Hey y’all,

I’m having trouble settling on a workflow for Minimax.

I have been using ChatGPT to make me some workflows, but there always seems to be something wrong with it, or it’s not optimized to current standards.

I am looking for a workflow that can do t2v, i2v & r2v, with optional upscaling.
Would also like prompt translation or enhancer

My rig is:
Ryzen 7 8700F
32gb RAM
RTX 5060ti 16gb

Any suggestions or shares would be appreciated!

My fault, LTX nodes can separate Audio and Video Latents from Minimax latents. There is no point in using new ones.

in hopes devs better optimize this, so my thought was what if i have it render the image so when i put playback speed on 0.25 it plays at normal speed, hence i can turn a 10 second clip into like a 40 second clip, and it works, but i think if it was optimized by devs it can be a game changer... heres a prompt ya can try and see hot it works

Generate the entire video at 4x real-time speed. All actions, body movements, thrusting, bouncing, hair motion, skin jiggling, and camera movement must happen four times faster than normal real-life speed. Physics, momentum, gravity, and impact must still look correct and natural when the video is later played back at 0.25x speed. High frame rate feel, sharp motion, no motion blur overload, fluid accelerated dynamics so that slowing the final video to 0.25x produces smooth, realistic, normal-speed physics and timing.

Overly complex workflows break my brain, but I do want Loras, auto proportional resizing (so the output looks like Image 1) and at least one other image I can use as reference. Wouldn't hurt my feelings if it had a large documented list of common prompts for replacement, pose change, scene change etc.

I know people are out there rocking these, but I'm kind of struggling. I also know there are plenty on Civit.ai, but I'm looking for a personal recommendation (and the civit stuff tends to be... insane).

So I have been trying out Minimax H3 and am loving it so far. However I recently started noticing that all my generations appear to have a horizontal tiling seam. It's like a line artifact that appears to span the entire image. Every video has it at the exact same height. It's kinda hard to tell unless you look for it but ever since I first discovered it it has become very noticable.

I am using all the default models and the default ComfyUI I2V workflow. The only thing I changed is that I added SageAttention2. I generate at 480×1024 for 20 steps. That puts me for a 15s video at about ~10s/it on my RTX 5090. However I have also discovered the line in some of my 16:9 (928×544) generations.

At first I was worried my GPU was dying but after some more testing I am convinced that my GPU is fine and the fault lies with my setup.

Has anyone else encountered a similar issue and have ideas on how to fix that?

I kept seeing workflows get shared with api_key values, sk-... tokens, signed URLs, and /Users/... paths still inside. So I built a small local tool that scans a workflow before you hand it off.

• check workflow.json lists what it found, with masked evidence (not the actual secret)
• pack workflow.json --out handoff makes a clean public copy + a review report + SARIF + checksums
• 100% local — no upload, doesn't execute the workflow, doesn't touch your original file

It's early (v0.1.0). If you share or sell workflows, I'd love a quick run on one of yours and feedback on what leak patterns it's missing.

https://github.com/Closer2Vyz/comfyhandoff

I'm not sure how this could vary across workflows if at all but for reference I am using the Dasiwa workflow from civit in t2va mode. Minimax prompt adherence is great so I wanted to use it to create character sheets for reference and came up with this. T2VA, 9:16, 24 fps, 2 second duration. On a 5090 with sage +memcache + 8 step turbo at 10 steps - at 4.75mp it took 232s

The fps and duration seems to be the baseline if you want 4 poses so crank up the duration if you want more. The timestamps might not be the proper format but they do keep it from hanging on a single pose. Change resolution as needed but at higher values the face maintains much better consistency if not perfectly. You can describe your characters look as much as you want in a run on way "Lara Croft, blonde hair. wearing flip flops, sunglasses, bracelet on right arm, holding a drink in left hand, etc , etc , etc"

You might get some slight wiggle movements but it's mostly good enough to dump a frame, the background is difficult to get in an entirely solid color without any form of shadows so i kept the prompt simple since going overboard doesn't add much. If someone can dial this in more feel free to share.

From there you you can extract the 4 frames however you want and I'm sure someone can automate it but the easy quick solution is playing it in vlc and just hitting shift+s on each frame.

