mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-05-16 12:07:26 +08:00
Merge branch 'master' into feature/implement-morphology-with-max-pool
This commit is contained in:
Omri Marom
GitHub
commit
25f8e58376
1
.gitignore
vendored
1
.gitignore
vendored
@ -21,6 +21,5 @@ venv*/
|
||||
*.log
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||||
web_custom_versions/
|
||||
.DS_Store
|
||||
openapi.yaml
|
||||
filtered-openapi.yaml
|
||||
uv.lock
|
||||
|
||||
@ -2,7 +2,6 @@
|
||||
precision mediump float;
|
||||
|
||||
uniform sampler2D u_image0;
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uniform vec2 u_resolution;
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uniform int u_int0; // Blend mode
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||||
uniform int u_int1; // Color tint
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uniform float u_float0; // Intensity
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@ -75,7 +74,7 @@ void main() {
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float t0 = threshold - 0.15;
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float t1 = threshold + 0.15;
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|
||||
vec2 texelSize = 1.0 / u_resolution;
|
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vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));
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float radius2 = radius * radius;
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|
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float sampleScale = clamp(radius * 0.75, 0.35, 1.0);
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|
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@ -12,7 +12,6 @@ const int RADIAL_SAMPLES = 12;
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const float RADIAL_STRENGTH = 0.0003;
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||||
|
||||
uniform sampler2D u_image0;
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uniform vec2 u_resolution;
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uniform int u_int0; // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)
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uniform float u_float0; // Blur radius/amount
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uniform int u_pass; // Pass index (0 = horizontal, 1 = vertical)
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@ -25,7 +24,7 @@ float gaussian(float x, float sigma) {
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}
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void main() {
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vec2 texelSize = 1.0 / u_resolution;
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vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));
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float radius = max(u_float0, 0.0);
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// Radial (angular) blur - single pass, doesn't use separable
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@ -2,14 +2,13 @@
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precision highp float;
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uniform sampler2D u_image0;
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uniform vec2 u_resolution;
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uniform float u_float0; // strength [0.0 – 2.0] typical: 0.3–1.0
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in vec2 v_texCoord;
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layout(location = 0) out vec4 fragColor0;
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void main() {
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vec2 texel = 1.0 / u_resolution;
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vec2 texel = 1.0 / vec2(textureSize(u_image0, 0));
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// Sample center and neighbors
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vec4 center = texture(u_image0, v_texCoord);
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@ -2,7 +2,6 @@
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precision highp float;
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uniform sampler2D u_image0;
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uniform vec2 u_resolution;
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uniform float u_float0; // amount [0.0 - 3.0] typical: 0.5-1.5
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uniform float u_float1; // radius [0.5 - 10.0] blur radius in pixels
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uniform float u_float2; // threshold [0.0 - 0.1] min difference to sharpen
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@ -19,7 +18,7 @@ float getLuminance(vec3 color) {
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}
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void main() {
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vec2 texel = 1.0 / u_resolution;
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vec2 texel = 1.0 / vec2(textureSize(u_image0, 0));
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float radius = max(u_float1, 0.5);
|
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float amount = u_float0;
|
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float threshold = u_float2;
|
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|
||||
1620
blueprints/Crop Images 2x2.json
Normal file
1620
blueprints/Crop Images 2x2.json
Normal file
File diff suppressed because it is too large
Load Diff
2957
blueprints/Crop Images 3x3.json
Normal file
2957
blueprints/Crop Images 3x3.json
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File diff suppressed because it is too large
Load Diff
@ -160,7 +160,7 @@
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||||
},
|
||||
"revision": 0,
|
||||
"config": {},
|
||||
"name": "local-Depth to Image (Z-Image-Turbo)",
|
||||
"name": "Depth to Image (Z-Image-Turbo)",
|
||||
"inputNode": {
|
||||
"id": -10,
|
||||
"bounding": [
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@ -2482,4 +2482,4 @@
|
||||
"VHS_KeepIntermediate": true
|
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},
|
||||
"version": 0.4
|
||||
}
|
||||
}
|
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@ -261,7 +261,7 @@
|
||||
},
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||||
"revision": 0,
|
||||
"config": {},
|
||||
"name": "local-Depth to Video (LTX 2.0)",
|
||||
"name": "Depth to Video (LTX 2.0)",
|
||||
"inputNode": {
|
||||
"id": -10,
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||||
"bounding": [
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||||
@ -5208,4 +5208,4 @@
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
}
|
||||
3360
blueprints/First-Last-Frame to Video (LTX-2.3).json
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3360
blueprints/First-Last-Frame to Video (LTX-2.3).json
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File diff suppressed because it is too large
Load Diff
@ -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"
|
||||
]
|
||||
},
|
||||
|
||||
@ -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"
|
||||
]
|
||||
}
|
||||
|
||||
2148
blueprints/Image Edit (FireRed Image Edit 1.1).json
Normal file
2148
blueprints/Image Edit (FireRed Image Edit 1.1).json
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File diff suppressed because it is too large
Load Diff
@ -128,7 +128,7 @@
|
||||
},
|
||||
"revision": 0,
|
||||
"config": {},
|
||||
"name": "local-Image Edit (Flux.2 Klein 4B)",
|
||||
"name": "Image Edit (Flux.2 Klein 4B)",
|
||||
"inputNode": {
|
||||
"id": -10,
|
||||
"bounding": [
|
||||
@ -1837,4 +1837,4 @@
|
||||
}
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
}
|
||||
1427
blueprints/Image Edit (LongCat Image Edit).json
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blueprints/Image Edit (LongCat Image Edit).json
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Load Diff
1205
blueprints/Image Inpainting (Flux.1 Fill Dev).json
Normal file
1205
blueprints/Image Inpainting (Flux.1 Fill Dev).json
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Load Diff
@ -124,7 +124,7 @@
|
||||
},
|
||||
"revision": 0,
|
||||
"config": {},
|
||||
"name": "local-Image Inpainting (Qwen-image)",
|
||||
"name": "Image Inpainting (Qwen-image)",
|
||||
"inputNode": {
|
||||
"id": -10,
|
||||
"bounding": [
|
||||
@ -1923,4 +1923,4 @@
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
}
|
||||
@ -204,7 +204,7 @@
|
||||
},
|
||||
"revision": 0,
|
||||
"config": {},
|
||||
"name": "local-Image Outpainting (Qwen-Image)",
|
||||
"name": "Image Outpainting (Qwen-Image)",
|
||||
"inputNode": {
|
||||
"id": -10,
|
||||
"bounding": [
|
||||
@ -2749,4 +2749,4 @@
|
||||
}
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
}
|
||||
@ -1,15 +1,14 @@
|
||||
{
|
||||
"id": "1a761372-7c82-4016-b9bf-fa285967e1e9",
|
||||
"revision": 0,
|
||||
"last_node_id": 83,
|
||||
"last_node_id": 176,
|
||||
"last_link_id": 0,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 83,
|
||||
"type": "f754a936-daaf-4b6e-9658-41fdc54d301d",
|
||||
"id": 176,
|
||||
