoder
+            geo_prompts = geo_enc(points=points, boxes=boxes, image_features=img_flat)
+            geo_cls = geo_enc.norm(geo_enc.final_proj(cast_to_input(geo_enc.cls_embed.weight, img_flat).view(1, 1, -1).expand(B, -1, -1)))
+            for layer in geo_enc.encode:
+                geo_cls = self._run_geo_layer(layer, geo_cls, img_flat, pos_flat)
+            geo_cls = geo_enc.encode_norm(geo_cls)
+            if text_embeddings is not None and text_embeddings.shape[0] != B:
+                text_embeddings = text_embeddings.expand(B, -1, -1)
+            if text_mask is not None and text_mask.shape[0] != B:
+                text_mask = text_mask.expand(B, -1)
+            parts = [t for t in [text_embeddings, geo_prompts, geo_cls] if t is not None]
+            text_embeddings = torch.cat(parts, dim=1)
+            n_new = text_embeddings.shape[1] - (text_mask.shape[1] if text_mask is not None else 0)
+            if text_mask is not None:
+                text_mask = torch.cat([text_mask, torch.ones(B, n_new, dtype=torch.bool, device=text_mask.device)], dim=1)
+            else:
+                text_mask = torch.ones(B, text_embeddings.shape[1], dtype=torch.bool, device=text_embeddings.device)
+
+        memory = self.transformer.encoder(img_flat, pos_flat, text_embeddings, text_mask)
+        dec_out = self.transformer.decoder(memory, pos_flat, text_embeddings, text_mask, H, W)
+        query_out, pred_boxes = dec_out["decoder_output"], dec_out["pred_boxes"]
+
+        if text_embeddings is not None:
+            scores = self.dot_prod_scoring(query_out, text_embeddings, text_mask)
+        else:
+            scores = torch.zeros(B, query_out.shape[1], device=query_out.device)
+
+        masks = self.segmentation_head(query_out, seg_features, encoder_hidden_states=memory, prompt=text_embeddings, prompt_mask=text_mask)
+        return box_cxcywh_to_xyxy(pred_boxes), scores, masks, dec_out
+
+    def forward(self, images, text_embeddings=None, text_mask=None, points=None, boxes=None, threshold=0.3, orig_size=None):
+        features, positions, _, _ = self._get_backbone_features(images)
+
+        if text_embeddings is not None:
+            text_embeddings = self.backbone["language_backbone"]["resizer"](text_embeddings)
+            if text_mask is not None:
+                text_mask = text_mask.bool()
+
+        boxes_xyxy, scores, masks, dec_out = self._detect(
+            features, positions, text_embeddings, text_mask, points, boxes)
+
+        if orig_size is not None:
+            oh, ow = orig_size
+            boxes_xyxy = boxes_xyxy * torch.tensor([ow, oh, ow, oh], device=boxes_xyxy.device, dtype=boxes_xyxy.dtype)
+            masks = F.interpolate(masks, size=orig_size, mode="bilinear", align_corners=False)
+
+        return {
+            "boxes": boxes_xyxy,
+            "scores": scores,
+            "masks": masks,
+            "presence": dec_out.get("presence"),
+        }
+
+    def forward_from_trunk(self, trunk_out, text_embeddings, text_mask):
+        """Run detection using a pre-computed ViTDet trunk output.
+
+        text_embeddings must already be resized through language_backbone.resizer.
+        Returns dict with boxes (normalized xyxy), scores, masks at detector resolution.
+        """
+        bb = self.backbone["vision_backbone"]
+        features = [conv(trunk_out) for conv in bb.convs]
+        positions = [cast_to_input(bb.position_encoding(f), f) for f in features]
+
+        if text_mask is not None:
+            text_mask = text_mask.bool()
+
+        boxes_xyxy, scores, masks, _ = self._detect(features, positions, text_embeddings, text_mask)
+        return {"boxes": boxes_xyxy, "scores": scores, "masks": masks}
+
+
+class SAM3Model(nn.Module):
+    def __init__(self, device=None, dtype=None, operations=None, **kwargs):
+        super().__init__()
+        self.dtype = dtype
+        image_model = kwargs.get("image_model", "SAM3")
+        tracker_cls = TRACKER_CLASSES[image_model]
+        self.detector = SAM3Detector(device=device, dtype=dtype, operations=operations, **kwargs)
+        self.tracker = tracker_cls(device=device, dtype=dtype, operations=operations, **kwargs)
+
+    def forward(self, images, **kwargs):
+        return self.detector(images, **kwargs)
+
+    def forward_segment(self, images, point_inputs=None, box_inputs=None, mask_inputs=None):
+        """Interactive segmentation using SAM decoder with point/box/mask prompts.
+
+        Args:
+            images: [B, 3, 1008, 1008] preprocessed images
+            point_inputs: {"point_coords": [B, N, 2], "point_labels": [B, N]} in 1008x1008 pixel space
+            box_inputs: [B, 2, 2] box corners (top-left, bottom-right) in 1008x1008 pixel space
+            mask_inputs: [B, 1, H, W] coarse mask logits to refine
+        Returns:
+            [B, 1, image_size, image_size] high-res mask logits
+        """
+        bb = self.detector.backbone["vision_backbone"]
+        if bb.multiplex:
+            _, _, tracker_features, tracker_positions = bb(images, tracker_mode="interactive")
+        else:
+            _, _, tracker_features, tracker_positions = bb(images, need_tracker=True)
+            if self.detector.scalp > 0:
+                tracker_features = tracker_features[:-self.detector.scalp]
+                tracker_positions = tracker_positions[:-self.detector.scalp]
+
+        high_res = list(tracker_features[:-1])
+        backbone_feat = tracker_features[-1]
+        B, C, H, W = backbone_feat.shape
+        # Add no-memory embedding (init frame path)
+        no_mem = getattr(self.tracker, 'interactivity_no_mem_embed', None)
+        if no_mem is None:
+            no_mem = getattr(self.tracker, 'no_mem_embed', None)
+        if no_mem is not None:
+            feat_flat = backbone_feat.flatten(2).permute(0, 2, 1)
+            feat_flat = feat_flat + cast_to_input(no_mem, feat_flat)
+            backbone_feat = feat_flat.view(B, H, W, C).permute(0, 3, 1, 2)
+
+        num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1)
+        _, high_res_masks, _, _ = self.tracker._forward_sam_heads(
+            backbone_features=backbone_feat,
+            point_inputs=point_inputs,
+            mask_inputs=mask_inputs,
+            box_inputs=box_inputs,
+            high_res_features=high_res,
+            multimask_output=(0 < num_pts <= 1),
+        )
+        return high_res_masks
+
+    def forward_video(self, images, initial_masks, pbar=None, text_prompts=None,
+                       new_det_thresh=0.5, max_objects=0, detect_interval=1):
+        """Track video with optional per-frame text-prompted detection."""
+        bb = self.detector.backbone["vision_backbone"]
+
+        def backbone_fn(frame, frame_idx=None):
+            trunk_out = bb.trunk(frame)
+            if bb.multiplex:
+                _, _, tf, tp = bb(frame, tracker_mode="propagation", cached_trunk=trunk_out, tracker_only=True)
+            else:
+                _, _, tf, tp = bb(frame, need_tracker=True, cached_trunk=trunk_out, tracker_only=True)
+            return tf, tp, trunk_out
+
+        detect_fn = None
+        if text_prompts:
+            resizer = self.detector.backbone["language_backbone"]["resizer"]
+            resized = [(resizer(emb), m.bool() if m is not None else None) for emb, m in text_prompts]
+            def detect_fn(trunk_out):
+                all_scores, all_masks = [], []
+                for emb, mask in resized:
+                    det = self.detector.forward_from_trunk(trunk_out, emb, mask)
+                    all_scores.append(det["scores"])
+                    all_masks.append(det["masks"])
+                return {"scores": torch.cat(all_scores, dim=1), "masks": torch.cat(all_masks, dim=1)}
+
+        if hasattr(self.tracker, 'track_video_with_detection'):
+            return self.tracker.track_video_with_detection(
+                backbone_fn, images, initial_masks, detect_fn,
+                new_det_thresh=new_det_thresh, max_objects=max_objects,
+                detect_interval=detect_interval, backbone_obj=bb, pbar=pbar)
+        # SAM3 (non-multiplex) — no detection support, requires initial masks
+        if initial_masks is None:
+            raise ValueError("SAM3 (non-multiplex) requires initial_mask for video tracking")
+        return self.tracker.track_video(backbone_fn, images, initial_masks, pbar=pbar, backbone_obj=bb)

comfy/ldm/sam3/sam.py

@@ -0,0 +1,425 @@
+# SAM3 shared components: primitives, ViTDet backbone, FPN neck, position encodings.
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from comfy.ldm.modules.attention import optimized_attention
+from comfy.ldm.flux.math import apply_rope
+from comfy.ldm.flux.layers import EmbedND
+from comfy.ops import cast_to_input
+
+
+class MLP(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, sigmoid_output=False, device=None, dtype=None, operations=None):
+        super().__init__()
+        dims = [input_dim] + [hidden_dim] * (num_layers - 1) + [output_dim]
+        self.layers = nn.ModuleList([operations.Linear(dims[i], dims[i + 1], device=device, dtype=dtype) for i in range(num_layers)])
+        self.sigmoid_output = sigmoid_output
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < len(self.layers) - 1 else layer(x)
+        return torch.sigmoid(x) if self.sigmoid_output else x
+
+
+class SAMAttention(nn.Module):
+    def __init__(self, embedding_dim, num_heads, downsample_rate=1, kv_in_dim=None, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.num_heads = num_heads
+        internal_dim = embedding_dim // downsample_rate
+        kv_dim = kv_in_dim if kv_in_dim is not None else embedding_dim
+        self.q_proj = operations.Linear(embedding_dim, internal_dim, device=device, dtype=dtype)
+        self.k_proj = operations.Linear(kv_dim, internal_dim, device=device, dtype=dtype)
+        self.v_proj = operations.Linear(kv_dim, internal_dim, device=device, dtype=dtype)
+        self.out_proj = operations.Linear(internal_dim, embedding_dim, device=device, dtype=dtype)
+
+    def forward(self, q, k, v):
+        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, low_precision_attention=False))
+
+
+class TwoWayAttentionBlock(nn.Module):
+    def __init__(self, embedding_dim, num_heads, mlp_dim=2048, attention_downsample_rate=2, skip_first_layer_pe=False, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.skip_first_layer_pe = skip_first_layer_pe
+        self.self_attn = SAMAttention(embedding_dim, num_heads, device=device, dtype=dtype, operations=operations)
+        self.cross_attn_token_to_image = SAMAttention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate, device=device, dtype=dtype, operations=operations)
+        self.cross_attn_image_to_token = SAMAttention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate, device=device, dtype=dtype, operations=operations)
+        self.mlp = nn.Sequential(operations.Linear(embedding_dim, mlp_dim, device=device, dtype=dtype), nn.ReLU(), operations.Linear(mlp_dim, embedding_dim, device=device, dtype=dtype))
+        self.norm1 = operations.LayerNorm(embedding_dim, device=device, dtype=dtype)
+        self.norm2 = operations.LayerNorm(embedding_dim, device=device, dtype=dtype)
+        self.norm3 = operations.LayerNorm(embedding_dim, device=device, dtype=dtype)
+        self.norm4 = operations.LayerNorm(embedding_dim, device=device, dtype=dtype)
+
+    def forward(self, queries, keys, query_pe, key_pe):
+        if self.skip_first_layer_pe:
+            queries = self.norm1(self.self_attn(queries, queries, queries))
+        else:
+            q = queries + query_pe
+            queries = self.norm1(queries + self.self_attn(q, q, queries))
+        q, k = queries + query_pe, keys + key_pe
+        queries = self.norm2(queries + self.cross_attn_token_to_image(q, k, keys))
+        queries = self.norm3(queries + self.mlp(queries))
+        q, k = queries + query_pe, keys + key_pe
+        keys = self.norm4(keys + self.cross_attn_image_to_token(k, q, queries))
+        return queries, keys
+
+
+class TwoWayTransformer(nn.Module):
+    def __init__(self, depth=2, embedding_dim=256, num_heads=8, mlp_dim=2048, attention_downsample_rate=2, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.layers = nn.ModuleList([
+            TwoWayAttentionBlock(embedding_dim, num_heads, mlp_dim, attention_downsample_rate,
+                                 skip_first_layer_pe=(i == 0), device=device, dtype=dtype, operations=operations)
+            for i in range(depth)
+        ])
+        self.final_attn_token_to_image = SAMAttention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate, device=device, dtype=dtype, operations=operations)
+        self.norm_final = operations.LayerNorm(embedding_dim, device=device, dtype=dtype)
+
+    def forward(self, image_embedding, image_pe, point_embedding):
+        queries, keys = point_embedding, image_embedding
+        for layer in self.layers:
+            queries, keys = layer(queries, keys, point_embedding, image_pe)
+        q, k = queries + point_embedding, keys + image_pe
+        queries = self.norm_final(queries + self.final_attn_token_to_image(q, k, keys))
+        return queries, keys
+
+
+class PositionEmbeddingRandom(nn.Module):
+    """Fourier feature positional encoding with random gaussian projection."""
+    def __init__(self, num_pos_feats=64, scale=None):
+        super().__init__()
+        self.register_buffer("positional_encoding_gaussian_matrix", (scale or 1.0) * torch.randn(2, num_pos_feats))
+
+    def _encode(self, normalized_coords):
+        """Map normalized [0,1] coordinates to fourier features via random projection. Computes in fp32."""
+        orig_dtype = normalized_coords.dtype
+        proj_matrix = self.positional_encoding_gaussian_matrix.to(device=normalized_coords.device, dtype=torch.float32)
+        projected = 2 * math.pi * (2 * normalized_coords.float() - 1) @ proj_matrix
+        return torch.cat([projected.sin(), projected.cos()], dim=-1).to(orig_dtype)
+
+    def forward(self, size, device=None):
+        h, w = size
+        dev = device if device is not None else self.positional_encoding_gaussian_matrix.device
+        ones = torch.ones((h, w), device=dev, dtype=torch.float32)
+        norm_xy = torch.stack([(ones.cumsum(1) - 0.5) / w, (ones.cumsum(0) - 0.5) / h], dim=-1)
+        return self._encode(norm_xy).permute(2, 0, 1).unsqueeze(0)
+
+    def forward_with_coords(self, pixel_coords, image_size):
+        norm = pixel_coords.clone()
+        norm[:, :, 0] /= image_size[1]
+        norm[:, :, 1] /= image_size[0]
+        return self._encode(norm)
+
+
+# ViTDet backbone + FPN neck
+
+def window_partition(x: torch.Tensor, window_size: int):
+    B, H, W, C = x.shape
+    pad_h = (window_size - H % window_size) % window_size
+    pad_w = (window_size - W % window_size) % window_size
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+    Hp, Wp = H + pad_h, W + pad_w
+    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows, (Hp, Wp)
+
+
+def window_unpartition(windows: torch.Tensor, window_size: int, pad_hw, hw):
+    Hp, Wp = pad_hw
+    H, W = hw
+    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+    if Hp > H or Wp > W:
+        x = x[:, :H, :W, :].contiguous()
+    return x
+
+
+def rope_2d(end_x: int, end_y: int, dim: int, theta: float = 10000.0, scale_pos: float = 1.0):
+    """Generate 2D axial RoPE using flux EmbedND. Returns [1, 1, HW, dim//2, 2, 2]."""
+    t = torch.arange(end_x * end_y, dtype=torch.float32)
+    ids = torch.stack([(t % end_x) * scale_pos,
+                       torch.div(t, end_x, rounding_mode="floor") * scale_pos], dim=-1)
+    return EmbedND(dim=dim, theta=theta, axes_dim=[dim // 2, dim // 2])(ids.unsqueeze(0))
+
+
+class _ViTMLP(nn.Module):
+    def __init__(self, dim, mlp_ratio=4.0, device=None, dtype=None, operations=None):
+        super().__init__()
+        hidden = int(dim * mlp_ratio)
+        self.fc1 = operations.Linear(dim, hidden, device=device, dtype=dtype)
+        self.act = nn.GELU()
+        self.fc2 = operations.Linear(hidden, dim, device=device, dtype=dtype)
+
+    def forward(self, x):
+        return self.fc2(self.act(self.fc1(x)))
+
+
+class Attention(nn.Module):
+    """ViTDet multi-head attention with fused QKV projection."""
+
+    def __init__(self, dim, num_heads=8, qkv_bias=True, use_rope=False, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.use_rope = use_rope
+        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, device=device, dtype=dtype)
+        self.proj = operations.Linear(dim, dim, device=device, dtype=dtype)
+
+    def forward(self, x, freqs_cis=None):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim)
+        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, low_precision_attention=False))
+
+
+class Block(nn.Module):
+    def __init__(self, dim, num_heads, mlp_ratio=4.0, qkv_bias=True, window_size=0, use_rope=False, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.window_size = window_size
+        self.norm1 = operations.LayerNorm(dim, device=device, dtype=dtype)
+        self.attn = Attention(dim, num_heads, qkv_bias, use_rope, device=device, dtype=dtype, operations=operations)
+        self.norm2 = operations.LayerNorm(dim, device=device, dtype=dtype)
+        self.mlp = _ViTMLP(dim, mlp_ratio, device=device, dtype=dtype, operations=operations)
+
+    def forward(self, x, freqs_cis=None):
+        shortcut = x
+        x = self.norm1(x)
+        if self.window_size > 0:
+            H, W = x.shape[1], x.shape[2]
+            x, pad_hw = window_partition(x, self.window_size)
+            x = x.view(x.shape[0], self.window_size * self.window_size, -1)
+            x = self.attn(x, freqs_cis=freqs_cis)
+            x = x.view(-1, self.window_size, self.window_size, x.shape[-1])
+            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
+        else:
+            B, H, W, C = x.shape
+            x = x.view(B, H * W, C)
+            x = self.attn(x, freqs_cis=freqs_cis)
+            x = x.view(B, H, W, C)
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class PatchEmbed(nn.Module):
+    def __init__(self, patch_size=14, in_chans=3, embed_dim=1024, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=False, device=device, dtype=dtype)
+
+    def forward(self, x):
+        return self.proj(x)
+
+
+class ViTDet(nn.Module):
+    def __init__(self, img_size=1008, patch_size=14, embed_dim=1024, depth=32, num_heads=16, mlp_ratio=4.625, qkv_bias=True, window_size=24,
+                 global_att_blocks=(7, 15, 23, 31), use_rope=True, pretrain_img_size=336, device=None, dtype=None, operations=None, **kwargs):
+        super().__init__()
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.global_att_blocks = set(global_att_blocks)
+
+        self.patch_embed = PatchEmbed(patch_size, 3, embed_dim, device=device, dtype=dtype, operations=operations)
+
+        num_patches = (pretrain_img_size // patch_size) ** 2 + 1  # +1 for cls token
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim, device=device, dtype=dtype))
+
+        self.ln_pre = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
+
+        grid_size = img_size // patch_size
+        pretrain_grid = pretrain_img_size // patch_size
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            is_global = i in self.global_att_blocks
+            self.blocks.append(Block(
+                embed_dim, num_heads, mlp_ratio, qkv_bias,
+                window_size=0 if is_global else window_size,
+                use_rope=use_rope,
+                device=device, dtype=dtype, operations=operations,
+            ))
+
+        if use_rope:
+            rope_scale = pretrain_grid / grid_size
+            self.register_buffer("freqs_cis", rope_2d(grid_size, grid_size, embed_dim // num_heads, scale_pos=rope_scale), persistent=False)
+            self.register_buffer("freqs_cis_window", rope_2d(window_size, window_size, embed_dim // num_heads), persistent=False)
+        else:
+            self.freqs_cis = None
+            self.freqs_cis_window = None
+
+    def _get_pos_embed(self, num_tokens):
+        pos = self.pos_embed
+        if pos.shape[1] == num_tokens:
+            return pos
+        cls_pos = pos[:, :1]
+        spatial_pos = pos[:, 1:]
+        old_size = int(math.sqrt(spatial_pos.shape[1]))
+        new_size = int(math.sqrt(num_tokens - 1)) if num_tokens > 1 else old_size
+        spatial_2d = spatial_pos.reshape(1, old_size, old_size, -1).permute(0, 3, 1, 2)
+        tiles_h = new_size // old_size + 1
+        tiles_w = new_size // old_size + 1
+        tiled = spatial_2d.tile([1, 1, tiles_h, tiles_w])[:, :, :new_size, :new_size]
+        tiled = tiled.permute(0, 2, 3, 1).reshape(1, new_size * new_size, -1)
+        return torch.cat([cls_pos, tiled], dim=1)
+
+    def forward(self, x):
+        x = self.patch_embed(x)
+        B, C, Hp, Wp = x.shape
+        x = x.permute(0, 2, 3, 1).reshape(B, Hp * Wp, C)
+
+        pos = cast_to_input(self._get_pos_embed(Hp * Wp + 1), x)
+        x = x + pos[:, 1:Hp * Wp + 1]
+
+        x = x.view(B, Hp, Wp, C)
+        x = self.ln_pre(x)
+
+        freqs_cis_global = self.freqs_cis
+        freqs_cis_win = self.freqs_cis_window
+        if freqs_cis_global is not None:
+            freqs_cis_global = cast_to_input(freqs_cis_global, x)
+        if freqs_cis_win is not None:
+            freqs_cis_win = cast_to_input(freqs_cis_win, x)
+
+        for block in self.blocks:
+            fc = freqs_cis_win if block.window_size > 0 else freqs_cis_global
+            x = block(x, freqs_cis=fc)
+
+        return x.permute(0, 3, 1, 2)
+
+
+class FPNScaleConv(nn.Module):
+    def __init__(self, in_dim, out_dim, scale, device=None, dtype=None, operations=None):
+        super().__init__()
+        if scale == 4.0:
+            self.dconv_2x2_0 = operations.ConvTranspose2d(in_dim, in_dim // 2, kernel_size=2, stride=2, device=device, dtype=dtype)
+            self.dconv_2x2_1 = operations.ConvTranspose2d(in_dim // 2, in_dim // 4, kernel_size=2, stride=2, device=device, dtype=dtype)
+            proj_in = in_dim // 4
+        elif scale == 2.0:
+            self.dconv_2x2 = operations.ConvTranspose2d(in_dim, in_dim // 2, kernel_size=2, stride=2, device=device, dtype=dtype)
+            proj_in = in_dim // 2
+        elif scale == 1.0:
+            proj_in = in_dim
+        elif scale == 0.5:
+            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+            proj_in = in_dim
+        self.scale = scale
+        self.conv_1x1 = operations.Conv2d(proj_in, out_dim, kernel_size=1, device=device, dtype=dtype)
+        self.conv_3x3 = operations.Conv2d(out_dim, out_dim, kernel_size=3, padding=1, device=device, dtype=dtype)
+
+    def forward(self, x):
+        if self.scale == 4.0:
+            x = F.gelu(self.dconv_2x2_0(x))
+            x = self.dconv_2x2_1(x)
+        elif self.scale == 2.0:
+            x = self.dconv_2x2(x)
+        elif self.scale == 0.5:
+            x = self.pool(x)
+        x = self.conv_1x1(x)
+        x = self.conv_3x3(x)
+        return x
+
+
+class PositionEmbeddingSine(nn.Module):
+    """2D sinusoidal position encoding (DETR-style) with result caching."""
+    def __init__(self, num_pos_feats=256, temperature=10000.0, normalize=True, scale=None):
+        super().__init__()
+        assert num_pos_feats % 2 == 0
+        self.half_dim = num_pos_feats // 2
+        self.temperature = temperature
+        self.normalize = normalize
+        self.scale = scale if scale is not None else 2 * math.pi
+        self._cache = {}
+
+    def _sincos(self, vals):
+        """Encode 1D values to interleaved sin/cos features."""
+        freqs = self.temperature ** (2 * (torch.arange(self.half_dim, dtype=torch.float32, device=vals.device) // 2) / self.half_dim)
+        raw = vals[..., None] * self.scale / freqs
+        return torch.stack((raw[..., 0::2].sin(), raw[..., 1::2].cos()), dim=-1).flatten(-2)
+
+    def _encode_xy(self, x, y):
+        """Encode normalized x, y coordinates to sinusoidal features. Returns (pos_x, pos_y) each [N, half_dim]."""
+        dim_t = self.temperature ** (2 * (torch.arange(self.half_dim, dtype=torch.float32, device=x.device) // 2) / self.half_dim)
+        pos_x = x[:, None] * self.scale / dim_t
+        pos_y = y[:, None] * self.scale / dim_t
+        pos_x = torch.stack((pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2).flatten(1)
+        pos_y = torch.stack((pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2).flatten(1)
+        return pos_x, pos_y
+
+    def encode_boxes(self, cx, cy, w, h):
+        """Encode box center + size to [N, d_model+2] features."""
+        pos_x, pos_y = self._encode_xy(cx, cy)
+        return torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1)
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        key = (H, W, x.device)
+        if key not in self._cache:
+            gy = torch.arange(H, dtype=torch.float32, device=x.device)
+            gx = torch.arange(W, dtype=torch.float32, device=x.device)
+            if self.normalize:
+                gy, gx = gy / (H - 1 + 1e-6), gx / (W - 1 + 1e-6)
+            yy, xx = torch.meshgrid(gy, gx, indexing="ij")
+            self._cache[key] = torch.cat((self._sincos(yy), self._sincos(xx)), dim=-1).permute(2, 0, 1).unsqueeze(0)
+        return self._cache[key].expand(B, -1, -1, -1)
+
+
+class SAM3VisionBackbone(nn.Module):
+    def __init__(self, embed_dim=1024, d_model=256, multiplex=False, device=None, dtype=None, operations=None, **kwargs):
+        super().__init__()
+        self.trunk = ViTDet(embed_dim=embed_dim, device=device, dtype=dtype, operations=operations, **kwargs)
+        self.position_encoding = PositionEmbeddingSine(num_pos_feats=d_model, normalize=True)
+        self.multiplex = multiplex
+
+        fpn_args = dict(device=device, dtype=dtype, operations=operations)
+        if multiplex:
+            scales = [4.0, 2.0, 1.0]
+            self.convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales])
+            self.propagation_convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales])
+            self.interactive_convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales])
+        else:
+            scales = [4.0, 2.0, 1.0, 0.5]
+            self.convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales])
+            self.sam2_convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales])
+
+    def forward(self, images, need_tracker=False, tracker_mode=None, cached_trunk=None, tracker_only=False):
+        backbone_out = cached_trunk if cached_trunk is not None else self.trunk(images)
+
+        if tracker_only:
+            # Skip detector FPN when only tracker features are needed (video tracking)
+            if self.multiplex:
+                tracker_convs = self.propagation_convs if tracker_mode == "propagation" else self.interactive_convs
+            else:
+                tracker_convs = self.sam2_convs
+            tracker_features = [conv(backbone_out) for conv in tracker_convs]
+            tracker_positions = [cast_to_input(self.position_encoding(f), f) for f in tracker_features]
+            return None, None, tracker_features, tracker_positions
+
+        features = [conv(backbone_out) for conv in self.convs]
+        positions = [cast_to_input(self.position_encoding(f), f) for f in features]
+
+        if self.multiplex:
+            if tracker_mode == "propagation":
+                tracker_convs = self.propagation_convs
+            elif tracker_mode == "interactive":
+                tracker_convs = self.interactive_convs
+            else:
+                return features, positions, None, None
+        elif need_tracker:
+            tracker_convs = self.sam2_convs
+        else:
+            return features, positions, None, None
+
+        tracker_features = [conv(backbone_out) for conv in tracker_convs]
+        tracker_positions = [cast_to_input(self.position_encoding(f), f) for f in tracker_features]
+        return features, positions, tracker_features, tracker_positions

