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Stable Diffusion: Social Media Visual Kit

Create a consistent branded social media graphics kit with Stable Diffusion: templates for posts, stories, banners, and ads in one unified style.

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Your Prompt
prompt.txt

                                

What this prompt does

This prompt produces a complete set of Stable Diffusion prompts for a branded social media visual kit. You supply [brand_name], [industry], [visual_style], [color_palette], and [photo_style], and it generates prompts for Instagram feed posts, stories, LinkedIn, Twitter/X headers, YouTube thumbnails, and an extra platform — all sharing one consistent look.

The structure is built around brand consistency. Every asset prompt includes your [color_palette] and [visual_style], and the kit defines a reusable base-prompt snippet to append to every generation plus a shared negative prompt (no text, no watermark, no stock-photo feel). It sets generation parameters through [cfg_scale], [steps], and [sampler], recommends a model via [recommended_model], and explains a seed strategy and ControlNet usage so batches stay layout-consistent. That base-prompt-plus-seed discipline is what stops ten posts from looking like ten different designers made them.

When to use it

  • When you need an on-brand social graphics kit across multiple platforms quickly.
  • Launching a product and you want consistent visuals for posts, stories, banners, and thumbnails.
  • When your social graphics drift in style and you want a locked base prompt to anchor them.
  • To get platform-correct dimensions and a content-calendar template in one pass.
  • When you want a post-processing workflow for upscaling and brand-accurate color grading.

Example output

You get Stable Diffusion prompts grouped by asset: Instagram feed templates (quote card, product showcase, behind-the-scenes), stories, LinkedIn posts, a Twitter/X header, a YouTube thumbnail, and an extra-platform set — each at the correct dimensions with --cfg, --steps, and --sampler values. Then a style-consistency system (base prompt snippet, shared negative prompt, model and ControlNet guidance, seed strategy), a post-processing workflow for upscaling and color grading, and a weekly content-calendar plan mapping templates to content types.

Pro tips

  • Put exact hex codes in [color_palette]; the base-prompt snippet repeats them on every asset, so precise colors keep the kit cohesive.
  • Lock a seed strategy and reuse the base prompt across assets — that's the single biggest factor in making every post look like the same brand.
  • Match [recommended_model] to your [visual_style]: a photorealistic checkpoint and a stylized one produce very different kits.
  • Tune [cfg_scale] and [steps] together; higher CFG follows the prompt more tightly, more steps add detail at the cost of generation time.
  • Always keep the shared negative prompt — Stable Diffusion mangles text, so generate clean backgrounds and add copy and logos later in Figma or Canva.
  • Use [additional_platform] to cover a launch-specific need, like Product Hunt gallery images, in the same kit.

Frequently Asked Questions

How does this keep all my graphics on-brand?
It defines a reusable base-prompt snippet with your `[color_palette]` and `[visual_style]` to append to every generation, plus a seed strategy and ControlNet guidance. Reusing the base prompt and locked seeds is what keeps every asset visually consistent.
Which Stable Diffusion model should I use?
It recommends a model through `[recommended_model]`, defaulting to options like SDXL with Juggernaut XL for photorealistic or Dreamshaper XL for stylized. Match the model to your `[visual_style]` since the two produce very different looks.
Can Stable Diffusion add the text to my posts?
No, and the prompt deliberately avoids it. The shared negative prompt excludes text and letters because Stable Diffusion renders them poorly. You generate clean backgrounds and add copy and logos afterward in Figma or Canva.
Does it give correct dimensions for each platform?
Yes. Each asset prompt specifies platform-correct dimensions, such as 1080x1080 for Instagram feed, 1080x1920 for stories, 1200x627 for LinkedIn, and 1280x720 for YouTube thumbnails.
What do the cfg, steps, and sampler settings do?
`[cfg_scale]` controls how closely the image follows your prompt, `[steps]` sets how many denoising iterations run (more adds detail but takes longer), and `[sampler]` picks the algorithm. The defaults are a balanced starting point you can tune.
Engr Mejba Ahmed

Need this built for real?

Engr Mejba Ahmed

AI Developer · Software Engineer

I'm Mejba — I design and ship production AI systems, automations, and full-stack apps. If you want this turned into a working solution for your team, let's talk.

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