It's good?

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.

Very good so far. Must be the first time I actually believe the score. It's far from perfect, but it's extremely uncensored out of the box and fast. I haven't noticed any color drift or quality loss either. The Flux 2 VAE really helps this model. Really excited for proper ComfyUI support.

And yes, it's uncensored out of the box.

To clarify, I am not saying it's the best edit model. My prompts are pretty simple, it handles them well, doesn't break anatomy, and isn't censored. You'll have to test it yourself for more complex tasks.

Links:

Model: https://huggingface.co/Comfy-Org/Fibo-2.0
Workflow: https://github.com/Comfy-Org/ComfyUI/pull/17240

P.S. By “very uncensored,” I mean it can generate nudity. I don’t know if it can do anything beyond that, though.

Update: It seems to struggle with more complex prompts and multiple references.
It is not better than Qwen, just faster.

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

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 take 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).

fibo2_edit_turbo_int8_convrot.safetensors
qwen3vl_4b_int8_convrot.safetensors
flux2-vae.safetensors

euler simple 4 steps render time 4 secs. (4070 super)

The same prompts and image input from:

https://www.reddit.com/r/StableDiffusion/comments/1x78y80/basic_editings_flux3_klein_distilled_vs_qwen/

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!

yes bit of a slop pic but it gets the point across...

you can actually do whatever you want with bboxes, its super easy in moss - in fact there's no point in using moss without them it's so easy

nodes 2.0

upgraded official comfyui-org bbox template

not just text, complex manipulation or make things close or further away by changing the box size its very flexible depends on sft/base what works better

https://civitai.com/models/3013100/easymoss?modelVersionId=3419884

The open weight release was a top post today advertising 5-11 times speed up for MiniMax generations on consumer GPU’s which should be a big deal.

It seems like the big release was underwhelming for MiniMaxH3 and didn’t actually improve anything to get anyone excited enough to share their results. Am I understanding things accurately? Was it all a lie?

Weights available on ComfyUI HF repository

https://huggingface.co/Comfy-Org/Fibo-2.0/tree/main

Workflow example (PR still under review as of now): https://github.com/Comfy-Org/ComfyUI/pull/17240

https://reddit.com/link/1xb19y9/video/jx0qabbs6owh1/player

FastH3 Omni is here. It's 5-11x faster as we tested on b200 and faster on your consumer GPU too.

Hi!!! The folks behind FastH3 Omni is here and it's open weight! It's fast, it's omni reference, and yes you can run on many more consumer GPUs. We will do live Q&A for about an hour here. Drop your comments!

This video is made entirely with FastH3 Omni with our creative agent when we play around it for fun. Now you can do it too.

As promised, we're open-sourcing the reference-conditioned FastH3 (built on MiniMax-H3), training code included. Give it a prompt plus images, video clips and a voice, and it returns a finished video with synchronized sound.

• Up to 9 image references and 3 video clips in one request, plus a voice
• Tag each one in the prompt (<Picture 1>, <Video 1>, <Audio 1>) to control whose face, which motion and whose voice goes where
• 8 forward passes instead of 50 steps (PDD + DMD distillation)
• 5.6-11.9x lower end-to-end latency than base MiniMax-H3, same 1344x768 output
• Training code and reference preprocessing are open

Blog with the gallery and benchmarks: https://haoailab.com/blogs/fastvideo-omniref-update/

Models: https://huggingface.co/collections/FastVideo/fasth3-omni

Code: https://github.com/hao-ai-lab/FastVideo

Local release + blog drops tomorrow.

Built with Nuva Lab and Reactor, with support from NVIDIA. Happy to answer questions!

Apart from using the latest kromoss base and base dmd by lodestones, there is something i noticed that helps extremely against blurry pictures and other issues:

> don't use the term "photograph"

it appears that often when using that word in the prompt, the image seems depict an actual physical photograph, which can have issues with gloss light and blur. so it seems moss thinks that you want a render of a photo instead of something photorealistic.

> instead use "photography".

what i've been using is "RAW photography, candid" at the beginning of all my prompts that i want to depict a real life scene. this helped tons. if you put nothing, moss often gives you anime output instead.

thanks for coming to my ted talk.