comfy/ldm/supir/supir_modules.py

@@ -0,0 +1,226 @@
+import torch
+import torch.nn as nn
+
+from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
+from comfy.ldm.modules.diffusionmodules.openaimodel import Downsample, TimestepEmbedSequential, ResBlock, SpatialTransformer
+from comfy.ldm.modules.attention import optimized_attention
+
+
+class ZeroSFT(nn.Module):
+    def __init__(self, label_nc, norm_nc, concat_channels=0, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        ks = 3
+        pw = ks // 2
+
+        self.param_free_norm = operations.GroupNorm(32, norm_nc + concat_channels, dtype=dtype, device=device)
+
+        nhidden = 128
+
+        self.mlp_shared = nn.Sequential(
+            operations.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw, dtype=dtype, device=device),
+            nn.SiLU()
+        )
+        self.zero_mul = operations.Conv2d(nhidden, norm_nc + concat_channels, kernel_size=ks, padding=pw, dtype=dtype, device=device)
+        self.zero_add = operations.Conv2d(nhidden, norm_nc + concat_channels, kernel_size=ks, padding=pw, dtype=dtype, device=device)
+
+        self.zero_conv = operations.Conv2d(label_nc, norm_nc, 1, 1, 0, dtype=dtype, device=device)
+        self.pre_concat = bool(concat_channels != 0)
+
+    def forward(self, c, h, h_ori=None, control_scale=1):
+        if h_ori is not None and self.pre_concat:
+            h_raw = torch.cat([h_ori, h], dim=1)
+        else:
+            h_raw = h
+
+        h = h + self.zero_conv(c)
+        if h_ori is not None and self.pre_concat:
+            h = torch.cat([h_ori, h], dim=1)
+        actv = self.mlp_shared(c)
+        gamma = self.zero_mul(actv)
+        beta = self.zero_add(actv)
+        h = self.param_free_norm(h)
+        h = torch.addcmul(h + beta, h, gamma)
+        if h_ori is not None and not self.pre_concat:
+            h = torch.cat([h_ori, h], dim=1)
+        return torch.lerp(h_raw, h, control_scale)
+
+
+class _CrossAttnInner(nn.Module):
+    """Inner cross-attention module matching the state_dict layout of the original CrossAttention."""
+    def __init__(self, query_dim, context_dim, heads, dim_head, dtype=None, device=None, operations=None):
+        super().__init__()
+        inner_dim = dim_head * heads
+        self.heads = heads
+        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_out = nn.Sequential(
+            operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
+        )
+
+    def forward(self, x, context):
+        q = self.to_q(x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+        return self.to_out(optimized_attention(q, k, v, self.heads))
+
+
+class ZeroCrossAttn(nn.Module):
+    def __init__(self, context_dim, query_dim, dtype=None, device=None, operations=None):
+        super().__init__()
+        heads = query_dim // 64
+        dim_head = 64
+        self.attn = _CrossAttnInner(query_dim, context_dim, heads, dim_head, dtype=dtype, device=device, operations=operations)
+        self.norm1 = operations.GroupNorm(32, query_dim, dtype=dtype, device=device)
+        self.norm2 = operations.GroupNorm(32, context_dim, dtype=dtype, device=device)
+
+    def forward(self, context, x, control_scale=1):
+        b, c, h, w = x.shape
+        x_in = x
+
+        x = self.attn(
+            self.norm1(x).flatten(2).transpose(1, 2),
+            self.norm2(context).flatten(2).transpose(1, 2),
+        ).transpose(1, 2).unflatten(2, (h, w))
+
+        return x_in + x * control_scale
+
+
+class GLVControl(nn.Module):
+    """SUPIR's Guided Latent Vector control encoder. Truncated UNet (input + middle blocks only)."""
+    def __init__(
+        self,
+        in_channels=4,
+        model_channels=320,
+        num_res_blocks=2,
+        attention_resolutions=(4, 2),
+        channel_mult=(1, 2, 4),
+        num_head_channels=64,
+        transformer_depth=(1, 2, 10),
+        context_dim=2048,
+        adm_in_channels=2816,
+        use_linear_in_transformer=True,
+        use_checkpoint=False,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs,
+    ):
+        super().__init__()
+        self.model_channels = model_channels
+        time_embed_dim = model_channels * 4
+
+        self.time_embed = nn.Sequential(
+            operations.Linear(model_channels, time_embed_dim, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device),
+        )
+
+        self.label_emb = nn.Sequential(
+            nn.Sequential(
+                operations.Linear(adm_in_channels, time_embed_dim, dtype=dtype, device=device),
+                nn.SiLU(),
+                operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device),
+            )
+        )
+
+        self.input_blocks = nn.ModuleList([
+            TimestepEmbedSequential(
+                operations.Conv2d(in_channels, model_channels, 3, padding=1, dtype=dtype, device=device)
+            )
+        ])
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for nr in range(num_res_blocks):
+                layers = [
+                    ResBlock(ch, time_embed_dim, 0, out_channels=mult * model_channels,
+                             dtype=dtype, device=device, operations=operations)
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    num_heads = ch // num_head_channels
+                    layers.append(
+                        SpatialTransformer(ch, num_heads, num_head_channels,
+                                           depth=transformer_depth[level], context_dim=context_dim,
+                                           use_linear=use_linear_in_transformer,
+                                           use_checkpoint=use_checkpoint,
+                                           dtype=dtype, device=device, operations=operations)
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+            if level != len(channel_mult) - 1:
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        Downsample(ch, True, out_channels=ch, dtype=dtype, device=device, operations=operations)
+                    )
+                )
+                ds *= 2
+
+        num_heads = ch // num_head_channels
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(ch, time_embed_dim, 0, dtype=dtype, device=device, operations=operations),
+            SpatialTransformer(ch, num_heads, num_head_channels,
+                               depth=transformer_depth[-1], context_dim=context_dim,
+                               use_linear=use_linear_in_transformer,
+                               use_checkpoint=use_checkpoint,
+                               dtype=dtype, device=device, operations=operations),
+            ResBlock(ch, time_embed_dim, 0, dtype=dtype, device=device, operations=operations),
+        )
+
+        self.input_hint_block = TimestepEmbedSequential(
+            operations.Conv2d(in_channels, model_channels, 3, padding=1, dtype=dtype, device=device)
+        )
+
+    def forward(self, x, timesteps, xt, context=None, y=None, **kwargs):
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
+        emb = self.time_embed(t_emb) + self.label_emb(y)
+
+        guided_hint = self.input_hint_block(x, emb, context)
+
+        hs = []
+        h = xt
+        for module in self.input_blocks:
+            if guided_hint is not None:
+                h = module(h, emb, context)
+                h += guided_hint
+                guided_hint = None
+            else:
+                h = module(h, emb, context)
+            hs.append(h)
+        h = self.middle_block(h, emb, context)
+        hs.append(h)
+        return hs
+
+
+class SUPIR(nn.Module):
+    """
+    SUPIR model containing GLVControl (control encoder) and project_modules (adapters).
+    State dict keys match the original SUPIR checkpoint layout:
+      control_model.*           -> GLVControl
+      project_modules.*         -> nn.ModuleList of ZeroSFT/ZeroCrossAttn
+    """
+    def __init__(self, device=None, dtype=None, operations=None):
+        super().__init__()
+
+        self.control_model = GLVControl(dtype=dtype, device=device, operations=operations)
+
+        project_channel_scale = 2
+        cond_output_channels = [320] * 4 + [640] * 3 + [1280] * 3
+        project_channels = [int(c * project_channel_scale) for c in [160] * 4 + [320] * 3 + [640] * 3]
+        concat_channels = [320] * 2 + [640] * 3 + [1280] * 4 + [0]
+        cross_attn_insert_idx = [6, 3]
+
+        self.project_modules = nn.ModuleList()
+        for i in range(len(cond_output_channels)):
+            self.project_modules.append(ZeroSFT(
+                project_channels[i], cond_output_channels[i],
+                concat_channels=concat_channels[i],
+                dtype=dtype, device=device, operations=operations,
+            ))
+
+        for i in cross_attn_insert_idx:
+            self.project_modules.insert(i, ZeroCrossAttn(
+                cond_output_channels[i], concat_channels[i],
+                dtype=dtype, device=device, operations=operations,
+            ))

comfy/ldm/supir/supir_patch.py

@@ -0,0 +1,103 @@
+import torch
+from comfy.ldm.modules.diffusionmodules.openaimodel import Upsample
+
+
+class SUPIRPatch:
+    """
+    Holds GLVControl (control encoder) + project_modules (ZeroSFT/ZeroCrossAttn adapters).
+    Runs GLVControl lazily on first patch invocation per step, applies adapters through
+    middle_block_after_patch, output_block_merge_patch, and forward_timestep_embed_patch.
+    """
+    SIGMA_MAX = 14.6146
+
+    def __init__(self, model_patch, project_modules, hint_latent, strength_start, strength_end):
+        self.model_patch = model_patch           # CoreModelPatcher wrapping GLVControl
+        self.project_modules = project_modules   # nn.ModuleList of ZeroSFT/ZeroCrossAttn
+        self.hint_latent = hint_latent           # encoded LQ image latent
+        self.strength_start = strength_start
+        self.strength_end = strength_end
+        self.cached_features = None
+        self.adapter_idx = 0
+        self.control_idx = 0
+        self.current_control_idx = 0
+        self.active = True
+
+    def _ensure_features(self, kwargs):
+        """Run GLVControl on first call per step, cache results."""
+        if self.cached_features is not None:
+            return
+        x = kwargs["x"]
+        b = x.shape[0]
+        hint = self.hint_latent.to(device=x.device, dtype=x.dtype)
+        if hint.shape[0] != b:
+            hint = hint.expand(b, -1, -1, -1) if hint.shape[0] == 1 else hint.repeat((b + hint.shape[0] - 1) // hint.shape[0], 1, 1, 1)[:b]
+        self.cached_features = self.model_patch.model.control_model(
+            hint, kwargs["timesteps"], x,
+            kwargs["context"], kwargs["y"]
+        )
+        self.adapter_idx = len(self.project_modules) - 1
+        self.control_idx = len(self.cached_features) - 1
+
+    def _get_control_scale(self, kwargs):
+        if self.strength_start == self.strength_end:
+            return self.strength_end
+        sigma = kwargs["transformer_options"].get("sigmas")
+        if sigma is None:
+            return self.strength_end
+        s = sigma[0].item() if sigma.dim() > 0 else sigma.item()
+        t = min(s / self.SIGMA_MAX, 1.0)
+        return t * (self.strength_start - self.strength_end) + self.strength_end
+
+    def middle_after(self, kwargs):
+        """middle_block_after_patch: run GLVControl lazily, apply last adapter after middle block."""
+        self.cached_features = None  # reset from previous step
+        self.current_scale = self._get_control_scale(kwargs)
+        self.active = self.current_scale > 0
+        if not self.active:
+            return {"h": kwargs["h"]}
+        self._ensure_features(kwargs)
+        h = kwargs["h"]
+        h = self.project_modules[self.adapter_idx](
+            self.cached_features[self.control_idx], h, control_scale=self.current_scale
+        )
+        self.adapter_idx -= 1
+        self.control_idx -= 1
+        return {"h": h}
+
+    def output_block(self, h, hsp, transformer_options):
+        """output_block_patch: ZeroSFT adapter fusion replaces cat([h, hsp]). Returns (h, None) to skip cat."""
+        if not self.active:
+            return h, hsp
+        self.current_control_idx = self.control_idx
+        h = self.project_modules[self.adapter_idx](
+            self.cached_features[self.control_idx], hsp, h, control_scale=self.current_scale
+        )
+        self.adapter_idx -= 1
+        self.control_idx -= 1
+        return h, None
+
+    def pre_upsample(self, layer, x, emb, context, transformer_options, output_shape, *args, **kw):
+        """forward_timestep_embed_patch for Upsample: extra cross-attn adapter before upsample."""
+        block_type, _ = transformer_options["block"]
+        if block_type == "output" and self.active and self.cached_features is not None:
+            x = self.project_modules[self.adapter_idx](
+                self.cached_features[self.current_control_idx], x, control_scale=self.current_scale
+            )
+            self.adapter_idx -= 1
+        return layer(x, output_shape=output_shape)
+
+    def to(self, device_or_dtype):
+        if isinstance(device_or_dtype, torch.device):
+            self.cached_features = None
+            if self.hint_latent is not None:
+                self.hint_latent = self.hint_latent.to(device_or_dtype)
+        return self
+
+    def models(self):
+        return [self.model_patch]
+
+    def register(self, model_patcher):
+        """Register all patches on a cloned model patcher."""
+        model_patcher.set_model_patch(self.middle_after, "middle_block_after_patch")
+        model_patcher.set_model_output_block_patch(self.output_block)
+        model_patcher.set_model_patch((Upsample, self.pre_upsample), "forward_timestep_embed_patch")

comfy/model_base.py

@@ -54,6 +54,7 @@ import comfy.ldm.anima.model
 import comfy.ldm.ace.ace_step15
 import comfy.ldm.rt_detr.rtdetr_v4
 import comfy.ldm.ernie.model
+import comfy.ldm.sam3.detector
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -578,8 +579,8 @@ class Stable_Zero123(BaseModel):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None):
         super().__init__(model_config, model_type, device=device)
         self.cc_projection = comfy.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device)
-        self.cc_projection.weight.copy_(cc_projection_weight)
-        self.cc_projection.bias.copy_(cc_projection_bias)
+        self.cc_projection.weight = torch.nn.Parameter(cc_projection_weight.clone())
+        self.cc_projection.bias = torch.nn.Parameter(cc_projection_bias.clone())
 
     def extra_conds(self, **kwargs):
         out = {}
@@ -1974,3 +1975,7 @@ class ErnieImage(BaseModel):
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         return out
+
+class SAM3(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.sam3.detector.SAM3Model)

comfy/model_detection.py

@@ -718,6 +718,14 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["image_model"] = "ernie"
         return dit_config
 
+    if 'detector.backbone.vision_backbone.trunk.blocks.0.attn.qkv.weight' in state_dict_keys: # SAM3 / SAM3.1
+        if 'detector.transformer.decoder.query_embed.weight' in state_dict_keys:
+            dit_config = {}
+            dit_config["image_model"] = "SAM3"
+            if 'detector.backbone.vision_backbone.propagation_convs.0.conv_1x1.weight' in state_dict_keys:
+                dit_config["image_model"] = "SAM31"
+            return dit_config
+
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 
@@ -873,6 +881,10 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
     return model_config
 
 def unet_prefix_from_state_dict(state_dict):
+    # SAM3: detector.* and tracker.* at top level, no common prefix
+    if any(k.startswith("detector.") for k in state_dict) and any(k.startswith("tracker.") for k in state_dict):
+        return ""
+
     candidates = ["model.diffusion_model.", #ldm/sgm models
                   "model.model.", #audio models
                   "net.", #cosmos

comfy/model_management.py

@@ -1801,7 +1801,7 @@ def debug_memory_summary():
         return torch.cuda.memory.memory_summary()
     return ""
 
-class InterruptProcessingException(Exception):
+class InterruptProcessingException(BaseException):
     pass
 
 interrupt_processing_mutex = threading.RLock()

comfy/model_patcher.py

@@ -506,6 +506,10 @@ class ModelPatcher:
     def set_model_noise_refiner_patch(self, patch):
         self.set_model_patch(patch, "noise_refiner")
 
+    def set_model_middle_block_after_patch(self, patch):
+        self.set_model_patch(patch, "middle_block_after_patch")
+
+
     def set_model_rope_options(self, scale_x, shift_x, scale_y, shift_y, scale_t, shift_t, **kwargs):
         rope_options = self.model_options["transformer_options"].get("rope_options", {})
         rope_options["scale_x"] = scale_x
@@ -681,9 +685,9 @@ class ModelPatcher:
                         sd.pop(k)
             return sd
 
-    def patch_weight_to_device(self, key, device_to=None, inplace_update=False, return_weight=False):
+    def patch_weight_to_device(self, key, device_to=None, inplace_update=False, return_weight=False, force_cast=False):
         weight, set_func, convert_func = get_key_weight(self.model, key)
-        if key not in self.patches:
+        if key not in self.patches and not force_cast:
             return weight
 
         inplace_update = self.weight_inplace_update or inplace_update
@@ -691,7 +695,7 @@ class ModelPatcher:
         if key not in self.backup and not return_weight:
             self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update)
 
-        temp_dtype = comfy.model_management.lora_compute_dtype(device_to)
+        temp_dtype = comfy.model_management.lora_compute_dtype(device_to) if key in self.patches else None
         if device_to is not None:
             temp_weight = comfy.model_management.cast_to_device(weight, device_to, temp_dtype, copy=True)
         else:
@@ -699,9 +703,10 @@ class ModelPatcher:
         if convert_func is not None:
             temp_weight = convert_func(temp_weight, inplace=True)
 
-        out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key)
+        out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key) if key in self.patches else temp_weight
         if set_func is None:
-            out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=comfy.utils.string_to_seed(key))
+            if key in self.patches:
+                out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=comfy.utils.string_to_seed(key))
             if return_weight:
                 return out_weight
             elif inplace_update:
@@ -1580,7 +1585,7 @@ class ModelPatcherDynamic(ModelPatcher):
                     key = key_param_name_to_key(n, param_key)
                     if key in self.backup:
                         comfy.utils.set_attr_param(self.model, key, self.backup[key].weight)
-                    self.patch_weight_to_device(key, device_to=device_to)
+                    self.patch_weight_to_device(key, device_to=device_to, force_cast=True)
                     weight, _, _ = get_key_weight(self.model, key)
                     if weight is not None:
                         self.model.model_loaded_weight_memory += weight.numel() * weight.element_size()
@@ -1605,6 +1610,10 @@ class ModelPatcherDynamic(ModelPatcher):
                             m._v = vbar.alloc(v_weight_size)
                         allocated_size += v_weight_size
 
+                    for param in params:
+                        if param not in ("weight", "bias"):
+                            force_load_param(self, param, device_to)
+
                 else:
                     for param in params:
                         key = key_param_name_to_key(n, param)

comfy/sd.py

@@ -12,6 +12,7 @@ from .ldm.cascade.stage_c_coder import StageC_coder
 from .ldm.audio.autoencoder import AudioOobleckVAE
 import comfy.ldm.genmo.vae.model
 import comfy.ldm.lightricks.vae.causal_video_autoencoder
+import comfy.ldm.lightricks.vae.audio_vae
 import comfy.ldm.cosmos.vae
 import comfy.ldm.wan.vae
 import comfy.ldm.wan.vae2_2
@@ -805,6 +806,24 @@ class VAE:
                     self.downscale_index_formula = (4, 8, 8)
                     self.memory_used_encode = lambda shape, dtype: (700 * (max(1, (shape[-3] ** 0.66 * 0.11)) * shape[-2] * shape[-1]) * model_management.dtype_size(dtype))
                     self.memory_used_decode = lambda shape, dtype: (50 * (max(1, (shape[-3] ** 0.65 * 0.26)) * shape[-2] * shape[-1] * 32 * 32) * model_management.dtype_size(dtype))
+            elif "vocoder.resblocks.0.convs1.0.weight" in sd or "vocoder.vocoder.resblocks.0.convs1.0.weight" in sd: # LTX Audio
+                sd = comfy.utils.state_dict_prefix_replace(sd, {"audio_vae.": "autoencoder."})
+                self.first_stage_model = comfy.ldm.lightricks.vae.audio_vae.AudioVAE(metadata=metadata)
+                self.memory_used_encode = lambda shape, dtype: (shape[2] * 330) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype)
+                self.latent_channels = self.first_stage_model.latent_channels
+                self.audio_sample_rate_output = self.first_stage_model.output_sample_rate
+                self.autoencoder = self.first_stage_model.autoencoder  # TODO: remove hack for ltxv custom nodes
+                self.output_channels = 2
+                self.pad_channel_value = "replicate"
+                self.upscale_ratio = 4096
+                self.downscale_ratio = 4096
+                self.latent_dim = 2
+                self.process_output = lambda audio: audio
+                self.process_input = lambda audio: audio
+                self.working_dtypes = [torch.float32]
+                self.disable_offload = True
+                self.extra_1d_channel = 16
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None

comfy/supported_models.py

@@ -1781,6 +1781,57 @@ class ErnieImage(supported_models_base.BASE):
         return supported_models_base.ClipTarget(comfy.text_encoders.ernie.ErnieTokenizer, comfy.text_encoders.ernie.te(**hunyuan_detect))
 
