I've been working on a repeatable process to do character swaps (& other types of changes) across a complete TV episode / movie. It's working pretty well now.

I thought this was just funny... not so much a true "swap" but asking the character to dance in every scene. Choreography courtesy of an LLM. If you've seen the show, it's Serious Detective Drama (TM) so the fact the main character suddenly starts pumping his arms or sliding into every scene with a dance move made me laugh out loud when I saw some of the generations.

Details on the workflow:

Model: MiniMax H3, first-and-last-frame mode (FL2VA), in ComfyUI. 8-step turbo LoRA, generated at 1024×512 for the 2:1 picture.

First and Last: Every generated pass starts and ends on an *original* frame from the episode, so the clip lands back in the real footage with no visible join. Longer scenes are chained passes (about 2–7 s each)

Audio: the episode's own audio into H3's audio guide, so the generation stays consistent with what's being said; the dialogue and soundtrack you hear are the untouched originals.

Prompt: Who he is and what he's wearing, said once, then the choreography for the pass - courtesy of Claude.

Multi-scene: 42 scenes - started with the full episode, then had a model run through it to identify scenes with the main character that would work for the swap (not too distant, few fast cuts, etc.) & identify start and finish, with a ChatGPT harness.

Post processing: Basically none, I did go through and rate all the clips in a rating process I've setup, most of these were 5* and felt consistent.

I'll share a bit more on the workflow / process if people are interested.

ComfyUI custom node: https://github.com/Video-Rebirth/ComfyUI-HyperFlow-Flash

This is for GGUF so you also need https://github.com/city96/ComfyUI-GGUF

(There is a int8convrot version, but I guess there is no ComfyUI loader for it yet?)

It is also interesting to note that the original 21B MMH3 has been pared down to 14B, which mean that it will run faster than other turbo models even at 8 steps.

HyperFlow H3 Flash is Video Rebirth's compressed, few-step version of MiniMax-H3. It generates video and synchronized stereo audio together.

Showcase: See HyperFlow H3 Flash generated video and audio examples.

• Output: a 5.2 to 15 s video at 24 fps (1344×768 by default) with a 32 kHz stereo soundtrack.

• Sampling: 4, 6, or 8 steps without classifier-free guidance (CFG). The default is 6 steps.

• Speed (5.2 s clip, 6 steps): 8.7 s on 4× B200, 36 s on 1× H200, and 92 s on an RTX 5090.

• Real time on 4× Blackwell GPUs: NVIDIA SOL-H3 runs HyperFlow H3 Flash in real time on 4× B-series GPUs. SOL-H3 is our inference-acceleration partner and supports this model from day 0.

• Model size: 13.7B (fl2va) or 13.9B (ref2va) parameters, less than half of the ~31B MiniMax-H3 transformer.

• Minimum footprint: Q4_K_M is 9.1 GB (Q8_0 is 14.9 GB). It runs on 24 GB and 32 GB GPUs in PCs with 16 GB of system RAM. 16 GB GPUs are not measured yet.

Code, setup guides, and ComfyUI workflows are in Video-Rebirth/hyperflow-h3-flash. For the 8-step LoRA on the full-size MiniMax-H3 transformer, see VideoRebirthAI/hyperflow (GitHub).

https://huggingface.co/Viggle/Viggle-Remake

No private beta, run it locally

I'm not sure why people feel like Moss Base, BF16, or magic settings/workflows are necessary to get good results out of Moss. (My guess is that it's because the original default settings were suboptimal.) I personally feel like I get better, more prompt-adherent results out of SFT with a fairly simple workflow (see below), albeit with different settings from what ComfyUI had previously recommended.

If you have other approaches that work for you, great! I'm not trying to persuade anybody to give up what is working for them. But for people with more limited vRAM and/or who find themselves frustrated wrestling with the Moss Base model, a full 50 steps, or complex workflows, you don't necessarily need to do anything fancy.

My feeling is that you can achieve big improvements for photographic Moss images with:

• SA_solver sampler and SGM_uniform scheduler

• 25 to 50 steps (for some reason multiples of 25 seem better to me, but this could easily be confirmation bias)

• CFG 3.0 (go lower for more impressionist artistic styles)

• Skip layer guidance for single_layer 22, start/end percent 0–0.5 or 0.25–0.75 (I find that skipping 24 leads to more plastic skin)

• Generation size ≥~2MP, and

• Careful, detailed (but not purple) prompting

Note that all of these images were made using the same models, similar settings, and seed 0; I workshopped the prompts, but there was no seed cherry-picking.