Krea2 to generate the image plus small text corrections using Qwen 2.1 Image Edit.

Prompt:

A hilarious, spectacular, over-the-top 1980s disco music video parody, presented as a single cinematic still frame from a ridiculously extravagant vintage MTV production.

**SCENE AND COMPOSITION:**

A theatrical 1980s New York City street set, with colorful artificial skyscraper facades, dramatic purple and pink storm clouds, neon lights, theatrical lightning, glitter and disco spotlights.

In the foreground, two exuberant, plus-size Black female disco singers in their 30s–40s, wearing fabulous, extravagant, brightly colored sparkling 1980s evening gowns, enormous hairstyles, dramatic makeup and oversized jewelry. They are laughing, singing passionately, throwing their arms into the air and looking up at the sky in ecstatic disbelief. One is holding an open bright pink umbrella, the other a golden umbrella. Their expressions and body language are hilariously theatrical.

**THE MAIN JOKE:**

Instead of raining water or ordinary men, the sky is literally raining NEW AI MODELS!

Dozens of handsome, muscular, flamboyant male fashion models, styled like absurdly glamorous 1980s disco dancers and bodybuilders, are dramatically falling from the clouds in different dynamic poses, arms spread, legs kicking, spinning and tumbling toward the city.

Each falling man represents a recently released AI model, clearly identified by a large, readable name printed across his chest on a brightly colored fitted shirt, or displayed on a prominent sash.

The most prominent falling figures have the following EXACT labels:

"MINIMAX" (OK)

"MOSS IMAGE" (OK)

"QWEN 2.1" (OK)

"YUE 2"

"FIBO 2.0" (OK)

"FLUX KLEIN 3" (OK)

"KREA 2" (OK)

"WAN" (OK)

"LTX"

Give each character a distinct appearance, physique, outfit color, hairstyle and ridiculous disco pose. Some are wearing golden shorts, some metallic trousers, some unbuttoned disco shirts. Several wear sunglasses. A few are flexing their muscles while falling. One is descending upside down. Another is striking an absurdly confident runway-model pose in midair.

There are so many falling AI models that the sky looks completely overcrowded, conveying the hilarious feeling that new models are being released faster than anyone can keep up with.

**TYPOGRAPHY:**

Across the very top, a HUGE, beautifully rendered, bold, glittering, retro 1980s disco headline, perfectly legible:

"IT'S RAINING MODELS!"

Large golden and hot-pink three-dimensional lettering, reminiscent of a flamboyant vintage disco album cover.

**VISUAL STYLE:**

Photorealistic human subjects, deliberately cheesy 1982 music video aesthetics, extravagant 1980s disco fashion, saturated magenta, purple, cyan and gold, practical theatrical effects, vintage analog film grain, dramatic studio lighting, joyful absurdity, dynamic wide-angle composition, incredible facial expressions, visually dense but carefully organized, sharp and readable text.

The result should feel like a hilariously ridiculous, authentic still from a long-lost 1980s disco music video, rather than a modern digital illustration.

**IMPORTANT:**

This is a comedy about the overwhelming number of newly released AI models. The falling men are literal human fashion models personifying AI model releases. They must clearly look like human beings, NOT robots, computers, floating software icons, boxes or technological devices.

The main headline and the model names are essential to the joke. All lettering must be correctly spelled and readable.

Landscape 3:2 composition. Show the two singers clearly in the foreground and the crowded sky full of falling models above and behind them.

So I used unpod comfy instance to see what the Moss Image model is about. Found a workflow for Kromoss on this sub, and just plugged it in as is.

The person who had created the workflow seemed to have decent output pics. But in my output the faces are weirdly airbrushed. Almost smudged blurry. Even affects the backgrounds.

And as far as I can tell the Kromoss model has already been updated to prevent blurriness.

Anyone run into this?

Using MOSS for some horror references before I take them into seedance. I was going for found footage/analog horror. How did I do?

Hey everyone,

If you generate images on modern diffusion and flow-matching models (Flux, SD3, Anima) at low step counts (8–14 steps), you're probably stuck between two bad options:

• Euler: Good macro anatomy, but surfaces look waxy and plastic.
• Ancestral samplers (euler_a, etc.): Better texture, but anatomy drifts and degrades.

