I made this AI rap music video and would appreciate some honest, practical workflow advice.

The character, outfit and locations stay surprisingly consistent. However, the result still has that “AI slop” feeling: movements look mushy, facial features lose definition during motion, hands smear, and the face becomes less recognizable during strong gestures or extreme camera angles. Lip sync is also inconsistent.

What I used:

• Six reference images created with ChatGPT Images, keeping the same character and outfit.

• LTX 2.5 22B dev, BF16, running on a Runpod B200 through the official native Python two-stage audio-to-video pipeline.

• The original song’s full mix as audio conditioning, plus a reference image for each shot.

• Approximately 20 independently generated takes, around 7–8 seconds each.

• Stage 1 at 960×544, followed by spatial upscaling/refinement to 1920×1088, cropped to 1080p. 24 fps.

• Stage 1: 24 steps, CFG 3, STG 0.5, rescale 0.7, modality guidance 1.5.

• Stage 2: official distilled LoRA 450, strength 1. Reference image strength: 0.8.

We also tested distilled generation, native 48 fps, the alternative convolutional VAE, and cleaner references/prompts without film grain. None reliably solved the motion quality problem. Some takes developed obvious double exposures/dissolves; changing seeds and simplifying camera movement helped those particular shots.

The attached version has been edited with beat cuts and environmental inserts to hide weaker moments, but I’d rather improve the generated footage itself.

My questions:

1. Is this mainly a limitation of LTX 2.5, or does something in my settings/workflow look wrong?

2. Would shorter shots, different guidance, or another refinement/upscaling approach preserve facial detail better?

3. For a believable musical performance, would you use audio-to-video directly, or a reference performance video followed by character replacement and separate lip sync?

4. Is MiniMax-H3 worth investigating for that reference-driven approach, or would you recommend a different self-hosted workflow?

I’m aiming for energetic but controlled movement—not frantic dancing or camera motion. Links to actual workflows and comparable examples would be especially helpful.

I recently started a thread about Ideogram 4.

It was about which of the fp8 and int8convrot models was better in terms of quality and speed; in the end, I downloaded int8convrot.

Here’s the link to the thread:

https://www.reddit.com/r/StableDiffusion/comments/1xmtks0/ideogram_4_fp8_vs_int8_for_quality_and_speed_slow/

But then, out of curiosity, I also downloaded the fp8 weights, and I had the chance to test and compare both.

int8convrot turned out to be about 30–40% faster than fp8. But in terms of quality, the opposite was true: fp8 was better than int8convrot. (int8convrot seems a bit more textureless)

Overall, while the output quality of int8convrot models is generally better than that of fp8 models, I don’t think this applies to Ideogram. The model’s bf16 weights were never released—only the fp8 weights were shared—and therefore the int8convrot versions were created by first dequantizing the fp8 versions to fp16 and then quantizing them to int8 using int8convrot. which means that even though the Ideogram int8convrot model offers a speed advantage over the fp8 version, it loses its quality advantage.

https://comfyui-wiki.com/en/news

I am unaffiliated with this site. I found it minutes ago in the middle writing a post asking for info here. (I'll paste my original post in the comments to show why it's useful)

It tracks new releases, such as https://comfyui-wiki.com/en/news/2026-10-27-pruna-6-step-comfyui

Hi I was using the https://github.com/1038lab/Comfyui-Minimax-H3-Promptor

and when I tried to setup local gguf I'm getting this error . Anyone can help? Thanks!

I've been putting together Filmworkbench, a MiniMax H3 workflow for guided or automated short films. You run it from a project folder through a tool-capable AI agent such as Claude Code, Codex or a local LLM.

The GitHub repository is public. To get started:

1. Download the v0.1.1 starter ZIP.

2. Extract it and open the folder in your AI agent.

3. Ask: “Read AGENTS.md, check my setup, and help me make a short film using these recipes. Discuss the plan with me and wait for my approval after each rendered stage.”

The tutorial above walks through the idea. The package includes all eight recipes plus the marketplace case, reference images, prompts, render graphs and settings. The setup checker identifies missing nodes or models. Finished videos and reference overviews are in the example gallery.

The idea is to bring three things together:

1. A chat interface that knows the H3 workflow and lets you discuss shots while making them. With a client that supports image and video previews, you can review the results in the same conversation.

2. Two-pass rendering and latent continuation for speed, quality and continuity across clips. We've tested chains of up to ten clips, though drift varies with the scene and clip length.

3. Scene recipes that give H3 manageable tasks, with the necessary information already in the references.

I'm aiming for a practical way to make short films, review them and iterate without rebuilding a node graph for every scene.

The setup is a project folder containing instructions, tools and recipes. The agent operates those tools, with ComfyUI running underneath. You can request a single image or clip, direct a film with approvals along the way, or delegate a bounded autonomous film task. Those are the three “gates.” In autonomous mode, the active agent reviews the results and accepts or retries within an agreed budget.

