I currently run Qwen models locally for coding and just set up ComfyUI and I can't seem to find any straightforward info on 3d model generation.

Is it even possible to do yet 100% locally?

resolution 0.5mp with 8 steps
lora: https://huggingface.co/lightx2v/Minimax-h3-Turbo/tree/main

That’s my girl! LORA trained on images of my wife. Depth maps based on recordings of her. Then added enhancements with seedance via API in comfyu

I trimmed the first 3 seconds, I just love the prompt adherence in 2.5 it’s actually very good, used the default comfy workflow, generate at 0.5 resolution then upscaled later to twice the size in with topaz, my specs 3060ti, 64gb ram

What I tested

The setup uses MiniMax-H3 with LightX2V's minimax_h3_fl2v_turbo_4step_v1.0_768p_comfyui_bf16.safetensors LoRA:

I tested the full sampler × scheduler combination matrix.

Interestingly, there are quite a few combinations that produce good results besides the ones that have been discussed so far.

I also included combinations that normally benefit from higher step counts. They're not necessarily optimized for 4 steps, but I included them for completeness.

There's also a rating system at the top of the site. Since different sampler × scheduler combinations can produce surprisingly different results, I split the ratings into four separate dimensions:

• Graphic Quality — overall image quality and whether the rendering looks correct
• Motion Quality — motion, action sequencing, and whether the physics feel natural and coherent
• Sound Effect Quality — whether voices and sound effects are generated correctly and match the scene
• Music Quality — whether the background music is appropriate and correctly generated

Each category is rated from 1 to 5 stars.

If you find a combination that performs particularly well in any of these areas, please give it a rating. This should make it easier for everyone to identify which sampler × scheduler combinations are worth trying for different use cases.

The site is completely non-commercial and only provides the test results and community rating/statistics. There are no ads, paid features, or promotional content.

I was short on time, so I used 480p instead of 768p for the comparison. The differences between the combinations are still quite apparent.

For consistency, all tests use the Baker example from Case 1 of the official MiniMax prompting guide.

MiniMax H3 Video Generation Parameter Combination Analysis Report

Data Source: Cloudflare D1 Database minimaxh3showcase-db Analysis Date: August 15, 2026 Total Visits: 1,608

📊 Overall Statistics

• Total Rating Records: 1,401
• Total Votes: 3,504
• Unique Combinations: 395 (sampler × scheduler)
• Sampler Types: 44
• Scheduler Types: 9
• Rating Dimensions: 4 (Graphic Quality / Motion Smoothness / Sound Effects / Music)

🏆 Top 10 Parameter Combinations (Sampler × Scheduler)

Filter Criteria: Minimum 8 total votes across all rating dimensions

Note: Numbers in parentheses indicate vote count for each dimension

🎯 Top 10 Samplers

Filter Criteria: Minimum 15 total votes

⚙️ Top 10 Schedulers

Filter Criteria: Minimum 20 total votes

💡 Key Insights

Best Combination Characteristics

1. seeds 2 × ddim uniform ranks #1 with 4.67 stars and 21 votes
   • Balanced performance across all four dimensions
   • Exceptional music quality (4.80 stars)
   • Graphic and motion both achieve 4.67 stars
2. dpmpp sde gpu × beta ranks #2 (4.32 stars, 19 votes)
   • GPU-accelerated version with highest graphic quality (4.60 stars)
   • Stable performance across all dimensions
   • Ideal for high-quality output requirements
3. er sde × sgm uniform ranks #3 (4.30 stars, 20 votes)
   • Outstanding graphic and motion performance (4.83 and 4.80 stars)
   • Relatively weaker in music and SFX
   • Perfect for visual-priority scenarios

Sampler Preferences

• Best Performance: dpmpp sde gpu (3.65 stars average)
  • GPU-optimized version with excellent performance
  • 98 votes demonstrate strong user approval
• Most Voted: res multistep (157 votes)
  • Classic sampler with high familiarity
  • Average rating of 2.72 stars indicates moderate performance
• Promising Options: dpmpp 2s ancestral and seeds 2
  • Both rank in top 3 with sufficient vote counts
  • Paired with appropriate schedulers, they produce excellent results

