As the title suggests. I used Claude code (Sonnet 5.5) with a Krea2_api workflow to create over 2000 images overnight. Attached are some of Claude's and my personal favorites that it made. I gave it freedom to use any of the loras I had downloaded, it could edit/make new workflows. It looked out the outputs and judged them and experimented with settings. It recorded notes and its thoughts. The final notes.MD file has over 1700 lines of text which I had it summarize into a shorter file. edit: notes.md + summary.md; and all ~2100 images zipped in the comments!

YT Video:
https://www.youtube.com/watch?v=cLc3l3eb2-Q

MiniMax H3 Singularity V2 Dual-Sampling Workflow:
https://www.runninghub.ai/zh-cn/post/2112912294795993090

Edit. -> Pruned: https://huggingface.co/WAS/Minimax-h3_Singularity_v2_pruned

P.S.: I'm not the author!

Download UDA here

UDA is a lora for creating anime and digital paintings with moss image. I've found the base model is quite good at most things but I've seen many weird artifacts and errors especially with digital artwork. This lora definitely helps with that and can produce quite nice outputs but be careful not to accidentally slopify your images.

For other fine tuners, I trained on about 250 images, 10,000 steps and the training dataset used the structured json bbox prompt type (but plain natural language prompts seems to work quite well). I trained off of the combined and converted datasets from my anime/digital painting, studio ghibli, and vintage anime ideogram loras. Let me know what you think!

I am not associated with the project in any way. Not sure if this will work with the regular ComfyUI LoRA loader.

We apply PTD (Parametric Trajectory Distillation) to MiniMax-H3, distilling the Ref2VA (reference-image → video + audio) trajectory into a few-step student. We release an 8-step and a 4-step accelerator for the Ref2VA task.

Unlike a single shared acceleration LoRA, PTD trains one independent low-rank adapter per inference step ("per-hop" LoRA) on the diffusion backbone, on top of the per-interval output heads introduced by Parallel Decoding Distillation (PDD). At inference, each denoising step selects its own hop's adapter and fuses that step's output head, so an 8-hop checkpoint runs in 8 transformer evaluations (NFE) and a 4-hop checkpoint in 4.

H3 t2v is just so damn good looking compared to ref2va/fl2va and even RefMod. So I attempted to create a LoRA of an original character, using 8 Z-Image Turbo stills, a custom ElevenLabs voice, and 14 short clips of her talking (from 5 Seedance gens with the Z-Image refs).

Left is H3 with a RefMod (photos and voice sample). Right is the same seed with the LoRA at 0.8, followed by DLSS 5. The RefMod keeps her face consistent, but everything looks a bit too polished. With the LoRA, she looks closer to t2v quality.

I trained her in Fizgig, running headless: rank 16, Automagic v3, Circlestone adapter, EMA 0.98, 40 epochs (3,520 steps) on the pruned int8 fl2va model. Dataset was the images plus face crops, 14 clips cut to the 17n+5 frame grid, and 45 voice clips trimmed to exact grid lengths. Training took 41 minutes on a rented H200 (free Modal credits).

Rendering on an RTX 3090: 864x480 at 8 steps with the DMAD 4-step LoRA, 3D latent upscale to 2 MP, 2-step refine at 0.4 denoise, then DLSS 5 at 0.8 intensity. About 4.5 minutes for an 8-second clip, plus 15 seconds for DLSS on a second card (5060 Ti).

A few things I figured out:

• LoRA at 0.8 is the sweet spot. At 1.0 she looked she was wearing too much foundation. Prompting "no makeup" somehow made it worse.
• Don't trust the trainer previews. Late epochs looked terrible there, then great in the actual workflow.
• RefMods pick up lighting from the reference photos. Studio lit stills made everything look like a moisturizer commercial, switching to candid photos fixed it. The strength slider did basically nothing.
• The second pass loses the cloned voice. Save the stage-1 audio and put it back afterward.
• Strong prior training data can still take over. Put her in an X-Files basement with Mulder and Scully, and suddenly she looks like Agent Reyes (the weird seasons) but with a British voice.

Workflows (API format, drag onto the canvas): H3 + LoRA, H3 + LoRA + RefMods on ref2va, DLSS 5, and both RefMod builders:

https://drive.google.com/drive/folders/1T-ELnDiOQ2pOJOVaaL_MvyfXcmeUdXeK?usp=sharing

Props to shootthesound (Fizgig), Luisacaotica (RefMod nodes), Blueforcer and Merserk (DLSS 5 Enhancer), ZhengmingYu and Kijai (DMAD), and circlestone-labs (training adapter).