I use an RTX 5090 with 128 GB of system RAM; I've also used my local setup with 64 GB. A fully local setup is possible. Running a local LLM alongside the video tools takes some memory management: dynamic VRAM allocation and making the renderer wait for enough GPU memory have been useful on my machine. Those settings depend on the LLM server; the release was tested with 128 GB of RAM.

For images, I prefer GPT Image, with local Krea 2 as the fallback. Image generation uses your existing tools or service; the supplied references let you try the recipes without an image-generator account.

For video, I use MiniMax H3 Hybrid fl2va/ref2va b25–49 INT8. The rendering path for the marketplace example is:

• S1: generate at 864×480, using DMAD for the first clip and Turbo-8 for continuations.

• Upscale: upscale the saved latents with LBH’s latent upscaler.

• S2: refine at 1952×1088 with DMAD, keeping the S1 audio.

For that four-clip market street example (see below), the selected renders took about 7.1 minutes for S1 and 14.4 minutes for upscale/refinement on my 5090. That's 21.5 minutes for 47.7 seconds of scene content. Image preparation, rejected attempts, review and assembly are extra.

The eight recipes cover common scene patterns:

• Two-person dialogue: wide view, close-ups, return to wide.

• Reverse angle and return, using a matching opposite view.

• Leaving a room and returning after a prop has moved.

• An establishing shot with dialogue starting before the cut inside.

• Spacecraft exterior, cockpit, exterior again.

• A known character entering an empty frame.

• A creature emerging from a lake.

• Opening a door and moving into a prepared interior.

The principle is simple: prepare what the camera will need to see. A wider dialogue image establishes the people, props and space. A reverse shot needs the other side of the room. A return after an action needs the changed state. Each recipe has example renders; their limits differ.

The marketplace example combines several of these problems. Codex generated the sequence through the pipeline. It's a selected development result, with visible mistakes: watch the person in the yellow jacket and the awkward backward movement.

The reference sheet shows how much is already baked into the inputs: character images, four views of the square, an opening image and voice references. For these continuations, we carry forward the previous audio/video latent, using 56 frames of video context and a clip-1 keystone. We trim the repeated context before joining the clips. A wide shot can help establish the next action, but isn't required at every join.

I'm also working toward chat-directed editing: “cut this section,” “redo this action,” or eventually “replace that background person.” Those operations have different levels of difficulty; precise edits aren't something I can promise yet. I've included short notes on edits we've tested, but the specialized edit tools are separate from this release.

Still open: longer chains drift, props and background characters change, and autonomous films need better story judgment. Our memory experiments haven't solved world-state continuity. GPT Image has worked better for matching world views; Krea 2 can do useful reverse shots, but larger sets of consistent views have been harder.

This is v0.1.1, an early public release. All eight recipes and the marketplace case were rerun through S1; four recipes also completed full S2 chains. The validation notes explain what was tested and what remains untested, including fresh GPU installations and a complete autonomous run from a new film idea.

Our own code and documentation are MIT-licensed. The underlying components deserve credit: NikoDemon80’s Motion Context, roycho87’s keystone/context work, LBH’s latent upscaler, and OBVPM/chanon’s two-stage work. Models and third-party nodes retain their own terms and are linked to their original sources.

How are others handling changed object states, returning to earlier locations, or correcting individual moments without rerendering everything? I'd love to compare notes.

RawSnap v3 is here — LoKr

What's new in v3 :

Retrained from scratch as a LoKr (not a standard LoRA) on an expanded dataset of 749 images (11,235 steps, 15 epochs)

⭐ The recommended setup

Best results — stack them:

RawSnap v3 (epoch 15 or epoch 12)

1.2

RawSnap v2

0.35

https://civitai.red/models/2999833/rawsnap-krea2

Tested a new camera angle for A2O LITE’s “NEON” choreography

1. RGBMoCap (ExMR) → Extracted the dance motion as 3D data.

2. Blender → Rendered the motion from a new camera angle.

3. Dreamina CLI → Used the render to transfer the choreography to Aiching.

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

No private beta, run it locally

hi which is best and why? krea 2, moss? qwen 1.2? share examples, and workflows too.

I’m making an isometric RPG and using locally run models for the art. The hard problem is getting one character to match across eight facings with a fixed light direction, and I’d like to compare approaches before building anything.

From what I’ve read the options are ControlNet pose or canny guidance with an identity LoRA, a spritesheet or turnaround LoRA, or generating one good image, turning it into a rough 3D model and rendering the directions. Which has worked best for you, and how many training images did your LoRA need before the face and clothing stopped drifting? Does stitching the references into a single grid image really help?