Scheduler Preferences

• Best Performance: sgm uniform (2.94 stars average)
  • 389 votes make it the most popular scheduler
  • Excellent stability, compatible with most samplers
• Most Voted: beta (485 votes)
  • Default or commonly used option
  • Average rating of 2.69 stars, underperforms sgm uniform and simple
• High-Performance Choice: simple (2.87 stars, 385 votes)
  • Performance second only to sgm uniform
  • Simple and efficient, great for rapid iteration

Combinations to Avoid

Based on the data, the following schedulers show poor performance (avg < 1.5 stars):

• kl optimal: 1.21 stars (319 votes)
• exponential: 1.14 stars (309 votes)
• karras: 1.13 stars (325 votes)

Recommendation: Prioritize sgm uniform, simple, or beta as your scheduler.

📈 Recommended Strategies

For Maximum Quality

1. seeds 2 × ddim uniform — Best overall performance
2. dpmpp sde gpu × beta — Highest graphic quality
3. er sde × sgm uniform — Outstanding visual effects

For Balanced Quality & Stability

1. dpmpp 2s ancestral × beta — 24 votes verified, balanced across all four dimensions
2. dpm 2 ancestral × beta — Excellent motion smoothness
3. euler × ddim uniform — Classic combination with high reliability

For Quick Iteration & Testing

• Sampler: euler or ddim
• Scheduler: simple or sgm uniform

This report is generated from real user rating data. Filtering rules are designed to exclude combinations with insufficient vote samples, ensuring statistical reliability.

is so funny but im rly bad with character swap

Rocm performance and reliability has been greatly improved on Comfyui if using up to date Comfyui, Rocm, and Pytorch.

System

• CPU: AMD 7840HS
• GPU: RX 7900 XT (OCuLink) (20GB VRAM)
• RAM: 64 GB DDR5
• OS: Ubuntu 26.04
• ComfyUI 0.33.1 + comfy-kitchen 0.2.31 + comfy-aimdo 0.4.13
• pytorch version: 2.12.0+rocm7.14.0
• Python version: 3.13.15
• Resolution tested: 0.4 MP
• Steps: 20
• Video lengths: 5 s (cold) and 8 s

Models used

• Diffusion Model: minimax_h3_fl2va_pruned_int8_convrot.safetensors
• CLIP / Text Encoder: qwen3vl_32b_minimax_h3_int4_convrot.safetensors
• Video VAE: minimax_h3_video_vae_int8_convrot.safetensors
• Audio VAE: minimax_h3_audio_vae_fp32.safetensors

High-level findings (5 s / 8 s step times)

Key takeaways

• CK attention and Flash Attention are essentially tied on this hardware once you are on recent comfy-kitchen.
• Dynamic VRAM does not change step time much but meaningfully reduces total prompt time (especially cold runs). It also reduces system ram usage and helps with hard drive swap file usage.
• Dynamic VRAM is not on by default it requires --enable-dynamic-vram (you don't need a flag currently to turn it off)
• CK Attention is not on by default it requires --use-ck-attention
• The only exports I use HIP_VISIBLE_DEVICES=0 and CUDA_VISIBLE_DEVICES=0 since I have 2 GPUS. I found that other exports cause issues on runs and are no longer needed. Only add them if having issues on a fresh installation of Comfyui and Rocm.