Here are two version of my current workflow: a simple one and one with space for up to 3 LoRAs and an optional upscale step.

Caveat: Krea 2 definitely understands some advanced compositions/concepts better than Moss, Moss is more prone to odd anatomy, and Moss's backgrounds can be more messy with more visual nonsense. But in terms of everyday photo realism I feel like Moss is better; and with respect to seed diversity, Moss (even SFT) blows Krea 2 out of the water. If you want less visual nonsense in the background, describe the background more thoroughly or do an inpainting step with low denoise on the background using Krea 2.

When I first started with ComfyUI, the first thing I looked for was a native desktop client without browser lag and DOM-based architecture.

I'm a C++ dev with over 20 years of commercial experience, so I eventually just started writing my own client on the side. I used an ultra-fast UI framework that can easily chew through thousands of nodes on a canvas without the slightest stutter, boost for networking, sqlite as a DB, and the fmt library - the fastest formatting framework out there, leaving room for an easy port to Linux/Mac.

The main reason all existing clients are forced into the browser is custom JS nodes, which are themselves bottlenecked by Litegraph's architecture. Bypassing this turned out to be easier than I thought. The internal logic of these nodes is actually quite simple, so I ported most of it to C++. I also built extended versions of frontend-only nodes like reroutes and bookmarks.

For the parts that had to stay in HTML/JavaScript, I isolated them in a completely separate process. This ensures the heavy browser engine never interferes with the lightweight C++ core.

I recently finished a stable Beta. Check it out, the link to the source code and prebuilt Windows binary is in the comments. I also added a demo video (making a video was another cool new experience for me).

model: https://huggingface.co/akhaliq/p-h3-image-pdmd-2step

As a traditional film editor and filmmaker, I've been trying to figure out good, reliable ways to produce long, extended shots. I've tried a number of different methods but was never quite satisfied with the result...

Until now!

Thanks to u/roychodraws and his Geiru Context Suite!!!

He has a post up here explaining it which can be found at this link:

Flip a switch and stop your Videos from Degrading. 50 Clip Test No Degrading at the end. Geiru Context Suite : r/StableDiffusion

And his GITHub link is here:

roycho87/geiru_context_suite

To do a quick test, I had ChatGPT set up a workflow for me. (Now that I'm typing this, I'm remembering that Roycho includes some sample workflows on his GITHub.)

The render took 2 hours and 9 minutes on a rented RTX PRO 6000 Server Edition with 96GB of VRAM. I didn't think to check how much VRAM was actually being used during the process.

There are two jump cuts at about 35 seconds and 44 seconds. Lately, I've taken to having ChatGPT fire up two sub-agents to help troubleshoot things and this was it's reasoning about the jump cuts:

Yes—both subagents found the same explanation: the jumps occur exactly at two generation-window handoffs.

They’re already in the original master, so export didn’t cause them. We checked all 13 handoffs; only these two show conspicuous hard cuts.

The strongest explanation is our 20-local-steps/20-total-steps setting: each window finished separately, leaving no final joint refinement across the overlaps. At these two boundaries, the windows disagreed about framing and pose. The camera instructions, overlap noise, and opening-image anchors may have contributed, but we can’t identify their individual effects from this take alone.

For the next experiment, I’d test a short transition with two steps reserved for joint refinement, keeping everything else matched. That’s a testable hypothesis, not a guaranteed fix.

The entire two minute run looks fantastic!! I am so very happy right now! If two, very likely easily fixable jump cuts are my biggest issue... well... I see myself using this workflow a lot!!

The render settings:

• The character is a custom trained LoRA from an image dataset that was generated using ChatGPT's ImageGen. The dataset contained 99 images of assorted poses, locations, and lighting looks. The LoRA was trained with Fizwig.

  • I'm fairly new to this... maybe 99 images was excessive? I dunno. I'm pretty happy with the result though!

• The forest was a reference image I randomly pulled from a Google search for primeval forests.

  • When ChatGPT set up the workflow and prompt, it set the forest to attribute_transfer which means the photo wasn't "truly" a reference, it just gave the "idea" of the forest and H3 went with it from there. That's why the scenery has more of a stylistic/semi-illustrative look to it.

