Creating Your First Workflow

Learn how to build your first text-to-image workflow in ComfyUI

This guide will walk you through creating your first complete workflow in ComfyUI. By the end, you’ll understand how nodes connect together to generate images from text prompts.

What You’ll Build

A basic text-to-image workflow that:

• Loads a Stable Diffusion model
• Encodes positive and negative prompts
• Generates an image
• Saves the result

Prerequisites

• ComfyUI installed and running
• A Stable Diffusion checkpoint (e.g., v1-5-pruned-emaonly.safetensors) in models/checkpoints/

1. Load the model
3. Create your prompts
5. The CLIP model converts your text descriptions into embeddings the model can understand.
6. Set up the latent space
8. This creates a blank canvas in latent space where your image will be generated.
9. Configure the sampler
11. Decode to pixels
13. This converts the latent representation back into a visible image.
14. Save your image
16. Images are saved to the output/ directory by default.
17. Generate
18. Press Ctrl+Enter (or Cmd+Enter on Mac) to queue your workflow. Watch the progress bar and see your first generated image!

Complete Workflow JSON

Here’s the API format for this basic workflow:

{
  "3": {
    "class_type": "KSampler",
    "inputs": {
      "cfg": 8,
      "denoise": 1,
      "latent_image": ["5", 0],
      "model": ["4", 0],
      "negative": ["7", 0],
      "positive": ["6", 0],
      "sampler_name": "euler",
      "scheduler": "normal",
      "seed": 8566257,
      "steps": 20
    }
  },
  "4": {
    "class_type": "CheckpointLoaderSimple",
    "inputs": {
      "ckpt_name": "v1-5-pruned-emaonly.safetensors"
    }
  },
  "5": {
    "class_type": "EmptyLatentImage",
    "inputs": {
      "batch_size": 1,
      "height": 512,
      "width": 512
    }
  },
  "6": {
    "class_type": "CLIPTextEncode",
    "inputs": {
      "clip": ["4", 1],
      "text": "masterpiece best quality girl"
    }
  },
  "7": {
    "class_type": "CLIPTextEncode",
    "inputs": {
      "clip": ["4", 1],
      "text": "bad hands"
    }
  },
  "8": {
    "class_type": "VAEDecode",
    "inputs": {
      "samples": ["3", 0],
      "vae": ["4", 2]
    }
  },
  "9": {
    "class_type": "SaveImage",
    "inputs": {
      "filename_prefix": "ComfyUI",
      "images": ["8", 0]
    }
  }
}

Understanding the Workflow

Node Connections

Each node ID (like “3”, “4”, etc.) references a node. Inputs like ["4", 0] mean:

• "4": Node ID to connect from
• 0: Output index from that node

The Generation Process

1. CheckpointLoaderSimple loads the model weights
2. CLIPTextEncode converts text prompts into embeddings
3. EmptyLatentImage creates a random noise tensor
4. KSampler iteratively denoises the latent based on your prompts
5. VAEDecode converts the latent to pixel space
6. SaveImage writes the PNG to disk

Next Steps

• Experiment with different prompts
• Try changing the seed for variations
• Adjust CFG scale (higher = follows prompt more closely)
• Increase steps for potentially better quality
• Learn about image-to-image workflows
• Explore ControlNet for precise control

Tips

• Use (word:1.2) to increase emphasis on specific terms
• Use (word:0.8) to decrease emphasis
• Escape special characters: \(, \), \{, \}
• Try different samplers (euler, dpm++, etc.) for different styles
• The scheduler controls noise reduction - “normal” works for most cases