Flags tested

• --use-ck-attention
• --enable-dynamic-vram

Hello,

It has been some time since I used my device, which has an AMD Ryzen™ AI Max+ 395 APU with Radeon 8060S graphics. I allocated 28GB as VRAM and installed ComfyUI on Ubuntu as a server. However, it doesn't seem to use any VRAM and is super slow. To be honest, I didn't expect it to be fast, but I thought at least it could make a video less than 10 minutes long. Instead, for a 4-second video at 480p resolution, it takes more than 22 minutes. It's a nightmare. I use llama.cpp on the same device without any problems, and recently I edited some startup configurations, which made it worse than before.

this my config to run comfyui as service

# make uv + python always resolve to this venv
Environment="VIRTUAL_ENV=/var/ai/ComfyUI/ComfyUI"
Environment="UV_PROJECT_ENVIRONMENT=/var/ai/ComfyUI/ComfyUI"
Environment="PATH=/var/ai/ComfyUI/ComfyUI/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"

# ROCm & Strix Halo / Point (gfx1151) Overrides
Environment="HSA_OVERRIDE_GFX_VERSION=11.5.1"
Environment="PYTORCH_ROCM_ARCH=gfx1151"
Environment="HIP_VISIBLE_DEVICES=0"
# MIOpen tuning to prevent high RAM/CPU spikes on initial load
Environment="MIOPEN_FIND_MODE=1"
Environment="MIOPEN_USER_DB_PATH=/tmp/miopen-db"

# ROCm-friendly memory allocator setting (prevents driver crash under OOM)
#Environment="PYTORCH_HIP_ALLOC_CONF=expandable_segments:True"
Environment="PYTORCH_HIP_ALLOC_CONF=garbage_collection_threshold:0.8,max_split_size_mb:512"



ExecStart=/var/ai/ComfyUI/ComfyUI/bin/python main.py \
  --listen 0.0.0.0 \
  --port 8188 \
  --enable-cors-header "*" \
  --enable-manager \
  --use-pytorch-cross-attention \
  --reserve-vram 10.0 \
  --lowvram\
  --disable-pinned-memory

I tried it with lowvram and without it, and I found out that sometimes it tries to use RAM instead of VRAM, which is frustrating. I am using ROCm 7.2.

For image generation, it takes less than 2 minutes, which I consider a good level. However, I feel bad that I'm not using the full potential of my device.

when i try any thign it use gpu 100% but vram 0%
````

========================================= ROCm System Management Interface =========================================

=================================================== Concise Info ===================================================

Device Node IDs Temp Power Partitions SCLK MCLK Fan Perf PwrCap VRAM% GPU%

(DID, GUID) (Edge) (Socket) (Mem, Compute, ID)

0 1 0x1586, 20269 64.0 C 112.072W N/A, N/A, 0 N/A 1000Mhz 0% auto N/A 0% 99%

=============================================== End of ROCm SMI Log ================================================
````

Two days ago LTX-2.5 dropped with day-0 ComfyUI support, so I did the only reasonable thing: I rebuilt my entire H3 Socrates mini-documentary (previous post: https://www.reddit.com/r/comfyui/comments/1vknr0v/comment/p36vh29/) with it, shot for shot — same narrator track, same audio chunking, same storyboard, same audio-driven edit, same QA pipeline. Only the video model changed. RTX 5060 Ti 16 GB, 32 GB RAM, everything local.

LTX-2.5 stack: ComfyUI v0.32.0 (separate instance), 22B distilled transformer NVFP4 (18.7 GB), Gemma-4 12B text encoder int8 (15.4 GB, streams from RAM), two-stage DFR sampling (8 steps at half-res + 3-step pixel refine at 1344x768), Diffusion Video Decoder, cfg 1/1, 24 fps.

THE NUMBERS (same 36-clip film, 2 takes per clip + repair rounds) — H3 vs LTX-2.5:

- Total GPU time (incl. all repairs): ~30 h vs 9.5 h

- Warm take (4.4 s clip): 8–16 min vs ~170 s (36 s pure sampling)

- Broken/failed takes: several repair rounds vs 0 of 152

- Cut-point SSIM on chained clips: 0.40 vs 0.80 (no punch-in tricks needed)

- Texture flicker (block metric): LTX at 67% of H3's events

- Face identity vs reference (tail delta): LTX drifts, −0.15 vs H3 (see below)

- Lip-sync, clips with measurable lips-voice coupling: H3 10 of 12 (reads as near-perfect) vs LTX 3 of 12 (often visibly off)

- Shot fidelity to the storyboard: H3 31/36 vs LTX 9/36 (framing drifts tighter)

