Detailed Explanation of txt2img
txt2img is the abbreviation of "text to image" and is commonly used especially in the Stable Diffusion WebUI (AUTOMATIC1111, ComfyUI) ecosystem. This mode refers to generating images from scratch using only a text prompt; it does not require any reference image.
In txt2img mode, the user specifies parameters such as a text prompt, optionally a negative prompt, image dimensions, number of steps, CFG scale (prompt adherence scale), and sampler/scheduler selection. The model generates an image from completely random noise according to these parameters.
Key txt2img parameters include: Width/Height (image dimensions, typically 512x512 or 1024x1024), Steps (number of denoising steps, between 20-50), CFG Scale (how closely the prompt is followed, between 5-15), Seed (randomness seed, same seed produces same image), and Sampler (denoising algorithm like Euler, DPM++).
Compared to img2img, txt2img produces completely original images but has more limited structural control over the output. Structural control can be achieved even in txt2img mode by adding ControlNet.
As a practical example, the txt2img workflow in Stable Diffusion's ComfyUI interface works as follows: you create a prompt node and write "cyberpunk city street at night, neon signs, rain, cinematic," select a checkpoint model (such as SDXL), set sampler parameters (Euler A, 25 steps, CFG 7), and start generation. The result is typically ready within 5-15 seconds. With the Flux model, the txt2img process is faster and prompt adherence is higher than traditional diffusion models.
Tools on tasarim.ai that directly feature txt2img include Stable Diffusion (full control via ComfyUI and Automatic1111) and Flux (ultra-fast generation with Schnell, high quality with Pro). While Midjourney, DALL-E 3, Leonardo AI, and Ideogram also fundamentally perform txt2img, the term is technically used more in the Stable Diffusion ecosystem context.
Tip for beginners: When starting with txt2img, learn the basic parameters like sampler, steps, and CFG scale. Generally, Euler A sampler, 20-30 steps, and CFG 7-8 values provide a good starting point. Use the batch count parameter to generate multiple variations from the same prompt and select the best one. You can try Stable Diffusion without local installation by running it on Google Colab.