It has been a busy week, only just two days ago I posted about MOSS and today I am diving into FIBO 2.0 (Edit Turbo model specifically). You can find below my quick summary, and you can also see my deep dive at my HF: huggingface.co/lvladikov/FIBO2-Edit-Turbo-T2I where I have run comparisons of images created with direct prompt and captioned (json) prompts, and also provided ComfyUI workflows, and diffusion python scripts for torch, mlx and mflux!

Bria's FIBO 2.0 came out with two checkpoints: Fibo-2.0-dev (text-to-image and editing, 50 steps, CFG 5) and Fibo-2.0-edit-turbo, which the model card presents as the few-step, guidance-free editing model.

Edit Turbo runs text-to-image too. It's the same BriaFibo2Pipeline, so you call it with no input image: 4 steps, guidance_scale=1.0. A render is 4 transformer passes, where Dev takes 100 (50 steps, each with a CFG pass).

The prompt does most of the work. FIBO 2 is trained on structured JSON captions (Bria's VGL). Bria's own captioner, bria-vgl, runs on Gemini, but you don't need it: FIBO 2's text encoder is a complete Qwen3-VL-4B-Instruct, so the pipeline can write its own caption. Give it bria-vgl's open "generate" system prompt and field guide (github.com/Bria-AI/vgl, under src/bria_vgl/dialects/thales/), take the JSON it writes, and pass that as the prompt. Plain prompts work too.

import json, torch
from diffusers import BriaFibo2Pipeline   # diffusers from git main (FIBO 2 landed Oct 20)

pipe = BriaFibo2Pipeline.from_pretrained("briaai/Fibo-2.0-edit-turbo", torch_dtype=torch.bfloat16)
pipe.to("cuda")   # "mps" on a Mac; pipe.enable_model_cpu_offload() on a smaller GPU

# 1. FIBO's own Qwen3-VL-4B-Instruct writes the VGL caption, with bria-vgl's open prompts:
#    prompts/generate/system_prompt.txt and input_schema/generate.txt
system = open("system_prompt.txt").read().replace("{style_preset_block}", "").replace(
    "{llm_input_schema}", open("generate.txt").read())
idea = "a woman in a cream cable-knit sweater on a misty lake pier at dawn, holding a steaming enamel mug"
user = ("Generate a detailed JSON object, adhering to the expected schema, for an imagined scene "
        f"based on the following request: {idea}.")
messages = [{"role": "system", "content": system},
            {"role": "user", "content": [{"type": "text", "text": user}]}]
text = pipe.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = pipe.processor(text=[text], return_tensors="pt").to(pipe.device)
out = pipe.text_encoder.generate(**inputs, max_new_tokens=2048, do_sample=False)
reply = pipe.processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0]
caption = json.loads(reply[reply.find("{"): reply.rfind("}") + 1])

# 2. Edit Turbo as text-to-image: no image, 4 steps, no CFG
image = pipe(prompt=json.dumps(caption), num_inference_steps=4, guidance_scale=1.0, width=1024, height=1024,
             generator=torch.Generator().manual_seed(4242)).images[0]
image.save("fibo2_turbo_t2i.png")

What 22 prompts showed

I rendered 22 prompts twice at seed 4242, 1024×1024: once as typed, and once through the caption the model writes for itself. Most came out clean either way. Counts held for five friends, three kittens and two dogs, and text rendered where the caption asked for it (a neon "Café Lumière" sign). Writing a caption took about 70% as long as rendering the image.

Three prompts went wrong, each for a different reason:

• "A family of four having dinner … seen from directly above" gave six people, as typed and with the caption. The model miscounts on its own here, and the written caption didn't help: it never lists the people as objects, and it adds walls and a framed family photo that can't be seen from above. A caption with four person objects and a straight-down camera gave exactly four.
• "Close-up of a pianist's two hands": the written caption added the piano's ornate legs seen from the side, and the render pulled back to a toy keyboard played by three hands. As typed, it was a good grand piano with one pianist, plus a second player's hands at the far end. In every version the black keys run evenly instead of in groups of two and three. No caption fixed that, so it's the model.
• "A gleaming white colony ship … clean sci-fi concept art, bold simple shapes": as typed, a detailed ship over an ocean planet. With the caption, a featureless egg under a daytime sky, because the writer read "bold simple shapes" as no windows, no rivets and no stars. Another seed looked the same, and three sampled captions asked for the same. Removing those lines from the caption fixed it.

