Swap in the custom MiniMax H3 graph and bind prompts, seed, size, steps, and LoRA to its node IDs.

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-08-25 18:02:25 -05:00
parent a9bf4f6a95
commit 30669c7a27
2 changed files with 480 additions and 156 deletions
@@ -12,38 +12,131 @@ export interface GenerateParams {
  length: number
 }

-type WorkflowGraph = Record<string, { class_type: string; inputs: Record<string, unknown>; _meta?: { title?: string } }>
+type WorkflowNode = { class_type: string; inputs: Record<string, unknown>; _meta?: { title?: string } }
+type WorkflowGraph = Record<string, WorkflowNode>
+
+const PROMPT_CLASSES = new Set([
+  'MiniMaxH3ImageToVideo',
+  'CLIPTextEncode',
+  'CLIPTextEncodeQwen3VL',
+  'TextEncodeQwen3VL'
+])
+const SEED_CLASSES = new Set(['RandomNoise', 'KSampler', 'SamplerMiniMax', 'KSamplerAdvanced'])
+const IMAGE_CLASSES = new Set(['LoadImage'])
+const LORA_CLASSES = new Set(['LoraLoaderModelOnly', 'LoraLoader'])
+
+function entriesOf(graph: WorkflowGraph, classType: string) {
+  return Object.entries(graph).filter(([, node]) => node.class_type === classType)
+}
+
+function linkId(value: unknown): string | null {
+  return Array.isArray(value) && typeof value[0] === 'string' ? value[0] : null
+}
+
+function snap32(value: number) {
+  return Math.max(32, Math.round(value / 32) * 32)
+}
+
+function titleOf(node: WorkflowNode) {
+  return node._meta?.title || ''
+}

 export function buildWorkflow(params: GenerateParams) {
  const graph = structuredClone(workflowTemplate) as WorkflowGraph
-  graph['1'].inputs.image = params.imageName
-  graph['8'].inputs.prompt = params.prompt
-  graph['8'].inputs.width = params.width
-  graph['8'].inputs.height = params.height
-  graph['8'].inputs.length = params.length
-  graph['9'].inputs.noise_seed = params.seed
-  graph['11'].inputs.steps = params.steps
-  graph['7'].inputs.switch = params.turbo
-  graph['6'].inputs.strength_model = params.turbo ? 1 : 0
+  const width = snap32(params.width)
+  const height = snap32(params.height)
+
+  for (const [, node] of Object.entries(graph)) {
+    if (IMAGE_CLASSES.has(node.class_type) && 'image' in node.inputs) {
+      node.inputs.image = params.imageName
+    }
+
+    if (PROMPT_CLASSES.has(node.class_type) || node.class_type.includes('Qwen3')) {
+      if ('prompt' in node.inputs) node.inputs.prompt = params.prompt
+      if ('text' in node.inputs) node.inputs.text = params.prompt
+    }
+
+    if (node.class_type === 'MiniMaxH3ImageToVideo') {
+      node.inputs.prompt = params.prompt
+      node.inputs.width = width
+      node.inputs.height = height
+      node.inputs.length = params.length
+    }
+
+    if (SEED_CLASSES.has(node.class_type)) {
+      if ('noise_seed' in node.inputs) node.inputs.noise_seed = params.seed
+      if ('seed' in node.inputs) node.inputs.seed = params.seed
+    }
+
+    if (LORA_CLASSES.has(node.class_type)) {
+      node.inputs.strength_model = params.turbo ? 1 : 0
+    }
+
+    if (node.class_type === 'PrimitiveBoolean') {
+      node.inputs.value = params.turbo
+    }
+
+    if (node.class_type === 'ComfySwitchNode' && typeof node.inputs.switch === 'boolean') {
+      node.inputs.switch = params.turbo
+    }
+
+    if (node.class_type === 'BasicScheduler' && typeof node.inputs.steps === 'number') {
+      node.inputs.steps = params.steps
+    }
+  }
+
+  for (const [, node] of entriesOf(graph, 'ComfySwitchNode')) {
+    if (!/step/i.test(titleOf(node))) continue
+    const qualityId = linkId(node.inputs.on_false)
+    const turboId = linkId(node.inputs.on_true)
+    if (qualityId && graph[qualityId]?.class_type === 'PrimitiveInt') {
+      graph[qualityId].inputs.value = params.turbo ? 20 : params.steps
+    }
+    if (turboId && graph[turboId]?.class_type === 'PrimitiveInt') {
+      graph[turboId].inputs.value = params.turbo ? params.steps : 8
+    }
+  }
+
  return graph
 }

+const LABEL_OVERRIDES: Record<string, string> = {
+  '114': 'Loading image',
+  '128': 'Scaling image',
+  '127': 'Reading image size',
+  '119': 'Scaling image',
+  '120': 'Reading image size',
+  '92': 'Saving MP4',
+  '105:6': 'Loading MiniMax H3 model',
+  '105:11': 'Loading video VAE',
+  '105:13': 'Loading Qwen3-VL encoder',
+  '105:15': 'Preparing seed',
+  '105:9': 'Building schedule',
+  '105:10': 'Decoding video frames',
+  '105:14': 'Sampling',
+  '105:16': 'Preparing guider',
+  '105:17': 'Selecting sampler',
+  '105:23': 'Decoding audio',
+  '105:24': 'Loading audio VAE',
+  '105:91': 'Encoding video',
+  '105:104': 'Encoding prompt & latent',
+  '105:107': 'Computing duration',
+  '105:111': 'Setting duration',
+  '105:121': 'Applying turbo LoRA',
+  '105:122': 'Selecting model path',
+  '105:123': 'Selecting steps',
+  '105:124': 'Quality steps',
+  '105:125': 'Turbo steps',
+  '105:126': 'Selecting turbo LoRA'
+}
+
 export const NODE_LABELS: Record<string, string> = {
-  '1': 'Loading image',
-  '2': 'Loading Qwen3-VL encoder',
-  '3': 'Loading MiniMax H3 model',
-  '4': 'Loading video VAE',
-  '5': 'Loading audio VAE',
-  '6': 'Applying turbo LoRA',
-  '7': 'Selecting model path',
-  '8': 'Encoding prompt & latent',
-  '9': 'Preparing seed',
-  '10': 'Selecting sampler',
-  '11': 'Building schedule',
-  '12': 'Preparing guider',
-  '13': 'Sampling',
-  '14': 'Decoding video frames',
-  '15': 'Decoding audio',
-  '16': 'Encoding video',
-  '17': 'Saving MP4'
+  ...Object.fromEntries(
+    Object.entries(workflowTemplate as WorkflowGraph).map(([id, node]) => [id, node._meta?.title || `Node ${id}`])
+  ),
+  ...LABEL_OVERRIDES
+}
+
+export function isEncodingNode(node: string) {
+  return node === '92' || node === '105:91' || /encoding|saving mp4/i.test(NODE_LABELS[node] || '')
 }