"type": "2d2e3c8e-53b3-4618-be52-6d1d99382f0e",
|
||||
"pos": [
|
||||
61.999827823554256,
|
||||
153.3332507624185
|
||||
-1150,
|
||||
200
|
||||
],
|
||||
"size": [
|
||||
400,
|
||||
@ -56,6 +55,38 @@
|
||||
"name": "layers"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"name": "seed",
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "seed"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"name": "unet_name",
|
||||
"type": "COMBO",
|
||||
"widget": {
|
||||
"name": "unet_name"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"name": "clip_name",
|
||||
"type": "COMBO",
|
||||
"widget": {
|
||||
"name": "clip_name"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"name": "vae_name",
|
||||
"type": "COMBO",
|
||||
"widget": {
|
||||
"name": "vae_name"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
@ -66,28 +97,41 @@
|
||||
"links": []
|
||||
}
|
||||
],
|
||||
"title": "Image to Layers (Qwen-Image-Layered)",
|
||||
"properties": {
|
||||
"proxyWidgets": [
|
||||
[
|
||||
"-1",
|
||||
"6",
|
||||
"text"
|
||||
],
|
||||
[
|
||||
"-1",
|
||||
"3",
|
||||
"steps"
|
||||
],
|
||||
[
|
||||
"-1",
|
||||
"3",
|
||||
"cfg"
|
||||
],
|
||||
[
|
||||
"-1",
|
||||
"83",
|
||||
"layers"
|
||||
],
|
||||
[
|
||||
"3",
|
||||
"seed"
|
||||
],
|
||||
[
|
||||
"37",
|
||||
"unet_name"
|
||||
],
|
||||
[
|
||||
"38",
|
||||
"clip_name"
|
||||
],
|
||||
[
|
||||
"39",
|
||||
"vae_name"
|
||||
],
|
||||
[
|
||||
"3",
|
||||
"control_after_generate"
|
||||
@ -95,6 +139,11 @@
|
||||
],
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.5.1",
|
||||
"ue_properties": {
|
||||
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|
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|
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|
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},
|
||||
"enableTabs": false,
|
||||
"tabWidth": 65,
|
||||
"tabXOffset": 10,
|
||||
@ -103,25 +152,20 @@
|
||||
"secondTabOffset": 80,
|
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"secondTabWidth": 65
|
||||
},
|
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"widgets_values": [
|
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"",
|
||||
20,
|
||||
2.5,
|
||||
2
|
||||
]
|
||||
"widgets_values": []
|
||||
}
|
||||
],
|
||||
"links": [],
|
||||
"groups": [],
|
||||
"version": 0.4,
|
||||
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|
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"subgraphs": [
|
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{
|
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"id": "f754a936-daaf-4b6e-9658-41fdc54d301d",
|
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"id": "2d2e3c8e-53b3-4618-be52-6d1d99382f0e",
|
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"version": 1,
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|
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"lastLinkId": 380,
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"lastRerouteId": 0
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},
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"revision": 0,
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@ -130,10 +174,10 @@
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"inputNode": {
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|
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720,
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140
|
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220
|
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]
|
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},
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"outputNode": {
|
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@ -156,8 +200,8 @@
|
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],
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"localized_name": "image",
|
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-410,
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543
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-620,
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740
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},
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{
|
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@ -168,8 +212,8 @@
|
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150
|
||||
],
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|
||||
-410,
|
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563
|
||||
-620,
|
||||
760
|
||||
]
|
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},
|
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{
|
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@ -180,8 +224,8 @@
|
||||
153
|
||||
],
|
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"pos": [
|
||||
-410,
|
||||
583
|
||||
-620,
|
||||
780
|
||||
]
|
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},
|
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{
|
||||
@ -192,8 +236,8 @@
|
||||
154
|
||||
],
|
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"pos": [
|
||||
-410,
|
||||
603
|
||||
-620,
|
||||
800
|
||||
]
|
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},
|
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{
|
||||
@ -204,8 +248,56 @@
|
||||
159
|
||||
],
|
||||
"pos": [
|
||||
-410,
|
||||
623
|
||||
-620,
|
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820
|
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]
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},
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{
|
||||
"id": "9f76338b-f4ca-4bb3-b61a-57b3f233061e",
|
||||
"name": "seed",
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"type": "INT",
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"linkIds": [
|
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377
|
||||
],
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-620,
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840
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]
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},
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{
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"id": "8d0422d5-5eee-4f7e-9817-dc613cc62eca",
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"name": "unet_name",
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"type": "COMBO",
|
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"linkIds": [
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378
|
||||
],
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"pos": [
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-620,
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860
|
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]
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},
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{
|
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"id": "552eece2-a735-4d00-ae78-ded454622bc1",
|
||||
"name": "clip_name",
|
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"type": "COMBO",
|
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"linkIds": [
|
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379
|
||||
],
|
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"pos": [
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-620,
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880
|
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]
|
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},
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{
|
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"id": "1e6d141c-d0f9-4a2b-895c-b6780e57cfa0",
|
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"name": "vae_name",
|
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"type": "COMBO",
|
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"linkIds": [
|
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380
|
||||
],
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"pos": [
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-620,
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900
|
||||
]
|
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}
|
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],
|
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@ -231,14 +323,14 @@
|
||||
"type": "CLIPLoader",
|
||||
"pos": [
|
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-320,
|
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310
|
||||
360
|
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],
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"size": [
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346.7470703125,
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106
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350,
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150
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],
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"flags": {},
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"order": 0,
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"order": 5,
|
||||
"mode": 0,
|
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"inputs": [
|
||||
{
|