So when a count, a layout or the amount of detail matters, open the caption it saved next to the image, fix it, and render it again.

Running it

• ComfyUI (native FIBO 2 support since Oct 21, PR #17240): two minimal workflows, one for the bf16 model and one for vantagewithai's GGUF quants. No custom nodes beyond ComfyUI-GGUF for the GGUF one.
• diffusers: a script for one prompt or a batch, with --plain (the prompt as typed), --sample N (another take on the caption) and --json (render a saved or edited caption). It keeps one part of the model in memory at a time: the text encoder, then the transformer, then the VAE.
• Apple silicon (MLX): a standalone MLX port of the same model, no PyTorch needed, plus a variant that uses mflux's VAE. Float16 compute is about a quarter faster than bf16, and a 1024×1024 run peaks at about 17.4 GB. With PyTorch installed (it only draws the starting noise), a seed gives the same picture as the diffusers script.

The workflows, the scripts and the 22 examples, both sets side by side with the notes above: huggingface.co/lvladikov/FIBO2-Edit-Turbo-T2I. No models are hosted there; they download from Bria's and the community's repositories. Both checkpoints are CC BY-NC 4.0 (non-commercial).

So here you go, have fun and share your FIBO 2 Edit Turbo generated images from text. FIBO2 is very good, for all I've seen, and I've not even focused on Edit where everyone says it's amazing. As Text to Image (for Turbo Edit model) it is pretty good!

https://github.com/NyckM/Bruxosdovfx-SyncTile-MiniMax-H3-v5.4/tree/main

Workflow https://github.com/NyckM/Bruxosdovfx-SyncTile-MiniMax-H3-v5.4/blob/main/workflow/minimaxH3BackgroundSwap_v18_LatentF.json

The idea in one sentence

A large or long video is split into overlapping regions (spatial tiles × temporal windows). At every model call all regions are predicted and blended into one canvas, so there is a single denoising trajectory: no tile seams, no chained clips, and VRAM is one region's.

Install

Easy way (Windows): put bruxosdovfx_EasyInstall_MiniMaxH3.bat in the ComfyUI folder (or the ComfyUI-Easy-Install folder) and double-click it. It finds the folders, installs the custom nodes (SyncTile, Media IO, Latent Upscaler, Easy-Use) and downloads models and LoRAs; anything already present with the same name is skipped, and interrupted downloads resume. Options at the top of the file: text encoder int8/nvfp4, VAE light/full, optional LoRAs, update nodes.

Manual:

1. git clone https://github.com/NyckM/Bruxosdovfx-SyncTile-MiniMax-H3-v5.4 inside ComfyUI/custom_nodes/ (or copy the bruxosdovfx-SyncTile folder). Delete ComfyUI-SyncTile-H3 if present (old version).
2. Restart. The console prints [bruxosdovfx • SyncTile] v5.5 carregado.
3. Also install the bruxosdovfx Media IO pack — https://github.com/NyckM/Video-Util-ComfYUI (Load Video Pro, Load Image, Save Video Pro — see below). Needs a ComfyUI with the native MiniMax H3 nodes. To write the video file: ffmpeg (on PATH, in FFMPEG_PATH, or pip install imageio-ffmpeg).

Background Replace v3 LoRA (summary)

Experimental merge of five LoRAs by other authors for MiniMax H3 (not an original training). Download: Background Replace v3.safetensors → models/loras/minimax/. BF16, ~1.91 GB, rank-concatenation merge (no SVD); no metadata (credits live in this README). Components and weights: Facial Realism CloseUp — prithivMLmods (1.0); H3 Character Swap — Akatz Labs (1.0); Realism People — Lovis Odin / fal (1.0); Head Swap v1.0 r32 — Alissonerdx / Alisson Anjos (0.2); VFX Edit v1.0 r128 — Alissonerdx / Alisson Anjos (0.2). Links in the table in the Portuguese section. Base model: MiniMax H3 — MiniMaxAI. Load from your LoRA folder at strength 1.0. It mixes FL2VA and REF2VA sources; the name does not guarantee background replacement. The exact Character Swap version is unconfirmed (metadata says v2, deltas resemble v1.5). Structure validated, not yet tested in generation. The source models' licenses apply; credits do not imply the authors endorse the merge.