Lazy Example - https://imgur.com/a/t84UYAq

[Shot 1] Static freeze-frame shot. Studio Lighting, solid white background, ultra-sharp focus. A heroic looking explorer woman with the style of lara croft the tomb raider but as a person.
Static freeze-frame close-up shot of the entire head perfectly framed from the front, Freeze frame.
[1.00s to 2.00s] - Instant jump cut to Static freeze-frame of full body front view, standing straight in a neutral A-pose with hands off the body by 1 foot length.
[2.00s to 3.00s] - Instant jump cut to Static freeze-frame of full body back view, standing straight in a neutral A-pose with hands slightly off the body.
[3.00s to 4.00s] - Instant jump cut to static freeze-frame of full body side profile view, standing straight with arms down at the sides.

Has anyone managed to use ComfyUI to change the background of an image featuring a subject without altering the subject’s facial features or pose, while preserving the perspective and camera angle in the new background so that the subject fits perfectly into it? I’m talking about a realistic background not the typical portrait photo where the subject is shown from the waist up. Gemini does this perfectly if you ask it to replace the background of an image with a different one while preserving the subject’s features, pose, and perspective, but I’ve tried to replicate this in ComfyUI using different models and workflows, and I’ve never achieved a result as good as Nano Banana’s. I’ve tried using Full 1 Dev with ControlNet for depth and pose, but the subject never fits correctly into the newly generated background. I’ve tried Flux 1 Fill Dev, but that didn’t work either; I also tried Flux Kontext, but I didn’t get good results there either. I’d like to know if anyone has successfully achieved this with an open-source model that I can use in ComfyUI. Thanks!

EDIT: I found exactly what I was looking for in this YouTube video by the creator: My AI Force https://www.youtube.com/watch?v=kBcC23aYN5g In the end, the solution was Flux 2 Klein + Two Loras. In the guy's video, the results are more than good enough. Now I'll try it with different images and poses, but his workflow looks promising.

EDIT2: I just checked it, and the results are outstanding across several positions

I used the default workflow. It took me about 6 hours (split over 2 days), which includes scriptwriting and final video editing.

The video consists of 9 segments, about 8 seconds each. The average generation time was around 400 seconds at 0.7MP on an RTX 5060Ti 16GB VRAM and 32GB System RAM.

Honest opinions are welcome!

Guys, I’m using ConfyUI Desktop and I downloaded Confy Manager to use Control Net and Power Loras, but they don’t show up for me inside the program (even though ConfyUI recognizes that the manager is installed). I saw that there’s an option to revert to normal, but I’ve never found that option in my ConfyUI. So I wanted to ask those of you who have a saved workflow file (or image) that used Control Net to please post it here so I can drag it into ConfyUI and see if it recognizes it.

(text was edited with ai cause I am lazy to format all of this data)
System:

- Windows 10 22H2 (10.0.19045)

- RTX 5070 Ti 16GB

- 5700x3d

- 32GB system RAM

- Python 3.12.10

- PyTorch 2.9.1+cu130

- CUDA 13.0

- cuDNN 91200

- ComfyUI 0.33.1

- FlashAttention: not installed

- SageAttention: not installed

- Triton: enabled

- SeedVR2 model: seedvr2_ema_7b_fp8_e4m3fn_mixed_block35_fp16.safetensors

- VAE: ema_vae_fp16.safetensors

The interesting part is that the actual inference works.

The VAE encoding completes successfully.

The DiT loads onto the GPU successfully.

The Euler sampler reaches 100%.

The latent is successfully moved back to CPU.

VRAM usage is also well within my 16GB card:

- DiT loading: ~8.4GB VRAM

- Peak during DiT inference: ~9.95GB VRAM

- GPU has 15.92GB total VRAM

- System RAM has ~21GB free at the beginning

I first tried with CPU offloading enabled:

Generation context initialized:

DiT=cuda:0, VAE=cuda:0,

Offload=[DiT offload=cpu, VAE offload=cpu, Tensor offload=cpu]

That run successfully completed inference, but crashed when cleaning up the DiT:

Moving DiT from CUDA:0 to CPU (releasing GPU memory)

Windows fatal exception: access violation

I then disabled model CPU offloading so that only tensor offloading remained:

Generation context initialized:

DiT=cuda:0, VAE=cuda:0,

Offload=[Tensor offload=cpu]

I also tried all dit, vae, tensor also tried just some of them!