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@ -248,7 +340,7 @@
|
||||
"widget": {
|
||||
"name": "clip_name"
|
||||
},
|
||||
"link": null
|
||||
"link": 379
|
||||
},
|
||||
{
|
||||
"localized_name": "type",
|
||||
@ -283,9 +375,14 @@
|
||||
}
|
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],
|
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"properties": {
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||||
"Node name for S&R": "CLIPLoader",
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||||
"cnr_id": "comfy-core",
|
||||
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||||
"ue_properties": {
|
||||
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|
||||
"input_ue_unconnectable": {},
|
||||
"version": "7.7"
|
||||
},
|
||||
"Node name for S&R": "CLIPLoader",
|
||||
"models": [
|
||||
{
|
||||
"name": "qwen_2.5_vl_7b_fp8_scaled.safetensors",
|
||||
@ -312,14 +409,14 @@
|
||||
"type": "VAELoader",
|
||||
"pos": [
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||||
-320,
|
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460
|
||||
580
|
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],
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346.7470703125,
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58
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|
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"mode": 0,
|
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"inputs": [
|
||||
{
|
||||
@ -329,7 +426,7 @@
|
||||
"widget": {
|
||||
"name": "vae_name"
|
||||
},
|
||||
"link": null
|
||||
"link": 380
|
||||
}
|
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],
|
||||
"outputs": [
|
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@ -345,9 +442,14 @@
|
||||
}
|
||||
],
|
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"properties": {
|
||||
"Node name for S&R": "VAELoader",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.5.1",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"input_ue_unconnectable": {},
|
||||
"version": "7.7"
|
||||
},
|
||||
"Node name for S&R": "VAELoader",
|
||||
"models": [
|
||||
{
|
||||
"name": "qwen_image_layered_vae.safetensors",
|
||||
@ -375,11 +477,11 @@
|
||||
420
|
||||
],
|
||||
"size": [
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425.27801513671875,
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180.6060791015625
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||||
430,
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190
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"flags": {},
|
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"order": 3,
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@ -411,9 +513,14 @@
|
||||
],
|
||||
"title": "CLIP Text Encode (Negative Prompt)",
|
||||
"properties": {
|
||||
"Node name for S&R": "CLIPTextEncode",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.5.1",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"input_ue_unconnectable": {},
|
||||
"version": "7.7"
|
||||
},
|
||||
"Node name for S&R": "CLIPTextEncode",
|
||||
"enableTabs": false,
|
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"tabXOffset": 10,
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@ -432,12 +539,12 @@
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"id": 70,
|
||||
"type": "ReferenceLatent",
|
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"pos": [
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||||
140,
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46
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],
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"flags": {
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"collapsed": true
|
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@ -470,9 +577,14 @@
|
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}
|
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],
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"properties": {
|
||||
"Node name for S&R": "ReferenceLatent",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.5.1",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
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"input_ue_unconnectable": {},
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"version": "7.7"
|
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},
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"Node name for S&R": "ReferenceLatent",
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"enableTabs": false,
|
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"tabWidth": 65,
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"tabXOffset": 10,
|
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@ -480,19 +592,18 @@
|
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"secondTabText": "Send Back",
|
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"secondTabOffset": 80,
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"secondTabWidth": 65
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},
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"widgets_values": []
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}
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},
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{
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"id": 69,
|
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"type": "ReferenceLatent",
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"pos": [
|
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||||
160,
|
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820
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],
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"size": [
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"flags": {
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|
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@ -525,9 +636,14 @@
|
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}
|
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"properties": {
|
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"Node name for S&R": "ReferenceLatent",
|
||||
"cnr_id": "comfy-core",
|
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"ver": "0.5.1",
|
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"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"input_ue_unconnectable": {},
|
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"version": "7.7"
|
||||
},
|
||||
"Node name for S&R": "ReferenceLatent",
|
||||
"enableTabs": false,
|
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"tabWidth": 65,
|
||||
"tabXOffset": 10,
|
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@ -535,8 +651,7 @@
|
||||
"secondTabText": "Send Back",
|
||||
"secondTabOffset": 80,
|
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"secondTabWidth": 65
|
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},
|
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}
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},
|
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{
|
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"id": 66,
|
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@ -547,10 +662,10 @@
|
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],
|
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"size": [
|
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|
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58
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110
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],
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"flags": {},
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|
||||
"order": 7,
|
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"mode": 0,
|
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"inputs": [
|
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{
|
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@ -580,9 +695,14 @@
|
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}
|
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],
|
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"properties": {
|
||||
"Node name for S&R": "ModelSamplingAuraFlow",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.5.1",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
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"input_ue_unconnectable": {},
|
||||
"version": "7.7"
|
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},
|
||||
"Node name for S&R": "ModelSamplingAuraFlow",
|
||||
"enableTabs": false,
|
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"tabWidth": 65,
|
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"tabXOffset": 10,
|
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@ -600,11 +720,11 @@
|
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"type": "LatentCutToBatch",
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"pos": [
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|
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||||
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],
|
||||
"size": [
|
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|
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82