Referências / References / 参考文献

Ideas we combined (no code copied unless stated)

https://huggingface.co/lodestones/Kromoss/blob/main/8-steps-dmd2-lora.safetensors

It's good?

Hi folks, I've been experimenting with Fibo 2.0 (both Dev and Edit Turbo), and one thing that really bothered me was how badly the model struggled with higher resolutions.

Even model trained with 1K images, the actual detail quality improves dramatically at 2K and above. Skin textures, clothing, hair, environmental details everything looks significantly better. But the model starts stretching and extending things unnaturally. People get elongated, environments repeat themselves, and objects lose their proportions(see the last image for a native 2K latent size only result).

And it's not just an anatomy issue. The same thing happens with landscapes, architecture, and ordinary objects.

So I started experimenting with custom sigmas, didn't worked then tried adjusting positional encoding (RoPE), and I'm sharing the best settings I have.

I know Reddit will destroy the images in this post so here are the full res comparisons: https://imgur.com/a/Ogcrw4V

I've managed to substantially reduce the stretching and get much more coherent images at native 2Kresolutions while retaining the additional detail, no upscaling, no second pass, no hires fix. Just direct high-resolution generation.

I'm still experimenting with different combinations of settings to find the best balance between sharpness, detail, and structural consistency, but it's pretty okay to share with the current state.

You can do upscaling with "enhance this image" prompt, or use text2img instead of edit, both works pretty great.

Speed is 19s with 1 reference, at 2K 4MP, with turbo 8 steps, on RTX6000PRO.

Workflows:

CivitAI
HuggingFace

Cheers!

I want to make this image a full body image, adding legs.

SDXL would do out painting, but the legs would be out of proportion.

What current model/method can do this?

Wooooooooooooo!

I saw this post yesterday about being able to feed reference images in directly: https://www.reddit.com/r/StableDiffusion/comments/1xb2h1k/mossimage_accepts_image_inputs/

and it does alright but as you can see, without a lora it often mashes the inputs together or doesnt really edit properly. I trained a lora for it with AI toolkit for 6,000 steps and it made a big difference although there was still plenty of room for improvement if I kept training.

I found that Ostris's AI toolkit also had his own Moss edit system that feeds the inputs in even more thuroughly to try to get better reference quality out of it. Without a lora it gives you essentially an overlaid version of the images but with a lora it does a pretty good job of editing. You can be the judge of which method is better. They both use their own lora trained for their reference/edit methods with the same dataset and step count.

Ostris' edit method takes about 45s to render one of these images while the simpler ref method renders them in about 15s. Training for the Ostris method takes about 3 times as long as well.

I'm working to expand my dataset and training methods then I will be trying again with a lokr that's significantly larger than the rank-16 lora I used for this comparison. I will probably want to train is atleast twice as long if not longer.

Bria released Fibo 2.0 yesterday and I wanted to see how it does with a character LoRA and create something that I can actually post (all my other datasets are not royalty-free).

The dragon is a plush mascot I use for my LoRA portfolio. 15 training images.

What I used:

• Bria's train_lora.py from the Fibo-2.0-dev repo
• 1024px, rank 64, alpha 64, lr 1e-4 constant, AdamW, batch 1, grad accum 2
• 400 steps, the likeness was already there at 200, and 300 was the best (this might hugely differ for human characters but I did not want to do a synthesized human)
• Rented an A100 40GB on Vast. It peaked at about 23 GB VRAM, ran at 6.2 s/step, and training took about 42 min. Each edit took about 80-90 seconds
• About $2 in GPU time in total

The lora works really well with a plain edit instruction and doesn't change much else

If you add a reference image from the dataset, it can give better results (It drew the outfits like the space suit.) but it often changes the whole picture

On Abbey Road I asked it to add the dragon as a fifth person but it just replaced one of the Beatles (I don't know which one, sorry.) I kept it because I found it funny.

The full settings and VRAM numbers are on my tracker, link in the comments. Fully free, no ads, just my referral links.

A big update to your local video workflow: the app has a redesigned Home page and sidebar, quick search, easier setup, and a new Assistant that can turn a chat into queued generations.