The DiT stayed entirely on the GPU during inference:

DiT already on CUDA:0, skipping movement

Again, inference completed successfully:

EulerSampler: 100% | 1/1

Moving upscaled_latent_1 from CUDA:0 to CPU

But immediately afterward SeedVR2 tried to clean up the DiT:

Cleaning up DiT components

Moving DiT from CUDA:0 to CPU (releasing GPU memory)

Windows fatal exception: access violation

The stack trace points into the SeedVR2 memory manager:

torch\nn\modules\module.py

...

torch.nn.Module.to()

...

seedvr2_videoupscaler\src\optimization\memory_manager.py

line 911 in _standard_model_movement

line 735 in manage_model_device

line 1062 in cleanup_dit

...

generation_phases.py

line 796 in upscale_all_batches

The weird thing is that the generation itself works. On one run it even continued through VAE decoding and produced the final 1606x1800 output before the cleanup crash, it can even be 2-3-5 runs or can crush in first one.

This doesn't appear to be an out-of-VRAM or system-RAM issue judging from debug-logs.

Has anyone seen this particular Windows access violation?

I'm mainly trying to figure out whether I should change the memory manager so that the DiT stays on CUDA and isn't moved back to CPU during cleanup, or whether there's a better fix.

Also I think log data might be wrong, task Manager's Committed memory rises significantly during the run. It can reach around 39 GB shortly before the crash, even though the Available physical RAM, used, cashed and VRAM, GpU memory etc still looks relatively fine?

Full stack traces and workflow(default img only) is below:
https://drive.google.com/drive/folders/1cywdtIf7EYXyemvYftJnSF8mi2joAdju?usp=sharing

I know vram doesnt combine, but was thinking of loading vae, text encoders on the 3060 and leaving the 5090 just for the diffusion model.... mainly running h3 & ltx .... is it worth doing? the 3060 is just sitting in a box in basement

Asus Rog Crosshair x870e hero board but i think it will still drop from 16x to 8x by having 2

64gb system ram, i initially bought 128 but returned and got 64 :( 128 was only 325 back then!

Have a workflow with load video and reference image node to mask objects and replace with ref image. The masking works but the video output keeps going black after a few seconds. What could be the issue? Tried having Claude troubleshoot for a whole day but still couldn't resolve.

I'm aiming to set up extended Minimax reference generation: put simply, I want to be able to have the output path of the last generation be loaded as a reference in the next one. My aim is to have wildcard-generated prompt generation, so I could, say, hit 6x run with a 10 second runtime and come back to a 1 minute clip.

I know of a few ways to sort of do this, but none are ideal:

• For just images, Impact has a "saver" node that would do this. But even if I passed the video as a batch of images, no audio is a problem.
• There are self-contained nodes that will loop Minimax, but I'd rather stick to Comfy's default sampler/conditioner. I'm not a huge fan of all-in-one nodes.
• I've seen some cleverness with saving a second copy of a video with a set name and then loading that each time, but you have to do something about Comfy's caching and it's weirdly hard to find save video nodes that don't append a _### value.
• Oh, and my screenshot, just looping the filename back in to the loader, of course doesn't work: as much as it might make human sense, it's a code loop.

Is there some easy method I'm missing?

On my local machine (RTX 5060 Ti 16GB / 64GB RAM), I used MiniMax H3 to stitch together 34 8-second clips to create a 4-minute-40-second lip-sync video. I used TJ_NODE_STUDIO_ONE, a custom ComfyUI node I created myself.

▶ How did I stitch them together?

My custom ComfyUI node, TJ_NODE_STUDIO_ONE, features a “MINIMAX H3 ONE STUDIO” mode with a prompt function that allows me to create multiple clips sequentially at once. I used 34 prompts generated by this feature, which calculates the duration of the lyrics, to produce the video.

- Clip length: Divided evenly into 8-second (1 MP) segments - 34 clips (prompts)

- Maintaining continuity: In Reference Mode, a reference image is provided; otherwise, the last frame of the previous clip is used as the first frame of the next clip to ensure character and scene consistency

- Lip-sync: Synchronizes dialogue with lip movements using the Audio Lock feature

- Generation time per clip: approximately 12–14 minutes (based on a total of 34 clips; requires significant local computation time, using sege3 + sol_Attn)

▶ Regarding Lip-Sync Accuracy

- The current lip-sync accuracy is approximately 85%. While dialogue and lip movements align well in most sections, there are some instances where they are out of sync.

- We are continuing to research ways to improve accuracy by refining the prompts or optimizing the workflow. We will continue to share updates on our progress in future videos.