|
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140
|
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],
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"flags": {},
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"order": 11,
|
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@ -646,9 +766,14 @@
|
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}
|
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],
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"properties": {
|
||||
"Node name for S&R": "LatentCutToBatch",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.5.1",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
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"input_ue_unconnectable": {},
|
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"version": "7.7"
|
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},
|
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"Node name for S&R": "LatentCutToBatch",
|
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"enableTabs": false,
|
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"tabWidth": 65,
|
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"tabXOffset": 10,
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@ -666,12 +791,12 @@
|
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"id": 71,
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"type": "VAEEncode",
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-280,
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|
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],
|
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"size": [
|
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|
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|
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|
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],
|
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"flags": {
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|
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@ -704,9 +829,14 @@
|
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}
|
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],
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"properties": {
|
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"Node name for S&R": "VAEEncode",
|
||||
"cnr_id": "comfy-core",
|
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"ver": "0.5.1",
|
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"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
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"input_ue_unconnectable": {},
|
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"version": "7.7"
|
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},
|
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"Node name for S&R": "VAEEncode",
|
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"enableTabs": false,
|
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"tabWidth": 65,
|
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"tabXOffset": 10,
|
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@ -714,24 +844,23 @@
|
||||
"secondTabText": "Send Back",
|
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"secondTabOffset": 80,
|
||||
"secondTabWidth": 65
|
||||
},
|
||||
"widgets_values": []
|
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}
|
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},
|
||||
{
|
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"id": 8,
|
||||
"type": "VAEDecode",
|
||||
"pos": [
|
||||
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|
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310
|
||||
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|
||||
],
|
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|
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|
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46
|
||||
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|
||||
],
|
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"flags": {
|
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|
||||
},
|
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"order": 7,
|
||||
"order": 3,
|
||||
"mode": 0,
|
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"inputs": [
|
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{
|
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@ -759,9 +888,14 @@
|
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}
|
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],
|
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"properties": {
|
||||
"Node name for S&R": "VAEDecode",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.5.1",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"input_ue_unconnectable": {},
|
||||
"version": "7.7"
|
||||
},
|
||||
"Node name for S&R": "VAEDecode",
|
||||
"enableTabs": false,
|
||||
"tabWidth": 65,
|
||||
"tabXOffset": 10,
|
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@ -769,8 +903,7 @@
|
||||
"secondTabText": "Send Back",
|
||||
"secondTabOffset": 80,
|
||||
"secondTabWidth": 65
|
||||
},
|
||||
"widgets_values": []
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 6,
|
||||
@ -780,11 +913,11 @@
|
||||
180
|
||||
],
|
||||
"size": [
|
||||
422.84503173828125,
|
||||
164.31304931640625
|
||||
430,
|
||||
170
|
||||
],
|
||||
"flags": {},
|
||||
"order": 6,
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@ -816,9 +949,14 @@
|
||||
],
|
||||
"title": "CLIP Text Encode (Positive Prompt)",
|
||||
"properties": {
|
||||
"Node name for S&R": "CLIPTextEncode",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.5.1",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"input_ue_unconnectable": {},
|
||||
"version": "7.7"
|
||||
},
|
||||
"Node name for S&R": "CLIPTextEncode",
|
||||
"enableTabs": false,
|
||||
"tabWidth": 65,
|
||||
"tabXOffset": 10,
|
||||
@ -838,14 +976,14 @@
|
||||
"type": "KSampler",
|
||||
"pos": [
|
||||
530,
|
||||
280
|
||||
340
|
||||
],
|
||||
"size": [
|
||||
270,
|
||||
400
|
||||
],
|
||||
"flags": {},
|
||||
"order": 5,
|
||||
"order": 0,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@ -879,7 +1017,7 @@
|
||||
"widget": {
|
||||
"name": "seed"
|
||||
},
|
||||
"link": null
|
||||
"link": 377
|
||||
},
|
||||
{
|
||||
"localized_name": "steps",
|
||||
@ -939,9 +1077,14 @@
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "KSampler",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.5.1",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"input_ue_unconnectable": {},
|
||||
"version": "7.7"
|
||||
},
|
||||
"Node name for S&R": "KSampler",
|
||||
"enableTabs": false,
|
||||
"tabWidth": 65,
|
||||
"tabXOffset": 10,
|
||||
@ -964,12 +1107,12 @@
|
||||
"id": 78,
|
||||
"type": "GetImageSize",
|
||||
"pos": [
|
||||
80,
|
||||
790
|
||||
-280,
|
||||
930
|
||||
],
|
||||
"size": [
|
||||
210,
|
||||
136
|
||||
230,
|
||||
140
|
||||
],
|
||||
"flags": {},
|
||||
"order": 12,
|
||||
@ -1007,9 +1150,14 @@
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "GetImageSize",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.5.1",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"input_ue_unconnectable": {},
|
||||
"version": "7.7"
|
||||
},
|
||||
"Node name for S&R": "GetImageSize",
|
||||
"enableTabs": false,
|
||||
"tabWidth": 65,
|
||||
"tabXOffset": 10,
|
||||
@ -1017,23 +1165,23 @@
|
||||
"secondTabText": "Send Back",
|
||||
"secondTabOffset": 80,
|
||||
"secondTabWidth": 65
|
||||
},
|
||||
"widgets_values": []
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 83,
|
||||
"type": "EmptyQwenImageLayeredLatentImage",
|
||||
"pos": [
|
||||
320,
|
||||
790
|
||||
-280,
|
||||
1120
|
||||
],
|
||||
"size": [
|
||||
330.9341796875,
|
||||
130
|
||||
340,
|
||||
200
|
||||
],
|
||||
"flags": {},
|
||||
"order": 13,
|
||||
"mode": 0,
|
||||
"showAdvanced": true,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "width",
|
||||
@ -1083,9 +1231,14 @@
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "EmptyQwenImageLayeredLatentImage",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.5.1",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"input_ue_unconnectable": {},
|
||||
"version": "7.7"
|
||||
},
|
||||
"Node name for S&R": "EmptyQwenImageLayeredLatentImage",
|
||||
"enableTabs": false,
|
||||
"tabWidth": 65,
|
||||
"tabXOffset": 10,
|
||||
@ -1109,11 +1262,11 @@
|
||||
180
|
||||
],
|
||||
"size": [
|
||||
346.7470703125,
|
||||
82
|
||||
350,
|
||||
110
|
||||
],
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"order": 4,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@ -1123,7 +1276,7 @@
|
||||
"widget": {
|
||||
"name": "unet_name"
|
||||
},
|
||||
"link": null
|
||||
"link": 378
|
||||
},
|
||||
{
|
||||
"localized_name": "weight_dtype",
|
||||
@ -1147,9 +1300,14 @@
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "UNETLoader",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.5.1",
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"input_ue_unconnectable": {},
|
||||
"version": "7.7"
|
||||
},
|
||||
"Node name for S&R": "UNETLoader",
|
||||
"models": [
|
||||
{
|
||||
"name": "qwen_image_layered_bf16.safetensors",