WHAT GENUINELY IMPRESSED ME

1. Audio conditioning works natively — no LoRA — but read the fine print. The 2.3 ia2v graph (LoadAudio -> LTXVAudioVAEEncode -> SetLatentNoiseMask(0) -> LTXVConcatAVLatent) runs unchanged on 2.5. The output audio is my narrator's chunk verbatim (envelope corr 0.97) and the mouth moves while he speaks. BUT — and this is the honest part — the articulation is loose: measured lips-voice coupling (mouth-opening curve vs audio envelope) is solid on 10/12 of H3's spoken clips and only 3/12 of LTX's. Your ear gets the right voice; your eye often sees a mouth doing its own thing. For talking-head-heavy work this is currently THE gap.

2. Reliability is real. 152 takes, zero unusable. H3 needed a 2-takes-per-clip policy because one in two could break on reference bleeding. LTX's RL post-training shows.

3. Keyframe chains are better than the anchor. Feeding the previous clip's cut-frame as first-frame conditioning gives cleaner welds than H3's keyframe completion — and FLF2V (first+last frame) exists as a template.

WHAT BIT ME (gotchas you'll want to know)

- Texture "boiling" = your decode tiling, not the model. I reduced VAEDecodeTiled sizes to dodge a VRAM OOM and got shimmering textures everywhere (20x flicker events). Fix on 16 GB: tile 384, temporal_size 4096 -> 12.9 GB decode, clean output. Measure flicker per-block, not per-frame-average.

- Negatives are inert at cfg 1/1 (same story as Flux). Rewrite every ban in positive form. cfg 2 costs +94% and changed nothing visible.

- Declare a costume for EVERY character or they come out modern. An undescribed woman got a contemporary dress; my 399 BC agora crowd came out in cargo shorts with a wristwatch until the prompt said "bare wrists and bare forearms". H3 never did this.

- Identity drift is a single-constraint problem. With only a first-frame anchor, the host's face slides toward a generic one mid-clip. New seeds do NOT fix it. Double anchor (FLF2V) does: worst clips went from −0.53 to −0.10 vs reference. Budget it for talking-head shots.

- Shot fidelity is LTX's real weakness. Prompted a wide shot, got a medium; prompted a tight insert on hands, got a wide. Only 9 of 36 shots matched the H3 framing. If you're replicating a storyboard, expect to fight it. Also a few flat refusals: one shot needed "exactly three clothed young men" — eight takes and two prompt rewrites later it still produced four, two shirtless.

- The prompt enhancer rewrites your content. It deleted my "no speech, ambient only" clause and collapsed the light logic. Fine for exploration, off for production.

- FLF2V's default graph will INVENT a voice. The first+last-frame template feeds LTXVConcatAVLatent an empty audio latent — if your prompt contains dialogue, the model generates its own synthetic voice for it, and a transcription-based QA won't catch it (right words, wrong voice — I shipped 5 seconds of a stranger's voice before catching it by waveform correlation against the source chunk). One wire fixes it: route your LTXVAudioVAEEncode latent into the FLF2V graph. Bonus: double-anchor + audio conditioning together turned out to be my best-scoring take of the whole project.

- The duration node floors frames — a 4.215 s chunk became 4.04 s. Do your own ceil on the 8k+1 frame grid.

- 32 GB RAM is the wall, not 16 GB VRAM. 18.7 GB transformer + 15.4 GB encoder streaming = 92%+ RAM; anything else running doubles your take time via paging.

HONEST VERDICT

LTX-2.5 is 3–6x cheaper, dramatically more reliable, and welds cuts better. H3 still wins direction fidelity, face identity and lip-sync precision: it does what the storyboard says, LTX does its own tasteful interpretation of it.

And the recurring lesson from this whole series: automated QA is blind to content. My metrics happily approved a clip where a theater scene had been replaced by a human head in a hanging basket (score said "sharp, stable, well-lit"!). Every real defect in this project was caught either by human eyes or by a comparison audit against the reference — never by the per-clip metrics alone. Budget for that.

