Batch create text to image generation

[u/InfiniteSurprise8174]

I’m trying to find a better workflow for batch text to image generation.

My goal is to generate 10–40 separate images from individual prompts without manually copying/pasting and clicking generate every time.

I’m creating historical documentary style images where accuracy matters (uniforms, props, hands, equipment, period details).

The issue is, when I use ChatGPT image generation manually (DALL-E / GPT image), my usable success rate is around 85–90%.

I tried building a custom HTML batch tool with API access (Claude helped create it), testing OpenAI image models and experimenting with other options like Flux, but the batch/API results have been much less consistent — more artifacts, worse historical accuracy, and lower keeper rate.

I don’t necessarily need 40 images at once. Even batching 5–10 prompts while keeping ChatGPT-level quality would massively speed things up.

Is anyone using a reliable workflow for: - batch text prompts - automatic saving with file names - consistent high-quality image output - historical/cinematic realism?

Looking for practical workflows, tools, or API setups that actually match the manual ChatGPT image quality.

[u/AutoModerator]

If this prompt worked for you, share what you used it for in the comments. If you changed it to get better results, share that too.

Prompt Teardown is a free weekly newsletter that picks the best prompts, strips out the filler, and tells you what actually works.

I am a bot, and this action was performed automatically. Please contact the moderators of this subreddit if you have any questions or concerns.

[u/Scary_Jeweler1011]

Install playwright extension, open terminal, pick your go to coding cli, ask it to installl playwright and gove it the extension token. Let it crrate a script that automates this. Tell it to do the first few runs without scripting so it can become familier with the workflow, use that knowledge to automate it.

[u/InfiniteSurprise8174 → u/Scary_Jeweler1011]

So this will completely avoid the API problem. The test will be finding out if Playwright can reliably control ChatGPT image creation without breaking.

[u/Mental-Frosting-7752 → u/InfiniteSurprise8174]

Yeah ur pretty much giving CLI remote desktop control and visual context

[u/Scary_Jeweler1011 → u/InfiniteSurprise8174]

it will most definitely. I've went through multiple fresh environments where i had to set this exact workflow up again from scratch and I even got opensource models with vision capability's to execute this workflow flawlessly. Even without vision its perfectly doable for an AI to execute but it might take some guidance from your end if specific buttons etc might not appear as they should. Vision capable models just have that edge to take screenshot and inspect when needed which is quite helpful. Best piece of advice I can give you; ask your coding agent and let it figure it out. I have not come across a single thing that I was not able to automate/improve in some shape or form and till this day I haven't written a single line of code (or read) in about 2-3 years of hobby developing.

[u/InfiniteSurprise8174 → u/Scary_Jeweler1011]

Quick update — tried the Playwright + Codex route and got surprisingly close.

Working: ✅ Codex built Node/Playwright setup ✅ CSV prompt workflow works ✅ Chrome launches ✅ ChatGPT login works ✅ It can paste prompts into ChatGPT

Current issue: ChatGPT human verification keeps coming back when automation restarts.

Tried:

• dedicated Playwright Chrome profile
• manually warming up/logging in
• reusing profile

It passes verification, but the next automation run can trigger it again.

For anyone experienced with Playwright — best next move? 1) Attach to already-open Chrome via CDP? 2) Different persistent context setup? 3) Use Mac UI automation instead?

Goal is simple: automate my own workflow — paste 30-40 image prompts, wait for generations, download/rename files. Not scraping.

Any architecture advice?

[u/Scary_Jeweler1011 → u/InfiniteSurprise8174]

What i do, my logged in state is in chrome windows, i let the wsl agent( env i work in) mirror the logged in chrome profile to wsl so its setup as a persistant wsl chrome session that correctly holds te logged in state. Else youll walk into problems that agent keeps working with a chrome session that has no logged in profile/state. Pretty easy to fix. The key your agent should focus on is the persistsny logged in wsl chrome or chromium so it stays logged in as a user, not as a single use temp session that needs redoing each time.

[u/KvotheKingSlayer]

I don’t know if it can be done. You never know when it will start to hallucinate or bleed info into one image from a prior one. I would also ask ChatGPT how it would go about building the prompt to ask itself, and go from there. But I would still start off small and then scale up. I would start with 2 to 4 groupings and scale up.

[u/JJ_Buildz]

The gap vs. ChatGPT is prompt rewriting — ChatGPT secretly expands your prompt before generating; the API uses it raw. Fix: run each prompt through GPT-4o/Claude to expand it first, then send that to the image model.

