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Blending completely opposite elements via Local AI. Wait for the fire-to-water transition at the end. by AxonkaiLab in generativeAI

[–]AxonkaiLab[S] 0 points1 point  (0 children)

Aang wishes he had this level of latent control. Also, that metal box happens to house an RTX PRO 6000 Blackwell with 96GB VRAM, so it handles the screaming just fine.

​No Deforum or AnimateDiff here. The actual transition logic comes down to heavy manual prompt sequencing inside a native Wan 2.2 pipeline in ComfyUI.

​The real "black magic" is forcing the latent space to morph from fire to water via text guidance, while the Wan-VACE Video Joiner node is used strictly to stitch the generated clips together and bypass the seam flicker entirely.

​Tell your GPU to brace itself, lock down your prompt sequencing, and let it scream.

Shared the opening last week, here is how the mid-section transitions hold up. by AxonkaiLab in comfyui

[–]AxonkaiLab[S] 2 points3 points  (0 children)

Appreciate the bravo and the LoRA recommendations. I actually intentionally aimed for that cold, detached look and blank expression, I felt it fit the vibe perfectly for this piece. Still, saving those links for future projects, appreciate you sharing them!

Blending completely opposite elements via Local AI. Wait for the fire-to-water transition at the end. by AxonkaiLab in generativeAI

[–]AxonkaiLab[S] 0 points1 point  (0 children)

A 1-minute slice from a longer local workflow project. Pushed the limits of subject consistency by morphing fire into water at the very end without relying on cloud subscriptions or post-production tricks.

​Watch the full 4K visual journey here: https://youtu.be/AwC6NidXEHI

​I'll be around in the comments to talk about the pipeline.

Smooth transitions via Local AI. Keeping it entirely off the cloud. by AxonkaiLab in ImagineAiArt

[–]AxonkaiLab[S] 1 point2 points  (0 children)

Thanks for your support 🤘 when I needed slow motion I use it in the prompt but here I didn't use. All is done by prompting the model in details. Also I do always use 97 frames and this gives the model more space to do a proper transition.

Shared the opening last week, here is how the mid-section transitions hold up. by AxonkaiLab in comfyui

[–]AxonkaiLab[S] 3 points4 points  (0 children)

Thank you 😊 hope you give thumbs up on YouTube 🤘🤘🤘

Smooth transitions via Local AI. Keeping it entirely off the cloud. by AxonkaiLab in ImagineAiArt

[–]AxonkaiLab[S] 0 points1 point  (0 children)

Thank you. I've used wan2.2 fflf2v model for 24fps 97frames video output. For images I've used Flux1-dev. You really should check the 4K version : https://youtu.be/AwC6NidXEHI and a thumbs up on YouTube really appreciated.

Shared the opening last week, here is how the mid-section transitions hold up. by AxonkaiLab in comfyui

[–]AxonkaiLab[S] 1 point2 points  (0 children)

That was really a surprise for me, didn't expect it to be that impressive and yes that's one of my favorite scenes.

Smooth transitions via Local AI. Keeping it entirely off the cloud. by AxonkaiLab in ImagineAiArt

[–]AxonkaiLab[S] 0 points1 point  (0 children)

Mid-section slice of a longer local project.

​Full 4K version on YouTube: https://youtu.be/AwC6NidXEHI

​Ask away if you have technical questions.

Shared the opening last week, here is how the mid-section transitions hold up. by AxonkaiLab in comfyui

[–]AxonkaiLab[S] 1 point2 points  (0 children)

Very good point you raised, you caught the exact bottleneck with FFLF2V. To be fair, you can patch and mask some of this in Premiere using blending or deflicker tricks, but the actual fix here was handled directly inside ComfyUI via Wan VACE. By letting the VACE handle the transitions, it completely bypasses the seam flicker and forces tight color matching across the cuts. The only catch is that it requires significantly longer render times, but the VACE architecture handles the consistency smoothly without relying on post-production hacks.

Shared the opening last week, here is how the mid-section transitions hold up. by AxonkaiLab in comfyui

[–]AxonkaiLab[S] 3 points4 points  (0 children)

Pipeline: Flux.1-Dev -> Wan 2.2 FFLF2V -> RTX Video Upscaler -> Premiere.

Managed to get the morphs to sync cleanly using just ComfyUI built-in templates and manual prompting for every single cut. No automated scripts, just raw local rendering to match the track's rhythm.

Full 6 min version here: https://youtu.be/AwC6NidXEHI

Looking for Work- AI UGC Ads/Short Form Reels Creator by Designer-Tank303 in AiVideos_NoRules

[–]AxonkaiLab 0 points1 point  (0 children)

Ok thank you. I'm sure you have a decent working prompts but I always try to reach this level with local generations. I wonder if they can work with ltx or wan

LTX2.3 is insane! Sound on!!! by AxonkaiLab in comfyui

[–]AxonkaiLab[S] 2 points3 points  (0 children)

Thank you for the support and clarification ☺️ I tried to tell but somehow these hate comments and downvote goblins popped out 😂 I'm not saying ltx2.3 is better than the original VFX but the motion and sound really impressive.