|
||||
@ -1191,8 +1349,8 @@
|
||||
"bounding": [
|
||||
-330,
|
||||
110,
|
||||
366.7470703125,
|
||||
421.6
|
||||
370,
|
||||
610
|
||||
],
|
||||
"color": "#3f789e",
|
||||
"font_size": 24,
|
||||
@ -1391,6 +1549,38 @@
|
||||
"target_id": 83,
|
||||
"target_slot": 2,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 377,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 5,
|
||||
"target_id": 3,
|
||||
"target_slot": 4,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 378,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 6,
|
||||
"target_id": 37,
|
||||
"target_slot": 0,
|
||||
"type": "COMBO"
|
||||
},
|
||||
{
|
||||
"id": 379,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 7,
|
||||
"target_id": 38,
|
||||
"target_slot": 0,
|
||||
"type": "COMBO"
|
||||
},
|
||||
{
|
||||
"id": 380,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 8,
|
||||
"target_id": 39,
|
||||
"target_slot": 0,
|
||||
"type": "COMBO"
|
||||
}
|
||||
],
|
||||
"extra": {
|
||||
@ -1400,7 +1590,6 @@
|
||||
}
|
||||
]
|
||||
},
|
||||
"config": {},
|
||||
"extra": {
|
||||
"ds": {
|
||||
"scale": 1.14,
|
||||
@ -1409,7 +1598,6 @@
|
||||
6.855893974423647
|
||||
]
|
||||
},
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
"ue_links": []
|
||||
}
|
||||
}
|
||||
4233
blueprints/Image to Video (LTX-2.3).json
Normal file
4233
blueprints/Image to Video (LTX-2.3).json
Normal file
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -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"
|
||||
]
|
||||
}
|
||||
|
||||
1046
blueprints/Text to Image (Flux.1 Dev).json
Normal file
1046
blueprints/Text to Image (Flux.1 Dev).json
Normal file
File diff suppressed because it is too large
Load Diff
1040
blueprints/Text to Image (Flux.1 Krea Dev).json
Normal file
1040
blueprints/Text to Image (Flux.1 Krea Dev).json
Normal file
File diff suppressed because it is too large
Load Diff
1468
blueprints/Text to Image (NetaYume Lumina).json
Normal file
1468
blueprints/Text to Image (NetaYume Lumina).json
Normal file
File diff suppressed because it is too large
Load Diff
1951
blueprints/Text to Image (Qwen-Image 2512).json
Normal file
1951
blueprints/Text to Image (Qwen-Image 2512).json
Normal file
File diff suppressed because it is too large
Load Diff
1881
blueprints/Text to Image (Qwen-Image).json
Normal file
1881
blueprints/Text to Image (Qwen-Image).json
Normal file
File diff suppressed because it is too large
Load Diff
4296
blueprints/Text to Video (LTX-2.3).json
Normal file
4296
blueprints/Text to Video (LTX-2.3).json
Normal file
File diff suppressed because it is too large
Load Diff
@ -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"
|
||||
]
|
||||
}
|
||||
|
||||
@ -54,7 +54,7 @@ class SplitMHA(nn.Module):
|
||||
if mask is not None and mask.ndim == 2:
|
||||
mask = mask[:, None, None, :] # [B, T] -> [B, 1, 1, T] for SDPA broadcast
|
||||
dtype = q.dtype # manual_cast may produce mixed dtypes
|
||||
out = optimized_attention(q, k.to(dtype), v.to(dtype), self.num_heads, mask=mask)
|
||||
out = optimized_attention(q, k.to(dtype), v.to(dtype), self.num_heads, mask=mask, low_precision_attention=False)
|
||||
return self.out_proj(out)
|
||||
|
||||
|
||||
|
||||
@ -40,7 +40,7 @@ class SAMAttention(nn.Module):
|
||||
q = self.q_proj(q)
|
||||
k = self.k_proj(k)
|
||||
v = self.v_proj(v)
|
||||
return self.out_proj(optimized_attention(q, k, v, self.num_heads))
|
||||
return self.out_proj(optimized_attention(q, k, v, self.num_heads, low_precision_attention=False))
|
||||
|
||||
|
||||
class TwoWayAttentionBlock(nn.Module):
|
||||
@ -179,7 +179,7 @@ class Attention(nn.Module):
|
||||
q, k, v = qkv.permute(2, 0, 3, 1, 4).unbind(dim=0)
|
||||
if self.use_rope and freqs_cis is not None:
|
||||
q, k = apply_rope(q, k, freqs_cis)
|
||||
return self.proj(optimized_attention(q, k, v, self.num_heads, skip_reshape=True))
|
||||
return self.proj(optimized_attention(q, k, v, self.num_heads, skip_reshape=True, low_precision_attention=False))
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
|
||||
@ -364,7 +364,7 @@ class SplitAttn(nn.Module):
|
||||
v = self.v_proj(v)
|
||||
if rope is not None:
|
||||
q, k = apply_rope_memory(q, k, rope, self.num_heads, num_k_exclude_rope)
|
||||
out = optimized_attention(q, k, v, self.num_heads)
|
||||
out = optimized_attention(q, k, v, self.num_heads, low_precision_attention=False)
|
||||
return self.out_proj(out)
|
||||
|
||||
|
||||
@ -657,7 +657,7 @@ class DecoupledMemoryAttnLayer(nn.Module):
|
||||
v = self.self_attn_v_proj(normed)
|
||||
if rope is not None:
|
||||
q, k = apply_rope_memory(q, k, rope, self.num_heads, 0)
|
||||
x = x + self.self_attn_out_proj(optimized_attention(q, k, v, self.num_heads))
|
||||
x = x + self.self_attn_out_proj(optimized_attention(q, k, v, self.num_heads, low_precision_attention=False))
|
||||
|
||||
# Decoupled cross-attention: fuse image and memory projections
|
||||
normed = self.norm2(x)
|
||||
@ -668,7 +668,7 @@ class DecoupledMemoryAttnLayer(nn.Module):
|
||||
v = self.cross_attn_v_proj(memory)
|
||||
if rope is not None:
|
||||
q, k = apply_rope_memory(q, k, rope, self.num_heads, num_k_exclude_rope)
|
||||
x = x + self.cross_attn_out_proj(optimized_attention(q, k, v, self.num_heads))
|
||||
x = x + self.cross_attn_out_proj(optimized_attention(q, k, v, self.num_heads, low_precision_attention=False))
|
||||
|
||||
# FFN
|
||||
x = x + self.linear2(F.gelu(self.linear1(self.norm3(x))))
|
||||
|
||||
@ -9,6 +9,7 @@ from comfy_api.latest._input import (
|
||||
CurveInput,
|
||||
MonotoneCubicCurve,
|
||||
LinearCurve,
|
||||
RangeInput,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
@ -21,4 +22,5 @@ __all__ = [
|
||||
"CurveInput",
|
||||
"MonotoneCubicCurve",
|
||||
"LinearCurve",
|
||||
"RangeInput",
|
||||
]
|
||||
|
||||
@ -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",
|
||||
]
|
||||
|
||||
70
comfy_api/latest/_input/range_types.py
Normal file
70
comfy_api/latest/_input/range_types.py
Normal file
@ -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})"
|
||||
@ -240,19 +240,34 @@ class VideoFromFile(VideoInput):
|
||||
start_time = self.__start_time
|
||||
# Get video frames
|
||||
frames = []
|
||||
alphas = None
|
||||
start_pts = int(start_time / video_stream.time_base)
|
||||
end_pts = int((start_time + self.__duration) / video_stream.time_base)
|
||||
container.seek(start_pts, stream=video_stream)
|
||||
image_format = 'gbrpf32le'
|
||||
for frame in container.decode(video_stream):
|
||||
if alphas is None:
|
||||
for comp in frame.format.components:
|
||||
if comp.is_alpha:
|
||||
alphas = []
|
||||
image_format = 'gbrapf32le'
|
||||
break
|
||||
|
||||
if frame.pts < start_pts:
|
||||
continue
|
||||
if self.__duration and frame.pts >= end_pts:
|
||||
break
|
||||
img = frame.to_ndarray(format='rgb24') # shape: (H, W, 3)
|
||||
img = torch.from_numpy(img) / 255.0 # shape: (H, W, 3)
|
||||
frames.append(img)
|
||||
|
||||
images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 3, 0, 0)
|
||||
img = frame.to_ndarray(format=image_format) # shape: (H, W, 4)
|
||||
if alphas is None:
|
||||
frames.append(torch.from_numpy(img))
|
||||
else:
|
||||
frames.append(torch.from_numpy(img[..., :-1]))
|
||||
alphas.append(torch.from_numpy(img[..., -1:]))
|
||||
|
||||
images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 0, 0, 3)
|
||||
if alphas is not None:
|
||||
alphas = torch.stack(alphas) if len(alphas) > 0 else torch.zeros(0, 0, 0, 1)
|
||||
|
||||
# Get frame rate
|
||||
frame_rate = Fraction(video_stream.average_rate) if video_stream.average_rate else Fraction(1)
|
||||
@ -295,7 +310,7 @@ class VideoFromFile(VideoInput):
|
||||
})
|
||||
|
||||
metadata = container.metadata
|
||||
return VideoComponents(images=images, audio=audio, frame_rate=frame_rate, metadata=metadata)
|
||||
return VideoComponents(images=images, alpha=alphas, audio=audio, frame_rate=frame_rate, metadata=metadata)
|
||||
|
||||
def get_components(self) -> VideoComponents:
|
||||
if isinstance(self.__file, io.BytesIO):
|
||||
|
||||
@ -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",
|
||||
]
|
||||
|
||||
@ -3,7 +3,7 @@ from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
from fractions import Fraction
|
||||
from typing import Optional
|
||||
from .._input import ImageInput, AudioInput
|
||||
from .._input import ImageInput, AudioInput, MaskInput
|
||||
|
||||
class VideoCodec(str, Enum):
|
||||
AUTO = "auto"
|
||||
@ -48,5 +48,4 @@ class VideoComponents:
|
||||
frame_rate: Fraction
|
||||
audio: Optional[AudioInput] = None
|
||||
metadata: Optional[dict] = None
|
||||
|
||||
|
||||
alpha: Optional[MaskInput] = None
|
||||
|
||||
@ -118,7 +118,7 @@ class Wan27ReferenceVideoInputField(BaseModel):
|
||||
class Wan27ReferenceVideoParametersField(BaseModel):
|
||||
resolution: str = Field(...)
|
||||
ratio: str | None = Field(None)
|
||||
duration: int = Field(5, ge=2, le=10)
|
||||
duration: int = Field(5, ge=2, le=15)
|
||||
watermark: bool = Field(False)
|
||||
seed: int = Field(..., ge=0, le=2147483647)
|
||||
|
||||
@ -157,7 +157,7 @@ class Wan27VideoEditInputField(BaseModel):
|
||||
class Wan27VideoEditParametersField(BaseModel):
|
||||
resolution: str = Field(...)
