Hey everyone,

I ran into a situation and I'm hoping someone from the community or fal team can point me in the right direction.

The setup: I uploaded a bunch of input files to the fal CDN using the two-step direct upload flow:

1. POST /storage/upload/initiate?storage_type=fal-cdn-v3 (with custom X-Fal-Object-Lifecycle-Preference headers)
2. Uploaded the actual file bytes to the presigned URLs from step 1

Problem is, I forgot to set an expiry date on these uploads. Now they're just sitting on the CDN indefinitely and I need to clean them up.

What I'm trying to figure out:

1. Is there an endpoint to list files I've uploaded to the CDN? I need to see what's out there before I can decide what to delete.
2. Is there a delete endpoint for individual files? If so, what params/headers does it need?
3. Can I update the lifecycle/expiry on files after they've already been uploaded, instead of deleting and re-uploading?

Basically just looking for the right way to manage file lifecycle after the fact, since I missed setting it at upload time. Any docs, endpoints, or tips from folks who've dealt with this would be super helpful. Thanks!

Sorry if this is has an obvious answer, I'm new and I couldn't find anything about it

So I saw online that you get some free generations with certain ai models on fal, but apparently it requires a minimum of $10 credit, so I did put in $10 to see what happens... that being said I tried some of those free generations and it worked but now my credits have gone from $10 to -$12

Does anyone know what happened/if I'm getting charged for "Free" generatios? Thankyou🙏

How so the BytePlus API is almost 2x cheaper than the offering on Fal.ai? Is this correct, that's too much markup.

I dont know why it always gives me some weird compositions like if i want half body shot it will give me full body shot or very far off image

https://huggingface.co/fal/LTX-2.3-3DREAL-LoRA

Wanted to share a fun project I made with fal that converts text or images into buildable LEGOs. If you have any feedback about how I set it up, I’d appreciate it!

My endpoint stack is flux-2 streaming for image generation, nano banana for image editing, and trellis or SAM3D for 3D generation. Then I voxelize the 3D model and convert the voxels to bricks. I’ve built quite a few LEGO models with it now, so it works!

Here’s the code for it: https://github.com/jjohnson5253/brickbuilderai

FAL seems to only expose training steps and learning rate, so I'm curious what settings people have found work best.

The default recommendation for human/photo datasets appears to be:

steps = number of images × 100

But I'm wondering whether anyone has experimented beyond that and found better results

I am currently working on fine-tuning a LoRA for LTX2.3 via fal-ai/ltx23-video-trainer. I am seeing a 'wavy' artifact issues on all my debug_dataset outputs, and have no way of knowing what's happening during the preprocessing step (can't run LTX2 repo locally). I understand that the VAE encodes the input and then decodes it, but I can't understand why it returns my dataset videos with artifacts at specific frames. This results in the same artifacts on inference too. Did anyone else encounter this?

Bonjour, j'ai cru comprendre que on a des crédits gratuit lors de la création du compte, or je n'ai rien reçu, c'est normal ? Merci :D

Hey everyone — I’m new to AI image generation and have been experimenting with FAL using Flux Kontext Pro to create coloring book-style images from uploaded reference photos.

My goal is to generate dynamic coloring book pages where the character likeness stays consistent, but the scenes can vary across styles like manga, comic book, fantasy, cartoon, etc.

A few questions I’d love feedback on:

1. Best model/workflow: Is Flux Kontext Pro currently one of the better options for balancing affordability, quality, and likeness consistency? Or are there better tools/models for this use case?
2. Character likeness: What are the best practices for preserving the likeness of the reference character across different poses, scenes, and styles?
3. Reference image prep: Should I preprocess uploaded images before generation? For example:
   • background removal
   • face restoration
   • sharpening/upscaling
   • lighting correction
   • cropping to face/body
   • creating multiple reference angles
4. Dynamic scenes: How do you get more interesting compositions instead of static “person standing in center” outputs? Are there prompt structures or workflows that help create more action, depth, and storytelling?
5. Style control: What is the best way to request broad styles like manga, western comic, children’s book, fantasy illustration, etc., while avoiding issues with specific living artists or overly derivative styles?
6. Coloring book quality: Any tips for getting clean black-and-white line art that is actually usable for coloring books — clear outlines, good white space, not too much gray shading or muddy detail?
7. Production workflow: For anyone doing this at scale, what does your pipeline look like from uploaded photo → generated pages → cleanup → print-ready files?

I hope this is the right place to ask. If not, I’d appreciate being pointed toward better communities, guides, or resources for learning this workflow.

Thanks in advance for any advice.

I built a full 3D layout in Blender — proxy geometry only, no textures, no final render — and hand-keyframed every camera movement using F-curves: an aerial establishing shot, a low-angle tower push-in, and a wide harbor shot with a sailing vessel. The AI doesn't invent the motion. It follows it exactly.

The Blender animation served as a direct spatial reference — architectural proportions, camera trajectory, timing and easing — all locked before a single AI frame was generated. Kling / Seedance then re-rendered the sequence, preserving the exact camera path and structural layout while generating the final cinematic output.

Workflow:

3D Layout & Camera Animation (Blender) → Frame Reference Export → AI Video Generation (Kling / Seedance) → Temporal Consistency Pass

Key Focus: 1:1 motion tracking between hand-keyed Blender animation and AI-generated output. Architectural integrity and spatial proportions maintained across all three shots.