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima, RT_DETR_v4, ErnieImage]
+class SAM3(supported_models_base.BASE):
+    unet_config = {"image_model": "SAM3"}
+    supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+    text_encoder_key_prefix = ["detector.backbone.language_backbone."]
+    unet_extra_prefix = ""
+
+    def process_clip_state_dict(self, state_dict):
+        clip_keys = getattr(self, "_clip_stash", {})
+        clip_keys = utils.state_dict_prefix_replace(clip_keys, {"detector.backbone.language_backbone.": "", "backbone.language_backbone.": ""}, filter_keys=True)
+        clip_keys = utils.clip_text_transformers_convert(clip_keys, "encoder.", "sam3_clip.transformer.")
+        return {k: v for k, v in clip_keys.items() if not k.startswith("resizer.")}
+
+    def process_unet_state_dict(self, state_dict):
+        self._clip_stash = {k: state_dict.pop(k) for k in list(state_dict.keys()) if "language_backbone" in k and "resizer" not in k}
+        # SAM3.1: remap tracker.model.* -> tracker.*
+        for k in list(state_dict.keys()):
+            if k.startswith("tracker.model."):
+                state_dict["tracker." + k[len("tracker.model."):]] = state_dict.pop(k)
+        # SAM3.1: remove per-block freqs_cis buffers (computed dynamically)
+        for k in [k for k in list(state_dict.keys()) if ".attn.freqs_cis" in k]:
+            state_dict.pop(k)
+        # Split fused QKV projections
+        for k in [k for k in list(state_dict.keys()) if k.endswith((".in_proj_weight", ".in_proj_bias"))]:
+            t = state_dict.pop(k)
+            base, suffix = k.rsplit(".in_proj_", 1)
+            s = ".weight" if suffix == "weight" else ".bias"
+            d = t.shape[0] // 3
+            state_dict[base + ".q_proj" + s] = t[:d]
+            state_dict[base + ".k_proj" + s] = t[d:2*d]
+            state_dict[base + ".v_proj" + s] = t[2*d:]
+        # Remap tracker SAM decoder transformer key names to match sam.py TwoWayTransformer
+        for k in list(state_dict.keys()):
+            if "sam_mask_decoder.transformer." not in k:
+                continue
+            new_k = k.replace(".mlp.lin1.", ".mlp.0.").replace(".mlp.lin2.", ".mlp.2.").replace(".norm_final_attn.", ".norm_final.")
+            if new_k != k:
+                state_dict[new_k] = state_dict.pop(k)
+        return state_dict
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return model_base.SAM3(self, device=device)
+
+    def clip_target(self, state_dict={}):
+        import comfy.text_encoders.sam3_clip
+        return supported_models_base.ClipTarget(comfy.text_encoders.sam3_clip.SAM3TokenizerWrapper, comfy.text_encoders.sam3_clip.SAM3ClipModelWrapper)
+
+
+class SAM31(SAM3):
+    unet_config = {"image_model": "SAM31"}
+
+
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima, RT_DETR_v4, ErnieImage, SAM3, SAM31]
 
 models += [SVD_img2vid]

comfy/text_encoders/sam3_clip.py

@@ -0,0 +1,97 @@
+import re
+from comfy import sd1_clip
+
+SAM3_CLIP_CONFIG = {
+    "architectures": ["CLIPTextModel"],
+    "hidden_act": "quick_gelu",
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "max_position_embeddings": 32,
+    "projection_dim": 512,
+    "vocab_size": 49408,
+    "layer_norm_eps": 1e-5,
+    "eos_token_id": 49407,
+}
+
+
+class SAM3ClipModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, max_length=32, layer="last", textmodel_json_config=SAM3_CLIP_CONFIG, special_tokens={"start": 49406, "end": 49407, "pad": 0}, return_projected_pooled=False, return_attention_masks=True, enable_attention_masks=True, model_options=model_options)
+
+
+class SAM3Tokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(max_length=32, pad_with_end=False, pad_token=0, embedding_directory=embedding_directory, embedding_size=1024, embedding_key="sam3_clip", tokenizer_data=tokenizer_data)
+        self.disable_weights = True
+
+
+def _parse_prompts(text):
+    """Split comma-separated prompts with optional :N max detections per category"""
+    text = text.replace("(", "").replace(")", "")
+    parts = [p.strip() for p in text.split(",") if p.strip()]
+    result = []
+    for part in parts:
+        m = re.match(r'^(.+?)\s*:\s*([\d.]+)\s*$', part)
+        if m:
+            text_part = m.group(1).strip()
+            val = m.group(2)
+            max_det = max(1, round(float(val)))
+            result.append((text_part, max_det))
+        else:
+            result.append((part, 1))
+    return result
+
+
+class SAM3TokenizerWrapper(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="l", tokenizer=SAM3Tokenizer, name="sam3_clip")
+
+    def tokenize_with_weights(self, text: str, return_word_ids=False, **kwargs):
+        parsed = _parse_prompts(text)
+        if len(parsed) <= 1 and (not parsed or parsed[0][1] == 1):
+            return super().tokenize_with_weights(text, return_word_ids, **kwargs)
+        # Tokenize each prompt part separately, store per-part batches and metadata
+        inner = getattr(self, self.clip)
+        per_prompt = []
+        for prompt_text, max_det in parsed:
+            batches = inner.tokenize_with_weights(prompt_text, return_word_ids, **kwargs)
+            per_prompt.append((batches, max_det))
+        # Main output uses first prompt's tokens (for compatibility)
+        out = {self.clip_name: per_prompt[0][0], "sam3_per_prompt": per_prompt}
+        return out
+
+
+class SAM3ClipModelWrapper(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+        super().__init__(device=device, dtype=dtype, model_options=model_options, clip_name="l", clip_model=SAM3ClipModel, name="sam3_clip")
+
+    def encode_token_weights(self, token_weight_pairs):
+        per_prompt = token_weight_pairs.pop("sam3_per_prompt", None)
+        if per_prompt is None:
+            return super().encode_token_weights(token_weight_pairs)
+
+        # Encode each prompt separately, pack into extra dict
+        inner = getattr(self, self.clip)
+        multi_cond = []
+        first_pooled = None
+        for batches, max_det in per_prompt:
+            out = inner.encode_token_weights(batches)
+            cond, pooled = out[0], out[1]
+            extra = out[2] if len(out) > 2 else {}
+            if first_pooled is None:
+                first_pooled = pooled
+            multi_cond.append({
+                "cond": cond,
+                "attention_mask": extra.get("attention_mask"),
+                "max_detections": max_det,
+            })
+
+        # Return first prompt as main (for non-SAM3 consumers), all prompts in metadata
+        main = multi_cond[0]
+        main_extra = {}
+        if main["attention_mask"] is not None:
+            main_extra["attention_mask"] = main["attention_mask"]
+        main_extra["sam3_multi_cond"] = multi_cond
+        return (main["cond"], first_pooled, main_extra)

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/_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_nodes/apis/bytedance.py

@@ -122,6 +122,41 @@ class TaskStatusResponse(BaseModel):
     usage: TaskStatusUsage | None = Field(None)
 
 
+class GetAssetResponse(BaseModel):
+    id: str = Field(...)
+    name: str | None = Field(None)
+    url: str | None = Field(None)
+    asset_type: str = Field(...)
+    group_id: str = Field(...)
+    status: str = Field(...)
+    error: TaskStatusError | None = Field(None)
+
+
+class SeedanceCreateVisualValidateSessionResponse(BaseModel):
+    session_id: str = Field(...)
+    h5_link: str = Field(...)
+
+
+class SeedanceGetVisualValidateSessionResponse(BaseModel):
+    session_id: str = Field(...)
+    status: str = Field(...)
+    group_id: str | None = Field(None)
+    error_code: str | None = Field(None)
+    error_message: str | None = Field(None)
+
+
+class SeedanceCreateAssetRequest(BaseModel):
+    group_id: str = Field(...)
+    url: str = Field(...)
+    asset_type: str = Field(...)
+    name: str | None = Field(None, max_length=64)
+    project_name: str | None = Field(None)
+
+
+class SeedanceCreateAssetResponse(BaseModel):
+    asset_id: str = Field(...)
+
+
 # Dollars per 1K tokens, keyed by (model_id, has_video_input).
 SEEDANCE2_PRICE_PER_1K_TOKENS = {
     ("dreamina-seedance-2-0-260128", False): 0.007,
@@ -158,10 +193,17 @@ RECOMMENDED_PRESETS_SEEDREAM_4 = [
     ("Custom", None, None),
 ]
 
-# Seedance 2.0 reference video pixel count limits per model.
+# Seedance 2.0 reference video pixel count limits per model and output resolution.
 SEEDANCE2_REF_VIDEO_PIXEL_LIMITS = {
-    "dreamina-seedance-2-0-260128": {"min": 409_600, "max": 927_408},
-    "dreamina-seedance-2-0-fast-260128": {"min": 409_600, "max": 927_408},
+    "dreamina-seedance-2-0-260128": {
+        "480p": {"min": 409_600, "max": 927_408},
+        "720p": {"min": 409_600, "max": 927_408},
+        "1080p": {"min": 409_600, "max": 2_073_600},
+    },
+    "dreamina-seedance-2-0-fast-260128": {
+        "480p": {"min": 409_600, "max": 927_408},
+        "720p": {"min": 409_600, "max": 927_408},
+    },
 }
 
 # The time in this dictionary are given for 10 seconds duration.

comfy_api_nodes/nodes_bytedance.py

@@ -1,5 +1,6 @@
 import logging
 import math
+import re
 
 import torch
 from typing_extensions import override
@@ -11,9 +12,14 @@ from comfy_api_nodes.apis.bytedance import (
     SEEDANCE2_PRICE_PER_1K_TOKENS,
     SEEDANCE2_REF_VIDEO_PIXEL_LIMITS,
     VIDEO_TASKS_EXECUTION_TIME,
+    GetAssetResponse,
     Image2VideoTaskCreationRequest,
     ImageTaskCreationResponse,
     Seedance2TaskCreationRequest,
+    SeedanceCreateAssetRequest,
+    SeedanceCreateAssetResponse,
+    SeedanceCreateVisualValidateSessionResponse,
+    SeedanceGetVisualValidateSessionResponse,
     Seedream4Options,
     Seedream4TaskCreationRequest,
     TaskAudioContent,
@@ -35,6 +41,7 @@ from comfy_api_nodes.util import (
     get_number_of_images,
     image_tensor_pair_to_batch,
     poll_op,
+    resize_video_to_pixel_budget,
     sync_op,
     upload_audio_to_comfyapi,
     upload_image_to_comfyapi,
@@ -43,10 +50,16 @@ from comfy_api_nodes.util import (
     validate_image_aspect_ratio,
     validate_image_dimensions,
     validate_string,
+    validate_video_dimensions,
+    validate_video_duration,
 )
+from server import PromptServer
 
 BYTEPLUS_IMAGE_ENDPOINT = "/proxy/byteplus/api/v3/images/generations"
 
+_VERIFICATION_POLL_TIMEOUT_SEC = 120
+_VERIFICATION_POLL_INTERVAL_SEC = 3
+
 SEEDREAM_MODELS = {
     "seedream 5.0 lite": "seedream-5-0-260128",
     "seedream-4-5-251128": "seedream-4-5-251128",
@@ -69,9 +82,12 @@ DEPRECATED_MODELS = {"seedance-1-0-lite-t2v-250428", "seedance-1-0-lite-i2v-2504
 logger = logging.getLogger(__name__)
 
 
-def _validate_ref_video_pixels(video: Input.Video, model_id: str, index: int) -> None:
-    """Validate reference video pixel count against Seedance 2.0 model limits."""
-    limits = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id)
+def _validate_ref_video_pixels(video: Input.Video, model_id: str, resolution: str, index: int) -> None:
+    """Validate reference video pixel count against Seedance 2.0 model limits for the selected resolution."""
+    model_limits = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id)
+    if not model_limits:
+        return
+    limits = model_limits.get(resolution)
     if not limits:
         return
     try:
@@ -92,6 +108,169 @@ def _validate_ref_video_pixels(video: Input.Video, model_id: str, index: int) ->
         )
 
 
+async def _resolve_reference_assets(
+    cls: type[IO.ComfyNode],
+    asset_ids: list[str],
+) -> tuple[dict[str, str], dict[str, str], dict[str, str]]:
+    """Look up each asset, validate Active status, group by asset_type.
+
+    Returns (image_assets, video_assets, audio_assets), each mapping asset_id -> "asset://<asset_id>".
+    """
+    image_assets: dict[str, str] = {}
+    video_assets: dict[str, str] = {}
+    audio_assets: dict[str, str] = {}
+    for i, raw_id in enumerate(asset_ids, 1):
+        asset_id = (raw_id or "").strip()
+        if not asset_id:
+            continue
+        result = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/seedance/assets/{asset_id}"),
+            response_model=GetAssetResponse,
+        )
+        if result.status != "Active":
+            extra = f" {result.error.code}: {result.error.message}" if result.error else ""
+            raise ValueError(f"Reference asset {i} (Id={asset_id}) is not Active (Status={result.status}).{extra}")
+        asset_uri = f"asset://{asset_id}"
+        if result.asset_type == "Image":
+            image_assets[asset_id] = asset_uri
+        elif result.asset_type == "Video":
+            video_assets[asset_id] = asset_uri
+        elif result.asset_type == "Audio":
+            audio_assets[asset_id] = asset_uri
+    return image_assets, video_assets, audio_assets
+
+
+_ASSET_REF_RE = re.compile(r"\basset ?(\d{1,2})\b", re.IGNORECASE)
+
+
+def _build_asset_labels(
+    reference_assets: dict[str, str],
+    image_asset_uris: dict[str, str],
+    video_asset_uris: dict[str, str],
+    audio_asset_uris: dict[str, str],
+    n_reference_images: int,
+    n_reference_videos: int,
+    n_reference_audios: int,
+) -> dict[int, str]:
+    """Map asset slot number (from 'asset_N' keys) to its positional label.
+
+    Asset entries are appended to `content` after the reference_images/videos/audios,
+    so their 1-indexed labels continue from the count of existing same-type refs:
+    one reference_images entry + one Image-type asset -> asset labelled "Image 2".
+    """
+    image_n = n_reference_images
+    video_n = n_reference_videos
+    audio_n = n_reference_audios
+    labels: dict[int, str] = {}
+    for slot_key, raw_id in reference_assets.items():
+        asset_id = (raw_id or "").strip()
+        if not asset_id:
+            continue
+        try:
+            slot_num = int(slot_key.rsplit("_", 1)[-1])
+        except ValueError:
+            continue
+        if asset_id in image_asset_uris:
+            image_n += 1
+            labels[slot_num] = f"Image {image_n}"
+        elif asset_id in video_asset_uris:
+            video_n += 1
+            labels[slot_num] = f"Video {video_n}"
+        elif asset_id in audio_asset_uris:
+            audio_n += 1
+            labels[slot_num] = f"Audio {audio_n}"
+    return labels
+
+
+def _rewrite_asset_refs(prompt: str, labels: dict[int, str]) -> str:
+    """Case-insensitively replace 'assetNN' (1-2 digit) tokens with their labels."""
+    if not labels:
+        return prompt
+
+    def _sub(m: "re.Match[str]") -> str:
+        return labels.get(int(m.group(1)), m.group(0))
+
+    return _ASSET_REF_RE.sub(_sub, prompt)
+
+
+async def _obtain_group_id_via_h5_auth(cls: type[IO.ComfyNode]) -> str:
+    session = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/seedance/visual-validate/sessions", method="POST"),
+        response_model=SeedanceCreateVisualValidateSessionResponse,
+    )
+    logger.warning("Seedance authentication required. Open link: %s", session.h5_link)
+
+    h5_text = f"Open this link in your browser and complete face verification:\n\n{session.h5_link}"
+
+    result = await poll_op(
+        cls,
+        ApiEndpoint(path=f"/proxy/seedance/visual-validate/sessions/{session.session_id}"),
+        response_model=SeedanceGetVisualValidateSessionResponse,
+        status_extractor=lambda r: r.status,
+        completed_statuses=["completed"],
+        failed_statuses=["failed"],
+        poll_interval=_VERIFICATION_POLL_INTERVAL_SEC,
+        max_poll_attempts=(_VERIFICATION_POLL_TIMEOUT_SEC // _VERIFICATION_POLL_INTERVAL_SEC) - 1,
+        estimated_duration=_VERIFICATION_POLL_TIMEOUT_SEC - 1,
+        extra_text=h5_text,
+    )
+
+    if not result.group_id:
+        raise RuntimeError(f"Seedance session {session.session_id} completed without a group_id")
+
+    logger.warning("Seedance authentication complete. New GroupId: %s", result.group_id)
+    PromptServer.instance.send_progress_text(
+        f"Authentication complete. New GroupId: {result.group_id}", cls.hidden.unique_id
+    )
+    return result.group_id
+
+
+async def _resolve_group_id(cls: type[IO.ComfyNode], group_id: str) -> str:
+    if group_id and group_id.strip():
+        return group_id.strip()
+    return await _obtain_group_id_via_h5_auth(cls)
+
+
+async def _create_seedance_asset(
+    cls: type[IO.ComfyNode],
+    *,
+    group_id: str,
+    url: str,
+    name: str,
+    asset_type: str,
+) -> str:
+    req = SeedanceCreateAssetRequest(
+        group_id=group_id,
+        url=url,
+        asset_type=asset_type,
+        name=name or None,
+    )
+    result = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/seedance/assets", method="POST"),
+        response_model=SeedanceCreateAssetResponse,
+        data=req,
+    )
+    return result.asset_id
+
+
+async def _wait_for_asset_active(cls: type[IO.ComfyNode], asset_id: str, group_id: str) -> GetAssetResponse:
+    """Poll the newly created asset until its status becomes Active."""
+    return await poll_op(
+        cls,
+        ApiEndpoint(path=f"/proxy/seedance/assets/{asset_id}"),
+        response_model=GetAssetResponse,
+        status_extractor=lambda r: r.status,
+        completed_statuses=["Active"],
+        failed_statuses=["Failed"],
+        poll_interval=5,
+        max_poll_attempts=1200,
+        extra_text=f"Waiting for asset pre-processing...\n\nasset_id: {asset_id}\n\ngroup_id: {group_id}",
+    )
+
+
 def _seedance2_price_extractor(model_id: str, has_video_input: bool):
     """Returns a price_extractor closure for Seedance 2.0 poll_op."""
     rate = SEEDANCE2_PRICE_PER_1K_TOKENS.get((model_id, has_video_input))
@@ -1224,12 +1403,27 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
                 IO.Image.Input(
                     "first_frame",
                     tooltip="First frame image for the video.",
+                    optional=True,
                 ),
                 IO.Image.Input(
                     "last_frame",
                     tooltip="Last frame image for the video.",
                     optional=True,
                 ),
+                IO.String.Input(
+                    "first_frame_asset_id",
+                    default="",
+                    tooltip="Seedance asset_id to use as the first frame. "
+                            "Mutually exclusive with the first_frame image input.",
+                    optional=True,
+                ),
+                IO.String.Input(
+                    "last_frame_asset_id",
+                    default="",
+                    tooltip="Seedance asset_id to use as the last frame. "
+                            "Mutually exclusive with the last_frame image input.",
+                    optional=True,
+                ),
                 IO.Int.Input(
                     "seed",
                     default=0,
@@ -1282,24 +1476,54 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
     async def execute(
         cls,
         model: dict,
-        first_frame: Input.Image,
         seed: int,
         watermark: bool,
+        first_frame: Input.Image | None = None,
         last_frame: Input.Image | None = None,
+        first_frame_asset_id: str = "",
+        last_frame_asset_id: str = "",
     ) -> IO.NodeOutput:
         validate_string(model["prompt"], strip_whitespace=True, min_length=1)
         model_id = SEEDANCE_MODELS[model["model"]]
 
+        first_frame_asset_id = first_frame_asset_id.strip()
+        last_frame_asset_id = last_frame_asset_id.strip()
+
+        if first_frame is not None and first_frame_asset_id:
+            raise ValueError("Provide only one of first_frame or first_frame_asset_id, not both.")
+        if first_frame is None and not first_frame_asset_id:
+            raise ValueError("Either first_frame or first_frame_asset_id is required.")
+        if last_frame is not None and last_frame_asset_id:
+            raise ValueError("Provide only one of last_frame or last_frame_asset_id, not both.")
+
+        asset_ids_to_resolve = [a for a in (first_frame_asset_id, last_frame_asset_id) if a]
+        image_assets: dict[str, str] = {}
+        if asset_ids_to_resolve:
+            image_assets, _, _ = await _resolve_reference_assets(cls, asset_ids_to_resolve)
+            for aid in asset_ids_to_resolve:
+                if aid not in image_assets:
+                    raise ValueError(f"Asset {aid} is not an Image asset.")
+
+        if first_frame_asset_id:
+            first_frame_url = image_assets[first_frame_asset_id]
+        else:
+            first_frame_url = await upload_image_to_comfyapi(cls, first_frame, wait_label="Uploading first frame.")
+
         content: list[TaskTextContent | TaskImageContent] = [
             TaskTextContent(text=model["prompt"]),
             TaskImageContent(
-                image_url=TaskImageContentUrl(
-                    url=await upload_image_to_comfyapi(cls, first_frame, wait_label="Uploading first frame.")
-                ),
+                image_url=TaskImageContentUrl(url=first_frame_url),
                 role="first_frame",
             ),
         ]
-        if last_frame is not None:
+        if last_frame_asset_id:
+            content.append(
+                TaskImageContent(
+                    image_url=TaskImageContentUrl(url=image_assets[last_frame_asset_id]),
+                    role="last_frame",
+                ),
+            )
+        elif last_frame is not None:
             content.append(
                 TaskImageContent(
                     image_url=TaskImageContentUrl(
@@ -1373,6 +1597,32 @@ def _seedance2_reference_inputs(resolutions: list[str]):
                 min=0,
             ),
         ),
+        IO.Boolean.Input(
+            "auto_downscale",
+            default=False,
+            advanced=True,
+            optional=True,
+            tooltip="Automatically downscale reference videos that exceed the model's pixel budget "
+            "for the selected resolution. Aspect ratio is preserved; videos already within limits are untouched.",
+        ),
+        IO.Autogrow.Input(
+            "reference_assets",
+            template=IO.Autogrow.TemplateNames(
+                IO.String.Input("reference_asset"),
+                names=[
+                    "asset_1",
+                    "asset_2",
+                    "asset_3",
+                    "asset_4",
+                    "asset_5",
+                    "asset_6",
+                    "asset_7",
+                    "asset_8",
+                    "asset_9",
+                ],
+                min=0,
+            ),
+        ),
     ]
 
 
@@ -1474,16 +1724,47 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
         reference_images = model.get("reference_images", {})
         reference_videos = model.get("reference_videos", {})
         reference_audios = model.get("reference_audios", {})
+        reference_assets = model.get("reference_assets", {})
 
-        if not reference_images and not reference_videos:
-            raise ValueError("At least one reference image or video is required.")
+        reference_image_assets, reference_video_assets, reference_audio_assets = await _resolve_reference_assets(
+            cls, list(reference_assets.values())
+        )
+
+        if not reference_images and not reference_videos and not reference_image_assets and not reference_video_assets:
+            raise ValueError("At least one reference image or video or asset is required.")
+
+        total_images = len(reference_images) + len(reference_image_assets)
+        if total_images > 9:
+            raise ValueError(
+                f"Too many reference images: {total_images} "
+                f"(images={len(reference_images)}, image assets={len(reference_image_assets)}). Maximum is 9."
+            )
+        total_videos = len(reference_videos) + len(reference_video_assets)
+        if total_videos > 3:
+            raise ValueError(
+                f"Too many reference videos: {total_videos} "
+                f"(videos={len(reference_videos)}, video assets={len(reference_video_assets)}). Maximum is 3."
+            )
+        total_audios = len(reference_audios) + len(reference_audio_assets)
+        if total_audios > 3:
+            raise ValueError(
+                f"Too many reference audios: {total_audios} "
+                f"(audios={len(reference_audios)}, audio assets={len(reference_audio_assets)}). Maximum is 3."
+            )
 
         model_id = SEEDANCE_MODELS[model["model"]]
-        has_video_input = len(reference_videos) > 0
+        has_video_input = total_videos > 0
+
+        if model.get("auto_downscale") and reference_videos:
+            max_px = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}).get(model["resolution"], {}).get("max")
+            if max_px:
+                for key in reference_videos:
+                    reference_videos[key] = resize_video_to_pixel_budget(reference_videos[key], max_px)
+
         total_video_duration = 0.0
         for i, key in enumerate(reference_videos, 1):
             video = reference_videos[key]
-            _validate_ref_video_pixels(video, model_id, i)
+            _validate_ref_video_pixels(video, model_id, model["resolution"], i)
             try:
                 dur = video.get_duration()
                 if dur < 1.8:
@@ -1506,8 +1787,19 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
         if total_audio_duration > 15.1:
             raise ValueError(f"Total reference audio duration is {total_audio_duration:.1f}s. Maximum is 15.1 seconds.")
 