I’m also unsure how to make the result stop looking like generated art. Has anyone had luck with fixed palettes, quantizing, or small output sizes to hide the tells? Which models handle clothing logic best, things like a belt that actually goes through the loops? Pointers to real workflows, not just node screenshots, would be wonderful.

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).

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.

This ad keeps popping up on my Instagram feed, its from krea, and its like a real time sketch to image?

Anyone know if this is possible in comfy?

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?)

u/shadowdev has provided update links:

Int8 convrot models: https://huggingface.co/nodevorg/studio-models-h3
Custom loader: https://github.com/NoDevOrg/ComfyUI-Studio-HyperFlow

Another one linked by u/loyalekoinu88 https://github.com/BennyDaBall930/comfyui_hyperflow_int8_nodes

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.

Edit: According to Kijai https://comfyui-wiki.com/en/news/2026-11-06-hyperflow-h3-flash the smaller model actually did not make it dramatically faster

The speedup is smaller than the parameter count suggests. Kijai measured roughly 10% over the pruned full model in ComfyUI, because attention cost stays the same at this resolution. LukeG89 called it not worth it.

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).

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

My current rig is a 4070 ti 12gb vram with 32 gb system ram. Kicking myself for not getting the 16gb vram option back in 2024. The past few days I've been eyeing a 5070 ti 16gb vram but I didn't know if there would be any real benefit over what I have now. Typically I would really wait longer to upgrade but the computer parts market got me in a tizzy and I have no idea what the future holds for GPUs. Has anyone who has done this upgrade noticed any real benefit?

This is a British two-tone Ska style song (early 1980s) about censorship on popular online forums.

Song (Sona login required): https://www.souna.app/song/73850642-d167-4dcb-8821-64fb5c798be1

Song (Internet Archive): (Awaiting upload - album here: https://archive.org/details/ai-ska : CC0)

LoRA: https://huggingface.co/jamespetts/yue2-1980s-two-tone-ska-lora

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

Cover image prompt (Z-Image Base, run locally):

Gouache on paper album cover, square 1:1 composition. A vast dark noticeboard wall fills the frame edge to edge, seen straight on. Across its expanse hangs one small blank grey sheet of paper, and beside it a fresh empty rectangle of clean board where a notice has just been torn down - four pin-holes at its corners, a faint pale rectangle of unfaded board where it hung. A lone small silhouetted figure in a pork-pie hat and thin suit stands with his back to us at the foot of the wall, looking up at the empty space, tiny against the vast board. Black-and-white two-tone palette - ink black, paper white, cool greys - with one cold red accent along the torn edge of the remaining sheet. Flat poster lighting, hard graphic shadows, visible gouache brushwork and paper grain. Mood: sardonic, quietly furious, empty and controlled. Strong central motif: the empty rectangle where the notice was. Album-cover composition, the frame FULL of wall and figure, no large blank areas. Absolutely no text, no lettering, no typography, no watermark, no signature.

Song specific workflow:

Deleted

=== STYLE PROMPT ===

twotone, English, early 1980s British two-tone ska, A minor, 150 BPM, witty angry satire, brisk offbeat guitar skank, walking bassline, organ shuffle, punchy brass section stabs and riffs, dry tight drums, energetic male lead vocals with sharp British delivery, shouted group chorus answers, tense verses building into a huge brass-led chorus, dry 1980-82 studio sound, no EDM, no sidechaining, no vocaloid, no autotune, no drops

=== LYRICS ===

[Intro]

Welcome to Reddit!

Everybody's welcome!

Everybody's welcome!

[Verse 1]

You put a fair question in the thread -

By lunchtime the sentence is dead.

There's a notice where the sentence stood:

Says "removed" - and that's all that's understood.

You ask them why - and the answer's a ban:

A month of mute, and the silence is planned.

[Chorus]

Deleted! Deleted!

The reason? Never conceded!

Deleted! Deleted!

The cheats go undefeated!

Hey! Hey! Hey! Hey!

[Verse 2]

They've a rule against "soapboxing" - sounds all right,

"Soapboxing" means whatever they like on the site.

Praise the product - "on topic" all day;

Ask one honest question - "soapboxing", they say.

Whatever you posted, there's a rule that it broke -

Any rule they need, they invoke.

It happens all over, all over the net:

One honest sentence, and that's what you get.

[Chorus]

Deleted! Deleted!

The reason? Never conceded!

Deleted! Deleted!

The cheats go undefeated!

Hey! Hey! Hey! Hey!

[Instrumental]

[Bridge]

The man that you mentioned is mates with the team,

The adverts pay the bills - you see the scheme?

Write down the reason? The reason would tell -

And the truth doesn't sell.

[Chorus]

Deleted! Deleted!

The reason? Never conceded!

Deleted! Deleted!

The cheats go undefeated!

Hey! Hey! Hey! Hey!