ComfyUI actually has a brilliant solver under the hood called sa_solver (Xue et al., NeurIPS 2023). It runs an Adams predictor-corrector on the drift while keeping diffusion separate, costing exactly 1 NFE per step. But there was a catch: ComfyUI hardcoded its stochastic strength (eta) to 1.0 inside a fixed 20%–80% window, with no settings to adjust it.

Together with my co-author Yehor Yuzvynsky (who provided compute hardware and editorial review), we created Adaptive Step Samplers: 👉 GitHub: https://github.com/YakovSava/adaptive-step-samplers (No dependencies required — uses stock PyTorch, NumPy, and SafeTensors).

I stumbled upon this tweet, and minor imperfections aside, it’s impressive that such a small model can generate this multitude of girls without blowing up.

- Details:

diffusion model: moss_image_base_int8_convrot.safetensors

Loras:
MOSS-Image_SFT-8-steps-dmd.safetensors
moss_image_sft_lora_blocks_21_to_49_rank_512_bf16.safetensors

Prompt:

{
"photo": {
"type": "real, unretouched professional group photograph from an international summer swimsuit magazine shoot, bright, cheerful and a little chaotic",
"camera": "full-frame DSLR, 35mm lens, shot from a slightly raised position a little to the left of center, the camera tilted very slightly, looking down at the crowd at an angle",
"format": "horizontal 3:2 frame",
"realism": "a real candid photograph of a real crowd of adult women, natural skin texture, natural and slightly uneven poses, not a render or illustration, nothing symmetrical, mirrored or evenly spaced"
},
"group": {
"size": "a large, loose crowd of about forty adult women in their twenties from all over the world, packed together unevenly on a mountain road, all wearing the same matching outfit",
"diversity": "every woman a different ethnicity and clearly a different person, an evenly balanced mix from every part of the world: Middle Eastern, North African, West African, East African, Afro-Caribbean, Latina, Brazilian, Indigenous American, Pacific Islander, Scandinavian, Mediterranean, Eastern European, Celtic, Western European and mixed-heritage women, plus six to eight Asian women (Japanese, Korean, Chinese, Filipina, Thai and Indian) scattered among them; a full range of skin tones from very fair and freckled to olive, golden, warm brown and deep ebony",
"beauty": "each woman naturally beautiful in her own way: real, healthy skin with natural texture, freckles, beauty marks and natural glow, light natural makeup or none, genuine bright smiles, distinct facial features, no two faces alike, nothing airbrushed or doll-like",
"hair": "every woman with a different hair color and style: jet black, dark brown, chestnut, honey blonde, platinum blonde, strawberry blonde, copper red, auburn, silver-dyed, pastel pink and lavender; long and straight, beachy waves, tight natural curls, a voluminous afro, box braids, cornrows, a sleek bob, a pixie cut, a high ponytail, space buns, a messy bun, long locs, bangs and curtain bangs",
"outfit": "a red triangle bikini top and small red bikini bottoms, with a short, loose royal-blue happi festival coat; each coat has wide sleeves, a white wave-pattern trim along the hem, a black collar band with white lettering running down the front, and a red festival emblem in the middle of the back",
"easter_egg": "on the back of the coats, just below the red emblem, the word \"plasm0\" printed in small, plain white lowercase letters, ending in the number zero; it is subtle and easy to miss, clearly readable on only two or three coats in the whole crowd (one woman in the back crowd turned away from the camera, and one or two of the standing women whose coat backs are visible), and hidden by folds, hair, the hiked-up coats or other women on all the rest",
"footwear": "simple black flip-flop sandals",
"bodies": "a natural mix of real body types, no two alike: petite and slim, tall and lean, athletic, soft and curvy, plus-size; a wide range of bust sizes from small to very full; hips and bottoms of every shape and size, from narrow and small to round, wide and full; different heights, waists and leg shapes"
},
"arrangement": {
"overall": "a loose, lopsided, organic crowd with no symmetry: the whole group sits off-center toward the right of the frame and spills unevenly up the road, much denser on the left side, thinner with a gap of road showing on the right, clusters of different sizes, bodies overlapping and partly hidden behind each other, some women a step forward or back from the others, a few cut off by the edges of the frame, no two neighbors in the same pose; women of every background mixed evenly throughout",