|
||||
ratio: str | None = Field(None)
|
||||
duration: int = Field(0)
|
||||
duration: int | None = Field(0)
|
||||
audio_setting: str = Field("auto")
|
||||
watermark: bool = Field(False)
|
||||
seed: int = Field(..., ge=0, le=2147483647)
|
||||
|
||||
@ -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))
|
||||
|
||||
@ -357,6 +357,10 @@ def calculate_tokens_price_image_1_5(response: OpenAIImageGenerationResponse) ->
|
||||
return ((response.usage.input_tokens * 8.0) + (response.usage.output_tokens * 32.0)) / 1_000_000.0
|
||||
|
||||
|
||||
def calculate_tokens_price_image_2_0(response: OpenAIImageGenerationResponse) -> float | None:
|
||||
return ((response.usage.input_tokens * 8.0) + (response.usage.output_tokens * 30.0)) / 1_000_000.0
|
||||
|
||||
|
||||
class OpenAIGPTImage1(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
@ -401,7 +405,17 @@ class OpenAIGPTImage1(IO.ComfyNode):
|
||||
IO.Combo.Input(
|
||||
"size",
|
||||
default="auto",
|
||||
options=["auto", "1024x1024", "1024x1536", "1536x1024"],
|
||||
options=[
|
||||
"auto",
|
||||
"1024x1024",
|
||||
"1024x1536",
|
||||
"1536x1024",
|
||||
"2048x2048",
|
||||
"2048x1152",
|
||||
"1152x2048",
|
||||
"3840x2160",
|
||||
"2160x3840",
|
||||
],
|
||||
tooltip="Image size",
|
||||
optional=True,
|
||||
),
|
||||
@ -427,7 +441,7 @@ class OpenAIGPTImage1(IO.ComfyNode):
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"model",
|
||||
options=["gpt-image-1", "gpt-image-1.5", 'gpt-image-2'],
|
||||
options=["gpt-image-1", "gpt-image-1.5", "gpt-image-2"],
|
||||
default="gpt-image-2",
|
||||
optional=True,
|
||||
),
|
||||
@ -442,23 +456,36 @@ class OpenAIGPTImage1(IO.ComfyNode):
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(widgets=["quality", "n"]),
|
||||
depends_on=IO.PriceBadgeDepends(widgets=["quality", "n", "model"]),
|
||||
expr="""
|
||||
(
|
||||
$ranges := {
|
||||
"low": [0.011, 0.02],
|
||||
"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 }
|
||||
}
|
||||
)
|
||||
""",
|
||||
@ -483,12 +510,18 @@ class OpenAIGPTImage1(IO.ComfyNode):
|
||||
if mask is not None and image is None:
|
||||
raise ValueError("Cannot use a mask without an input image")
|
||||
|
||||
if model in ("gpt-image-1", "gpt-image-1.5"):
|
||||
if size not in ("auto", "1024x1024", "1024x1536", "1536x1024"):
|
||||
raise ValueError(f"Resolution {size} is only supported by GPT Image 2 model")
|
||||
|
||||
if model == "gpt-image-1":
|
||||
price_extractor = calculate_tokens_price_image_1
|
||||
elif model == "gpt-image-1.5":
|
||||
price_extractor = calculate_tokens_price_image_1_5
|
||||
elif model == "gpt-image-2":
|
||||
price_extractor = calculate_tokens_price_image_1_5
|
||||
price_extractor = calculate_tokens_price_image_2_0
|
||||
if background == "transparent":
|
||||
raise ValueError("Transparent background is not supported for GPT Image 2 model")
|
||||
else:
|
||||
raise ValueError(f"Unknown model: {model}")
|
||||
|
||||
|
||||
@ -33,9 +33,13 @@ class OpenAIVideoSora2(IO.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="OpenAIVideoSora2",
|
||||
display_name="OpenAI Sora - Video",
|
||||
display_name="OpenAI Sora - Video (Deprecated)",
|
||||
category="api node/video/Sora",
|
||||
description="OpenAI video and audio generation.",
|
||||
description=(
|
||||
"OpenAI video and audio generation.\n\n"
|
||||
"DEPRECATION NOTICE: OpenAI will stop serving the Sora v2 API in September 2026. "
|
||||
"This node will be removed from ComfyUI at that time."
|
||||
),
|
||||
inputs=[
|
||||
IO.Combo.Input(
|
||||
"model",
|
||||
|
||||
@ -1646,6 +1646,557 @@ class Wan2ReferenceVideoApi(IO.ComfyNode):
|
||||
return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
|
||||
|
||||
|
||||
class HappyHorseTextToVideoApi(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="HappyHorseTextToVideoApi",
|
||||
display_name="HappyHorse Text to Video",
|
||||
category="api node/video/Wan",
|
||||
description="Generates a video based on a text prompt using the HappyHorse model.",
|
||||
inputs=[
|
||||
IO.DynamicCombo.Input(
|
||||
"model",
|
||||
options=[
|
||||
IO.DynamicCombo.Option(
|
||||
"happyhorse-1.0-t2v",
|
||||
[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
tooltip="Prompt describing the elements and visual features. "
|
||||
"Supports English and Chinese.",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"resolution",
|
||||
options=["720P", "1080P"],
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"ratio",
|
||||
options=["16:9", "9:16", "1:1", "4:3", "3:4"],
|
||||
),
|
||||
IO.Int.Input(
|
||||
"duration",
|
||||
default=5,
|
||||
min=3,
|
||||
max=15,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
IO.Int.Input(
|
||||
"seed",
|
||||
default=0,
|
||||
min=0,
|
||||
max=2147483647,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
control_after_generate=True,
|
||||
tooltip="Seed to use for generation.",
|
||||
),
|
||||
IO.Boolean.Input(
|
||||
"watermark",
|
||||
default=False,
|
||||
tooltip="Whether to add an AI-generated watermark to the result.",
|
||||
advanced=True,
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Video.Output(),
|
||||
],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
|
||||
expr="""
|
||||
(
|
||||
$res := $lookup(widgets, "model.resolution");
|
||||
$dur := $lookup(widgets, "model.duration");
|
||||
$ppsTable := { "720p": 0.14, "1080p": 0.24 };
|
||||
$pps := $lookup($ppsTable, $res);
|
||||
{ "type": "usd", "usd": $pps * $dur }
|
||||
)
|
||||
""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
model: dict,
|
||||
seed: int,
|
||||
watermark: bool,
|
||||
):
|
||||
validate_string(model["prompt"], strip_whitespace=False, min_length=1)
|
||||
initial_response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(
|
||||
path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
|
||||
method="POST",
|
||||
),
|
||||
response_model=TaskCreationResponse,
|
||||
data=Wan27Text2VideoTaskCreationRequest(
|
||||
model=model["model"],
|
||||
input=Text2VideoInputField(
|
||||
prompt=model["prompt"],
|
||||
negative_prompt=None,
|
||||
),
|
||||
parameters=Wan27Text2VideoParametersField(
|
||||
resolution=model["resolution"],
|
||||
ratio=model["ratio"],
|
||||
duration=model["duration"],
|
||||
seed=seed,
|
||||
watermark=watermark,
|
||||
),
|
||||
),
|
||||
)
|
||||
if not initial_response.output:
|
||||
raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
|
||||
response = await poll_op(
|
||||
cls,
|
||||
ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
|
||||
response_model=VideoTaskStatusResponse,
|
||||
status_extractor=lambda x: x.output.task_status,
|
||||
