How do I get better text in my images without training a LoRA?

I keep seeing people jump straight to training a LoRA to fix text generation in Stable Diffusion. That's like launching a fleet of g4dn.16xlarge instances to host a static website—massive overkill and a huge time/money sink before you've exhausted the obvious, cheaper options.

The core issue is that SD models are trained on noisy image-text pairs, so they're inherently bad at spelling. You need to use the tools you already have more effectively. Here are the most cost-efficient methods, in order of effort:

* **Prompt Engineering & Negative Promixes:** This is your first line of defense. Be explicit and repetitive.
* **Positive:** `"logo of text that says 'CLOUD HAWK'", ((white text)), ((clean typography)), ((sharp edges)), ((vector graphic)), ((high contrast))`
* **Negative:** `blurry text, messy, distorted letters, runes, symbols, glyphs, watermark, signature, cursive`
* Force the composition with `"text at the center"` or `"on a sign that reads: YOURTEXT"`.

* **ControlNet is your most powerful tool here.** Use it to guide the generation precisely.
1. Generate your desired text in an image editor or even MS Paint. White text on black background.
2. Use **ControlNet with Canny or Scribble** preprocessor on that text image. It will lock in the shapes.
3. Use **ControlNet with Depth** if you want the text on a specific surface. Generate a depth map of a wall or sign, then combine with the Canny text control.
The key is setting the ControlNet weight high (1.0 to 1.2) and starting its influence early (`"Starting Control Step": 0.0`). You may need to lower the "Ending Control Step" to let the model refine details later.

* **Model Choice Matters:** Some models are marginally better at text than others. Try a model known for coherence, like SDXL or fine-tunes that mention "legible text." Don't waste GPU hours on a model fundamentally unsuited for the task.

* **Inpainting for Final Fixes:** Generate a scene, then use inpainting to mask *just the text area*. Use your original text prompt again here, often with higher denoising strength (0.7-0.8). This is a targeted correction, not a full retrain.

Here's a basic ComfyUI workflow snippet for the ControlNet approach:

```json
{
"nodes": [
{
"class_type": "ControlNetLoader",
"inputs": { "control_net_name": "control_v11p_sd15_scribble.pth" }
},
{
"class_type": "LoadImage",
"inputs": { "image": "your_text_scribble.png" }
},
// ... connecting to your KSampler, with ControlNet applied to the positive conditioning
]
}
```

Training a LoRA should be your absolute last resort. The inference cost (time) of iterating with these techniques is pennies compared to the training compute and hours spent collecting/data-prepping a dataset. Optimize your pipeline first.