+        asset_labels = _build_asset_labels(
+            reference_assets,
+            reference_image_assets,
+            reference_video_assets,
+            reference_audio_assets,
+            len(reference_images),
+            len(reference_videos),
+            len(reference_audios),
+        )
+        prompt_text = _rewrite_asset_refs(model["prompt"], asset_labels)
+
         content: list[TaskTextContent | TaskImageContent | TaskVideoContent | TaskAudioContent] = [
-            TaskTextContent(text=model["prompt"]),
+            TaskTextContent(text=prompt_text),
         ]
         for i, key in enumerate(reference_images, 1):
             content.append(
@@ -1548,6 +1840,21 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
                     ),
                 ),
             )
+        for url in reference_image_assets.values():
+            content.append(
+                TaskImageContent(
+                    image_url=TaskImageContentUrl(url=url),
+                    role="reference_image",
+                ),
+            )
+        for url in reference_video_assets.values():
+            content.append(
+                TaskVideoContent(video_url=TaskVideoContentUrl(url=url)),
+            )
+        for url in reference_audio_assets.values():
+            content.append(
+                TaskAudioContent(audio_url=TaskAudioContentUrl(url=url)),
+            )
         initial_response = await sync_op(
             cls,
             ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"),
@@ -1602,6 +1909,156 @@ async def process_video_task(
     return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
 
 
+class ByteDanceCreateImageAsset(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ByteDanceCreateImageAsset",
+            display_name="ByteDance Create Image Asset",
+            category="api node/image/ByteDance",
+            description=(
+                "Create a Seedance 2.0 personal image asset. Uploads the input image and "
+                "registers it in the given asset group. If group_id is empty, runs a real-person "
+                "H5 authentication flow to create a new group before adding the asset."
+            ),
+            inputs=[
+                IO.Image.Input("image", tooltip="Image to register as a personal asset."),
+                IO.String.Input(
+                    "group_id",
+                    default="",
+                    tooltip="Reuse an existing Seedance asset group ID to skip repeated human verification for the "
+                    "same person. Leave empty to run real-person authentication in the browser and create a new group.",
+                ),
+                # IO.String.Input(
+                #     "name",
+                #     default="",
+                #     tooltip="Asset name (up to 64 characters).",
+                # ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="asset_id"),
+                IO.String.Output(display_name="group_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            # is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        group_id: str = "",
+        # name: str = "",
+    ) -> IO.NodeOutput:
+        # if len(name) > 64:
+        #     raise ValueError("Name of asset can not be greater then 64 symbols")
+        validate_image_dimensions(image, min_width=300, max_width=6000, min_height=300, max_height=6000)
+        validate_image_aspect_ratio(image, min_ratio=(0.4, 1), max_ratio=(2.5, 1))
+        resolved_group = await _resolve_group_id(cls, group_id)
+        asset_id = await _create_seedance_asset(
+            cls,
+            group_id=resolved_group,
+            url=await upload_image_to_comfyapi(cls, image),
+            name="",
+            asset_type="Image",
+        )
+        await _wait_for_asset_active(cls, asset_id, resolved_group)
+        PromptServer.instance.send_progress_text(
+            f"Please save the asset_id and group_id for reuse.\n\nasset_id: {asset_id}\n\n"
+            f"group_id: {resolved_group}",
+            cls.hidden.unique_id,
+        )
+        return IO.NodeOutput(asset_id, resolved_group)
+
+
+class ByteDanceCreateVideoAsset(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="ByteDanceCreateVideoAsset",
+            display_name="ByteDance Create Video Asset",
+            category="api node/video/ByteDance",
+            description=(
+                "Create a Seedance 2.0 personal video asset. Uploads the input video and "
+                "registers it in the given asset group. If group_id is empty, runs a real-person "
+                "H5 authentication flow to create a new group before adding the asset."
+            ),
+            inputs=[
+                IO.Video.Input("video", tooltip="Video to register as a personal asset."),
+                IO.String.Input(
+                    "group_id",
+                    default="",
+                    tooltip="Reuse an existing Seedance asset group ID to skip repeated human verification for the "
+                    "same person. Leave empty to run real-person authentication in the browser and create a new group.",
+                ),
+                # IO.String.Input(
+                #     "name",
+                #     default="",
+                #     tooltip="Asset name (up to 64 characters).",
+                # ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="asset_id"),
+                IO.String.Output(display_name="group_id"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            # is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        group_id: str = "",
+        # name: str = "",
+    ) -> IO.NodeOutput:
+        # if len(name) > 64:
+        #     raise ValueError("Name of asset can not be greater then 64 symbols")
+        validate_video_duration(video, min_duration=2, max_duration=15)
+        validate_video_dimensions(video, min_width=300, max_width=6000, min_height=300, max_height=6000)
+
+        w, h = video.get_dimensions()
+        if h > 0:
+            ratio = w / h
+            if not (0.4 <= ratio <= 2.5):
+                raise ValueError(f"Asset video aspect ratio (W/H) must be in [0.4, 2.5], got {ratio:.3f} ({w}x{h}).")
+        pixels = w * h
+        if not (409_600 <= pixels <= 927_408):
+            raise ValueError(
+                f"Asset video total pixels (W×H) must be in [409600, 927408], " f"got {pixels:,} ({w}x{h})."
+            )
+
+        fps = float(video.get_frame_rate())
+        if not (24 <= fps <= 60):
+            raise ValueError(f"Asset video FPS must be in [24, 60], got {fps:.2f}.")
+
+        resolved_group = await _resolve_group_id(cls, group_id)
+        asset_id = await _create_seedance_asset(
+            cls,
+            group_id=resolved_group,
+            url=await upload_video_to_comfyapi(cls, video),
+            name="",
+            asset_type="Video",
+        )
+        await _wait_for_asset_active(cls, asset_id, resolved_group)
+        PromptServer.instance.send_progress_text(
+            f"Please save the asset_id and group_id for reuse.\n\nasset_id: {asset_id}\n\n"
+            f"group_id: {resolved_group}",
+            cls.hidden.unique_id,
+        )
+        return IO.NodeOutput(asset_id, resolved_group)
+
+
 class ByteDanceExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1615,6 +2072,8 @@ class ByteDanceExtension(ComfyExtension):
             ByteDance2TextToVideoNode,
             ByteDance2FirstLastFrameNode,
             ByteDance2ReferenceNode,
+            ByteDanceCreateImageAsset,
+            ByteDanceCreateVideoAsset,
         ]
 
 

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": 0.084, "pro": 0.112};
+                    ? {"std": 0.112, "pro": 0.14, "4k": 0.42}
+                    : {"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": 0.084, "pro": 0.112};
+                    ? {"std": 0.112, "pro": 0.14, "4k": 0.42}
+                    : {"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": 0.084, "pro": 0.112};
+                    ? {"std": 0.112, "pro": 0.14, "4k": 0.42}
+                    : {"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

@@ -357,13 +357,17 @@ 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
     def define_schema(cls):
         return IO.Schema(
             node_id="OpenAIGPTImage1",
-            display_name="OpenAI GPT Image 1.5",
+            display_name="OpenAI GPT Image 2",
             category="api node/image/OpenAI",
             description="Generates images synchronously via OpenAI's GPT Image endpoint.",
             inputs=[
@@ -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,
                 ),
@@ -427,8 +441,8 @@ class OpenAIGPTImage1(IO.ComfyNode):
                 ),
                 IO.Combo.Input(
                     "model",
-                    options=["gpt-image-1", "gpt-image-1.5"],
-                    default="gpt-image-1.5",
+                    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],
-                    "medium": [0.046, 0.07],
-                    "high":   [0.167, 0.3]
+                    "gpt-image-1": {
+                      "low":    [0.011, 0.02],
+                      "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);
-                  $n := widgets.n;
+                  $range := $lookup($lookup($ranges, widgets.model), widgets.quality);
+                  $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],
-                        "max_usd": $range[1],
-                        "format": { "suffix": " x " & $string($n) & "/Run" }
+                        "min_usd": $range[0] * $n,
+                        "max_usd": $range[1] * $n,
+                        "format": { "suffix": "/Run", "approximate": true }
                       }
                 )
                 """,
@@ -483,10 +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_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_veo2.py

@@ -24,8 +24,9 @@ from comfy_api_nodes.util import (
 AVERAGE_DURATION_VIDEO_GEN = 32
 MODELS_MAP = {
     "veo-2.0-generate-001": "veo-2.0-generate-001",
-    "veo-3.1-generate": "veo-3.1-generate-preview",
-    "veo-3.1-fast-generate": "veo-3.1-fast-generate-preview",
+    "veo-3.1-generate": "veo-3.1-generate-001",
+    "veo-3.1-fast-generate": "veo-3.1-fast-generate-001",
+    "veo-3.1-lite": "veo-3.1-lite-generate-001",
     "veo-3.0-generate-001": "veo-3.0-generate-001",
     "veo-3.0-fast-generate-001": "veo-3.0-fast-generate-001",
 }
@@ -247,17 +248,8 @@ class VeoVideoGenerationNode(IO.ComfyNode):
         raise Exception("Video generation completed but no video was returned")
 
 
-class Veo3VideoGenerationNode(VeoVideoGenerationNode):
-    """
-    Generates videos from text prompts using Google's Veo 3 API.
-
-    Supported models:
-    - veo-3.0-generate-001
-    - veo-3.0-fast-generate-001
-
-    This node extends the base Veo node with Veo 3 specific features including
-    audio generation and fixed 8-second duration.
-    """
+class Veo3VideoGenerationNode(IO.ComfyNode):
+    """Generates videos from text prompts using Google's Veo 3 API."""
 
     @classmethod
     def define_schema(cls):
@@ -279,6 +271,13 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                     default="16:9",
                     tooltip="Aspect ratio of the output video",
                 ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["720p", "1080p", "4k"],
+                    default="720p",
+                    tooltip="Output video resolution. 4K is not available for veo-3.1-lite and veo-3.0 models.",
+                    optional=True,
+                ),
                 IO.String.Input(
                     "negative_prompt",
                     multiline=True,
@@ -289,11 +288,11 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                 IO.Int.Input(
                     "duration_seconds",
                     default=8,
-                    min=8,
+                    min=4,
                     max=8,
-                    step=1,
+                    step=2,
                     display_mode=IO.NumberDisplay.number,
-                    tooltip="Duration of the output video in seconds (Veo 3 only supports 8 seconds)",
+                    tooltip="Duration of the output video in seconds",
                     optional=True,
                 ),
                 IO.Boolean.Input(
@@ -332,10 +331,10 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                     options=[
                         "veo-3.1-generate",
                         "veo-3.1-fast-generate",
+                        "veo-3.1-lite",
                         "veo-3.0-generate-001",
                         "veo-3.0-fast-generate-001",
                     ],
-                    default="veo-3.0-generate-001",
                     tooltip="Veo 3 model to use for video generation",
                     optional=True,
                 ),
@@ -356,21 +355,111 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio", "resolution", "duration_seconds"]),
                 expr="""
                 (
                   $m := widgets.model;
+                  $r := widgets.resolution;
                   $a := widgets.generate_audio;
-                  ($contains($m,"veo-3.0-fast-generate-001") or $contains($m,"veo-3.1-fast-generate"))
-                    ? {"type":"usd","usd": ($a ? 1.2 : 0.8)}
-                    : ($contains($m,"veo-3.0-generate-001") or $contains($m,"veo-3.1-generate"))
-                      ? {"type":"usd","usd": ($a ? 3.2 : 1.6)}
-                      : {"type":"range_usd","min_usd":0.8,"max_usd":3.2}
+                  $seconds := widgets.duration_seconds;
+                  $pps :=
+                    $contains($m, "lite")
+                      ? ($r = "1080p" ? ($a ? 0.08 : 0.05) : ($a ? 0.05 : 0.03))
+                    : $contains($m, "3.1-fast")
+                      ? ($r = "4k" ? ($a ? 0.30 : 0.25) : $r = "1080p" ? ($a ? 0.12 : 0.10) : ($a ? 0.10 : 0.08))
+                    : $contains($m, "3.1-generate")
+                      ? ($r = "4k" ? ($a ? 0.60 : 0.40) : ($a ? 0.40 : 0.20))
+                    : $contains($m, "3.0-fast")
+                      ? ($a ? 0.15 : 0.10)
+                    : ($a ? 0.40 : 0.20);
+                  {"type":"usd","usd": $pps * $seconds}
                 )
                 """,
             ),
         )
 
+    @classmethod
+    async def execute(
+        cls,
+        prompt,
+        aspect_ratio="16:9",
+        resolution="720p",
+        negative_prompt="",
+        duration_seconds=8,
+        enhance_prompt=True,
+        person_generation="ALLOW",
+        seed=0,
+        image=None,
+        model="veo-3.0-generate-001",
+        generate_audio=False,
+    ):
+        if resolution == "4k" and ("lite" in model or "3.0" in model):
+            raise Exception("4K resolution is not supported by the veo-3.1-lite or veo-3.0 models.")
+
+        model = MODELS_MAP[model]
+
+        instances = [{"prompt": prompt}]
+        if image is not None:
+            image_base64 = tensor_to_base64_string(image)
+            if image_base64:
+                instances[0]["image"] = {"bytesBase64Encoded": image_base64, "mimeType": "image/png"}
+
+        parameters = {
+            "aspectRatio": aspect_ratio,
+            "personGeneration": person_generation,
+            "durationSeconds": duration_seconds,
+            "enhancePrompt": True,
+            "generateAudio": generate_audio,
+        }
+        if negative_prompt:
+            parameters["negativePrompt"] = negative_prompt
+        if seed > 0:
+            parameters["seed"] = seed
+        if "veo-3.1" in model:
+            parameters["resolution"] = resolution
+
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/veo/{model}/generate", method="POST"),
+            response_model=VeoGenVidResponse,
+            data=VeoGenVidRequest(
+                instances=instances,
+                parameters=parameters,
+            ),
+        )
+
+        poll_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/veo/{model}/poll", method="POST"),
+            response_model=VeoGenVidPollResponse,
+            status_extractor=lambda r: "completed" if r.done else "pending",
+            data=VeoGenVidPollRequest(operationName=initial_response.name),
+            poll_interval=9.0,
+            estimated_duration=AVERAGE_DURATION_VIDEO_GEN,
+        )
+
+        if poll_response.error:
+            raise Exception(f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})")
+
+        response = poll_response.response
+        filtered_count = response.raiMediaFilteredCount
+        if filtered_count:
+            reasons = response.raiMediaFilteredReasons or []
+            reason_part = f": {reasons[0]}" if reasons else ""
+            raise Exception(
+                f"Content blocked by Google's Responsible AI filters{reason_part} "
+                f"({filtered_count} video{'s' if filtered_count != 1 else ''} filtered)."
+            )
+
+        if response.videos:
+            video = response.videos[0]
+            if video.bytesBase64Encoded:
+                return IO.NodeOutput(InputImpl.VideoFromFile(BytesIO(base64.b64decode(video.bytesBase64Encoded))))
+            if video.gcsUri:
+                return IO.NodeOutput(await download_url_to_video_output(video.gcsUri))
+            raise Exception("Video returned but no data or URL was provided")
+        raise Exception("Video generation completed but no video was returned")
+
 
 class Veo3FirstLastFrameNode(IO.ComfyNode):
 
@@ -394,7 +483,7 @@ class Veo3FirstLastFrameNode(IO.ComfyNode):
                     default="",
                     tooltip="Negative text prompt to guide what to avoid in the video",
                 ),
-                IO.Combo.Input("resolution", options=["720p", "1080p"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p", "4k"]),
                 IO.Combo.Input(
                     "aspect_ratio",
                     options=["16:9", "9:16"],
@@ -424,8 +513,7 @@ class Veo3FirstLastFrameNode(IO.ComfyNode):
                 IO.Image.Input("last_frame", tooltip="End frame"),
                 IO.Combo.Input(
                     "model",
-                    options=["veo-3.1-generate", "veo-3.1-fast-generate"],
-                    default="veo-3.1-fast-generate",
+                    options=["veo-3.1-generate", "veo-3.1-fast-generate", "veo-3.1-lite"],
                 ),
                 IO.Boolean.Input(
                     "generate_audio",
@@ -443,26 +531,20 @@ class Veo3FirstLastFrameNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio", "duration"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio", "duration", "resolution"]),
                 expr="""
                 (
-                  $prices := {
-                    "veo-3.1-fast-generate": { "audio": 0.15, "no_audio": 0.10 },
-                    "veo-3.1-generate":      { "audio": 0.40, "no_audio": 0.20 }
-                  };
                   $m := widgets.model;
-                  $ga := (widgets.generate_audio = "true");
+                  $r := widgets.resolution;
+                  $ga := widgets.generate_audio;
                   $seconds := widgets.duration;
-                  $modelKey :=
-                    $contains($m, "veo-3.1-fast-generate") ? "veo-3.1-fast-generate" :
-                    $contains($m, "veo-3.1-generate")      ? "veo-3.1-generate" :
-                    "";
-                  $audioKey := $ga ? "audio" : "no_audio";
-                  $modelPrices := $lookup($prices, $modelKey);
-                  $pps := $lookup($modelPrices, $audioKey);
-                  ($pps != null)
-                    ? {"type":"usd","usd": $pps * $seconds}
-                    : {"type":"range_usd","min_usd": 0.4, "max_usd": 3.2}
+                  $pps :=
+                    $contains($m, "lite")
+                      ? ($r = "1080p" ? ($ga ? 0.08 : 0.05) : ($ga ? 0.05 : 0.03))
+                    : $contains($m, "fast")
+                      ? ($r = "4k" ? ($ga ? 0.30 : 0.25) : $r = "1080p" ? ($ga ? 0.12 : 0.10) : ($ga ? 0.10 : 0.08))
+                    : ($r = "4k" ? ($ga ? 0.60 : 0.40) : ($ga ? 0.40 : 0.20));
+                  {"type":"usd","usd": $pps * $seconds}
                 )
                 """,
             ),
@@ -482,6 +564,9 @@ class Veo3FirstLastFrameNode(IO.ComfyNode):
         model: str,
         generate_audio: bool,
     ):
+        if "lite" in model and resolution == "4k":
+            raise Exception("4K resolution is not supported by the veo-3.1-lite model.")
+
         model = MODELS_MAP[model]
         initial_response = await sync_op(
             cls,
@@ -519,7 +604,7 @@ class Veo3FirstLastFrameNode(IO.ComfyNode):
             data=VeoGenVidPollRequest(
                 operationName=initial_response.name,
             ),
-            poll_interval=5.0,
+            poll_interval=9.0,
             estimated_duration=AVERAGE_DURATION_VIDEO_GEN,
         )
 

comfy_api_nodes/util/__init__.py

@@ -19,6 +19,7 @@ from .conversions import (
     image_tensor_pair_to_batch,
     pil_to_bytesio,
     resize_mask_to_image,
+    resize_video_to_pixel_budget,
     tensor_to_base64_string,
     tensor_to_bytesio,
     tensor_to_pil,
@@ -90,6 +91,7 @@ __all__ = [
     "image_tensor_pair_to_batch",
     "pil_to_bytesio",
     "resize_mask_to_image",
+    "resize_video_to_pixel_budget",
     "tensor_to_base64_string",
     "tensor_to_bytesio",
     "tensor_to_pil",

comfy_api_nodes/util/client.py

@@ -156,6 +156,7 @@ async def poll_op(
     estimated_duration: int | None = None,
     cancel_endpoint: ApiEndpoint | None = None,
     cancel_timeout: float = 10.0,
+    extra_text: str | None = None,
 ) -> M:
     raw = await poll_op_raw(
         cls,
@@ -176,6 +177,7 @@ async def poll_op(
         estimated_duration=estimated_duration,
         cancel_endpoint=cancel_endpoint,
         cancel_timeout=cancel_timeout,
+        extra_text=extra_text,
     )
     if not isinstance(raw, dict):
         raise Exception("Expected JSON response to validate into a Pydantic model, got non-JSON (binary or text).")
@@ -260,6 +262,7 @@ async def poll_op_raw(
     estimated_duration: int | None = None,
     cancel_endpoint: ApiEndpoint | None = None,
     cancel_timeout: float = 10.0,
+    extra_text: str | None = None,
 ) -> dict[str, Any]:
     """
     Polls an endpoint until the task reaches a terminal state. Displays time while queued/processing,
@@ -299,6 +302,7 @@ async def poll_op_raw(
                     price=state.price,
                     is_queued=state.is_queued,
                     processing_elapsed_seconds=int(proc_elapsed),
+                    extra_text=extra_text,
                 )
                 await asyncio.sleep(1.0)
         except Exception as exc:
@@ -389,6 +393,7 @@ async def poll_op_raw(
                     price=state.price,
                     is_queued=False,
                     processing_elapsed_seconds=int(state.base_processing_elapsed),
+                    extra_text=extra_text,
                 )
                 return resp_json
 
@@ -462,6 +467,7 @@ def _display_time_progress(
     price: float | None = None,
     is_queued: bool | None = None,
     processing_elapsed_seconds: int | None = None,
+    extra_text: str | None = None,
 ) -> None:
     if estimated_total is not None and estimated_total > 0 and is_queued is False:
         pe = processing_elapsed_seconds if processing_elapsed_seconds is not None else elapsed_seconds
@@ -469,7 +475,8 @@ def _display_time_progress(
         time_line = f"Time elapsed: {int(elapsed_seconds)}s (~{remaining}s remaining)"
     else:
         time_line = f"Time elapsed: {int(elapsed_seconds)}s"
-    _display_text(node_cls, time_line, status=status, price=price)
+    text = f"{time_line}\n\n{extra_text}" if extra_text else time_line
+    _display_text(node_cls, text, status=status, price=price)
 
 
 async def _diagnose_connectivity() -> dict[str, bool]:

comfy_api_nodes/util/conversions.py

@@ -129,22 +129,38 @@ def pil_to_bytesio(img: Image.Image, mime_type: str = "image/png") -> BytesIO:
     return img_byte_arr
 