poll_interval=7,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
|
||||
|
||||
|
||||
class HappyHorseImageToVideoApi(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="HappyHorseImageToVideoApi",
|
||||
display_name="HappyHorse Image to Video",
|
||||
category="api node/video/Wan",
|
||||
description="Generate a video from a first-frame image using the HappyHorse model.",
|
||||
inputs=[
|
||||
IO.DynamicCombo.Input(
|
||||
"model",
|
||||
options=[
|
||||
IO.DynamicCombo.Option(
|
||||
"happyhorse-1.0-i2v",
|
||||
[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
tooltip="Prompt describing the elements and visual features. "
|
||||
"Supports English and Chinese.",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"resolution",
|
||||
options=["720P", "1080P"],
|
||||
),
|
||||
IO.Int.Input(
|
||||
"duration",
|
||||
default=5,
|
||||
min=3,
|
||||
max=15,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
IO.Image.Input(
|
||||
"first_frame",
|
||||
tooltip="First frame image. The output aspect ratio is derived from this image.",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"seed",
|
||||
default=0,
|
||||
min=0,
|
||||
max=2147483647,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
control_after_generate=True,
|
||||
tooltip="Seed to use for generation.",
|
||||
),
|
||||
IO.Boolean.Input(
|
||||
"watermark",
|
||||
default=False,
|
||||
tooltip="Whether to add an AI-generated watermark to the result.",
|
||||
advanced=True,
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Video.Output(),
|
||||
],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
|
||||
expr="""
|
||||
(
|
||||
$res := $lookup(widgets, "model.resolution");
|
||||
$dur := $lookup(widgets, "model.duration");
|
||||
$ppsTable := { "720p": 0.14, "1080p": 0.24 };
|
||||
$pps := $lookup($ppsTable, $res);
|
||||
{ "type": "usd", "usd": $pps * $dur }
|
||||
)
|
||||
""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
model: dict,
|
||||
first_frame: Input.Image,
|
||||
seed: int,
|
||||
watermark: bool,
|
||||
):
|
||||
media = [
|
||||
Wan27MediaItem(
|
||||
type="first_frame",
|
||||
url=await upload_image_to_comfyapi(cls, image=first_frame),
|
||||
)
|
||||
]
|
||||
initial_response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(
|
||||
path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
|
||||
method="POST",
|
||||
),
|
||||
response_model=TaskCreationResponse,
|
||||
data=Wan27ImageToVideoTaskCreationRequest(
|
||||
model=model["model"],
|
||||
input=Wan27ImageToVideoInputField(
|
||||
prompt=model["prompt"] or None,
|
||||
negative_prompt=None,
|
||||
media=media,
|
||||
),
|
||||
parameters=Wan27ImageToVideoParametersField(
|
||||
resolution=model["resolution"],
|
||||
duration=model["duration"],
|
||||
seed=seed,
|
||||
watermark=watermark,
|
||||
),
|
||||
),
|
||||
)
|
||||
if not initial_response.output:
|
||||
raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
|
||||
response = await poll_op(
|
||||
cls,
|
||||
ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
|
||||
response_model=VideoTaskStatusResponse,
|
||||
status_extractor=lambda x: x.output.task_status,
|
||||
poll_interval=7,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
|
||||
|
||||
|
||||
class HappyHorseVideoEditApi(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="HappyHorseVideoEditApi",
|
||||
display_name="HappyHorse Video Edit",
|
||||
category="api node/video/Wan",
|
||||
description="Edit a video using text instructions or reference images with the HappyHorse model. "
|
||||
"Output duration is 3-15s and matches the input video; inputs longer than 15s are truncated.",
|
||||
inputs=[
|
||||
IO.DynamicCombo.Input(
|
||||
"model",
|
||||
options=[
|
||||
IO.DynamicCombo.Option(
|
||||
"happyhorse-1.0-video-edit",
|
||||
[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
tooltip="Editing instructions or style transfer requirements.",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"resolution",
|
||||
options=["720P", "1080P"],
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"ratio",
|
||||
options=["16:9", "9:16", "1:1", "4:3", "3:4"],
|
||||
tooltip="Aspect ratio. If not changed, approximates the input video ratio.",
|
||||
),
|
||||
IO.Autogrow.Input(
|
||||
"reference_images",
|
||||
template=IO.Autogrow.TemplateNames(
|
||||
IO.Image.Input("reference_image"),
|
||||
names=[
|
||||
"image1",
|
||||
"image2",
|
||||
"image3",
|
||||
"image4",
|
||||
"image5",
|
||||
],
|
||||
min=0,
|
||||
),
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
IO.Video.Input(
|
||||
"video",
|
||||
tooltip="The video to edit.",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"seed",
|
||||
default=0,
|
||||
min=0,
|
||||
max=2147483647,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
control_after_generate=True,
|
||||
tooltip="Seed to use for generation.",
|
||||
),
|
||||
IO.Boolean.Input(
|
||||
"watermark",
|
||||
default=False,
|
||||
tooltip="Whether to add an AI-generated watermark to the result.",
|
||||
advanced=True,
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Video.Output(),
|
||||
],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution"]),
|
||||
expr="""
|
||||
(
|
||||
$res := $lookup(widgets, "model.resolution");
|
||||
$ppsTable := { "720p": 0.14, "1080p": 0.24 };
|
||||
$pps := $lookup($ppsTable, $res);
|
||||
{ "type": "usd", "usd": $pps, "format": { "suffix": "/second" } }
|
||||
)
|
||||
""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
model: dict,
|
||||
video: Input.Video,
|
||||
seed: int,
|
||||
watermark: bool,
|
||||
):
|
||||
validate_string(model["prompt"], strip_whitespace=False, min_length=1)
|
||||
validate_video_duration(video, min_duration=3, max_duration=60)
|
||||
media = [Wan27MediaItem(type="video", url=await upload_video_to_comfyapi(cls, video))]
|
||||
reference_images = model.get("reference_images", {})
|
||||
for key in reference_images:
|
||||
media.append(
|
||||
Wan27MediaItem(
|
||||
type="reference_image", url=await upload_image_to_comfyapi(cls, image=reference_images[key])
|
||||
)
|
||||
)
|
||||
initial_response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(
|
||||
path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
|
||||
method="POST",
|
||||
),
|
||||
response_model=TaskCreationResponse,
|
||||
data=Wan27VideoEditTaskCreationRequest(
|
||||
model=model["model"],
|
||||
input=Wan27VideoEditInputField(prompt=model["prompt"], media=media),
|
||||
parameters=Wan27VideoEditParametersField(
|
||||
resolution=model["resolution"],
|
||||
ratio=model["ratio"],
|
||||
duration=None,
|
||||
watermark=watermark,
|
||||
seed=seed,
|
||||
),
|
||||
),