Create videos locally with Text-to-Video, Image-to-Video, and Audio-to-Video. Retake or Extend clips, try IC-LoRAs like Ingredients, Day to Night, Deblur, and Water Simulation, then queue jobs in the background and reorder or cancel them. You can also use Remote from your phone or another browser on the same network. Generations and assets persist across restarts.

The update adds fast-preview resolutions, more duration, frame-rate, and aspect-ratio options, prompt enhancement controls, and seed settings. It also includes installation and memory fixes across Mac, Windows, and Linux.

Download: LTX Desktop · Full changelog

I wonder is there any style loras or models out there that are really good / unique and didn't get the spot light?

I was curious how Fibo fares against the rest of the solid editors and if it is time to swap.

Comparing edits is a pain on reddit so I've put together a small website:

Image Edit Showdon comarison page

Hotkeys:
[←][→] to cycle model / prompt combo
[↑][↓] for next / previous scene
[space] to see original image

TLDR: for my cases Qwen 2.1 still is the best, but FIbo might be a great option if the community supports it.

There are lots of caveats here.

1. All three models are very prompt sensitive.

Although I've run bare prompts as is and tried using "best" prompt enhancers for each model, the initial prompts are all over the place: some are super detailed, some are simple and a lot depends on enhancer model.

Enhanced prompts were generated with GPT 6 Luna xhigh with these system instructions:
- VGL JSON prompting for Fibo
- JSON for Klein
- Prose edit for Qwen 2.1 (based on their original PE pipeline)

In the future I might try:
- Qwen 2.1 & their tuned PE model
- Fibo with their skill package.
- Klein with my personal edit prompt that worked well in the past.

Lots of moving parts!

Even on meme swaps I was able to get much better results with Klein and Qwen with adjusted prompts and BFS + consistency loras. For Fibo I've used Kijai sample workflow so community might improve it drastically too.

2. The resolution is 1mp because Fibo breaks down above 2 megapixels. Qwen and Klein can generate up to 4 mps and their results improve dramatically. I might run 1.6-1.8 MP panel again to see if Fibo can fare with better resolution. (or they’ll all be obsolete with Krea 3 edit lol)

3. I am using 1-shot gens with int8 and same seed. These are not original models and bf16 versions might give better results. And 1-shot sample is not representative of the model overall performance - it might've been just bad seed luck. Also Fibo and Klein are 4 step and Qwen is 8 step. (I still haven't found good 4 step lora/distill with decent quality for it)

4. Fibo edit turbo is not meant for text2image
Devs specifically say Fibo edit distill is not good for t2i, but I still ran text2image panel in a separate page (top right). Ideally I’d like a generalist model and so far Qwen 2.1 is the best option for my cases. (Inpainting-edits, product photography, lifestyle photography)

All the sources, prompts, and workflows are in the bottom of the original page under the viewport

If you see more problems with methodology, caveats, have ideas to test or suggestions please submit an issue as this is my first time im doing something like this.

Also share your "model breaker" prompts or benchmark prompts to include in future version.

Built a custom node for MOSS-Image that feeds reference images directly through the Qwen3.5-9B vision tower and leaves its embeddings in the text conditioning - so the image conditions generation as part of the prompt, not as a VAE latent.

- Custom Node + Workflow: https://github.com/ThetaCursed/ComfyUI-MOSSImageTextEncoder
- LoRA used: https://huggingface.co/lodestones/MOSS-Image-SFT-8-steps-dmd/tree/main

I see people here raving about it being a really good edit model. HOW?

I tried some things, always got bad, low quality results or deformed anatomy.

I would highly appreciate if anyone could share their H3 image edit workflow, and an H3 image generation one if you have it.

Thanks.

Prompt: Completely change the art style of image 1 to fully lifelike real life professional editorial DSLR photograph style. Completely remove all of the text from image 1 including the large text behind the character in image 1. Completely replace the character in image 1 with the literal exact same woman from image 2. Preserve the original composition and original layout and original character's pose and original character's clothing and original rectangles on the wall to the right from image 1.

I'm finding MOSS to be so interesting, and also so frustrating at the same time.

I think that its prompt adherence is awesome, its speed is great, and its variety on the same prompt/different seeds makes it super fun.

But I feel like I'm constantly fighting its desire to (1) give cartoonish images, and (2) blur either some or all of the image. On this second one, it's like it just bought a DSLR and a f/2.8 lens and wants to show off its ability to get thin depth of field.

