> ## Documentation Index
> Fetch the complete documentation index at: https://docs.runcomfy.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Workflow Files
When you deploy a ComfyUI workflow as **Serverless API (ComfyUI)**, you’ll work with three JSON files:
* `workflow.json` — full UI export (graph + layout)
* `workflow_api.json` — execution graph optimized for API calls (what deployments run)
* `object_info.json` — schema registry for all nodes in a running ComfyUI instance
Examples in this guide use the [RunComfy/FLUX workflow](https://www.runcomfy.com/comfyui-workflows/comfyui-flux-a-new-art-image-generation).
***
## Quick comparison
| File | Contains | Typical use |
| ------------------- | -------------------------------------------------------------- | --------------------------------------------------- |
| `workflow.json` | Nodes + links + canvas layout (groups, positions, UI metadata) | Sharing/editing in the ComfyUI UI |
| `workflow_api.json` | Only what’s required to execute (node inputs + connections) | Referenced by `overrides` when calling a Deployment |
| `object_info.json` | Input/output schemas for every node in the running instance | Validating inputs, building tools/UIs, debugging |
***
## `workflow.json`
`workflow.json` is the **full workflow export**. It includes nodes, positions, links, and UI elements like groups.
To download `workflow.json`:
1. Open your workflow in the ComfyUI interface on RunComfy.
2. Click the **Workflow** menu in the top-left.
3. Select **Export**.
The file’s main content is in the `"nodes"` array, which lists each node as an object. Each node object includes keys like `"id"` (a unique number for the node), `"type"` (the node's class, e.g., "SamplerCustomAdvanced"), `"pos"` (an array \[x, y] for canvas position), `"size"` (an array \[width, height] for node dimensions), `"flags"` (an object for node states like collapsed), `"order"` (execution order index), `"mode"` (node mode, often 0 for active), `"inputs"` (an array of input objects with "name", "type", and "link" to a connection ID), `"outputs"` (an array of output objects with "name", "type", "slot\_index", and "links" array of connection IDs), `"properties"` (an object for node-specific settings), and `"widgets_values"` (an array of widget values if any).
For example:
```
{
"id": 13,
"type": "SamplerCustomAdvanced",
"pos": [
842,
215
],
"size": [
355.20001220703125,
106
],
"flags": {},
"order": 10,
"mode": 0,
"inputs": [
{
"name": "noise",
"type": "NOISE",
"link": 37
},
{
"name": "guider",
"type": "GUIDER",
"link": 30
},
{
"name": "sampler",
"type": "SAMPLER",
"link": 19
},
{
"name": "sigmas",
"type": "SIGMAS",
"link": 20
},
{
"name": "latent_image",
"type": "LATENT",
"link": 23
}
],
"outputs": [
{
"name": "output",
"type": "LATENT",
"slot_index": 0,
"links": [
24
]
},
{
"name": "denoised_output",
"type": "LATENT",
"links": null
}
],
"properties": {
"Node name for S&R": "SamplerCustomAdvanced"
},
"widgets_values": []
}
```
For the complete example, you can check [flux\_workflow.json](/static/flux_workflow.json).
***
## `workflow_api.json`
`workflow_api.json` is a **streamlined workflow export designed for API execution**.
It removes UI-related details (node positions, sizes, groups) and keeps only:
* node types (`class_type`)
* node inputs (`inputs`)
* connections between nodes
When you deploy a workflow on RunComfy, the platform stores this file internally and uses it as the basis for serverless API calls. During API calls, RunComfy references this stored file and applies your `overrides` without requiring you to resend the whole workflow.
To get `workflow_api.json`:
1. Open your workflow in the ComfyUI interface on RunComfy.
2. Click the **Workflow** menu in the top-left.
3. Select **Export (API)**.
The file is a single JSON object where:
* keys are node IDs (as strings)
* values are node definitions (`inputs`, `class_type`, and optional `_meta`)
For example:
```
{
"5": {
"inputs": {
"width": 1024,
"height": 1024,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"6": {
"inputs": {
"text": "n old tv with the word \\"FLUX\\" on it, sitting in an abandoned workshop environment, created in Unreal Engine 5 with Octane render in the style of ArtStation.",
"clip": [
"11",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
}
// other nodes
}
```
For the complete example, you can check [flux\_workflow\_api.json](/static/flux_workflow_api.json).