 
+def _compute_downscale_dims(src_w: int, src_h: int, total_pixels: int) -> tuple[int, int] | None:
+    """Return downscaled (w, h) with even dims fitting ``total_pixels``, or None if already fits.
+
+    Source aspect ratio is preserved; output may drift by a fraction of a percent because both dimensions
+    are rounded down to even values (many  codecs require divisible-by-2).
+    """
+    pixels = src_w * src_h
+    if pixels <= total_pixels:
+        return None
+    scale = math.sqrt(total_pixels / pixels)
+    new_w = max(2, int(src_w * scale))
+    new_h = max(2, int(src_h * scale))
+    new_w -= new_w % 2
+    new_h -= new_h % 2
+    return new_w, new_h
+
+
 def downscale_image_tensor(image: torch.Tensor, total_pixels: int = 1536 * 1024) -> torch.Tensor:
-    """Downscale input image tensor to roughly the specified total pixels."""
+    """Downscale input image tensor to roughly the specified total pixels.
+
+    Output dimensions are rounded down to even values so that the result is guaranteed to fit within ``total_pixels``
+    and is compatible with codecs that require even dimensions (e.g. yuv420p).
+    """
     samples = image.movedim(-1, 1)
-    total = int(total_pixels)
-    scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
-    if scale_by >= 1:
+    dims = _compute_downscale_dims(samples.shape[3], samples.shape[2], int(total_pixels))
+    if dims is None:
         return image
-    width = round(samples.shape[3] * scale_by)
-    height = round(samples.shape[2] * scale_by)
-
-    s = common_upscale(samples, width, height, "lanczos", "disabled")
-    s = s.movedim(1, -1)
-    return s
+    new_w, new_h = dims
+    return common_upscale(samples, new_w, new_h, "lanczos", "disabled").movedim(1, -1)
 
 
-def downscale_image_tensor_by_max_side(image: torch.Tensor, *,  max_side: int) -> torch.Tensor:
+def downscale_image_tensor_by_max_side(image: torch.Tensor, *, max_side: int) -> torch.Tensor:
     """Downscale input image tensor so the largest dimension is at most max_side pixels."""
     samples = image.movedim(-1, 1)
     height, width = samples.shape[2], samples.shape[3]
@@ -399,6 +415,72 @@ def trim_video(video: Input.Video, duration_sec: float) -> Input.Video:
         raise RuntimeError(f"Failed to trim video: {str(e)}") from e
 
 
+def resize_video_to_pixel_budget(video: Input.Video, total_pixels: int) -> Input.Video:
+    """Downscale a video to fit within ``total_pixels`` (w * h), preserving aspect ratio.
+
+    Returns the original video object untouched when it already fits. Preserves frame rate, duration, and audio.
+    Aspect ratio is preserved up to a fraction of a percent (even-dim rounding).
+    """
+    src_w, src_h = video.get_dimensions()
+    scale_dims = _compute_downscale_dims(src_w, src_h, total_pixels)
+    if scale_dims is None:
+        return video
+    return _apply_video_scale(video, scale_dims)
+
+
+def _apply_video_scale(video: Input.Video, scale_dims: tuple[int, int]) -> Input.Video:
+    """Re-encode ``video`` scaled to ``scale_dims`` with a single decode/encode pass."""
+    out_w, out_h = scale_dims
+    output_buffer = BytesIO()
+    input_container = None
+    output_container = None
+
+    try:
+        input_source = video.get_stream_source()
+        input_container = av.open(input_source, mode="r")
+        output_container = av.open(output_buffer, mode="w", format="mp4")
+
+        video_stream = output_container.add_stream("h264", rate=video.get_frame_rate())
+        video_stream.width = out_w
+        video_stream.height = out_h
+        video_stream.pix_fmt = "yuv420p"
+
+        audio_stream = None
+        for stream in input_container.streams:
+            if isinstance(stream, av.AudioStream):
+                audio_stream = output_container.add_stream("aac", rate=stream.sample_rate)
+                audio_stream.sample_rate = stream.sample_rate
+                audio_stream.layout = stream.layout
+                break
+
+        for frame in input_container.decode(video=0):
+            frame = frame.reformat(width=out_w, height=out_h, format="yuv420p")
+            for packet in video_stream.encode(frame):
+                output_container.mux(packet)
+        for packet in video_stream.encode():
+            output_container.mux(packet)
+
+        if audio_stream is not None:
+            input_container.seek(0)
+            for audio_frame in input_container.decode(audio=0):
+                for packet in audio_stream.encode(audio_frame):
+                    output_container.mux(packet)
+            for packet in audio_stream.encode():
+                output_container.mux(packet)
+
+        output_container.close()
+        input_container.close()
+        output_buffer.seek(0)
+        return InputImpl.VideoFromFile(output_buffer)
+
+    except Exception as e:
+        if input_container is not None:
+            input_container.close()
+        if output_container is not None:
+            output_container.close()
+        raise RuntimeError(f"Failed to resize video: {str(e)}") from e
+
+
 def _f32_pcm(wav: torch.Tensor) -> torch.Tensor:
     """Convert audio to float 32 bits PCM format. Copy-paste from nodes_audio.py file."""
     if wav.dtype.is_floating_point:

comfy_extras/frame_interpolation_models/film_net.py

@@ -0,0 +1,258 @@
+"""FILM: Frame Interpolation for Large Motion (ECCV 2022)."""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.ops
+
+ops = comfy.ops.disable_weight_init
+
+
+class FilmConv2d(nn.Module):
+    """Conv2d with optional LeakyReLU and FILM-style padding."""
+
+    def __init__(self, in_channels, out_channels, size, activation=True, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.even_pad = not size % 2
+        self.conv = operations.Conv2d(in_channels, out_channels, kernel_size=size, padding=size // 2 if size % 2 else 0, device=device, dtype=dtype)
+        self.activation = nn.LeakyReLU(0.2) if activation else None
+
+    def forward(self, x):
+        if self.even_pad:
+            x = F.pad(x, (0, 1, 0, 1))
+        x = self.conv(x)
+        if self.activation is not None:
+            x = self.activation(x)
+        return x
+
+
+def _warp_core(image, flow, grid_x, grid_y):
+    dtype = image.dtype
+    H, W = flow.shape[2], flow.shape[3]
+    dx = flow[:, 0].float() / (W * 0.5)
+    dy = flow[:, 1].float() / (H * 0.5)
+    grid = torch.stack([grid_x[None, None, :] + dx, grid_y[None, :, None] + dy], dim=3)
+    return F.grid_sample(image.float(), grid, mode="bilinear", padding_mode="border", align_corners=False).to(dtype)
+
+
+def build_image_pyramid(image, pyramid_levels):
+    pyramid = [image]
+    for _ in range(1, pyramid_levels):
+        image = F.avg_pool2d(image, 2, 2)
+        pyramid.append(image)
+    return pyramid
+
+
+def flow_pyramid_synthesis(residual_pyramid):
+    flow = residual_pyramid[-1]
+    flow_pyramid = [flow]
+    for residual_flow in residual_pyramid[:-1][::-1]:
+        flow = F.interpolate(flow, size=residual_flow.shape[2:4], mode="bilinear", scale_factor=None).mul_(2).add_(residual_flow)
+        flow_pyramid.append(flow)
+    flow_pyramid.reverse()
+    return flow_pyramid
+
+
+def multiply_pyramid(pyramid, scalar):
+    return [image * scalar[:, None, None, None] for image in pyramid]
+
+
+def pyramid_warp(feature_pyramid, flow_pyramid, warp_fn):
+    return [warp_fn(features, flow) for features, flow in zip(feature_pyramid, flow_pyramid)]
+
+
+def concatenate_pyramids(pyramid1, pyramid2):
+    return [torch.cat([f1, f2], dim=1) for f1, f2 in zip(pyramid1, pyramid2)]
+
+
+class SubTreeExtractor(nn.Module):
+    def __init__(self, in_channels=3, channels=64, n_layers=4, device=None, dtype=None, operations=ops):
+        super().__init__()
+        convs = []
+        for i in range(n_layers):
+            out_ch = channels << i
+            convs.append(nn.Sequential(
+                FilmConv2d(in_channels, out_ch, 3, device=device, dtype=dtype, operations=operations),
+                FilmConv2d(out_ch, out_ch, 3, device=device, dtype=dtype, operations=operations)))
+            in_channels = out_ch
+        self.convs = nn.ModuleList(convs)
+
+    def forward(self, image, n):
+        head = image
+        pyramid = []
+        for i, layer in enumerate(self.convs):
+            head = layer(head)
+            pyramid.append(head)
+            if i < n - 1:
+                head = F.avg_pool2d(head, 2, 2)
+        return pyramid
+
+
+class FeatureExtractor(nn.Module):
+    def __init__(self, in_channels=3, channels=64, sub_levels=4, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.extract_sublevels = SubTreeExtractor(in_channels, channels, sub_levels, device=device, dtype=dtype, operations=operations)
+        self.sub_levels = sub_levels
+
+    def forward(self, image_pyramid):
+        sub_pyramids = [self.extract_sublevels(image_pyramid[i], min(len(image_pyramid) - i, self.sub_levels))
+                        for i in range(len(image_pyramid))]
+        feature_pyramid = []
+        for i in range(len(image_pyramid)):
+            features = sub_pyramids[i][0]
+            for j in range(1, self.sub_levels):
+                if j <= i:
+                    features = torch.cat([features, sub_pyramids[i - j][j]], dim=1)
+            feature_pyramid.append(features)
+            # Free sub-pyramids no longer needed by future levels
+            if i >= self.sub_levels - 1:
+                sub_pyramids[i - self.sub_levels + 1] = None
+        return feature_pyramid
+
+
+class FlowEstimator(nn.Module):
+    def __init__(self, in_channels, num_convs, num_filters, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self._convs = nn.ModuleList()
+        for _ in range(num_convs):
+            self._convs.append(FilmConv2d(in_channels, num_filters, 3, device=device, dtype=dtype, operations=operations))
+            in_channels = num_filters
+        self._convs.append(FilmConv2d(in_channels, num_filters // 2, 1, device=device, dtype=dtype, operations=operations))
+        self._convs.append(FilmConv2d(num_filters // 2, 2, 1, activation=False, device=device, dtype=dtype, operations=operations))
+
+    def forward(self, features_a, features_b):
+        net = torch.cat([features_a, features_b], dim=1)
+        for conv in self._convs:
+            net = conv(net)
+        return net
+
+
+class PyramidFlowEstimator(nn.Module):
+    def __init__(self, filters=64, flow_convs=(3, 3, 3, 3), flow_filters=(32, 64, 128, 256), device=None, dtype=None, operations=ops):
+        super().__init__()
+        in_channels = filters << 1
+        predictors = []
+        for i in range(len(flow_convs)):
+            predictors.append(FlowEstimator(in_channels, flow_convs[i], flow_filters[i], device=device, dtype=dtype, operations=operations))
+            in_channels += filters << (i + 2)
+        self._predictor = predictors[-1]
+        self._predictors = nn.ModuleList(predictors[:-1][::-1])
+
+    def forward(self, feature_pyramid_a, feature_pyramid_b, warp_fn):
+        levels = len(feature_pyramid_a)
+        v = self._predictor(feature_pyramid_a[-1], feature_pyramid_b[-1])
+        residuals = [v]
+        # Coarse-to-fine: shared predictor for deep levels, then specialized predictors for fine levels
+        steps = [(i, self._predictor) for i in range(levels - 2, len(self._predictors) - 1, -1)]
+        steps += [(len(self._predictors) - 1 - k, p) for k, p in enumerate(self._predictors)]
+        for i, predictor in steps:
+            v = F.interpolate(v, size=feature_pyramid_a[i].shape[2:4], mode="bilinear").mul_(2)
+            v_residual = predictor(feature_pyramid_a[i], warp_fn(feature_pyramid_b[i], v))
+            residuals.append(v_residual)
+            v = v.add_(v_residual)
+        residuals.reverse()
+        return residuals
+
+
+def _get_fusion_channels(level, filters):
+    # Per direction: multi-scale features + RGB image (3ch) + flow (2ch), doubled for both directions
+    return (sum(filters << i for i in range(level)) + 3 + 2) * 2
+
+
+class Fusion(nn.Module):
+    def __init__(self, n_layers=4, specialized_layers=3, filters=64, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.output_conv = operations.Conv2d(filters, 3, kernel_size=1, device=device, dtype=dtype)
+        self.convs = nn.ModuleList()
+        in_channels = _get_fusion_channels(n_layers, filters)
+        increase = 0
+        for i in range(n_layers)[::-1]:
+            num_filters = (filters << i) if i < specialized_layers else (filters << specialized_layers)
+            self.convs.append(nn.ModuleList([
+                FilmConv2d(in_channels, num_filters, 2, activation=False, device=device, dtype=dtype, operations=operations),
+                FilmConv2d(in_channels + (increase or num_filters), num_filters, 3, device=device, dtype=dtype, operations=operations),
+                FilmConv2d(num_filters, num_filters, 3, device=device, dtype=dtype, operations=operations)]))
+            in_channels = num_filters
+            increase = _get_fusion_channels(i, filters) - num_filters // 2
+
+    def forward(self, pyramid):
+        net = pyramid[-1]
+        for k, layers in enumerate(self.convs):
+            i = len(self.convs) - 1 - k
+            net = layers[0](F.interpolate(net, size=pyramid[i].shape[2:4], mode="nearest"))
+            net = layers[2](layers[1](torch.cat([pyramid[i], net], dim=1)))
+        return self.output_conv(net)
+
+
+class FILMNet(nn.Module):
+    def __init__(self, pyramid_levels=7, fusion_pyramid_levels=5, specialized_levels=3, sub_levels=4,
+                 filters=64, flow_convs=(3, 3, 3, 3), flow_filters=(32, 64, 128, 256), device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.pyramid_levels = pyramid_levels
+        self.fusion_pyramid_levels = fusion_pyramid_levels
+        self.extract = FeatureExtractor(3, filters, sub_levels, device=device, dtype=dtype, operations=operations)
+        self.predict_flow = PyramidFlowEstimator(filters, flow_convs, flow_filters, device=device, dtype=dtype, operations=operations)
+        self.fuse = Fusion(sub_levels, specialized_levels, filters, device=device, dtype=dtype, operations=operations)
+        self._warp_grids = {}
+
+    def get_dtype(self):
+        return self.extract.extract_sublevels.convs[0][0].conv.weight.dtype
+
+    def _build_warp_grids(self, H, W, device):
+        """Pre-compute warp grids for all pyramid levels."""
+        if (H, W) in self._warp_grids:
+            return
+        self._warp_grids = {}  # clear old resolution grids to prevent memory leaks
+        for _ in range(self.pyramid_levels):
+            self._warp_grids[(H, W)] = (
+                torch.linspace(-(1 - 1 / W), 1 - 1 / W, W, dtype=torch.float32, device=device),
+                torch.linspace(-(1 - 1 / H), 1 - 1 / H, H, dtype=torch.float32, device=device),
+            )
+            H, W = H // 2, W // 2
+
+    def warp(self, image, flow):
+        grid_x, grid_y = self._warp_grids[(flow.shape[2], flow.shape[3])]
+        return _warp_core(image, flow, grid_x, grid_y)
+
+    def extract_features(self, img):
+        """Extract image and feature pyramids for a single frame. Can be cached across pairs."""
+        image_pyramid = build_image_pyramid(img, self.pyramid_levels)
+        feature_pyramid = self.extract(image_pyramid)
+        return image_pyramid, feature_pyramid
+
+    def forward(self, img0, img1, timestep=0.5, cache=None):
+        # FILM uses a scalar timestep per batch element (spatially-varying timesteps not supported)
+        t = timestep.mean(dim=(1, 2, 3)).item() if isinstance(timestep, torch.Tensor) else timestep
+        return self.forward_multi_timestep(img0, img1, [t], cache=cache)
+
+    def forward_multi_timestep(self, img0, img1, timesteps, cache=None):
+        """Compute flow once, synthesize at multiple timesteps. Expects batch=1 inputs."""
+        self._build_warp_grids(img0.shape[2], img0.shape[3], img0.device)
+
+        image_pyr0, feat_pyr0 = cache["img0"] if cache and "img0" in cache else self.extract_features(img0)
+        image_pyr1, feat_pyr1 = cache["img1"] if cache and "img1" in cache else self.extract_features(img1)
+
+        fwd_flow = flow_pyramid_synthesis(self.predict_flow(feat_pyr0, feat_pyr1, self.warp))[:self.fusion_pyramid_levels]
+        bwd_flow = flow_pyramid_synthesis(self.predict_flow(feat_pyr1, feat_pyr0, self.warp))[:self.fusion_pyramid_levels]
+
+        # Build warp targets and free full pyramids (only first fpl levels needed from here)
+        fpl = self.fusion_pyramid_levels
+        p2w = [concatenate_pyramids(image_pyr0[:fpl], feat_pyr0[:fpl]),
+               concatenate_pyramids(image_pyr1[:fpl], feat_pyr1[:fpl])]
+        del image_pyr0, image_pyr1, feat_pyr0, feat_pyr1
+
+        results = []
+        dt_tensors = torch.tensor(timesteps, device=img0.device, dtype=img0.dtype)
+        for idx in range(len(timesteps)):
+            batch_dt = dt_tensors[idx:idx + 1]
+            bwd_scaled = multiply_pyramid(bwd_flow, batch_dt)
+            fwd_scaled = multiply_pyramid(fwd_flow, 1 - batch_dt)
+            fwd_warped = pyramid_warp(p2w[0], bwd_scaled, self.warp)
+            bwd_warped = pyramid_warp(p2w[1], fwd_scaled, self.warp)
+            aligned = [torch.cat([fw, bw, bf, ff], dim=1)
+                       for fw, bw, bf, ff in zip(fwd_warped, bwd_warped, bwd_scaled, fwd_scaled)]
+            del fwd_warped, bwd_warped, bwd_scaled, fwd_scaled
+            results.append(self.fuse(aligned))
+            del aligned
+        return torch.cat(results, dim=0)

comfy_extras/frame_interpolation_models/ifnet.py

@@ -0,0 +1,128 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.ops
+
+ops = comfy.ops.disable_weight_init
+
+
+def _warp(img, flow, warp_grids):
+    B, _, H, W = img.shape
+    base_grid, flow_div = warp_grids[(H, W)]
+    flow_norm = torch.cat([flow[:, 0:1] / flow_div[0], flow[:, 1:2] / flow_div[1]], 1).float()
+    grid = (base_grid.expand(B, -1, -1, -1) + flow_norm).permute(0, 2, 3, 1)
+    return F.grid_sample(img.float(), grid, mode="bilinear", padding_mode="border", align_corners=True).to(img.dtype)
+
+
+class Head(nn.Module):
+    def __init__(self, out_ch=4, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.cnn0 = operations.Conv2d(3, 16, 3, 2, 1, device=device, dtype=dtype)
+        self.cnn1 = operations.Conv2d(16, 16, 3, 1, 1, device=device, dtype=dtype)
+        self.cnn2 = operations.Conv2d(16, 16, 3, 1, 1, device=device, dtype=dtype)
+        self.cnn3 = operations.ConvTranspose2d(16, out_ch, 4, 2, 1, device=device, dtype=dtype)
+        self.relu = nn.LeakyReLU(0.2, True)
+
+    def forward(self, x):
+        x = self.relu(self.cnn0(x))
+        x = self.relu(self.cnn1(x))
+        x = self.relu(self.cnn2(x))
+        return self.cnn3(x)
+
+
+class ResConv(nn.Module):
+    def __init__(self, c, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.conv = operations.Conv2d(c, c, 3, 1, 1, device=device, dtype=dtype)
+        self.beta = nn.Parameter(torch.ones((1, c, 1, 1), device=device, dtype=dtype))
+        self.relu = nn.LeakyReLU(0.2, True)
+
+    def forward(self, x):
+        return self.relu(torch.addcmul(x, self.conv(x), self.beta))
+
+
+class IFBlock(nn.Module):
+    def __init__(self, in_planes, c=64, device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.conv0 = nn.Sequential(
+            nn.Sequential(operations.Conv2d(in_planes, c // 2, 3, 2, 1, device=device, dtype=dtype), nn.LeakyReLU(0.2, True)),
+            nn.Sequential(operations.Conv2d(c // 2, c, 3, 2, 1, device=device, dtype=dtype), nn.LeakyReLU(0.2, True)))
+        self.convblock = nn.Sequential(*(ResConv(c, device=device, dtype=dtype, operations=operations) for _ in range(8)))
+        self.lastconv = nn.Sequential(operations.ConvTranspose2d(c, 4 * 13, 4, 2, 1, device=device, dtype=dtype), nn.PixelShuffle(2))
+
+    def forward(self, x, flow=None, scale=1):
+        x = F.interpolate(x, scale_factor=1.0 / scale, mode="bilinear")
+        if flow is not None:
+            flow = F.interpolate(flow, scale_factor=1.0 / scale, mode="bilinear").div_(scale)
+            x = torch.cat((x, flow), 1)
+        feat = self.convblock(self.conv0(x))
+        tmp = F.interpolate(self.lastconv(feat), scale_factor=scale, mode="bilinear")
+        return tmp[:, :4] * scale, tmp[:, 4:5], tmp[:, 5:]
+
+
+class IFNet(nn.Module):
+    def __init__(self, head_ch=4, channels=(192, 128, 96, 64, 32), device=None, dtype=None, operations=ops):
+        super().__init__()
+        self.encode = Head(out_ch=head_ch, device=device, dtype=dtype, operations=operations)
+        block_in = [7 + 2 * head_ch] + [8 + 4 + 8 + 2 * head_ch] * 4
+        self.blocks = nn.ModuleList([IFBlock(block_in[i], channels[i], device=device, dtype=dtype, operations=operations) for i in range(5)])
+        self.scale_list = [16, 8, 4, 2, 1]
+        self.pad_align = 64
+        self._warp_grids = {}
+
+    def get_dtype(self):
+        return self.encode.cnn0.weight.dtype
+
+    def _build_warp_grids(self, H, W, device):
+        if (H, W) in self._warp_grids:
+            return
+        self._warp_grids = {}  # clear old resolution grids to prevent memory leaks
+        grid_y, grid_x = torch.meshgrid(
+            torch.linspace(-1.0, 1.0, H, device=device, dtype=torch.float32),
+            torch.linspace(-1.0, 1.0, W, device=device, dtype=torch.float32), indexing="ij")
+        self._warp_grids[(H, W)] = (
+            torch.stack((grid_x, grid_y), dim=0).unsqueeze(0),
+            torch.tensor([(W - 1.0) / 2.0, (H - 1.0) / 2.0], dtype=torch.float32, device=device))
+
+    def warp(self, img, flow):
+        return _warp(img, flow, self._warp_grids)
+
+    def extract_features(self, img):
+        """Extract head features for a single frame. Can be cached across pairs."""
+        return self.encode(img)
+
+    def forward(self, img0, img1, timestep=0.5, cache=None):
+        if not isinstance(timestep, torch.Tensor):
+            timestep = torch.full((img0.shape[0], 1, img0.shape[2], img0.shape[3]), timestep, device=img0.device, dtype=img0.dtype)
+
+        self._build_warp_grids(img0.shape[2], img0.shape[3], img0.device)
+
+        B = img0.shape[0]
+        f0 = cache["img0"].expand(B, -1, -1, -1) if cache and "img0" in cache else self.encode(img0)
+        f1 = cache["img1"].expand(B, -1, -1, -1) if cache and "img1" in cache else self.encode(img1)
+        flow = mask = feat = None
+        warped_img0, warped_img1 = img0, img1
+        for i, block in enumerate(self.blocks):
+            if flow is None:
+                flow, mask, feat = block(torch.cat((img0, img1, f0, f1, timestep), 1), None, scale=self.scale_list[i])
+            else:
+                fd, mask, feat = block(
+                    torch.cat((warped_img0, warped_img1, self.warp(f0, flow[:, :2]), self.warp(f1, flow[:, 2:4]), timestep, mask, feat), 1),
+                    flow, scale=self.scale_list[i])
+                flow = flow.add_(fd)
+            warped_img0 = self.warp(img0, flow[:, :2])
+            warped_img1 = self.warp(img1, flow[:, 2:4])
+        return torch.lerp(warped_img1, warped_img0, torch.sigmoid(mask))
+
+
+def detect_rife_config(state_dict):
+    head_ch = state_dict["encode.cnn3.weight"].shape[1]  # ConvTranspose2d: (in_ch, out_ch, kH, kW)
+    channels = []
+    for i in range(5):
+        key = f"blocks.{i}.conv0.1.0.weight"
+        if key in state_dict:
+            channels.append(state_dict[key].shape[0])
+    if len(channels) != 5:
+        raise ValueError(f"Unsupported RIFE model: expected 5 blocks, found {len(channels)}")
+    return head_ch, channels

comfy_extras/nodes_ace.py

@@ -3,136 +3,136 @@ from typing_extensions import override
 
 import comfy.model_management
 import node_helpers
-from comfy_api.latest import ComfyExtension, io
+from comfy_api.latest import ComfyExtension, IO
 