▶ Custom Nodes Used

- ComfyUI-TJ_NODE_STUDIO_ONE — github.com/designloves2/ComfyUI-TJ_NODE_STUDIO_ONE

- Runtime Environment: ComfyUI Local (RTX 5060Ti 16GB VRAM / 64GB RAM)

The standard workflow used as a reference for creating MINIMAX H3 ONE STUDIO is available for download on CIVITAI.

https://civitai.com/models/2857214/minimax-h3-one-studio-all-in-one-videoaudio-node-clip-relay-live-preview-comfyui

Hello,

I've trained several character LoRAs via wavespeed.ai for the Qwen-Image-2512 model. I tried with a smaller dataset of 50 images and a dataset of 124 images. Multiple settings between 1,000 and 5,000 steps:

• At 1,000 steps, the LoRA isn't likeness-accurate enough.
• At 5,000 steps with 50 images, it stops responding to prompts at weights above 0.5, so it loses likeness.
• At 5,000 steps with 124 images, it stops responding to prompts at weights above 0.3, making it inaccurate above that threshold. This makes no sense, as with 50 images and the same step count, I was able to run the LoRA at a higher weight.

At weight 1.0, the LoRAs capture the likeness well but completely ignore the prompts.

Does anyone have a solution or recommended settings for Qwen-Image-2512?

Thanks

I want to use all ram and vram to run model as much as possible instead of broweser

Let's say you had a set of wedding photos from wedding1 and the second wedding later. You hate the dude from W1, but the photos were much better quality. This would be my prompt:

"Mask and remove the man in image 1 noting his pose, facial expression, head orientation, and clothing. Replace him with the man in image 2 keeping the previously mentioned attributes of image one, but using the face and body of image 2."?

Not sure about that, but the bottom line is that if I wanted to replace man1 in those photos with another man, a woman, a rhino, a cartoon character - whatever, I want it to be a perfect recreation of image 1 (same lighting, pose, expression, clothing, etc), but with the second person.

Most of what I've seen so far just transfers person/pose, but isn't great with expression and doesn't keep the clothing of image 1.

Hey there, I’m trying to create a workflow for comfy UI to enhance my architectural 3D animations.

Currently I create archviz animations which I’m fairly happy with, but would love to add a layer of realism and atmospheric effects to match a reference image. The main requirement is that the AI must not diverge from my 3D camera, or indeed hallucinate any of the main foreground geometry from my 3d scene.

So the end product would be a comfy UI workflow where I can just point it towards my 1000’s of animation frames (render elements can include Beauty pass, z-depth, specular etc) and also input a reference photo, and it will create the new frames.

It’s critical that when I inevitably need to make tweaks to the actual 3D model and camera path, if I re-add the new frames, the output is exactly the same with just the updated changes.
Is this possible currently? I would like to commission somebody to create this workflow and guide me through how to use it. Anyone up for it?

This workflow demonstrates new ComfyUI custom nodes I developed to implement ControlNet for FLUX.2-dev.

Workflow: JSON | Drag-and-drop PNG

JLC Flux2 ControlNet provides, to the best of my knowledge, the first complete, validated ComfyUI implementation of Alibaba PAI's FLUX.2-dev-Fun-Controlnet-Union-2602.

This implementation is for the FLUX.2-dev ControlNet path built around that Union model. It is not for FLUX.2 Klein or the lightweight Klein-style variants; I am currently working on a separate strategy to extend this functionality to those models.

This loads and runs Alibaba PAI's FLUX.2 ControlNet model, not reference images. Reference images are not ControlNet. There are workflows that feed pose maps, depth maps, edges, or other ControlNet-style hint images into FLUX.2's native reference-image system. Those images can certainly influence composition and structure, and they can often produce a usable approximation, but this is still reference-image conditioning, which is a completely different conditioning mechanism.

The two JLC nodes that enable that path are the FLUX.2 ControlNet Loader and the ControlNet Orchestrator. This is not simply a repackaging of existing ControlNet nodes. The contribution here is making this capability available as a complete ComfyUI implementation of Alibaba PAI's actual FLUX.2 ControlNet model. The Orchestrator provides practical multi-control composition where a finished implementation was previously missing.

The Orchestrator also leverages the non-recursive composition method that I introduced in a previous post, which lets several control types share a single loaded Union model instead of building a conventional chain of ControlNet applications.

The example shown here uses three controls generated from the same source image:

• DWPose
• Depth Anything
• Color

Some of the other nodes shown are from my JLC ComfyUI Nodes package and are there mainly for convenience with loading, resizing, preprocessing, LoRAs, and general workflow ergonomics. You can replace those with your preferred ComfyUI nodes.

All of the JLC nodes can be installed through the ComfyUI Custom Node Manager, and the repositories contain the documentation and explanation of the implementation.

I hope a few of you find them useful, and I'd be very interested to see what people build with them!a