Has anyone found either a prompting style or a checkpoint/lora that can reliable give the crisp, clear, sharp photo-style images that I've had no problem getting out of Krea 2?

(Currently using kromoss - today's version - and its latest 8 step lora, but i've had the same problem with other finetunes as well.)

Sorry in advance if this is hard to read, I'm not entirely sure how to format on Reddit.

All images generated using my local comfy workflow (Workflow, update to latest comfy version to use - the built-in comfy fibo node will automatically resize to 1 megapixel if width and height are left 0). Models used were fibo_dev_int8_convrot for diffusion model, qwen3vl_4b_int8_convrot for text encoder, and full_encoder_small_decoder for vae (flux2-vae with faster decoding). 50 steps, cfg 5, euler sampler, and simple scheduler. No negative prompt was used.

These are my unbridled outputs, i.e. one seed per prompt as my machine takes a while per each generation. So keep in mind that what you're about to see might not be 100 percent reflective of the model's capabilities. The prompts were also limited in scope so with further fine-tuning results may improve.

First two images represent a 360 'turntable' presentation. From left to right (with first image being the original so it doesn't have a prompt):

1. Turn the girl to face the right while preserving all other aspects of the image.

2. Turn the girl to face the right such that her body is perpendicular to the viewer, viewed from the side, while preserving all other aspects of the image.

3. Turn the girl 270 degrees counter-clockwise such that she's viewed from behind facing slightly to the right while preserving all other aspects of the image.

4. Turn the girl so she faces the opposite direction, viewed straight-on from behind while preserving all other aspects of the image.

5. Turn the girl 270 degrees clockwise while preserving all other aspects of the image.

6. Turn the girl to face the left such that her body is perpendicular to the viewer, viewed from the side, while preserving all other aspects of the image.

7. Turn the girl to face the left while preserving all other aspects of the image.

The overall quality of the images seems decent, however there are certainly some issues with the eyes and the hands at some points.

Interestingly it didn't just copy the same pose for every shot, it actually managed to preserve the book when she turned away from facing the viewer while still having her hold it naturally.

I specifically chose an image with a hair ornament as I wanted to test the model's sense of object permanence, i.e. if it understands that the hair ornament should be excluded when the character is facing in a manner that it would normally be out of visibility. I purposefully didn't mention this in the prompt to see what its innate abilities were.

Unfortunately it basically failed on all counts in this regard as the ornament was just pasted on randomly in the shots where it should have been occluded as a result of the angle. This isn't really any different from the likes of Qwen and Flux though.

Next two images (images 3 and 4) are variations of the same input image with two different prompts, left showing the original and right showing the output.

1. Edit the provided image so that only the single girl in the center foreground remains. Keep the orange-haired girl in the very front center of the image as the only character. Preserve her full appearance, including her face, expression, hair, pose, clothing, and proportions. She should remain the main subject in the foreground. Remove all other girls from the image completely. Remove every background character, including any visible faces, hair, hands, arms, legs, shoulders, or partial body parts belonging to the other girls. Remove all text and graphic elements from the image, including the large pink Japanese title text, the English subtitle text, decorative sparkles, and feather-like graphic elements near the text. After removing the other characters and text, reconstruct the missing background naturally and cleanly. Fill the empty areas with a believable continuation of the outdoor rooftop scene, including sky, water, distant mountains, railing, ground, and any appropriate environmental details. Make the composition look natural and seamless, as if the image originally only contained the single front girl. Preserve the anime illustration style, clean line art, bright colors, and overall visual quality of the original image. Keep the remaining girl centered and prominent. Do not add any new characters, objects, or text.

Prompt was enhanced by ChatGPT. This was actually pretty impressive, as it did remove all the characters except the one I intended and maintained her pose and expression almost exactly. It did duplicate her ahoge on the opposite side and mess up her left hand, but I wasn't really expecting anything too high quality from that much of a destructive action.

1. Remove the text and make every character's hair and eyes vibrant green while preserving the composition. Preserve the feathers behind the text and above and to the sides of the characters.

Yeah so this one was just a complete and utter failure. Not only did it turn the skin of every character green, it also barely changed any of the hair colors, and took my preserve instruction too strongly and didn't remove the Japanese text fully, only the English text.

Images 5 through 8 are not my outputs, but just something kind of curious/mildly alarming I wanted to point out.