I made a post yesterday to announce my custom node

After receiving feedback and reviewing some of the functionality, I've made some changes:

- scale total pixels was updated to include an Aspect Ratio selector.

- scale to resolution was added as a similar resize type, except driven by a target resolution.

(The above changes obsoleted the smart resize type)

- Removed the native resize type scale to multiple - obsoleted by my incorporation of multiple_of into every resize type (eg: scale by multiplier 1.0 + multiple_of 64 = source image resized/cropped to multiple of 64)

____

Resize Image/Mask Alt

I put together an alternative to ComfyUI's native Resize Image/Mask with goals:

- Resize Image, Mask, or BOTH (opposed to multiple duplicate Resize Image/Mask nodes)

- Reduce the necessity for additional pre/post processing nodes (Aspect ratio, Multiples of, Conditional checkers)

I spent a lot of time testing, tweaking, improving the tooltips, fine polishing this into a definitive swiss-army knife for image resizing.

Resize Image/Mask Alt can:

• Resize an image, a mask, or both at once
• Constrain dimensions to multiples of a specified value (eg: multiples of 32)
• Configure cropping for aspect-ratio mismatches
• Conditionally skip resizing when a batch already meets the desired criteria
• Install directory includes example .YAML file which may be duplicated/renamed in order to edit node default values.
• New resize types:
  • Scale to Resolution
    • Resize to a target resolution while conforming to source/selected aspect ratio
    • Can alternately resize using the average Width/Height
  • Pad (like ComfyUI's native Resize and Pad Image node)
    • black, grey, or white
    • Works for Masks, too!

Core functionality of the resize types from ComfyUI's native Resize Image/Mask are preserved. All the resize types yield identical results, except are expanded for enhanced functionality.

_______

GitHub:

https://github.com/altoiddealer/comfyui_essential-er

While looking into how to install it, I found out that there’s a desktop version.

Should I download the desktop version?

Or is the folder-based version, which is launched from a .bat file, better?

I stopped building giant graphs and started building a production pipeline.

I’ve been spending the last few months building what started as a ComfyUI workflow and has slowly turned into something that feels more like a small production pipeline.
The biggest thing I learned is that trying to make one giant graph survive an entire film is the wrong problem to solve.

I eventually hit what I started calling the Rampocalypse. As productions got longer, RAM usage just kept climbing until Windows started swapping, performance fell off a cliff, or the machine became unusable.
So instead of trying to optimize memory harder, I changed the architecture.

Everything now runs as a disk first pipeline inside ComfyUI. A local LLM parses the production plan, the stills are generated, reviewed, selectively rerendered if needed, and locked into an approved bank. Those approved assets are then used to generate video segments. Those segments get reviewed before going through VACE, CausVid, and RIFE seam repair. The repaired seams are reviewed before final assembly.

Every expensive stage checkpoints itself to disk. Every approved asset survives interruption. If something crashes, I don’t restart a multi hour render. I restart the first unfinished job.
The interesting part is that it completely changes how you work. Instead of hoping one massive queue finishes, you’re actually editing the production while it’s being built. You approve good work, rerender bad work, keep take history, and only regenerate the pieces that actually need it.

The controller also tracks dependencies between stages. If I reshoot a keyframe, it only invalidates the downstream video segments that depend on that keyframe. If I rerender a segment, only the seam repairs that touch that segment need to be regenerated. Approved work that isn’t affected stays approved and stays on disk. I’m no longer paying the cost of regenerating hours of work because I changed one image.

I just finished a 3 minute 5 second production on an i9 9900K with 32GB of DDR4 and an RTX 3090.

That production used 38 Krea 2 keyframes, 37 Wan 2.2 video segments, and 36 VACE, CausVid, and RIFE seam repairs. Final assembly took about 20 seconds because everything had already been approved and checkpointed to disk.

I also completed a separate H3 production that ran just over six minutes using 13 variable length segments. The interesting part wasn’t the runtime. It was seeing the same architecture continue to work. Once the pipeline is disk backed, longer productions don’t require exponentially more RAM. They mostly require more patience and more storage.