 
-class TextEncodeAceStepAudio(io.ComfyNode):
+class TextEncodeAceStepAudio(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
-        return io.Schema(
+        return IO.Schema(
             node_id="TextEncodeAceStepAudio",
             category="conditioning",
             inputs=[
-                io.Clip.Input("clip"),
-                io.String.Input("tags", multiline=True, dynamic_prompts=True),
-                io.String.Input("lyrics", multiline=True, dynamic_prompts=True),
-                io.Float.Input("lyrics_strength", default=1.0, min=0.0, max=10.0, step=0.01),
+                IO.Clip.Input("clip"),
+                IO.String.Input("tags", multiline=True, dynamic_prompts=True),
+                IO.String.Input("lyrics", multiline=True, dynamic_prompts=True),
+                IO.Float.Input("lyrics_strength", default=1.0, min=0.0, max=10.0, step=0.01),
             ],
-            outputs=[io.Conditioning.Output()],
+            outputs=[IO.Conditioning.Output()],
         )
 
     @classmethod
-    def execute(cls, clip, tags, lyrics, lyrics_strength) -> io.NodeOutput:
+    def execute(cls, clip, tags, lyrics, lyrics_strength) -> IO.NodeOutput:
         tokens = clip.tokenize(tags, lyrics=lyrics)
         conditioning = clip.encode_from_tokens_scheduled(tokens)
         conditioning = node_helpers.conditioning_set_values(conditioning, {"lyrics_strength": lyrics_strength})
-        return io.NodeOutput(conditioning)
+        return IO.NodeOutput(conditioning)
 
-class TextEncodeAceStepAudio15(io.ComfyNode):
+class TextEncodeAceStepAudio15(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
-        return io.Schema(
+        return IO.Schema(
             node_id="TextEncodeAceStepAudio1.5",
             category="conditioning",
             inputs=[
-                io.Clip.Input("clip"),
-                io.String.Input("tags", multiline=True, dynamic_prompts=True),
-                io.String.Input("lyrics", multiline=True, dynamic_prompts=True),
-                io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True),
-                io.Int.Input("bpm", default=120, min=10, max=300),
-                io.Float.Input("duration", default=120.0, min=0.0, max=2000.0, step=0.1),
-                io.Combo.Input("timesignature", options=['2', '3', '4', '6']),
-                io.Combo.Input("language", options=["en", "ja", "zh", "es", "de", "fr", "pt", "ru", "it", "nl", "pl", "tr", "vi", "cs", "fa", "id", "ko", "uk", "hu", "ar", "sv", "ro", "el"]),
-                io.Combo.Input("keyscale", options=[f"{root} {quality}" for quality in ["major", "minor"] for root in ["C", "C#", "Db", "D", "D#", "Eb", "E", "F", "F#", "Gb", "G", "G#", "Ab", "A", "A#", "Bb", "B"]]),
-                io.Boolean.Input("generate_audio_codes", default=True, tooltip="Enable the LLM that generates audio codes. This can be slow but will increase the quality of the generated audio. Turn this off if you are giving the model an audio reference.", advanced=True),
-                io.Float.Input("cfg_scale", default=2.0, min=0.0, max=100.0, step=0.1, advanced=True),
-                io.Float.Input("temperature", default=0.85, min=0.0, max=2.0, step=0.01, advanced=True),
-                io.Float.Input("top_p", default=0.9, min=0.0, max=2000.0, step=0.01, advanced=True),
-                io.Int.Input("top_k", default=0, min=0, max=100, advanced=True),
-                io.Float.Input("min_p", default=0.000, min=0.0, max=1.0, step=0.001, advanced=True),
+                IO.Clip.Input("clip"),
+                IO.String.Input("tags", multiline=True, dynamic_prompts=True),
+                IO.String.Input("lyrics", multiline=True, dynamic_prompts=True),
+                IO.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True),
+                IO.Int.Input("bpm", default=120, min=10, max=300),
+                IO.Float.Input("duration", default=120.0, min=0.0, max=2000.0, step=0.1),
+                IO.Combo.Input("timesignature", options=['2', '3', '4', '6']),
+                IO.Combo.Input("language", options=["en", "ja", "zh", "es", "de", "fr", "pt", "ru", "it", "nl", "pl", "tr", "vi", "cs", "fa", "id", "ko", "uk", "hu", "ar", "sv", "ro", "el"]),
+                IO.Combo.Input("keyscale", options=[f"{root} {quality}" for quality in ["major", "minor"] for root in ["C", "C#", "Db", "D", "D#", "Eb", "E", "F", "F#", "Gb", "G", "G#", "Ab", "A", "A#", "Bb", "B"]]),
+                IO.Boolean.Input("generate_audio_codes", default=True, tooltip="Enable the LLM that generates audio codes. This can be slow but will increase the quality of the generated audio. Turn this off if you are giving the model an audio reference.", advanced=True),
+                IO.Float.Input("cfg_scale", default=2.0, min=0.0, max=100.0, step=0.1, advanced=True),
+                IO.Float.Input("temperature", default=0.85, min=0.0, max=2.0, step=0.01, advanced=True),
+                IO.Float.Input("top_p", default=0.9, min=0.0, max=2000.0, step=0.01, advanced=True),
+                IO.Int.Input("top_k", default=0, min=0, max=100, advanced=True),
+                IO.Float.Input("min_p", default=0.000, min=0.0, max=1.0, step=0.001, advanced=True),
             ],
-            outputs=[io.Conditioning.Output()],
+            outputs=[IO.Conditioning.Output()],
         )
 
     @classmethod
-    def execute(cls, clip, tags, lyrics, seed, bpm, duration, timesignature, language, keyscale, generate_audio_codes, cfg_scale, temperature, top_p, top_k, min_p) -> io.NodeOutput:
+    def execute(cls, clip, tags, lyrics, seed, bpm, duration, timesignature, language, keyscale, generate_audio_codes, cfg_scale, temperature, top_p, top_k, min_p) -> IO.NodeOutput:
         tokens = clip.tokenize(tags, lyrics=lyrics, bpm=bpm, duration=duration, timesignature=int(timesignature), language=language, keyscale=keyscale, seed=seed, generate_audio_codes=generate_audio_codes, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p)
         conditioning = clip.encode_from_tokens_scheduled(tokens)
-        return io.NodeOutput(conditioning)
+        return IO.NodeOutput(conditioning)
 
 
-class EmptyAceStepLatentAudio(io.ComfyNode):
+class EmptyAceStepLatentAudio(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
-        return io.Schema(
+        return IO.Schema(
             node_id="EmptyAceStepLatentAudio",
             display_name="Empty Ace Step 1.0 Latent Audio",
             category="latent/audio",
             inputs=[
-                io.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1),
-                io.Int.Input(
+                IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1),
+                IO.Int.Input(
                     "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
                 ),
             ],
-            outputs=[io.Latent.Output()],
+            outputs=[IO.Latent.Output()],
         )
 
     @classmethod
-    def execute(cls, seconds, batch_size) -> io.NodeOutput:
+    def execute(cls, seconds, batch_size) -> IO.NodeOutput:
         length = int(seconds * 44100 / 512 / 8)
         latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
-        return io.NodeOutput({"samples": latent, "type": "audio"})
+        return IO.NodeOutput({"samples": latent, "type": "audio"})
 
 
-class EmptyAceStep15LatentAudio(io.ComfyNode):
+class EmptyAceStep15LatentAudio(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
-        return io.Schema(
+        return IO.Schema(
             node_id="EmptyAceStep1.5LatentAudio",
             display_name="Empty Ace Step 1.5 Latent Audio",
             category="latent/audio",
             inputs=[
-                io.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.01),
-                io.Int.Input(
+                IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.01),
+                IO.Int.Input(
                     "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
                 ),
             ],
-            outputs=[io.Latent.Output()],
+            outputs=[IO.Latent.Output()],
         )
 
     @classmethod
-    def execute(cls, seconds, batch_size) -> io.NodeOutput:
+    def execute(cls, seconds, batch_size) -> IO.NodeOutput:
         length = round((seconds * 48000 / 1920))
         latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
-        return io.NodeOutput({"samples": latent, "type": "audio"})
+        return IO.NodeOutput({"samples": latent, "type": "audio"})
 
-class ReferenceAudio(io.ComfyNode):
+class ReferenceAudio(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
-        return io.Schema(
+        return IO.Schema(
             node_id="ReferenceTimbreAudio",
             display_name="Reference Audio",
             category="advanced/conditioning/audio",
             is_experimental=True,
             description="This node sets the reference audio for ace step 1.5",
             inputs=[
-                io.Conditioning.Input("conditioning"),
-                io.Latent.Input("latent", optional=True),
+                IO.Conditioning.Input("conditioning"),
+                IO.Latent.Input("latent", optional=True),
             ],
             outputs=[
-                io.Conditioning.Output(),
+                IO.Conditioning.Output(),
             ]
         )
 
     @classmethod
-    def execute(cls, conditioning, latent=None) -> io.NodeOutput:
+    def execute(cls, conditioning, latent=None) -> IO.NodeOutput:
         if latent is not None:
             conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_audio_timbre_latents": [latent["samples"]]}, append=True)
-        return io.NodeOutput(conditioning)
+        return IO.NodeOutput(conditioning)
 
 class AceExtension(ComfyExtension):
     @override
-    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
             TextEncodeAceStepAudio,
             EmptyAceStepLatentAudio,

comfy_extras/nodes_audio.py

@@ -104,7 +104,7 @@ def vae_decode_audio(vae, samples, tile=None, overlap=None):
     std = torch.std(audio, dim=[1, 2], keepdim=True) * 5.0
     std[std < 1.0] = 1.0
     audio /= std
-    vae_sample_rate = getattr(vae, "audio_sample_rate", 44100)
+    vae_sample_rate = getattr(vae, "audio_sample_rate_output", getattr(vae, "audio_sample_rate", 44100))
     return {"waveform": audio, "sample_rate": vae_sample_rate if "sample_rate" not in samples else samples["sample_rate"]}
 
 

comfy_extras/nodes_frame_interpolation.py

@@ -0,0 +1,211 @@
+import torch
+from tqdm import tqdm
+from typing_extensions import override
+
+import comfy.model_patcher
+import comfy.utils
+import folder_paths
+from comfy import model_management
+from comfy_extras.frame_interpolation_models.ifnet import IFNet, detect_rife_config
+from comfy_extras.frame_interpolation_models.film_net import FILMNet
+from comfy_api.latest import ComfyExtension, io
+
+FrameInterpolationModel = io.Custom("INTERP_MODEL")
+
+
+class FrameInterpolationModelLoader(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FrameInterpolationModelLoader",
+            display_name="Load Frame Interpolation Model",
+            category="loaders",
+            inputs=[
+                io.Combo.Input("model_name", options=folder_paths.get_filename_list("frame_interpolation"),
+                               tooltip="Select a frame interpolation model to load. Models must be placed in the 'frame_interpolation' folder."),
+            ],
+            outputs=[
+                FrameInterpolationModel.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_name) -> io.NodeOutput:
+        model_path = folder_paths.get_full_path_or_raise("frame_interpolation", model_name)
+        sd = comfy.utils.load_torch_file(model_path, safe_load=True)
+
+        model = cls._detect_and_load(sd)
+        dtype = torch.float16 if model_management.should_use_fp16(model_management.get_torch_device()) else torch.float32
+        model.eval().to(dtype)
+        patcher = comfy.model_patcher.ModelPatcher(
+            model,
+            load_device=model_management.get_torch_device(),
+            offload_device=model_management.unet_offload_device(),
+        )
+        return io.NodeOutput(patcher)
+
+    @classmethod
+    def _detect_and_load(cls, sd):
+        # Try FILM
+        if "extract.extract_sublevels.convs.0.0.conv.weight" in sd:
+            model = FILMNet()
+            model.load_state_dict(sd)
+            return model
+
+        # Try RIFE (needs key remapping for raw checkpoints)
+        sd = comfy.utils.state_dict_prefix_replace(sd, {"module.": "", "flownet.": ""})
+        key_map = {}
+        for k in sd:
+            for i in range(5):
+                if k.startswith(f"block{i}."):
+                    key_map[k] = f"blocks.{i}.{k[len(f'block{i}.'):]}"
+        if key_map:
+            sd = {key_map.get(k, k): v for k, v in sd.items()}
+        sd = {k: v for k, v in sd.items() if not k.startswith(("teacher.", "caltime."))}
+
+        try:
+            head_ch, channels = detect_rife_config(sd)
+        except (KeyError, ValueError):
+            raise ValueError("Unrecognized frame interpolation model format")
+        model = IFNet(head_ch=head_ch, channels=channels)
+        model.load_state_dict(sd)
+        return model
+
+
+class FrameInterpolate(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FrameInterpolate",
+            display_name="Frame Interpolate",
+            category="image/video",
+            search_aliases=["rife", "film", "frame interpolation", "slow motion", "interpolate frames", "vfi"],
+            inputs=[
+                FrameInterpolationModel.Input("interp_model"),
+                io.Image.Input("images"),
+                io.Int.Input("multiplier", default=2, min=2, max=16),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, interp_model, images, multiplier) -> io.NodeOutput:
+        offload_device = model_management.intermediate_device()
+
+        num_frames = images.shape[0]
+        if num_frames < 2 or multiplier < 2:
+            return io.NodeOutput(images)
+
+        model_management.load_model_gpu(interp_model)
+        device = interp_model.load_device
+        dtype = interp_model.model_dtype()
+        inference_model = interp_model.model
+
+        # Free VRAM for inference activations (model weights + ~20x a single frame's worth)
+        H, W = images.shape[1], images.shape[2]
+        activation_mem = H * W * 3 * images.element_size() * 20
+        model_management.free_memory(activation_mem, device)
+        align = getattr(inference_model, "pad_align", 1)
+
+        # Prepare a single padded frame on device for determining output dimensions
+        def prepare_frame(idx):
+            frame = images[idx:idx + 1].movedim(-1, 1).to(dtype=dtype, device=device)
+            if align > 1:
+                from comfy.ldm.common_dit import pad_to_patch_size
+                frame = pad_to_patch_size(frame, (align, align), padding_mode="reflect")
+            return frame
+
+        # Count total interpolation passes for progress bar
+        total_pairs = num_frames - 1
+        num_interp = multiplier - 1
+        total_steps = total_pairs * num_interp
+        pbar = comfy.utils.ProgressBar(total_steps)
+        tqdm_bar = tqdm(total=total_steps, desc="Frame interpolation")
+
+        batch = num_interp  # reduced on OOM and persists across pairs (same resolution = same limit)
+        t_values = [t / multiplier for t in range(1, multiplier)]
+
+        out_dtype = model_management.intermediate_dtype()
+        total_out_frames = total_pairs * multiplier + 1
+        result = torch.empty((total_out_frames, 3, H, W), dtype=out_dtype, device=offload_device)
+        result[0] = images[0].movedim(-1, 0).to(out_dtype)
+        out_idx = 1
+
+        # Pre-compute timestep tensor on device (padded dimensions needed)
+        sample = prepare_frame(0)
+        pH, pW = sample.shape[2], sample.shape[3]
+        ts_full = torch.tensor(t_values, device=device, dtype=dtype).reshape(num_interp, 1, 1, 1)
+        ts_full = ts_full.expand(-1, 1, pH, pW)
+        del sample
+
+        multi_fn = getattr(inference_model, "forward_multi_timestep", None)
+        feat_cache = {}
+        prev_frame = None
+
+        try:
+            for i in range(total_pairs):
+                img0_single = prev_frame if prev_frame is not None else prepare_frame(i)
+                img1_single = prepare_frame(i + 1)
+                prev_frame = img1_single
+
+                # Cache features: img1 of pair N becomes img0 of pair N+1
+                feat_cache["img0"] = feat_cache.pop("next") if "next" in feat_cache else inference_model.extract_features(img0_single)
+                feat_cache["img1"] = inference_model.extract_features(img1_single)
+                feat_cache["next"] = feat_cache["img1"]
+
+                used_multi = False
+                if multi_fn is not None:
+                    # Models with timestep-independent flow can compute it once for all timesteps
+                    try:
+                        mids = multi_fn(img0_single, img1_single, t_values, cache=feat_cache)
+                        result[out_idx:out_idx + num_interp] = mids[:, :, :H, :W].to(out_dtype)
+                        out_idx += num_interp
+                        pbar.update(num_interp)
+                        tqdm_bar.update(num_interp)
+                        used_multi = True
+                    except model_management.OOM_EXCEPTION:
+                        model_management.soft_empty_cache()
+                        multi_fn = None  # fall through to single-timestep path
+
+                if not used_multi:
+                    j = 0
+                    while j < num_interp:
+                        b = min(batch, num_interp - j)
+                        try:
+                            img0 = img0_single.expand(b, -1, -1, -1)
+                            img1 = img1_single.expand(b, -1, -1, -1)
+                            mids = inference_model(img0, img1, timestep=ts_full[j:j + b], cache=feat_cache)
+                            result[out_idx:out_idx + b] = mids[:, :, :H, :W].to(out_dtype)
+                            out_idx += b
+                            pbar.update(b)
+                            tqdm_bar.update(b)
+                            j += b
+                        except model_management.OOM_EXCEPTION:
+                            if batch <= 1:
+                                raise
+                            batch = max(1, batch // 2)
+                            model_management.soft_empty_cache()
+
+                result[out_idx] = images[i + 1].movedim(-1, 0).to(out_dtype)
+                out_idx += 1
+        finally:
+            tqdm_bar.close()
+
+        # BCHW -> BHWC
+        result = result.movedim(1, -1).clamp_(0.0, 1.0)
+        return io.NodeOutput(result)
+
+
+class FrameInterpolationExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            FrameInterpolationModelLoader,
+            FrameInterpolate,
+        ]
+
+
+async def comfy_entrypoint() -> FrameInterpolationExtension:
+    return FrameInterpolationExtension()

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_lt_audio.py

@@ -3,9 +3,8 @@ import comfy.utils
 import comfy.model_management
 import torch
 
-from comfy.ldm.lightricks.vae.audio_vae import AudioVAE
 from comfy_api.latest import ComfyExtension, io
-
+from comfy_extras.nodes_audio import VAEEncodeAudio
 
 class LTXVAudioVAELoader(io.ComfyNode):
     @classmethod
@@ -28,10 +27,14 @@ class LTXVAudioVAELoader(io.ComfyNode):
     def execute(cls, ckpt_name: str) -> io.NodeOutput:
         ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
         sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True)
-        return io.NodeOutput(AudioVAE(sd, metadata))
+        sd = comfy.utils.state_dict_prefix_replace(sd, {"audio_vae.": "autoencoder.", "vocoder.": "vocoder."}, filter_keys=True)
+        vae = comfy.sd.VAE(sd=sd, metadata=metadata)
+        vae.throw_exception_if_invalid()
+
+        return io.NodeOutput(vae)
 
 
-class LTXVAudioVAEEncode(io.ComfyNode):
+class LTXVAudioVAEEncode(VAEEncodeAudio):
     @classmethod
     def define_schema(cls) -> io.Schema:
         return io.Schema(
@@ -50,15 +53,8 @@ class LTXVAudioVAEEncode(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, audio, audio_vae: AudioVAE) -> io.NodeOutput:
-        audio_latents = audio_vae.encode(audio)
-        return io.NodeOutput(
-            {
-                "samples": audio_latents,
-                "sample_rate": int(audio_vae.sample_rate),
-                "type": "audio",
-            }
-        )
+    def execute(cls, audio, audio_vae) -> io.NodeOutput:
+        return super().execute(audio_vae, audio)
 
 
 class LTXVAudioVAEDecode(io.ComfyNode):
@@ -80,12 +76,12 @@ class LTXVAudioVAEDecode(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, samples, audio_vae: AudioVAE) -> io.NodeOutput:
+    def execute(cls, samples, audio_vae) -> io.NodeOutput:
         audio_latent = samples["samples"]
         if audio_latent.is_nested:
             audio_latent = audio_latent.unbind()[-1]
-        audio = audio_vae.decode(audio_latent).to(audio_latent.device)
-        output_audio_sample_rate = audio_vae.output_sample_rate
+        audio = audio_vae.decode(audio_latent).movedim(-1, 1).to(audio_latent.device)
+        output_audio_sample_rate = audio_vae.first_stage_model.output_sample_rate
         return io.NodeOutput(
             {
                 "waveform": audio,
@@ -143,17 +139,17 @@ class LTXVEmptyLatentAudio(io.ComfyNode):
         frames_number: int,
         frame_rate: int,
         batch_size: int,
-        audio_vae: AudioVAE,
+        audio_vae,
     ) -> io.NodeOutput:
         """Generate empty audio latents matching the reference pipeline structure."""
 
         assert audio_vae is not None, "Audio VAE model is required"
 
         z_channels = audio_vae.latent_channels
-        audio_freq = audio_vae.latent_frequency_bins
-        sampling_rate = int(audio_vae.sample_rate)
+        audio_freq = audio_vae.first_stage_model.latent_frequency_bins
+        sampling_rate = int(audio_vae.first_stage_model.sample_rate)
 