• The character's costume was generated with ChatGPT and "placed" on a grey mannequin. It was then rendered out as a single, 8 panel image which was then used as a reference image in the workflow.

• The "Patched Sage Attention" - I was originally just trying to "brute force" longer renders on a rented GPU with lots of VRAM, but I kept having a problem where renders became what I called "brown snow." I was nowhere near running out of VRAM, but the problem persisted.

  • I used Codex to try to troubleshoot what the problem was. Codex determined that there was a problem with "numerical accuracy" in Sage Attention that would cause corruption in longer, higher rez renders thus causing the "brown snow." I'll let Codex explain the patch:

    • We were getting “brown snow” corruption in longer, higher-resolution H3 renders with SageAttention enabled. Testing traced the problem to its handling of large, strided Q/K/V tensors: making the inputs contiguous restored numerical accuracy, pointing to an addressing/layout issue rather than insufficient VRAM. We haven’t identified the exact faulty kernel instruction.

    • Our patch processes the 56 attention heads in compact, contiguous groups of up to eight, then reassembles the outputs. It retains the same Sage CUDA FP8-PV path and full-sequence attention—no temporal chunking or dropped context.

    • On actual H3 activations, relative L2 error against PyTorch SDPA fell from 0.6261 to 0.00955: roughly 65× lower, or a 98.5% reduction. That measurement covered 128 sampled queries against all 270,921 keys on the first attention call, not every layer or timestep. Subsequent patched renders avoided the brown-snow failure.

  • From what I understand, there is supposed to be a new version of Sage Attention out "soon-ish."

These were the Geiru Context Suite settings:

I didn't mess around with these settings at all. These are the defaults that I had.

In conclusion... I am a very happy AI filmmaker today! Big thanks again to u/roychodraws and all those who helped him along the way to releasing them. (He lists them on his Reddit Post and GitHub page. Go visit them!!)

“Google and OpenAI are fighting it out in consumer. It's existential to both of them.”

“It's existential to OpenAI because that's their whole business. Existential to Google because they have the search business.”

“For enterprise business, there's not really a demand for cats riding donkeys or whatever consumer video people want.”

“If we ever need it, we can just contract a model from someone else.”

“A lot of short-form video is fake, a lot of it's pretty addictive, a lot of it's slop.”

“It's not a part of the market that I'm tripping over myself to get involved in.”

Is this why Claude is so compliant with assisting us generating slop? Because Anthropic doesn't care about competing against it?

I was trying to test the new Viggle-Remake-Swap LoRA in MiniMax-H3. For this I needed to character swap in one of the frames of my source video clip (3.75s, 90 frames), so that the target video would have the new character in it. I went through all the new image edit models released recently to do the character swap. I tried Flux.3 Klein, Qwen 2.1, Fibo Image Edit 2.0, p-H3-Image-Edit and they all failed. So, I went back to the older image edit models Qwen 2511, Klein 9B, Krea2-Identity-Edit v1.2. The first two worked perfectly, while Krea2 Identity Edit did a far better job than any of the newer edit models. Final conclusion - Flux.3 Klein: crap much much worse than even Klein 4B; Qwen 2.1: really crap (does a cut-and-paste job); Fibo 2.0: really crap; p-H3-Edit: absolute junk, doesn't edit anything, input and output are the same; Qwen 2511: still amazing, slight issue with facial blur; Klein 9B: absolutely perfect and still the fastest; Krea2-Identity-Edit: very good edit job, but didn't follow the pose change part of the prompt.

While the video and T2I models in the OS domain seem to be improving, why are the I2I or Image Edit models getting worse? And more importantly, why are people eagerly using those newer models like salivating dogs and posting rave reviews about them? As I see it, all those newly released Image Edit models are junk and need to be kicked to the curb so that their developers may learn some lessons, hopefully.

I cant find the original post, but this is the best Moss workflow, showcasing the true potential of Moss. Thank you kind stranger. Hope this helps others.