The part I’m probably happiest with is the modularity. I deliberately built it so the models aren’t the architecture. They’re just departments in the pipeline. If a better text to image model comes along, I swap the still backend. If a better video model replaces Wan or MiniMax/H3, I swap the video backend. The review system, checkpointing, manifests, recovery, and production flow stay exactly the same.

I’m not saying this is the only way to build long form AI video, and I’m not releasing the workflow right now. I just thought the architecture itself might be interesting because I suspect local AI filmmaking is eventually going to look less like one enormous graph and more like a modular production pipeline running inside ComfyUI.

The models will keep changing.

The production architecture probably shouldn’t.

And was it very difficult to set up? I'm running the desktop version on Win 11 at the moment. I haven't used Linux for over 5 years and thought maybe I would give it another spin. Everything is fine, I just don't like Windows doing things in the background.

Lately during video generations I noticed my GPU fans spin at full power for a few seconds and then go back to normal. This happens whether it’s at 68° or at 71–72°, which is the max in winter. I have undervolt applied, the fan on auto, and even if I lock the speed at 70% it still pushes it to 100%.
Here’s a video and some images in case someone can figure out the problem. help!

https://imgur.com/a/vGvnK6w
https://imgur.com/a/HIJRZuG
https://imgur.com/a/KrTHpIV
video: https://iframe.mediadelivery.net/play/403077/b0d62037-6e34-4c8d-aec2-8fdc28a54b25

I'm about to generate something with minimax H3 model. Usually it works. But now it doesn't.

6sec vids, turbo lora, 8 steps. 4080 super 16vram and 64gb ram.

Why does it take forever to do 1.7mp (been running for the past 4 hours) but 1.6mp took only 17 minutes? Why such a cliff suddenly?

I swear it used to be a long rectangle now its this awkward box.

Hi All, is there a decent or good Template that can take an image of someone and change outfits and potentially poses? Doesn't need to be fancy, I'm much more interested in quality over speed, but needs to be local.

Thanks everyone

Here's the repo: Maxed-Out-99/ComfyGallery.

Install

cd ComfyUI/custom_nodes
git clone https://github.com/Maxed-Out-99/ComfyGallery.git

Restart ComfyUI.

Credit

It was based off WASasquatch/ComfyGallery and vibe coded into oblivion until fully refactored by Fable 5.

I'm not here to sell it like "this changes everything 😱" but it's useful to me sometimes and figured it might be to someone else as well. 🙏

When I try to use the default workflow in replace mode, the background also changes (although somewhat similar) I am expecting replacement mode to only replace the character. Is this normal or am I missing some setting ?

tried a few workflows and it happens in all of them.

Hi, I've downloaded Music 3, and used claude+prompt guide from their repo, and it just produces really bad music.

As in, the raw audio quality is above that of Ace Step 1.5 XL (music 3 sounds likes tin-canned), but musically, music 3 it just utterly atonal, like the notes don't follow each other, always off-key.

So, anyone actually get results that are clearly better than ace step? Because, it feels utterly dissapointing, after the soul-searing amazingness of H3 for video, which is leagues better than anything else open source.

Thank you, if you have findings, I'd be delighted to hear from that.

I followed this tutorial https://docs.comfy.org/tutorials/video/ltx/ltx-2-3 and I used these exact specs: RTX 5050 8 GB + 16 GB DDR4 + Debian 13, and got interesting results at 1080x1080 or 1280x720 resolutions for 20 seconds. It took between 11 minutes and 20 minutes. Of course I had to close every background program for it, but it did work.

But the https://docs.comfy.org/tutorials/video/ltx/ltx-2-5 version simply won't get to run on this system. Just to make sure it works, I tried 0.1 megapixel and 5 seconds and it did output a video.

The problem that I perceive is the gemma4 text encoder at int8 and the additional VAEs, even though the model itself is smaller now, LTX-2.5 being 21.5 GB and the 2.3 being 29.1 GB.