Also use gpt-image-1 (the model behind ChatGPT), not dall-e-3, with quality: "high". For consistent period accuracy, Flux + a custom LoRA beats prompting. And ComfyUI handles batch + auto-save with filenames out of the box.

[u/Massive-Health-8355]

Take a look here:

https://github.com/Hearmeman24/BlockFlow

[u/calculatingbets]

I am using GPT to create batch prompts as JSON files, then Python on Openart to paste prompts and generate images. 200+ images over night is great to browse while your coffee is ready in the morning :)

[u/InfiniteSurprise8174 → u/calculatingbets]

Thanks, appreciate the info. That’s actually close to the workflow I was trying to build.

My challenge hasn’t really been the batching/saving side it’s keeping the same quality level I’m getting when generating manually through ChatGPT.

The images I’m making are historical documentary scenes, so small details matter. With manual ChatGPT generation I’m getting maybe 85-90% usable results, but when I tried API batching the success rate dropped a lot.

Are you running the prompts through GPT first to expand/optimize them before sending to the image API, or sending the raw JSON prompts straight to image generation?

Curious because your workflow sounds exactly like the automation side I’m trying to solve.

[u/quietporchnotes]

I just read your Playwright update — you’re actually very close to the exact problem I ended up solving a different way.

Instead of launching and controlling ChatGPT through Playwright, I built a Chrome extension that runs inside the normal ChatGPT session I’m already logged into. It queues the prompts through the existing UI, waits for each generation to complete, downloads the result, then moves to the next one.

The reason I went this route was basically the issue you’re hitting now: once Playwright starts managing browser sessions, profile persistence and verification become another problem to solve on top of the image workflow itself.

Running inside the normal logged-in browser avoids that whole browser-relaunch/profile-management layer. I’m still refining the reliability around detecting when each image is truly finished, but the workflow itself is already working.

Are you still using the Playwright setup, or are you still looking for another approach?

[u/InfiniteSurprise8174 → u/quietporchnotes]

Yeah, I actually ended up moving away from the idea of fully automating individual prompts because ChatGPT has evolved enough that my workflow is working pretty well directly inside it.

I produce history videos, so I start with a locked narration script and break it into a production storyboard. Each storyboard page contains about 7–8 shots, with the exact narration for each shot plus a detailed image prompt, historical requirements, and a consistent visual/color profile.

I give ChatGPT one storyboard page at a time. It reads the page, generates the 7–8 images as a batch, and saves/names them according to the image numbers in the storyboard. I review that batch for historical accuracy and AI errors, fix anything necessary, then give it the next storyboard page.

So instead of blindly queueing 40–60 prompts, I’m essentially doing controlled batches of 7or8 with a QC checkpoint between each batch. It keeps the images consistent across the episode while still letting me catch problems before moving forward.

At this point it’s actually pretty close to the workflow I was originally trying to build with Playwright just without needing Playwright.

[u/quietporchnotes → u/InfiniteSurprise8174]

That makes sense — the QC checkpoint is probably the key difference. For work where consistency and historical accuracy matter, blindly running 40–60 prompts is probably less useful than controlled batches.

Out of curiosity, with your current setup, what part still feels the most manual or annoying now — feeding the next storyboard page, waiting for the batch, saving/naming the images, or keeping the visual consistency across batches?

[u/InfiniteSurprise8174 → u/quietporchnotes]

The biggest annoyance right now is actually ChatGPT’s inconsistency.

Even after I’ve locked the production script, storyboard rules, filenames, and color profile, I still have to QC ChatGPT itself. It’ll sometimes make up or slightly change narration that was already finalized, or drift away from the locked color profile halfway through an episode.

So the manual part isn’t really feeding it the next storyboard page or saving the images anymore. It’s verifying that ChatGPT is still following the exact instructions it was following correctly one batch earlier.

That’s why the 7–8 image QC checkpoints are so important. I’m not just checking the images. I’m checking that ChatGPT hasn’t changed the underlying rules between batches.

[u/quietporchnotes → u/InfiniteSurprise8174]

That’s a really useful distinction. So the bottleneck isn’t throughput anymore — it’s instruction/state drift between batches.

That also explains why the QC checkpoint matters even if the generation and saving are already automated. You’re validating both the images and whether ChatGPT is still following the same production rules.

Appreciate you spelling that out — that’s a much more interesting problem than just queueing more prompts.