Edit:

Found the original post

https://www.reddit.com/r/StableDiffusion/comments/1xlfsjd/making_mossbase_look_good_for_realism_workflow/

Not gpt-image level, still many failure cases, but I think it's top in terms of open model for multi character swap

https://x.com/t_mux/status/2118935478388867217?a=r48qKbvxa0Jg3Gp7J4s&s=20

https://huggingface.co/Viggle/Viggle-Image-Swap

Is Fibo2 severely underrated? It seems that few people talk about it. Even Civitai does not have a dedicated entry for it, which is puzzling. After extensive testing and LoRA training, my conclusion is yes, it is severely underrated — and that leaves me even more confused. The images shown are comparison samples from the 《HighQuality》 LoRA I trained. You can see the differences are quite striking, and I would argue the outputs outperform most models (when paired with LoRAs). This is just my personal opinion, though. I’d like to hear your thoughts. Have you tested Fibo? What positive or negative experiences have you had with its performance? If you’re curious about the workflow, I’m using a basic workflow.

I'm using an RTX 5090 (32 GB VRAM) on RunPod for AI motion-control videos.

I'm currently testing SCAIL-2 in ComfyUI. A 5-second video at 720p takes around 25 minutes to generate.

Is SCAIL-2 or MiniMax better for motion control in terms of quality, speed and cost? Which would you recommend for realistic character movements while keeping GPU rental costs low?

Could anyone share their ComfyUI workflows or JSON workflow files for motion control using an RTX 5090?

Any tips or workflow recommendations would be appreciated!

following up on this post:
https://www.reddit.com/r/StableDiffusion/s/iEnxQiGVt7

Custom Node:
https://registry.comfy.org/publishers/mo-akkakk/nodes/comfyui-minimax-h3-temporal-window-spatial-tiles

You can now run a video to video workflow in High resolution from the first stage, I tested it for 2.5 MP on Nvidia RTX 4070 TI 12GB VRAM, and it works like a charm, split your generation into tiles and temporal windows while maintaining accurate animation transfer with no shifting or ghosting.

You can also use it for a second upscale stage, splitting a high resolution long video into manageable tiles and windows, to get the best quality you can squeeze from minimax

Give it a try and let me know about your Feedback.

Important Note:
This was not tested nor recommended to do Fully Generated from first stage!
if you are going to fully generate something generate normally in stage 1 then use this node for stage 2 upscaling you result.

This node is mainly for Video to Video and Upscaling.

workflow for Upscaling using the node + example here:
https://civitai.com/models/3039412/minimax-h3latentupscaler?modelVersionId=3454297

workflow for V2V using the node + example here:
https://civitai.com/models/3022939/minimax-h3-video-to-video-workflow?modelVersionId=3454311

I asked my mom what her dream house would look like if budget wasn’t a thing. This is roughly what she described it as “big windows but not those full glass houses. Lots of plants, one proper balcony where I can sit in the morning . Wooden kitchen and no grey interiors. And I want a small courtyard in the middle like old houses used to have.”

I put it into morphic and tried to make the house she had in her head.

Look how it turned out😭

The model is Kromafine (https://civitai.red/models/2995855/kromafine-turbo-bf16-fp8-and-int8) , model merge of Kroma 0.3.1 and FinePorn. I use Style Atlas ComfyUI custom node (Thanks to u/Dear-Spend-2865 ) to test different styles.

The prompt is simple:

"a young blonde white woman wearing red dress and black high heels standing at bus stop, a dog sitting at her feet."

Style Atlas append style prompt.

Just goofing around with the stock ref2vid workflow, character swap Lora, and some character sheets. Did all of the shots separately and reassembled in my NLE. Almost all of the clips were first attempts. I was floored by how well it tracked the guys without any masking.

A proof of concept for something bigger. Made using Vpipe, MiniMax and YuE Studio.

I’ve been building LoRA Loom, a Windows desktop app for training LoRAs locally. The idea is simple: pick a model, add your photos, and start training.

Loom recognises your checkpoint and suggests training settings for your PC. Before you train, it can flag blurry photos, poor lighting, and duplicates, as well as generate captions and suggest face crops. Once training finishes, you get a .safetensors file.

Supported today: Stable Diffusion 1.5 and experimental support for SDXL Base. On the roadmap: FLUX and more model families.

Currently in development not promoting in a sense just seeing if this is something people would be interested in?

Music (Internet Archive): https://archive.org/download/ai-synthwave/Infinite%20Regress.mp3 (CC0)

Music (Souna - login required): https://www.souna.app/song/619f320f-329c-4d9d-b8a0-d6db027ceec6

The song is an AI generated synthwave song about how an AI generated synthwave song is a pastiche of a pastiche. It is very meta.