So briaai uploaded an official image editing demonstration to their model card on huggingface (link), which you can see in its entirety on image 5.

Image 6 is a cropped version of the second panel from image 5. If you look closely you can actually see there's a random finger emerging from nowhere (circled in red). Then circled in green it appears that the duck's right foot has been transformed into some kind of stump and its left foot seemingly has two feet emerging from the same area. Then in blue, this is relatively minor, but if you compare the nose piercing in the original (i.e. left panel in image 5) to the nose piercing in image 6, you can see it flipped direction to the right side of her nose instead of the left.

Image 7 is a cropped version of the third panel in image 5, if you look closely at the fingers you can see the pinky and ring finger (marked in yellow) are actually just stumps that don't continue past the viewer's visibility. Then again in blue the nose ring has been duplicated across both sides despite only being present on the left in the original.

Image 8 is a cropped version of the fourth panel in image 5. Again the pinky and ring finger have turned to stumps (marked in yellow) and the nose ring has mysteriously disappeared, once again in blue.

This tangent doesn't really serve a purpose, I just thought it was odd that if these are the images you're exposing to the consumer as the representation for your model, you would avoid including such blatant errors. If the entire series of panels was one prompt it would be understandable but it seems more like to me that they were three separate generations that were stitched together. I could be wrong, but the prompt is conveniently missing so it's hard to say.

But anyways back to the actual point of the post, based on my limited experience, I think Fibo 2.0 is a solid model, surely, but I'm not really seeing anything that designates it as the cream of the crop.

Only officially supporting one megapixel generations limits the quality of the outputs significantly I feel, so perhaps comparing it to Qwen and Flux is unfair in this instance. However, I feel if you're releasing an image editing model in 2026, native 4 megapixel support should more or less be the bare minimum.

Images 9 and 10 show the 'Artificial Analysis' rankings for the model which show Fibo 2.0 at number 1 in image editing with 1087 points and number 2 in text to image with 1038 points.

But if you look into what this actually entails, the ranking was taken from the model's 'Image Arena' score which is merely a subjective assessment of several models based on select prompts. Image 11 shows an example of what this might look like, though the official rankings are not influenced by the public.

In any case, it can be misleading at first glance looking at the chart since it appears that the model is objectively better than the rest of the competition, when in actuality the metrics are just based off of each rater's preference. Just something I thought I would bring up.

Every time a new model comes out you always see the hordes gravitating towards it and proclaiming it as the 'Best [x] Model of 2026!' which is typically based on subjective criteria, or frankly, in a lot of cases, misinformation.

I would do more prompts to compare but unfortunately I do have to actually go to sleep in order to wake up and go to work and I've already spent a lot of time on this post as it is. Let me know your guys' thoughts and experiences on this model. I'm interested to see if people will actually consider replacing/have already replaced their Flux or Qwen workflows with this and what the rationale is if so (genuinely, not intending to be facetious).

I put together some simple ComfyUI workflows for Fibo 2.0: text2image with Dev, and image editing (single and multi-reference) with both Dev and Edit Turbo. Each one comes in two versions, with and without structured prompt conversion.

https://comfyui.nomadoor.net/en/basic-workflows/fibo-2/

For the structured prompt conversion I'm using Qwen3.5 9B. You can reuse qwen3vl_4b, the text encoder Fibo already loads, but it just wasn't capable enough.

That said, the interesting part is that you don't strictly need structured prompts. Plain, simple instructions work reasonably well too, so don't worry, it won't end up like Ideogram 4.0 or FLUX 3 Image Klein 🤔

I haven't used it much yet, so I can't say much about overall quality. For text2image, there are lighter and faster models that can also go to higher resolutions, so it may be hard to find a place for it there. For image editing, though, Edit Turbo is very fast and performs well, so I think it'll have its moments.

As far as I know, the original FIBO was a pioneer of structured prompts, and I remember thinking it was a really interesting idea. I'm glad to see a successor with such strong editing capabilities released. Thanks to the Bria team!

is it known that the weights of the model are unencrypted in the cache?

I just made an easy test completly without the topaz suite from the unencrypted model i found at
'/Applications/Topaz Video.app/Contents/Resources/models/coreMLCache/ganim-v1-fp16-576x672-2x-OS15-mlp'
on macos.

Im not promoting anything i have official topaz i just found the models and just used codex to make a quick proof on concept.

First image is original,
second image is my proof of concept
third option is using topaz video directly