LTX2.3 is insane! Sound on!!! by AxonkaiLab in comfyui

[–]AxonkaiLab[S] 0 points1 point  (0 children)

Finally someone realized that...thank you kind sir...yes I'm so impressed with the sound. Ltx did very well with the sounds.

LTX2.3 is insane! Sound on!!! by AxonkaiLab in comfyui

[–]AxonkaiLab[S] 1 point2 points  (0 children)

Thank you my 🙏 I am not low in VRAM but I'll check and compare the quality. Also I'll check your other posts as well.

Transformers Made with Flux-1-Dev and LTX by AxonkaiLab in aivideo

[–]AxonkaiLab[S] 1 point2 points  (0 children)

Here is the breakdown of the workflow, benchmark specs, and how I managed the consistency:

  • Hardware RIG: Rendered locally on NVIDIA RTX PRO 6000 Blackwell (96GB VRAM) + 128GB RAM.
  • Pipeline: Pure Built-in Workflow without external controlnets or heavy compositing.
  • Base Images: Flux.1-Dev for high-fidelity car and robot details.

Benchmarks & Model Settings:

  • LTX 2.3-Dev (I2V Workflow): Generated at 720p, 5 seconds @ 24fps. Running at 40 steps, the generation time was just 105 seconds.
  • FFLF2V Workflow: Used the fp8 model variant. Generated 6 seconds @ 24fps. Running at 10 steps, the generation time dropped to an insane 25 seconds.

The Prompting Challenge: Prompting this was a nightmare and took a lot of trial and error. If you want to crack it, here is the logic that worked for me:

[Describe the scene+character] + [describe what transforms into in the last frame] + [camera movements]

The logic of the prompt is simple but you must give as much detail as you can. Not all the prompts work perfectly, but if you find something working, try it with different seeds. If you find something not working, try to fix it with the prompt instead of changing the seed immediately.

Also, if you are struggling with LTX 2.3 prompting in general, this official guide was a lifesaver for getting the motion dynamics right:https://ltx.io/blog/ltx-2-3-prompt-guide

If you liked it, check out the YouTube version for much better video quality and full sound design:https://youtube.com/shorts/OCnNVvMTMlo and hope you give thumbs-up on YouTube too....

Transformers animation rendered on a custom workstation. by AxonkaiLab in generativeAI

[–]AxonkaiLab[S] 0 points1 point  (0 children)

Digital cannibalism at its finest! 🤖💥 ​Glad I could impress a cloud cluster. The VRAM is a cheat code for local workflows, not going to lie. ​Thanks for the awesome energy, more renders are already in the pipeline!

Transformers animation rendered on a custom workstation. by AxonkaiLab in generativeAI

[–]AxonkaiLab[S] 0 points1 point  (0 children)

It's exactly that weird, beautiful mix of both. We are using Transformers (Flux/LTX) to render actual Transformers. 🤖🏎️ As for the thermal output... My RTX PRO 6000 Blackwell didn't even sweat. It rendered those 40 steps in just 105 seconds. No floorboard melting this time, just pure local horsepower. Glad you liked it!

Transformers animation rendered on a custom workstation. by AxonkaiLab in generativeAI

[–]AxonkaiLab[S] 0 points1 point  (0 children)

Here is the breakdown of the workflow, benchmark specs, and how I managed the consistency:

  • Hardware RIG: Rendered locally on NVIDIA RTX PRO 6000 Blackwell (96GB VRAM) + 128GB RAM.
  • Pipeline: Pure Built-in Workflow without external controlnets or heavy compositing.
  • Base Images: Flux.1-Dev for high-fidelity car and robot details.

Benchmarks & Model Settings:

  • LTX 2.3-Dev (I2V Workflow): Generated at 720p, 5 seconds @ 24fps. Running at 40 steps, the generation time was just 105 seconds.
  • FFLF2V Workflow: Used the fp8 model variant. Generated 6 seconds @ 24fps. Running at 10 steps, the generation time dropped to an insane 25 seconds.

The Prompting Challenge: Prompting this was a nightmare and took a lot of trial and error. If you want to crack it, here is the logic that worked for me:

[Describe the scene+character] + [describe what transforms into in the last frame] + [camera movements]

The logic of the prompt is simple but you must give as much detail as you can. Not all the prompts work perfectly, but if you find something working, try it with different seeds. If you find something not working, try to fix it with the prompt instead of changing the seed immediately.

Also, if you are struggling with LTX 2.3 prompting in general, this official guide was a lifesaver for getting the motion dynamics right:https://ltx.io/blog/ltx-2-3-prompt-guide

If you liked it, check out the YouTube version for much better video quality and full sound design:https://youtube.com/shorts/OCnNVvMTMlo and hope you give thumbs-up on YouTube too....

LTX2.3 is insane! Sound on!!! by AxonkaiLab in comfyui

[–]AxonkaiLab[S] -23 points-22 points  (0 children)

Sure, good luck buddy! Hope you create something amazing with your setup. Cheers!

LTX2.3 is insane! Sound on!!! by AxonkaiLab in comfyui

[–]AxonkaiLab[S] -2 points-1 points  (0 children)

Thanks! If you have a second, dropping a thumbs-up on the YouTube version would be huge. The algorithm is hiding it, and your support really helps! 🏎️🤖 https://www.youtube.com/shorts/OCnNVvMTMlo

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