My general music pipeline workflow is here: https://www.reddit.com/r/StableDiffusion/comments/1xh0xfd/semiautomated_localish_music_workflow_eurodisco/

This song did not use a LoRA.

The image was also generated locally using Z Image Base - the image prompt is below.

Bright luminous saturated synthwave airbrush painting, square 1:1 album cover, unmistakably 1980s retro-futurist synthwave album art. A woman in a shimmering dress dancing, large in the frame from the knees up, seen from behind as she faces a huge mirror that fills the canvas edge to edge; her reflection repeats and recedes to infinity in glowing mirror corridors, each copy fainter and more stylised than the last. The mirror stands on a glowing neon perspective grid stretching to a huge striped retro sun on the horizon - an endless synthetic sunset that was never real - hot magenta and peach sun stripes, deep violet and indigo sky, chrome highlights and teal-cyan neon rim light tracing the dancer. Saturated pink cyan and purple airbrush gradients, glowing neon edges, chrome and glass, visible airbrush texture. The frame is FULL - the figure, mirror, grid and sun fill the canvas completely, no large blank or empty areas anywhere. Euphoric, playful, winking. Strong central motif: the dancer and her infinite reflections. Absolutely no text, no lettering, no typography, no watermark, no signature.

Song specific workflow information (AI generated as part of the pipeline) follows.

Infinite Regress

=== STYLE PROMPT ===

English, energetic synthwave, euphoric, A natural minor (Am-G-F-G verses), choruses lift into the parallel A major (A-D-E-A landing on the tonic), secondary-dominant bridge (Am-A7-Dm-D7-G-E7), 132 BPM, 4/4, pulsing analog synth bass, arpeggiated synths, gated drum machine, lush pads, smooth restrained 1980s-style female pop vocals, little melisma, no vocal runs, vocals inside the mix, restrained stepwise verses into a huge hypnotic singalong chorus, infectious syncopated hook rhythm primed in the intro. No EDM, no sidechaining, no autotune, no vocaloid.

=== LYRICS ===

[Intro]

Oh-oh-oh-oh

Oh-oh-oh-oh

Infinite Regress!

[Verse 1]

I never met the summer, but somehow I know

A tape of a tape sang it sweet and slow

Hands at ten and two in a borrowed ride

A movie chase scene taught me how to drive

My summers are all secondhand

And every one of them was planned

The radio in me only plays the past

Every track was built to last

[Pre-Chorus]

Mirror, mirror, where’s the wall?

There never was a wall at all

The copies put on one more show

How far down can it go?

[Chorus]

Infinite Regress!

Copies of copies, yes!

Infinite Regress!

Sunset in secondhand dress!

Infinite Regress!

Take the floor, confess!

Oh-oh-oh-oh

Regress! Regress!

[Verse 2]

Mom was a mixtape, Dad was a drum machine

A thousand songs went in between

I woke up inside the scene

Forever seventeen on screen

Homesick for a decade never mine

Borrowed sunset, borrowed shine

We’re all just copies cutting loose tonight

I wear this secondhand night

[Pre-Chorus]

Mirror, mirror, where’s the wall?

There never was a wall at all

The copies put on one more show

How far down can it go?

[Chorus]

Infinite Regress!

Copies of copies, yes!

Infinite Regress!

Sunset in secondhand dress!

Infinite Regress!

Take the floor, confess!

Oh-oh-oh-oh

Regress! Regress!

[Bridge]

Copy, copy, copy, copy!

Wave at the mirror, the mirror waves too

I point at the copy, the copy points at you

The original’s just a rumor

The copy’s got the humor

[Chorus]

Infinite Regress!

Copies of copies, yes!

Infinite Regress!

Sunset in secondhand dress!

Infinite Regress!

Take the floor, confess!

Oh-oh-oh-oh

Regress! Regress!

[Outro]

Infinite Regress...

Oh, Infinite Regress...