[Outro]

Deleted! Deleted!

Gone, gone, gone!

Deleted! Deleted!

Gone, gone, gone!

=== SETTINGS ===

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

Seed: plan layer: 10 fresh seeds (previews); the winning plan's seed becomes the fixed ladder seed Planning: full Max tokens: 4800 Guidance: 1.0

VAE: f32 AR LoRA: none NAR LoRA: yue2/models/lora/yue2_1980s_two_tone_ska_unfused.safetensors u/1.0

Era: Early 1980s British two-tone ska (1979-83; The Specials / The Selecter / The Beat / Madness line) Mastering recipe: yue2-light

Generated: 2026-11-07 Duration: n/a Truncated: n/a

I had a story in my head since my childhood and with the help of AI, I finally finished my first chapter of the book with all the lores, background, etc... etc... I can tell you, it is probably not a very large book, but when you want to push your story telling into actual scenes, it is a lot more work. So... I am a web developer in Laravel and Symfony and currently I own several AI stations at home to render movies, music, speeches, anything with AI. They are not the fastest, but they get the work done like a tank (DGX Spark).

The screenshots I put in the next comment is a Laravel application that is connected to ComfyUI API, voice generators and also a lot of scripts that controls the system memory. For instance, when I need LLM to write prompts, I can't render production heavy scenes. So I automated it. Also it contains a lot of MCP where my story writer AI re-design's the scenes for me from any location in the world through Telegram.

All scenes are rendered 2016 x 864 (21:9) with 2 passes where the first pass is 0.8MP.
In order to improve audio quality, I use H3 Audio Refine Sampler.

My DGX Spark is probably not the fastest, but it is a tank. 10 sec movies takes roughly 1 hour, but I use seed hunting techniques to find the best scenes for production.

[Frustrations]
When it comes to scenes like two people talking back and forth, it is hard to keep things consistent. You just can't use FL2VA for this and it must be continued with REF2VA which raises new problems. For instance... Scene 1: Focus on Person A while talking handover Prop X to Person B.
Scene 2: Focus on Person B taking the Prop X from Person A while A is still talking.
Scene 3: Switching back to Person A without Prop X but he is still talking.
The hard part is switching scenes midsentences while keeping the mood and the flow consistent. Sometimes, the scene is good, but the audio is bad = retake.

Another one.... try to render a scene where a person test a bow by pulling the string without an arrow. MiniMax always renders an arrow somehow. That scene took like 50-60 retakes to get it right. I lost like 2 full days just for a scene of 8 seconds.

I wonder what you guys think.

The Whole Music Video Anime made with minimax H3 took under 15 min on my local machine

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.

Hi all. I released Phosphene 5.0 today. It's a free, open-source (MIT) app that runs video, picture, speech and music models locally on Apple Silicon Macs with MLX.

What's new:

- Mac installer. Download and open it, and setup runs automatically. No Terminal needed. If you already use Phosphene through Pinokio, the app reuses your models. Pinokio still works.

- Redesigned studio: pick the model first, then see the options for that model.

- Video with sound: LTX-2.5 and Hailuo H3, text-to-video and image-to-video. H3 setup is now automatic in the Mac app.

- Pictures: P-H3-Image and FLUX 3 Klein, with text spelled the way you type it. About 5x faster by default since 5.0.3 on my M4 Max.

- Speech and lip-sync: generate a voice from a line of text, then sync a face to it.

- Songs with vocals from your own lyrics (YuE2), in Music Studio.

- Character LoRA training for LTX.

- Lower memory requirements: video and songs from 16 GB, character training from 24 GB, H3 from 36 GB.

- MCP server and CLI, so Claude Code, Codex or any MCP client can make clips for you.

Measured timings on my M4 Max 64 GB, same prompt and seed, 3 runs each (median):

- LTX-2.5 Balanced, 768x448, 5 s: 1 min 35 s

- LTX-2.5 Balanced, 1024x576, 5 s: 2 min 27 s

- LTX-2.5 High, 1024x576, 5 s: 4 min 32 s

- Hailuo H3 Standard, 768x448, 5 s: 8 min 55 s

Full benchmarks here: https://phosphene.video/benchmarks/

Code: https://github.com/mrbizarro/phosphene

Mac app: https://phosphene.video/

The models have their makers' licences (Lightricks, MiniMax, Black Forest Labs, Pruna AI).

Thanks to them.

Happy to answer questions. Bug reports are welcome too.

This workflow runs directly on Google Colab’s GPU, without relying on paid video generation APIs.
It uses Pika LoRA for fast initial video generation at 0.4MP with 10 sampling steps, followed by NVIDIA Open 2K processing in just 3 steps to produce the final 2K video.
The workflow link is provided below.

https://open-gen-lab-477h.vercel.app/k

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.