-        num_audio_latents = audio_vae.num_of_latents_from_frames(frames_number, frame_rate)
+        num_audio_latents = audio_vae.first_stage_model.num_of_latents_from_frames(frames_number, frame_rate)
 
         audio_latents = torch.zeros(
             (batch_size, z_channels, num_audio_latents, audio_freq),

comfy_extras/nodes_model_patch.py

@@ -7,7 +7,10 @@ import comfy.model_management
 import comfy.ldm.common_dit
 import comfy.latent_formats
 import comfy.ldm.lumina.controlnet
+import comfy.ldm.supir.supir_modules
 from comfy.ldm.wan.model_multitalk import WanMultiTalkAttentionBlock, MultiTalkAudioProjModel
+from comfy_api.latest import io
+from comfy.ldm.supir.supir_patch import SUPIRPatch
 
 
 class BlockWiseControlBlock(torch.nn.Module):
@@ -266,6 +269,27 @@ class ModelPatchLoader:
                     out_dim=sd["audio_proj.norm.weight"].shape[0],
                     device=comfy.model_management.unet_offload_device(),
                     operations=comfy.ops.manual_cast)
+        elif 'model.control_model.input_hint_block.0.weight' in sd or 'control_model.input_hint_block.0.weight' in sd:
+            prefix_replace = {}
+            if 'model.control_model.input_hint_block.0.weight' in sd:
+                prefix_replace["model.control_model."] = "control_model."
+                prefix_replace["model.diffusion_model.project_modules."] = "project_modules."
+            else:
+                prefix_replace["control_model."] = "control_model."
+                prefix_replace["project_modules."] = "project_modules."
+
+            # Extract denoise_encoder weights before filter_keys discards them
+            de_prefix = "first_stage_model.denoise_encoder."
+            denoise_encoder_sd = {}
+            for k in list(sd.keys()):
+                if k.startswith(de_prefix):
+                    denoise_encoder_sd[k[len(de_prefix):]] = sd.pop(k)
+
+            sd = comfy.utils.state_dict_prefix_replace(sd, prefix_replace, filter_keys=True)
+            sd.pop("control_model.mask_LQ", None)
+            model = comfy.ldm.supir.supir_modules.SUPIR(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
+            if denoise_encoder_sd:
+                model.denoise_encoder_sd = denoise_encoder_sd
 
         model_patcher = comfy.model_patcher.CoreModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
         model.load_state_dict(sd, assign=model_patcher.is_dynamic())
@@ -565,9 +589,89 @@ class MultiTalkModelPatch(torch.nn.Module):
         )
 
 
+class SUPIRApply(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="SUPIRApply",
+            category="model_patches/supir",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.ModelPatch.Input("model_patch"),
+                io.Vae.Input("vae"),
+                io.Image.Input("image"),
+                io.Float.Input("strength_start", default=1.0, min=0.0, max=10.0, step=0.01,
+                               tooltip="Control strength at the start of sampling (high sigma)."),
+                io.Float.Input("strength_end", default=1.0, min=0.0, max=10.0, step=0.01,
+                               tooltip="Control strength at the end of sampling (low sigma). Linearly interpolated from start."),
+                io.Float.Input("restore_cfg", default=4.0, min=0.0, max=20.0, step=0.1, advanced=True,
+                               tooltip="Pulls denoised output toward the input latent. Higher = stronger fidelity to input. 0 to disable."),
+                io.Float.Input("restore_cfg_s_tmin", default=0.05, min=0.0, max=1.0, step=0.01, advanced=True,
+                               tooltip="Sigma threshold below which restore_cfg is disabled."),
+            ],
+            outputs=[io.Model.Output()],
+        )
+
+    @classmethod
+    def _encode_with_denoise_encoder(cls, vae, model_patch, image):
+        """Encode using denoise_encoder weights from SUPIR checkpoint if available."""
+        denoise_sd = getattr(model_patch.model, 'denoise_encoder_sd', None)
+        if not denoise_sd:
+            return vae.encode(image)
+
+        # Clone VAE patcher, apply denoise_encoder weights to clone, encode
+        orig_patcher = vae.patcher
+        vae.patcher = orig_patcher.clone()
+        patches = {f"encoder.{k}": (v,) for k, v in denoise_sd.items()}
+        vae.patcher.add_patches(patches, strength_patch=1.0, strength_model=0.0)
+        try:
+            return vae.encode(image)
+        finally:
+            vae.patcher = orig_patcher
+
+    @classmethod
+    def execute(cls, *, model: io.Model.Type, model_patch: io.ModelPatch.Type, vae: io.Vae.Type, image: io.Image.Type,
+                strength_start: float, strength_end: float, restore_cfg: float, restore_cfg_s_tmin: float) -> io.NodeOutput:
+        model_patched = model.clone()
+        hint_latent = model.get_model_object("latent_format").process_in(
+            cls._encode_with_denoise_encoder(vae, model_patch, image[:, :, :, :3]))
+        patch = SUPIRPatch(model_patch, model_patch.model.project_modules, hint_latent, strength_start, strength_end)
+        patch.register(model_patched)
+
+        if restore_cfg > 0.0:
+            # Round-trip to match original pipeline: decode hint, re-encode with regular VAE
+            latent_format = model.get_model_object("latent_format")
+            decoded = vae.decode(latent_format.process_out(hint_latent))
+            x_center = latent_format.process_in(vae.encode(decoded[:, :, :, :3]))
+            sigma_max = 14.6146
+
+            def restore_cfg_function(args):
+                denoised = args["denoised"]
+                sigma = args["sigma"]
+                if sigma.dim() > 0:
+                    s = sigma[0].item()
+                else:
+                    s = sigma.item()
+                if s > restore_cfg_s_tmin:
+                    ref = x_center.to(device=denoised.device, dtype=denoised.dtype)
+                    b = denoised.shape[0]
+                    if ref.shape[0] != b:
+                        ref = ref.expand(b, -1, -1, -1) if ref.shape[0] == 1 else ref.repeat((b + ref.shape[0] - 1) // ref.shape[0], 1, 1, 1)[:b]
+                    sigma_val = sigma.view(-1, 1, 1, 1) if sigma.dim() > 0 else sigma
+                    d_center = denoised - ref
+                    denoised = denoised - d_center * ((sigma_val / sigma_max) ** restore_cfg)
+                return denoised
+
+            model_patched.set_model_sampler_post_cfg_function(restore_cfg_function)
+
+        return io.NodeOutput(model_patched)
+
+
 NODE_CLASS_MAPPINGS = {
     "ModelPatchLoader": ModelPatchLoader,
     "QwenImageDiffsynthControlnet": QwenImageDiffsynthControlnet,
     "ZImageFunControlnet": ZImageFunControlnet,
     "USOStyleReference": USOStyleReference,
+    "SUPIRApply": SUPIRApply,
 }

comfy_extras/nodes_post_processing.py

@@ -6,6 +6,7 @@ from PIL import Image
 import math
 from enum import Enum
 from typing import TypedDict, Literal
+import kornia
 
 import comfy.utils
 import comfy.model_management
@@ -660,6 +661,228 @@ class BatchImagesMasksLatentsNode(io.ComfyNode):
         return io.NodeOutput(batched)
 
 
+class ColorTransfer(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ColorTransfer",
+            category="image/postprocessing",
+            description="Match the colors of one image to another using various algorithms.",
+            search_aliases=["color match", "color grading", "color correction", "match colors", "color transform", "mkl", "reinhard", "histogram"],
+            inputs=[
+                io.Image.Input("image_target", tooltip="Image(s) to apply the color transform to."),
+                io.Image.Input("image_ref", optional=True, tooltip="Reference image(s) to match colors to. If not provided, processing is skipped"),
+                io.Combo.Input("method", options=['reinhard_lab', 'mkl_lab', 'histogram'],),
+                io.DynamicCombo.Input("source_stats",
+                    tooltip="per_frame: each frame matched to image_ref individually. uniform: pool stats across all source frames as baseline, match to image_ref. target_frame: use one chosen frame as the baseline for the transform to image_ref, applied uniformly to all frames (preserves relative differences)",
+                    options=[
+                        io.DynamicCombo.Option("per_frame", []),
+                        io.DynamicCombo.Option("uniform", []),
+                        io.DynamicCombo.Option("target_frame", [
+                            io.Int.Input("target_index", default=0, min=0, max=10000,
+                                tooltip="Frame index used as the source baseline for computing the transform to image_ref"),
+                        ]),
+                    ]),
+                io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01),
+            ],
+            outputs=[
+                io.Image.Output(display_name="image"),
+            ],
+        )
+
+    @staticmethod
+    def _to_lab(images, i, device):
+        return kornia.color.rgb_to_lab(
+            images[i:i+1].to(device, dtype=torch.float32).permute(0, 3, 1, 2))
+
+    @staticmethod
+    def _pool_stats(images, device, is_reinhard, eps):
+        """Two-pass pooled mean + std/cov across all frames."""
+        N, C = images.shape[0], images.shape[3]
+        HW = images.shape[1] * images.shape[2]
+        mean = torch.zeros(C, 1, device=device, dtype=torch.float32)
+        for i in range(N):
+            mean += ColorTransfer._to_lab(images, i, device).view(C, -1).mean(dim=-1, keepdim=True)
+        mean /= N
+        acc = torch.zeros(C, 1 if is_reinhard else C, device=device, dtype=torch.float32)
+        for i in range(N):
+            centered = ColorTransfer._to_lab(images, i, device).view(C, -1) - mean
+            if is_reinhard:
+                acc += (centered * centered).mean(dim=-1, keepdim=True)
+            else:
+                acc += centered @ centered.T / HW
+        if is_reinhard:
+            return mean, torch.sqrt(acc / N).clamp_min_(eps)
+        return mean, acc / N
+
+    @staticmethod
+    def _frame_stats(lab_flat, hw, is_reinhard, eps):
+        """Per-frame mean + std/cov."""
+        mean = lab_flat.mean(dim=-1, keepdim=True)
+        if is_reinhard:
+            return mean, lab_flat.std(dim=-1, keepdim=True, unbiased=False).clamp_min_(eps)
+        centered = lab_flat - mean
+        return mean, centered @ centered.T / hw
+
+    @staticmethod
+    def _mkl_matrix(cov_s, cov_r, eps):
+        """Compute MKL 3x3 transform matrix from source and ref covariances."""
+        eig_val_s, eig_vec_s = torch.linalg.eigh(cov_s)
+        sqrt_val_s = torch.sqrt(eig_val_s.clamp_min(0)).clamp_min_(eps)
+
+        scaled_V = eig_vec_s * sqrt_val_s.unsqueeze(0)
+        mid = scaled_V.T @ cov_r @ scaled_V
+        eig_val_m, eig_vec_m = torch.linalg.eigh(mid)
+        sqrt_m = torch.sqrt(eig_val_m.clamp_min(0))
+
+        inv_sqrt_s = 1.0 / sqrt_val_s
+        inv_scaled_V = eig_vec_s * inv_sqrt_s.unsqueeze(0)
+        M_half = (eig_vec_m * sqrt_m.unsqueeze(0)) @ eig_vec_m.T
+        return inv_scaled_V @ M_half @ inv_scaled_V.T
+
+    @staticmethod
+    def _histogram_lut(src, ref, bins=256):
+        """Build per-channel LUT from source and ref histograms. src/ref: (C, HW) in [0,1]."""
+        s_bins = (src * (bins - 1)).long().clamp(0, bins - 1)
+        r_bins = (ref * (bins - 1)).long().clamp(0, bins - 1)
+        s_hist = torch.zeros(src.shape[0], bins, device=src.device, dtype=src.dtype)
+        r_hist = torch.zeros(src.shape[0], bins, device=src.device, dtype=src.dtype)
+        ones_s = torch.ones_like(src)
+        ones_r = torch.ones_like(ref)
+        s_hist.scatter_add_(1, s_bins, ones_s)
+        r_hist.scatter_add_(1, r_bins, ones_r)
+        s_cdf = s_hist.cumsum(1)
+        s_cdf = s_cdf / s_cdf[:, -1:]
+        r_cdf = r_hist.cumsum(1)
+        r_cdf = r_cdf / r_cdf[:, -1:]
+        return torch.searchsorted(r_cdf, s_cdf).clamp_max_(bins - 1).float() / (bins - 1)
+
+    @classmethod
+    def _pooled_cdf(cls, images, device, num_bins=256):
+        """Build pooled CDF across all frames, one frame at a time."""
+        C = images.shape[3]
+        hist = torch.zeros(C, num_bins, device=device, dtype=torch.float32)
+        for i in range(images.shape[0]):
+            frame = images[i].to(device, dtype=torch.float32).permute(2, 0, 1).reshape(C, -1)
+            bins = (frame * (num_bins - 1)).long().clamp(0, num_bins - 1)
+            hist.scatter_add_(1, bins, torch.ones_like(frame))
+        cdf = hist.cumsum(1)
+        return cdf / cdf[:, -1:]
+
+    @classmethod
+    def _build_histogram_transform(cls, image_target, image_ref, device, stats_mode, target_index, B):
+        """Build per-frame or uniform LUT transform for histogram mode."""
+        if stats_mode == 'per_frame':
+            return None  # LUT computed per-frame in the apply loop
+
+        r_cdf = cls._pooled_cdf(image_ref, device)
+        if stats_mode == 'target_frame':
+            ti = min(target_index, B - 1)
+            s_cdf = cls._pooled_cdf(image_target[ti:ti+1], device)
+        else:
+            s_cdf = cls._pooled_cdf(image_target, device)
+        return torch.searchsorted(r_cdf, s_cdf).clamp_max_(255).float() / 255.0
+
+    @classmethod
+    def _build_lab_transform(cls, image_target, image_ref, device, stats_mode, target_index, is_reinhard):
+        """Build transform parameters for Lab-based methods. Returns a transform function."""
+        eps = 1e-6
+        B, H, W, C = image_target.shape
+        B_ref = image_ref.shape[0]
+        single_ref = B_ref == 1
+        HW = H * W
+        HW_ref = image_ref.shape[1] * image_ref.shape[2]
+
+        # Precompute ref stats
+        if single_ref or stats_mode in ('uniform', 'target_frame'):
+            ref_mean, ref_sc = cls._pool_stats(image_ref, device, is_reinhard, eps)
+
+        # Uniform/target_frame: precompute single affine transform
+        if stats_mode in ('uniform', 'target_frame'):
+            if stats_mode == 'target_frame':
+                ti = min(target_index, B - 1)
+                s_lab = cls._to_lab(image_target, ti, device).view(C, -1)
+                s_mean, s_sc = cls._frame_stats(s_lab, HW, is_reinhard, eps)
+            else:
+                s_mean, s_sc = cls._pool_stats(image_target, device, is_reinhard, eps)
+
+            if is_reinhard:
+                scale = ref_sc / s_sc
+                offset = ref_mean - scale * s_mean
+                return lambda src_flat, **_: src_flat * scale + offset
+            T = cls._mkl_matrix(s_sc, ref_sc, eps)
+            offset = ref_mean - T @ s_mean
+            return lambda src_flat, **_: T @ src_flat + offset
+
+        # per_frame
+        def per_frame_transform(src_flat, frame_idx):
+            s_mean, s_sc = cls._frame_stats(src_flat, HW, is_reinhard, eps)
+
+            if single_ref:
+                r_mean, r_sc = ref_mean, ref_sc
+            else:
+                ri = min(frame_idx, B_ref - 1)
+                r_mean, r_sc = cls._frame_stats(cls._to_lab(image_ref, ri, device).view(C, -1), HW_ref, is_reinhard, eps)
+
+            centered = src_flat - s_mean
+            if is_reinhard:
+                return centered * (r_sc / s_sc) + r_mean
+            T = cls._mkl_matrix(centered @ centered.T / HW, r_sc, eps)
+            return T @ centered + r_mean
+
+        return per_frame_transform
+
+    @classmethod
+    def execute(cls, image_target, image_ref, method, source_stats, strength=1.0) -> io.NodeOutput:
+        stats_mode = source_stats["source_stats"]
+        target_index = source_stats.get("target_index", 0)
+
+        if strength == 0 or image_ref is None:
+            return io.NodeOutput(image_target)
+
+        device = comfy.model_management.get_torch_device()
+        intermediate_device = comfy.model_management.intermediate_device()
+        intermediate_dtype = comfy.model_management.intermediate_dtype()
+
+        B, H, W, C = image_target.shape
+        B_ref = image_ref.shape[0]
+        pbar = comfy.utils.ProgressBar(B)
+        out = torch.empty(B, H, W, C, device=intermediate_device, dtype=intermediate_dtype)
+
+        if method == 'histogram':
+            uniform_lut = cls._build_histogram_transform(
+                image_target, image_ref, device, stats_mode, target_index, B)
+
+            for i in range(B):
+                src = image_target[i].to(device, dtype=torch.float32).permute(2, 0, 1)
+                src_flat = src.reshape(C, -1)
+                if uniform_lut is not None:
+                    lut = uniform_lut
+                else:
+                    ri = min(i, B_ref - 1)
+                    ref = image_ref[ri].to(device, dtype=torch.float32).permute(2, 0, 1).reshape(C, -1)
+                    lut = cls._histogram_lut(src_flat, ref)
+                bin_idx = (src_flat * 255).long().clamp(0, 255)
+                matched = lut.gather(1, bin_idx).view(C, H, W)
+                result = matched if strength == 1.0 else torch.lerp(src, matched, strength)
+                out[i] = result.permute(1, 2, 0).clamp_(0, 1).to(device=intermediate_device, dtype=intermediate_dtype)
+                pbar.update(1)
+        else:
+            transform = cls._build_lab_transform(image_target, image_ref, device, stats_mode, target_index, is_reinhard=method == "reinhard_lab")
+
+            for i in range(B):
+                src_frame = cls._to_lab(image_target, i, device)
+                corrected = transform(src_frame.view(C, -1), frame_idx=i)
+                if strength == 1.0:
+                    result = kornia.color.lab_to_rgb(corrected.view(1, C, H, W))
+                else:
+                    result = kornia.color.lab_to_rgb(torch.lerp(src_frame, corrected.view(1, C, H, W), strength))
+                out[i] = result.squeeze(0).permute(1, 2, 0).clamp_(0, 1).to(device=intermediate_device, dtype=intermediate_dtype)
+                pbar.update(1)
+
+        return io.NodeOutput(out)
+
+
 class PostProcessingExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
@@ -673,6 +896,7 @@ class PostProcessingExtension(ComfyExtension):
             BatchImagesNode,
             BatchMasksNode,
             BatchLatentsNode,
+            ColorTransfer,
             # BatchImagesMasksLatentsNode,
         ]
 