***
## `object_info.json`
`object_info.json` is a **schema catalog for a running ComfyUI instance**. It includes each node’s:
* required/optional inputs
* accepted types and ranges
* output types
* tooltips/metadata
Use this file to validate inputs (for example in your own UI), or to build tools that dynamically generate/modify workflows.
Fetch it from a running server:
1. Launch a ComfyUI instance on RunComfy.
2. Note the server ID.
3. Visit `https://-comfyui.runcomfy.com/object_info` in your browser.
For example:
```
{
"KSampler": {
"input": {
"required": {
"model": [
"MODEL",
{
"tooltip": "The model used for denoising the input latent."
}
],
"seed": [
"INT",
{
"default": 0,
"min": 0,
"max": 18446744073709551615,
"control_after_generate": true,
"tooltip": "The random seed used for creating the noise."
}
],
"steps": [
"INT",
{
"default": 20,
"min": 1,
"max": 10000,
"tooltip": "The number of steps used in the denoising process."
}
],
"cfg": [
"FLOAT",
{
"default": 8.0,
"min": 0.0,
"max": 100.0,
"step": 0.1,
"round": 0.01,
"tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."
}
],
"sampler_name": [
[
"euler",
"euler_cfg_pp",
"euler_ancestral",
"euler_ancestral_cfg_pp",
"heun",
"heunpp2",
"dpm_2",
"dpm_2_ancestral",
"lms",
"dpm_fast",
"dpm_adaptive",
"dpmpp_2s_ancestral",
"dpmpp_2s_ancestral_cfg_pp",
"dpmpp_sde",
"dpmpp_sde_gpu",
"dpmpp_2m",
"dpmpp_2m_cfg_pp",
"dpmpp_2m_sde",
"dpmpp_2m_sde_gpu",
"dpmpp_3m_sde",
"dpmpp_3m_sde_gpu",
"ddpm",
"lcm",
"ipndm",
"ipndm_v",
"deis",
"res_multistep",
"res_multistep_cfg_pp",
"res_multistep_ancestral",
"res_multistep_ancestral_cfg_pp",
"gradient_estimation",
"gradient_estimation_cfg_pp",
"er_sde",
"seeds_2",
"seeds_3",
"sa_solver",
"sa_solver_pece",
"ddim",
"uni_pc",
"uni_pc_bh2"
],
{
"tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."
}
],
"scheduler": [
[
"simple",
"sgm_uniform",
"karras",
"exponential",
"ddim_uniform",
"beta",
"normal",
"linear_quadratic",
"kl_optimal"
],
{
"tooltip": "The scheduler controls how noise is gradually removed to form the image."
}
],
"positive": [
"CONDITIONING",
{
"tooltip": "The conditioning describing the attributes you want to include in the image."
}
],
"negative": [
"CONDITIONING",
{
"tooltip": "The conditioning describing the attributes you want to exclude from the image."
}
],
"latent_image": [
"LATENT",
{
"tooltip": "The latent image to denoise."
}
],
"denoise": [
"FLOAT",
{
"default": 1.0,
"min": 0.0,
"max": 1.0,
"step": 0.01,
"tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."
}
]
}
},
"input_order": {
"required": [
"model",
"seed",
"steps",
"cfg",
"sampler_name",
"scheduler",
"positive",
"negative",
"latent_image",
"denoise"
]
},
"output": [
"LATENT"
],
"output_is_list": [
false
],
"output_name": [
"LATENT"
],
"name": "KSampler",
"display_name": "KSampler",
"description": "Uses the provided model, positive and negative conditioning to denoise the latent image.",
"python_module": "nodes",
"category": "sampling",
"output_node": false,
"output_tooltips": [
"The denoised latent."
]
}
// other nodes
}
```
For the complete example, you can check [flux\_object\_info.json](/static/flux_object_info.json).
***
## Files in API calls
When making API requests to a deployed workflow:
* use `workflow_api.json` to find node IDs and inputs
* send only the values you want to change under `overrides` (you don’t include the full file in your request)
This keeps requests efficient and makes it easy to evolve your workflow over time.
For exact formatting and examples, refer to **[Async Queue Endpoints](/serverless/async-queue-endpoints)**.