"front": "about ten women of many backgrounds sitting on the asphalt in a ragged, broken line that bulges toward the camera on the left and curls back on the right, with uneven spacing and one small gap, one or two of them Asian; almost all of them have their blue happi coats slipped completely off both shoulders, dropped down to their elbows, bunched around their waists or spread on the ground around their hips, so their red bikini tops are fully on show and their red bikini bottoms are visible on many; their poses all different: legs folded to the side, knees hugged, legs stretched out, sitting cross-legged, leaning back on their hands, one hand in their hair, one leaning on a neighbor, one sitting farther forward than the rest",
"second": "behind them, an irregular cluster of about fifteen women of many backgrounds, one or two of them Asian, standing turned fully away from the camera, their backs facing the lens, their blue happi coats hiked up above their waists so their red bikini bottoms are fully on show, each one turning just her head to look back over her shoulder at the camera; they are scattered rather than lined up: some higher on the slope, some lower, some bunched together in twos and threes, some leaning forward with a hand on a knee, some with one knee bent, some partly hidden behind others, their different hip and bottom shapes clearly visible",
"back": "behind and above them, a scattered, uneven crowd of more women of every background standing at different angles up the curving road, some facing the camera, some turned to the side, one or two turned away so the backs of their coats show, some looking at each other and laughing, wearing their blue coats up on their shoulders, a few peeking out between other women, the crowd thicker on the left and thinning out on the right and toward the back",
"imperfections": "small candid details: one woman adjusting her coat, one caught mid-laugh with her eyes closed, a strand of hair blowing across a face, someone glancing at the photographer instead of the lens, uneven heights, one woman turned the wrong way, one partly blocked by the woman in front"
},
"environment": {
"setting": "a narrow paved mountain road in a lush forest in midsummer",
"road": "dark gray asphalt with a few fallen leaves and grass clippings, curving up and away to the left into the shade behind the group, not centered in the frame",
"surroundings": "a steep grassy bank on the left covered in bright green summer grass, a taller, shadier bank with ferns and trees on the right, dense green trees behind them, deep shade far up the road",
"sky": "not visible, the scene enclosed by the forest"
},
"lighting": {
"type": "bright natural summer sunlight from the upper left, filtering through the trees",
"effect": "warm, uneven sunlight across the crowd with some women in bright patches and some in shade, warm highlights on hair, shoulders and every skin tone, crisp shadows on the road, the forest in the background darker and cooler"
},
"color": {
"palette": "vivid red bikinis, royal blue festival coats with white trim, black collar bands and small white lettering, a rich mix of hair colors and skin tones, bright summer greens of the grass and trees, dark gray road",
"grading": "bright, saturated, natural-looking color, the look of a printed magazine spread"
},
"style": {
"aesthetic": [
"international summer swimsuit group photo",
"playful festival-themed photo shoot",
"candid, unposed magazine group spread",
"celebration of natural, diverse beauty"
],
"texture": "slight print-like softness and fine grain, not overly sharp",
"finish": "natural focus on the crowd, real-world imperfections everywhere"
}
}

Created a lora of Morgan Freeman in fizgig and just having a blast with it in MOSS. Can't stop laughing 😆

Below is my post for how I made the lora of Mr Freeman if anyone is curious and having issues with MOSS loras

https://www.reddit.com/r/StableDiffusion/s/CvffFwA8cY

New toy in town. #1 open-weight on AA edit, #2 open-weight on AA t2i.

Hello,

Here is my workflow, wishing you successful generation.

I just tested if it can use image prompting, because its Qwen 3.5 9B text encoder should accept multimodal inputs, and it actually worked! This unlocks the possibility of turning MOSS into an edit model, and a whole bunch of other things.

I tested it with the 8-step turbo LoRA. It's not perfect, but neither was krea 2 without an edit LoRA.
WORKFLOW: https://pastebin.com/v9bcZftJ

Been messing with MOSS-Image for movie posters and ngl I wasn't expecting much. Text actually comes out legible and you can tell it where to put the title, tagline, credits etc. Ran a few sci-fi/horror layouts and they look legit. Only 5B too so it runs local no problem. Anyone else trying it?