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_sam3.py

@@ -0,0 +1,529 @@
+"""
+SAM3 (Segment Anything 3) nodes for detection, segmentation, and video tracking.
+"""
+
+from typing_extensions import override
+
+import json
+import os
+import torch
+import torch.nn.functional as F
+import comfy.model_management
+import comfy.utils
+import folder_paths
+from comfy_api.latest import ComfyExtension, io, ui
+import av
+from fractions import Fraction
+
+
+def _extract_text_prompts(conditioning, device, dtype):
+    """Extract list of (text_embeddings, text_mask) from conditioning."""
+    cond_meta = conditioning[0][1]
+    multi = cond_meta.get("sam3_multi_cond")
+    prompts = []
+    if multi is not None:
+        for entry in multi:
+            emb = entry["cond"].to(device=device, dtype=dtype)
+            mask = entry["attention_mask"].to(device) if entry["attention_mask"] is not None else None
+            if mask is None:
+                mask = torch.ones(emb.shape[0], emb.shape[1], dtype=torch.int64, device=device)
+            prompts.append((emb, mask, entry.get("max_detections", 1)))
+    else:
+        emb = conditioning[0][0].to(device=device, dtype=dtype)
+        mask = cond_meta.get("attention_mask")
+        if mask is not None:
+            mask = mask.to(device)
+        else:
+            mask = torch.ones(emb.shape[0], emb.shape[1], dtype=torch.int64, device=device)
+        prompts.append((emb, mask, 1))
+    return prompts
+
+
+def _refine_mask(sam3_model, orig_image_hwc, coarse_mask, box_xyxy, H, W, device, dtype, iterations):
+    """Refine a coarse detector mask via SAM decoder, cropping to the detection box.
+
+    Returns: [1, H, W] binary mask
+    """
+    def _coarse_fallback():
+        return (F.interpolate(coarse_mask.unsqueeze(0).unsqueeze(0), size=(H, W),
+                              mode="bilinear", align_corners=False)[0] > 0).float()
+
+    if iterations <= 0:
+        return _coarse_fallback()
+
+    pad_frac = 0.1
+    x1, y1, x2, y2 = box_xyxy.tolist()
+    bw, bh = x2 - x1, y2 - y1
+    cx1 = max(0, int(x1 - bw * pad_frac))
+    cy1 = max(0, int(y1 - bh * pad_frac))
+    cx2 = min(W, int(x2 + bw * pad_frac))
+    cy2 = min(H, int(y2 + bh * pad_frac))
+    if cx2 <= cx1 or cy2 <= cy1:
+        return _coarse_fallback()
+
+    crop = orig_image_hwc[cy1:cy2, cx1:cx2, :3]
+    crop_1008 = comfy.utils.common_upscale(crop.unsqueeze(0).movedim(-1, 1), 1008, 1008, "bilinear", crop="disabled")
+    crop_frame = crop_1008.to(device=device, dtype=dtype)
+    crop_h, crop_w = cy2 - cy1, cx2 - cx1
+
+    # Crop coarse mask and refine via SAM on the cropped image
+    mask_h, mask_w = coarse_mask.shape[-2:]
+    mx1, my1 = int(cx1 / W * mask_w), int(cy1 / H * mask_h)
+    mx2, my2 = int(cx2 / W * mask_w), int(cy2 / H * mask_h)
+    if mx2 <= mx1 or my2 <= my1:
+        return _coarse_fallback()
+    mask_logit = coarse_mask[..., my1:my2, mx1:mx2].unsqueeze(0).unsqueeze(0)
+    for _ in range(iterations):
+        coarse_input = F.interpolate(mask_logit, size=(1008, 1008), mode="bilinear", align_corners=False)
+        mask_logit = sam3_model.forward_segment(crop_frame, mask_inputs=coarse_input)
+
+    refined_crop = F.interpolate(mask_logit, size=(crop_h, crop_w), mode="bilinear", align_corners=False)
+    full_mask = torch.zeros(1, 1, H, W, device=device, dtype=dtype)
+    full_mask[:, :, cy1:cy2, cx1:cx2] = refined_crop
+    coarse_full = F.interpolate(coarse_mask.unsqueeze(0).unsqueeze(0), size=(H, W), mode="bilinear", align_corners=False)
+    return ((full_mask[0] > 0) | (coarse_full[0] > 0)).float()
+
+
+
+class SAM3_Detect(io.ComfyNode):
+    """Open-vocabulary detection and segmentation using text, box, or point prompts."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SAM3_Detect",
+            display_name="SAM3 Detect",
+            category="detection/",
+            search_aliases=["sam3", "segment anything", "open vocabulary", "text detection", "segment"],
+            inputs=[
+                io.Model.Input("model", display_name="model"),
+                io.Image.Input("image", display_name="image"),
+                io.Conditioning.Input("conditioning", display_name="conditioning", optional=True, tooltip="Text conditioning from CLIPTextEncode"),
+                io.BoundingBox.Input("bboxes", display_name="bboxes", force_input=True, optional=True, tooltip="Bounding boxes to segment within"),
+                io.String.Input("positive_coords", display_name="positive_coords", force_input=True, optional=True, tooltip="Positive point prompts as JSON [{\"x\": int, \"y\": int}, ...] (pixel coords)"),
+                io.String.Input("negative_coords", display_name="negative_coords", force_input=True, optional=True, tooltip="Negative point prompts as JSON [{\"x\": int, \"y\": int}, ...] (pixel coords)"),
+                io.Float.Input("threshold", display_name="threshold", default=0.5, min=0.0, max=1.0, step=0.01),
+                io.Int.Input("refine_iterations", display_name="refine_iterations", default=2, min=0, max=5, tooltip="SAM decoder refinement passes (0=use raw detector masks)"),
+                io.Boolean.Input("individual_masks", display_name="individual_masks", default=False, tooltip="Output per-object masks instead of union"),
+            ],
+            outputs=[
+                io.Mask.Output("masks"),
+                io.BoundingBox.Output("bboxes"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, image, conditioning=None, bboxes=None, positive_coords=None, negative_coords=None, threshold=0.5, refine_iterations=2, individual_masks=False) -> io.NodeOutput:
+        B, H, W, C = image.shape
+        image_in = comfy.utils.common_upscale(image[..., :3].movedim(-1, 1), 1008, 1008, "bilinear", crop="disabled")
+
+        # Convert bboxes to normalized cxcywh format, per-frame list of [1, N, 4] tensors.
+        # Supports: single dict (all frames), list[dict] (all frames), list[list[dict]] (per-frame).
+        def _boxes_to_tensor(box_list):
+            coords = []
+            for d in box_list:
+                cx = (d["x"] + d["width"] / 2) / W
+                cy = (d["y"] + d["height"] / 2) / H
+                coords.append([cx, cy, d["width"] / W, d["height"] / H])
+            return torch.tensor([coords], dtype=torch.float32)  # [1, N, 4]
+
+        per_frame_boxes = None
+        if bboxes is not None:
+            if isinstance(bboxes, dict):
+                # Single box → same for all frames
+                shared = _boxes_to_tensor([bboxes])
+                per_frame_boxes = [shared] * B
+            elif isinstance(bboxes, list) and len(bboxes) > 0 and isinstance(bboxes[0], list):
+                # list[list[dict]] → per-frame boxes
+                per_frame_boxes = [_boxes_to_tensor(frame_boxes) if frame_boxes else None for frame_boxes in bboxes]
+                # Pad to B if fewer frames provided
+                while len(per_frame_boxes) < B:
+                    per_frame_boxes.append(per_frame_boxes[-1] if per_frame_boxes else None)
+            elif isinstance(bboxes, list) and len(bboxes) > 0:
+                # list[dict] → same boxes for all frames
+                shared = _boxes_to_tensor(bboxes)
+                per_frame_boxes = [shared] * B
+
+        # Parse point prompts from JSON (KJNodes PointsEditor format: [{"x": int, "y": int}, ...])
+        pos_pts = json.loads(positive_coords) if positive_coords else []
+        neg_pts = json.loads(negative_coords) if negative_coords else []
+        has_points = len(pos_pts) > 0 or len(neg_pts) > 0
+
+        comfy.model_management.load_model_gpu(model)
+        device = comfy.model_management.get_torch_device()
+        dtype = model.model.get_dtype()
+        sam3_model = model.model.diffusion_model
+
+        # Build point inputs for tracker SAM decoder path
+        point_inputs = None
+        if has_points:
+            all_coords = [[p["x"] / W * 1008, p["y"] / H * 1008] for p in pos_pts] + \
+                         [[p["x"] / W * 1008, p["y"] / H * 1008] for p in neg_pts]
+            all_labels = [1] * len(pos_pts) + [0] * len(neg_pts)
+            point_inputs = {
+                "point_coords": torch.tensor([all_coords], dtype=dtype, device=device),
+                "point_labels": torch.tensor([all_labels], dtype=torch.int32, device=device),
+            }
+
+        cond_list = _extract_text_prompts(conditioning, device, dtype) if conditioning is not None and len(conditioning) > 0 else []
+        has_text = len(cond_list) > 0
+
+        # Run per-image through detector (text/boxes) and/or tracker (points)
+        all_bbox_dicts = []
+        all_masks = []
+        pbar = comfy.utils.ProgressBar(B)
+
+        for b in range(B):
+            frame = image_in[b:b+1].to(device=device, dtype=dtype)
+            b_boxes = None
+            if per_frame_boxes is not None and per_frame_boxes[b] is not None:
+                b_boxes = per_frame_boxes[b].to(device=device, dtype=dtype)
+
+            frame_bbox_dicts = []
+            frame_masks = []
+
+            # Point prompts: tracker SAM decoder path with iterative refinement
+            if point_inputs is not None:
+                mask_logit = sam3_model.forward_segment(frame, point_inputs=point_inputs)
+                for _ in range(max(0, refine_iterations - 1)):
+                    mask_logit = sam3_model.forward_segment(frame, mask_inputs=mask_logit)
+                mask = F.interpolate(mask_logit, size=(H, W), mode="bilinear", align_corners=False)
+                frame_masks.append((mask[0] > 0).float())
+
+            # Box prompts: SAM decoder path (segment inside each box)
+            if b_boxes is not None and not has_text:
+                for box_cxcywh in b_boxes[0]:
+                    cx, cy, bw, bh = box_cxcywh.tolist()
+                    # Convert cxcywh normalized → xyxy in 1008 space → [1, 2, 2] corners
+                    sam_box = torch.tensor([[[(cx - bw/2) * 1008, (cy - bh/2) * 1008],
+                                             [(cx + bw/2) * 1008, (cy + bh/2) * 1008]]],
+                                           device=device, dtype=dtype)
+                    mask_logit = sam3_model.forward_segment(frame, box_inputs=sam_box)
+                    for _ in range(max(0, refine_iterations - 1)):
+                        mask_logit = sam3_model.forward_segment(frame, mask_inputs=mask_logit)
+                    mask = F.interpolate(mask_logit, size=(H, W), mode="bilinear", align_corners=False)
+                    frame_masks.append((mask[0] > 0).float())
+
+            # Text prompts: run detector per text prompt (each detects one category)
+            for text_embeddings, text_mask, max_det in cond_list:
+                results = sam3_model(
+                    frame, text_embeddings=text_embeddings, text_mask=text_mask,
+                    boxes=b_boxes, threshold=threshold, orig_size=(H, W))
+
+                pred_boxes = results["boxes"][0]
+                scores = results["scores"][0]
+                masks = results["masks"][0]
+
+                probs = scores.sigmoid()
+                keep = probs > threshold
+                kept_boxes = pred_boxes[keep].cpu()
+                kept_scores = probs[keep].cpu()
+                kept_masks = masks[keep]
+
+                order = kept_scores.argsort(descending=True)[:max_det]
+                kept_boxes = kept_boxes[order]
+                kept_scores = kept_scores[order]
+                kept_masks = kept_masks[order]
+
+                for box, score in zip(kept_boxes, kept_scores):
+                    frame_bbox_dicts.append({
+                        "x": float(box[0]), "y": float(box[1]),
+                        "width": float(box[2] - box[0]), "height": float(box[3] - box[1]),
+                        "score": float(score),
+                    })
+                for m, box in zip(kept_masks, kept_boxes):
+                    frame_masks.append(_refine_mask(
+                        sam3_model, image[b], m, box, H, W, device, dtype, refine_iterations))
+
+            all_bbox_dicts.append(frame_bbox_dicts)
+            if len(frame_masks) > 0:
+                combined = torch.cat(frame_masks, dim=0)  # [N_obj, H, W]
+                if individual_masks:
+                    all_masks.append(combined)
+                else:
+                    all_masks.append((combined > 0).any(dim=0).float())
+            else:
+                if individual_masks:
+                    all_masks.append(torch.zeros(0, H, W, device=comfy.model_management.intermediate_device()))
+                else:
+                    all_masks.append(torch.zeros(H, W, device=comfy.model_management.intermediate_device()))
+            pbar.update(1)
+
+        idev = comfy.model_management.intermediate_device()
+        all_masks = [m.to(idev) for m in all_masks]
+        mask_out = torch.cat(all_masks, dim=0) if individual_masks else torch.stack(all_masks)
+        return io.NodeOutput(mask_out, all_bbox_dicts)
+
+
+SAM3TrackData = io.Custom("SAM3_TRACK_DATA")
+
+class SAM3_VideoTrack(io.ComfyNode):
+    """Track objects across video frames using SAM3's memory-based tracker."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SAM3_VideoTrack",
+            display_name="SAM3 Video Track",
+            category="detection/",
+            search_aliases=["sam3", "video", "track", "propagate"],
+            inputs=[
+                io.Image.Input("images", display_name="images", tooltip="Video frames as batched images"),
+                io.Model.Input("model", display_name="model"),
+                io.Mask.Input("initial_mask", display_name="initial_mask", optional=True, tooltip="Mask(s) for the first frame to track (one per object)"),
+                io.Conditioning.Input("conditioning", display_name="conditioning", optional=True, tooltip="Text conditioning for detecting new objects during tracking"),
+                io.Float.Input("detection_threshold", display_name="detection_threshold", default=0.5, min=0.0, max=1.0, step=0.01, tooltip="Score threshold for text-prompted detection"),
+                io.Int.Input("max_objects", display_name="max_objects", default=0, min=0, tooltip="Max tracked objects (0=unlimited). Initial masks count toward this limit."),
+                io.Int.Input("detect_interval", display_name="detect_interval", default=1, min=1, tooltip="Run detection every N frames (1=every frame). Higher values save compute."),
+            ],
+            outputs=[
+                SAM3TrackData.Output("track_data", display_name="track_data"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, images, model, initial_mask=None, conditioning=None, detection_threshold=0.5, max_objects=0, detect_interval=1) -> io.NodeOutput:
+        N, H, W, C = images.shape
+
+        comfy.model_management.load_model_gpu(model)
+        device = comfy.model_management.get_torch_device()
+        dtype = model.model.get_dtype()
+        sam3_model = model.model.diffusion_model
+
+        frames = images[..., :3].movedim(-1, 1)
+        frames_in = comfy.utils.common_upscale(frames, 1008, 1008, "bilinear", crop="disabled").to(device=device, dtype=dtype)
+
+        init_masks = None
+        if initial_mask is not None:
+            init_masks = initial_mask.unsqueeze(1).to(device=device, dtype=dtype)
+
+        pbar = comfy.utils.ProgressBar(N)
+
+        text_prompts = None
+        if conditioning is not None and len(conditioning) > 0:
+            text_prompts = [(emb, mask) for emb, mask, _ in _extract_text_prompts(conditioning, device, dtype)]
+        elif initial_mask is None:
+            raise ValueError("Either initial_mask or conditioning must be provided")
+
+        result = sam3_model.forward_video(
+            images=frames_in, initial_masks=init_masks, pbar=pbar, text_prompts=text_prompts,
+            new_det_thresh=detection_threshold, max_objects=max_objects,
+            detect_interval=detect_interval)
+        result["orig_size"] = (H, W)
+        return io.NodeOutput(result)
+
+
+class SAM3_TrackPreview(io.ComfyNode):
+    """Visualize tracked objects with distinct colors as a video preview. No tensor output — saves to temp video."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SAM3_TrackPreview",
+            display_name="SAM3 Track Preview",
+            category="detection/",
+            inputs=[
+                SAM3TrackData.Input("track_data", display_name="track_data"),
+                io.Image.Input("images", display_name="images", optional=True),
+                io.Float.Input("opacity", display_name="opacity", default=0.5, min=0.0, max=1.0, step=0.05),
+                io.Float.Input("fps", display_name="fps", default=24.0, min=1.0, max=120.0, step=1.0),
+            ],
+            is_output_node=True,
+        )
+
+    COLORS = [
+        (0.12, 0.47, 0.71), (1.0, 0.5, 0.05), (0.17, 0.63, 0.17), (0.84, 0.15, 0.16),
+        (0.58, 0.4, 0.74), (0.55, 0.34, 0.29), (0.89, 0.47, 0.76), (0.5, 0.5, 0.5),
+        (0.74, 0.74, 0.13), (0.09, 0.75, 0.81), (0.94, 0.76, 0.06), (0.42, 0.68, 0.84),
+    ]
+
+    # 5x3 bitmap font atlas for digits 0-9 [10, 5, 3]
+    _glyph_cache = {}  # (device, scale) -> (glyphs, outlines, gh, gw, oh, ow)
+
+    @staticmethod
+    def _get_glyphs(device, scale=3):
+        key = (device, scale)
+        if key in SAM3_TrackPreview._glyph_cache:
+            return SAM3_TrackPreview._glyph_cache[key]
+        atlas = torch.tensor([
+            [[1,1,1],[1,0,1],[1,0,1],[1,0,1],[1,1,1]],
+            [[0,1,0],[1,1,0],[0,1,0],[0,1,0],[1,1,1]],
+            [[1,1,1],[0,0,1],[1,1,1],[1,0,0],[1,1,1]],
+            [[1,1,1],[0,0,1],[1,1,1],[0,0,1],[1,1,1]],
+            [[1,0,1],[1,0,1],[1,1,1],[0,0,1],[0,0,1]],
+            [[1,1,1],[1,0,0],[1,1,1],[0,0,1],[1,1,1]],
+            [[1,1,1],[1,0,0],[1,1,1],[1,0,1],[1,1,1]],
+            [[1,1,1],[0,0,1],[0,0,1],[0,0,1],[0,0,1]],
+            [[1,1,1],[1,0,1],[1,1,1],[1,0,1],[1,1,1]],
+            [[1,1,1],[1,0,1],[1,1,1],[0,0,1],[1,1,1]],
+        ], dtype=torch.bool)
+        glyphs, outlines = [], []
+        for d in range(10):
+            g = atlas[d].repeat_interleave(scale, 0).repeat_interleave(scale, 1)
+            padded = F.pad(g.float().unsqueeze(0).unsqueeze(0), (1,1,1,1))
+            o = (F.max_pool2d(padded, 3, stride=1, padding=1)[0, 0] > 0)
+            glyphs.append(g.to(device))
+            outlines.append(o.to(device))
+        gh, gw = glyphs[0].shape
+        oh, ow = outlines[0].shape
+        SAM3_TrackPreview._glyph_cache[key] = (glyphs, outlines, gh, gw, oh, ow)
+        return SAM3_TrackPreview._glyph_cache[key]
+
+    @staticmethod
+    def _draw_number_gpu(frame, number, cx, cy, color, scale=3):
+        """Draw a number on a GPU tensor [H, W, 3] float 0-1 at (cx, cy) with outline."""
+        H, W = frame.shape[:2]
+        device = frame.device
+        glyphs, outlines, gh, gw, oh, ow = SAM3_TrackPreview._get_glyphs(device, scale)
+        color_t = torch.tensor(color, device=device, dtype=frame.dtype)
+        digs = [int(d) for d in str(number)]
+        total_w = len(digs) * (gw + scale) - scale
+        x0 = cx - total_w // 2
+        y0 = cy - gh // 2
+        for i, d in enumerate(digs):
+            dx = x0 + i * (gw + scale)
+            # Black outline
+            oy0, ox0 = y0 - 1, dx - 1
+            osy1, osx1 = max(0, -oy0), max(0, -ox0)
+            osy2, osx2 = min(oh, H - oy0), min(ow, W - ox0)
+            if osy2 > osy1 and osx2 > osx1:
+                fy1, fx1 = oy0 + osy1, ox0 + osx1
+                frame[fy1:fy1+(osy2-osy1), fx1:fx1+(osx2-osx1)][outlines[d][osy1:osy2, osx1:osx2]] = 0
+            # Colored fill
+            sy1, sx1 = max(0, -y0), max(0, -dx)
+            sy2, sx2 = min(gh, H - y0), min(gw, W - dx)
+            if sy2 > sy1 and sx2 > sx1:
+                fy1, fx1 = y0 + sy1, dx + sx1
+                frame[fy1:fy1+(sy2-sy1), fx1:fx1+(sx2-sx1)][glyphs[d][sy1:sy2, sx1:sx2]] = color_t
+
+    @classmethod
+    def execute(cls, track_data, images=None, opacity=0.5, fps=24.0) -> io.NodeOutput:
+
+        from comfy.ldm.sam3.tracker import unpack_masks
+        packed = track_data["packed_masks"]
+        H, W = track_data["orig_size"]
+        if images is not None:
+            H, W = images.shape[1], images.shape[2]
+        if packed is None:
+            N, N_obj = track_data["n_frames"], 0
+        else:
+            N, N_obj = packed.shape[0], packed.shape[1]
+
+        import uuid
+        gpu = comfy.model_management.get_torch_device()
+        temp_dir = folder_paths.get_temp_directory()
+        filename = f"sam3_track_preview_{uuid.uuid4().hex[:8]}.mp4"
+        filepath = os.path.join(temp_dir, filename)
+        with av.open(filepath, mode='w') as output:
+            stream = output.add_stream('h264', rate=Fraction(round(fps * 1000), 1000))
+            stream.width = W
+            stream.height = H
+            stream.pix_fmt = 'yuv420p'
+
+            frame_cpu = torch.empty(H, W, 3, dtype=torch.uint8)
+            frame_np = frame_cpu.numpy()
+            if N_obj > 0:
+                colors_t = torch.tensor([cls.COLORS[i % len(cls.COLORS)] for i in range(N_obj)],
+                                       device=gpu, dtype=torch.float32)
+                grid_y = torch.arange(H, device=gpu).view(1, H, 1)
+                grid_x = torch.arange(W, device=gpu).view(1, 1, W)
+            for t in range(N):
+                if images is not None and t < images.shape[0]:
+                    frame = images[t].clone()
+                else:
+                    frame = torch.zeros(H, W, 3)
+
+                if N_obj > 0:
+                    frame_binary = unpack_masks(packed[t:t+1].to(gpu))  # [1, N_obj, H, W] bool
+                    frame_masks = F.interpolate(frame_binary.float(), size=(H, W), mode="nearest")[0]
+                    frame_gpu = frame.to(gpu)
+                    bool_masks = frame_masks > 0.5
+                    any_mask = bool_masks.any(dim=0)
+                    if any_mask.any():
+                        obj_idx_map = bool_masks.to(torch.uint8).argmax(dim=0)
+                        color_overlay = colors_t[obj_idx_map]
+                        mask_3d = any_mask.unsqueeze(-1)
+                        frame_gpu = torch.where(mask_3d, frame_gpu * (1 - opacity) + color_overlay * opacity, frame_gpu)
+                    area = bool_masks.sum(dim=(-1, -2)).clamp_(min=1)
+                    cy = (bool_masks * grid_y).sum(dim=(-1, -2)) // area
+                    cx = (bool_masks * grid_x).sum(dim=(-1, -2)) // area
+                    has = area > 1
+                    scores = track_data.get("scores", [])
+                    for obj_idx in range(N_obj):
+                        if has[obj_idx]:
+                            _cx, _cy = int(cx[obj_idx]), int(cy[obj_idx])
+                            color = cls.COLORS[obj_idx % len(cls.COLORS)]
+                            SAM3_TrackPreview._draw_number_gpu(frame_gpu, obj_idx, _cx, _cy, color)
+                            if obj_idx < len(scores) and scores[obj_idx] < 1.0:
+                                SAM3_TrackPreview._draw_number_gpu(frame_gpu, int(scores[obj_idx] * 100),
+                                                                   _cx, _cy + 5 * 3 + 3, color, scale=2)
+                    frame_cpu.copy_(frame_gpu.clamp_(0, 1).mul_(255).byte())
+                else:
+                    frame_cpu.copy_(frame.clamp_(0, 1).mul_(255).byte())
+
+                vframe = av.VideoFrame.from_ndarray(frame_np, format='rgb24')
+                output.mux(stream.encode(vframe.reformat(format='yuv420p')))
+            output.mux(stream.encode(None))
+        return io.NodeOutput(ui=ui.PreviewVideo([ui.SavedResult(filename, "", io.FolderType.temp)]))
+
+
+class SAM3_TrackToMask(io.ComfyNode):
+    """Select tracked objects by index and output as mask."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SAM3_TrackToMask",
+            display_name="SAM3 Track to Mask",
+            category="detection/",
+            inputs=[
+                SAM3TrackData.Input("track_data", display_name="track_data"),
+                io.String.Input("object_indices", display_name="object_indices", default="",
+                                tooltip="Comma-separated object indices to include (e.g. '0,2,3'). Empty = all objects."),
+            ],
+            outputs=[
+                io.Mask.Output("masks", display_name="masks"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, track_data, object_indices="") -> io.NodeOutput:
+        from comfy.ldm.sam3.tracker import unpack_masks
+        packed = track_data["packed_masks"]
+        H, W = track_data["orig_size"]
+
+        if packed is None:
+            N = track_data["n_frames"]
+            return io.NodeOutput(torch.zeros(N, H, W, device=comfy.model_management.intermediate_device()))
+
+        N, N_obj = packed.shape[0], packed.shape[1]
+
+        if object_indices.strip():
+            indices = [int(i.strip()) for i in object_indices.split(",") if i.strip().isdigit()]
+            indices = [i for i in indices if 0 <= i < N_obj]
+        else:
+            indices = list(range(N_obj))
+
+        if not indices:
+            return io.NodeOutput(torch.zeros(N, H, W, device=comfy.model_management.intermediate_device()))
+
+        selected = packed[:, indices]
+        binary = unpack_masks(selected)  # [N, len(indices), Hm, Wm] bool
+        union = binary.any(dim=1, keepdim=True).float()
+        mask_out = F.interpolate(union, size=(H, W), mode="bilinear", align_corners=False)[:, 0]
+        return io.NodeOutput(mask_out)
+
+
+class SAM3Extension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SAM3_Detect,
+            SAM3_VideoTrack,
+            SAM3_TrackPreview,
+            SAM3_TrackToMask,
+        ]
+
+
+async def comfy_entrypoint() -> SAM3Extension:
+    return SAM3Extension()

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 = (False, errors, unique_id)
-    else:
-        ret = (True, [], unique_id)
+    ret = validated.get(unique_id, (True, [], unique_id))
+    # Recursive cycle detection may have already populated an error on us. Join it.
+    ret = (
+        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

folder_paths.py

@@ -52,6 +52,8 @@ folder_names_and_paths["model_patches"] = ([os.path.join(models_dir, "model_patc
 
 folder_names_and_paths["audio_encoders"] = ([os.path.join(models_dir, "audio_encoders")], supported_pt_extensions)
 
+folder_names_and_paths["frame_interpolation"] = ([os.path.join(models_dir, "frame_interpolation")], supported_pt_extensions)
+
 output_directory = os.path.join(base_path, "output")
 temp_directory = os.path.join(base_path, "temp")
 input_directory = os.path.join(base_path, "input")

main.py

@@ -9,6 +9,8 @@ import folder_paths
 import time
 from comfy.cli_args import args, enables_dynamic_vram
 from app.logger import setup_logger
+setup_logger(log_level=args.verbose, use_stdout=args.log_stdout)
+
 from app.assets.seeder import asset_seeder
 from app.assets.services import register_output_files
 import itertools
@@ -27,8 +29,6 @@ if __name__ == "__main__":
     os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
     os.environ['DO_NOT_TRACK'] = '1'
 
-setup_logger(log_level=args.verbose, use_stdout=args.log_stdout)
-
 faulthandler.enable(file=sys.stderr, all_threads=False)
 
 import comfy_aimdo.control

manager_requirements.txt

@@ -1 +1 @@
-comfyui_manager==4.1
+comfyui_manager==4.2.1

nodes.py

@@ -2457,7 +2457,9 @@ async def init_builtin_extra_nodes():
         "nodes_number_convert.py",
         "nodes_painter.py",
         "nodes_curve.py",
-        "nodes_rtdetr.py"
+        "nodes_rtdetr.py",
+        "nodes_frame_interpolation.py",
+        "nodes_sam3.py"
     ]
 
     import_failed = []

requirements.txt

@@ -1,6 +1,6 @@
-comfyui-frontend-package==1.42.11
-comfyui-workflow-templates==0.9.57
-comfyui-embedded-docs==0.4.3
+comfyui-frontend-package==1.42.14
+comfyui-workflow-templates==0.9.62
+comfyui-embedded-docs==0.4.4
 torch
 torchsde
 torchvision
@@ -19,11 +19,11 @@ scipy
 tqdm
 psutil
 alembic
-SQLAlchemy
+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

utils/install_util.py

@@ -39,7 +39,7 @@ def get_required_packages_versions():
                 if len(s) == 2:
                     version_str = s[-1]
                     if not is_valid_version(version_str):
-                        logging.error(f"Invalid version format in requirements.txt: {version_str}")
+                        logging.debug(f"Invalid version format for {s[0]} in requirements.txt: {version_str}")
                         continue
                     out[s[0]] = version_str
         return out.copy()