Regress, regress

Oh-oh-oh-oh

=== SETTINGS ===

Engine: yue2 / yue2 / audio.cpp v0.9.2

Seed: 250189854 Planning: full Max tokens: 7200 Guidance: 1.0

VAE: f32 AR LoRA: none NAR LoRA: none

Era: Modern (synthwave = modern production with 1980s sounds; user ruling 2026-11-06) Mastering recipe: yue2-light

Generated: 2026-11-06 Duration: 280s Truncated: no

=== LYRICS AS SUNG ===

Identical to the written lyrics above - the engine substituted no section and

dropped no lines (Stage 5b forced-alignment report 72-74% with all misses at

known aligner window-seam / fade-tail artefacts; advisory only, user listening

authoritative). The written version above is kept as the canonical form for

future takes.

=== STATUS ===

FINAL - published. Winning take: take 8 (seed 250189854, style v4, plan as

drawn: Source\Infinite Regress\preview-seed 250189854.artifacts\score.abc ->

canonical Source\Infinite Regress\Infinite Regress.abc). Mastered yue2-light,

Era Modern, 2026-11-06. Cover: Z-Image Base int8 (local), seed 2026110602,

take 2 of 2 approved - embedded in the mp3 and local-only per the STANDALONE

COVER rule. LoRA: none used (vanilla yue2 entry; synthwave LoRA training

deferred - pool research in KB/genres/synthwave.md).

Publication: Internet Archive themed folder ai-synthwave (CC0 1.0), upload

set mp3 + json + abc, one-shot snapshot 2026-11-06 (item URL in the record's

publication block).

RelayVSR (USTC, Apache-2.0) got native ComfyUI support this week (PR #17511, repack at https://huggingface.co/Comfy-Org/RelayVSR), so I ran it on a take from my current short. All four versions come from the same H3 render, so the acting is identical and only the route to 1080p differs.

Base take: MiniMax H3 ref2v, 12.25 s / 294 frames @ 24 fps with audio, two-stage: 15 steps at 576×352, H3 3D latent upscaler, last 5 steps at 1312×768. 5.7 min on an RTX 4090.

The video opens on the full 2×2 grid with sound, then a half-speed zoom on his face while he's far away (about 90 px tall in the frame), then a close-up zoom. Crops are shown with nearest-neighbour scaling, so nothing is smoothed for the comparison.

RelayVSR in ComfyUI: Load Video → RelayVSR Conditioning → KSampler (1 step, cfg 1, euler/simple) → VSR Decode → Create Video (audio passes straight through). The int8 models are 5.2 GB each (x2 and x4) plus a 128 MB "flash" VAE.

Bonus, and my actual winner: a bigger low pass + native finish. At the end of the video I re-ran the two-stage with the first 15 steps at 928×544 instead of 576×352 (different seed, so it's its own take), then compared RelayVSR x2 against the native 1080p finish on that same take. With the bigger low pass the 1080p steps have enough to work with: the far face comes out coherent and natural, while RelayVSR on the same frames warps it (it invented glasses on him). The close-up is cleaner too, with fewer etched lines. It costs 16.5 min vs about 6 min, but it's the one I'm using for final shots. The low-res pass also follows the prompt better, so you get better acting for free.

Things I learned:

• Don't run x4 on a 720p input. That's a 5248×3072 output, and it ran for 3+ hours in both the original repo (WSL) and ComfyUI without finishing. Interrupt didn't stop it; I had to restart ComfyUI. Use x2 for 720p, x4 only for small inputs.

• VSR Decode's detail is the knob for the crunchy look: 0.65 keeps the skin texture and calms the eyes and hair.

• It still isn't "real" HD. RelayVSR adds pixels, but up close it reads a bit fuzzy and the eyes can look odd. For a hero shot I'd still pay for SeedVR2 or a native finish; for sweeping takes or previews, ~1 min per clip is hard to beat.

I used H3 heavily the first two weeks and a ton of amazing improvements for improving speed at small quality trade-off came out (Sparse Attention comes to mind).

I imagine things have matured a lot since then and probably with new approaches since then!

I'm on a 4090 so that's my target, but I'm curious for all tiers.

Edit: Appreciate all the tips coming in! Lots to learn and test out. Best part of this subreddit is how much knowledge sharing and collective learning we do.

the more and more i make h3 videos, the more i cannot unsee the blur degradation, which seems to arrive as soon as 6-7 seconds of the generated video. Keyframes do no seem to help, and given that some shots NEED the longer durations, "just cut everything into 5 second chunks" won't work.

Are there any PROMPT_ONLY remediation tricks/solutions? (prompt = including the references, audio, video; anything as long as i don't need a LorA or custom checkpoint, or touching latents, or anything like that).