|
||||
)
|
||||
if not initial_response.output:
|
||||
raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
|
||||
response = await poll_op(
|
||||
cls,
|
||||
ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
|
||||
response_model=VideoTaskStatusResponse,
|
||||
status_extractor=lambda x: x.output.task_status,
|
||||
poll_interval=7,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
|
||||
|
||||
|
||||
class HappyHorseReferenceVideoApi(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="HappyHorseReferenceVideoApi",
|
||||
display_name="HappyHorse Reference to Video",
|
||||
category="api node/video/Wan",
|
||||
description="Generate a video featuring a person or object from reference materials with the HappyHorse "
|
||||
"model. Supports single-character performances and multi-character interactions.",
|
||||
inputs=[
|
||||
IO.DynamicCombo.Input(
|
||||
"model",
|
||||
options=[
|
||||
IO.DynamicCombo.Option(
|
||||
"happyhorse-1.0-r2v",
|
||||
[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
tooltip="Prompt describing the video. Use identifiers such as 'character1' and "
|
||||
"'character2' to refer to the reference characters.",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"resolution",
|
||||
options=["720P", "1080P"],
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"ratio",
|
||||
options=["16:9", "9:16", "1:1", "4:3", "3:4"],
|
||||
),
|
||||
IO.Int.Input(
|
||||
"duration",
|
||||
default=5,
|
||||
min=3,
|
||||
max=15,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
),
|
||||
IO.Autogrow.Input(
|
||||
"reference_images",
|
||||
template=IO.Autogrow.TemplateNames(
|
||||
IO.Image.Input("reference_image"),
|
||||
names=[
|
||||
"image1",
|
||||
"image2",
|
||||
"image3",
|
||||
"image4",
|
||||
"image5",
|
||||
"image6",
|
||||
"image7",
|
||||
"image8",
|
||||
"image9",
|
||||
],
|
||||
min=1,
|
||||
),
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
IO.Int.Input(
|
||||
"seed",
|
||||
default=0,
|
||||
min=0,
|
||||
max=2147483647,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
control_after_generate=True,
|
||||
tooltip="Seed to use for generation.",
|
||||
),
|
||||
IO.Boolean.Input(
|
||||
"watermark",
|
||||
default=False,
|
||||
tooltip="Whether to add an AI-generated watermark to the result.",
|
||||
advanced=True,
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Video.Output(),
|
||||
],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]),
|
||||
expr="""
|
||||
(
|
||||
$res := $lookup(widgets, "model.resolution");
|
||||
$dur := $lookup(widgets, "model.duration");
|
||||
$ppsTable := { "720p": 0.14, "1080p": 0.24 };
|
||||
$pps := $lookup($ppsTable, $res);
|
||||
{ "type": "usd", "usd": $pps * $dur }
|
||||
)
|
||||
""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
model: dict,
|
||||
seed: int,
|
||||
watermark: bool,
|
||||
):
|
||||
validate_string(model["prompt"], strip_whitespace=False, min_length=1)
|
||||
media = []
|
||||
reference_images = model.get("reference_images", {})
|
||||
for key in reference_images:
|
||||
media.append(
|
||||
Wan27MediaItem(
|
||||
type="reference_image",
|
||||
url=await upload_image_to_comfyapi(cls, image=reference_images[key]),
|
||||
)
|
||||
)
|
||||
if not media:
|
||||
raise ValueError("At least one reference reference image must be provided.")
|
||||
|
||||
initial_response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(
|
||||
path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
|
||||
method="POST",
|
||||
),
|
||||
response_model=TaskCreationResponse,
|
||||
data=Wan27ReferenceVideoTaskCreationRequest(
|
||||
model=model["model"],
|
||||
input=Wan27ReferenceVideoInputField(
|
||||
prompt=model["prompt"],
|
||||
negative_prompt=None,
|
||||
media=media,
|
||||
),
|
||||
parameters=Wan27ReferenceVideoParametersField(
|
||||
resolution=model["resolution"],
|
||||
ratio=model["ratio"],
|
||||
duration=model["duration"],
|
||||
watermark=watermark,
|
||||
seed=seed,
|
||||
),
|
||||
),
|
||||
)
|
||||
if not initial_response.output:
|
||||
raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
|
||||
response = await poll_op(
|
||||
cls,
|
||||
ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
|
||||
response_model=VideoTaskStatusResponse,
|
||||
status_extractor=lambda x: x.output.task_status,
|
||||
poll_interval=7,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
|
||||
|
||||
|
||||
class WanApiExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
|
||||
@ -1660,6 +2211,10 @@ class WanApiExtension(ComfyExtension):
|
||||
Wan2VideoContinuationApi,
|
||||
Wan2VideoEditApi,
|
||||
Wan2ReferenceVideoApi,
|
||||
HappyHorseTextToVideoApi,
|
||||
HappyHorseImageToVideoApi,
|
||||
HappyHorseVideoEditApi,
|
||||
HappyHorseReferenceVideoApi,
|
||||
]
|
||||
|
||||
|
||||
|
||||
@ -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}
|
||||
|
||||
@ -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 = {
|
||||
|
||||
@ -54,7 +54,7 @@ class EmptySD3LatentImage(io.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def execute(cls, width, height, batch_size=1) -> io.NodeOutput:
|
||||
latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=comfy.model_management.intermediate_device())
|
||||
latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
|
||||
return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 8})
|
||||
|
||||
generate = execute # TODO: remove
|
||||
|
||||
@ -1,3 +1,3 @@
|
||||
# This file is automatically generated by the build process when version is
|
||||
# updated in pyproject.toml.
|
||||
__version__ = "0.19.3"
|
||||
__version__ = "0.20.0"
|
||||
|
||||
38
execution.py
38
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
|
||||
|
||||
3231
openapi.yaml
Normal file
3231
openapi.yaml
Normal file
File diff suppressed because it is too large
Load Diff
@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "ComfyUI"
|
||||
version = "0.19.3"
|
||||
version = "0.20.0"
|
||||
readme = "README.md"
|
||||
license = { file = "LICENSE" }
|
||||
requires-python = ">=3.10"
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
comfyui-frontend-package==1.42.14
|
||||
comfyui-workflow-templates==0.9.59
|
||||
comfyui-embedded-docs==0.4.3
|
||||
comfyui-frontend-package==1.42.15
|
||||
comfyui-workflow-templates==0.9.63
|
||||
comfyui-embedded-docs==0.4.4
|
||||
torch
|
||||
torchsde
|
||||
torchvision
|
||||
@ -23,7 +23,7 @@ SQLAlchemy>=2.0
|
||||
filelock
|
||||
av>=14.2.0
|
||||
comfy-kitchen>=0.2.8
|
||||
comfy-aimdo>=0.2.12
|
||||
comfy-aimdo==0.2.14
|
||||
requests
|
||||
simpleeval>=1.0.0
|
||||
blake3
|
||||
|
||||
Loading…
Reference in New Issue
Block a user