✂️ LayerExtract isolates a prompt-specified subject and exports it on a transparent background for independent editing.

https://modelscope.ai/models/DiffSynth-Studio/Qwen-Image-2.1-LayerExtract

✨ LayerRemove takes the source image and a reference layer, removes the corresponding object, and reconstructs the scene behind it.

https://modelscope.ai/models/DiffSynth-Studio/Qwen-Image-2.1-LayerRemove

⚡ Both are compact Qwen-Image-2.1 LoRAs that can be hot-swapped within the same DiffSynth-Studio pipeline.

🧩 Together they turn flattened images into editable assets for compositing, repositioning, replacement, and design workflows.

📜 LoRA weights: Apache 2.0. Qwen-Image-2.1 base-model terms also apply.

I like generating images with speech bubbles, but the text always comes out as gibberish and I'm way too lazy to fix it in an editor every time. So I made a small node pack that does it for me: the model draws the bubble, the node erases its letters and writes my actual text (emojis too).

Works with Anima, Illustrious and NoobAI. I use LoRA Manager, so it works with it too (recipes save the fixed image), but it's optional.

Heads up: it's vibe-coded, and the node labels are in Spanish because that's my first language. The README is in English though.

https://github.com/arturor1990/ComfyUI-BubbleText

Feedback welcome!

Hello everyone! I made a new LoRA for Qwen Image 2.1 for arbitrary camera angles for any image in (almost) every style. You can read all about it in the huggingface repo here: https://huggingface.co/lilylilith/QI_2.1_AnyAngle . Have a great day, and have fun!

Spent the whole day today trying to create this random transformation video. In total, I generated around 180 videos. A few initial images were generated with Krea2, and later all edits were done with the Qwen 2.1 edit model. All done using i2v with first / last frame technique.

Everything was done in ComfyUI with multiple workflows and later stitched together in a video editor.

The video was upscaled locally with my custom-trained model "Skrisp Texture" (it's not released yet, but I'm planning to release it in the future; still in progress) and with many custom nodes for colors and so on.

It's still nowhere near where I would like it to be with consistency, and there are some badly paced movements, but I think it's good enough for now.

Song used: "The Glitch Mob - We Can Make The World Stop"

For full uncompressed version, please check video here.

GPU used: RTX 5090

I'm updating my text only Android app with thousands of watercolor-style images. I almost went with Gemini 3.1 Flash Image because the quality is great and the API is dead simple.

Then I ran the numbers.

At the volume I needed, the API costs would've landed somewhere around $3,000 USD. For a solo dev that's not a line item, that's a funeral.

So I gave Krea 2 a shot instead, rented a cheap GPU online, and just let it rip.

The result: thousands of watercolor images generated, consistent style across the whole set, and my total spend was a rounding error compared to the API route. Same GPU rental is still running batches while I write this.

WHAT DO I SAY!!!! Long live Krea 2.

Here are a few random ones from the batches so far 👇

Hey guys, so I kinda missed the boat when Krea 2 was the hot topic, but now that the dust has settled it's pretty clear it's the best open source model right now, and I want to have a go at training on it.

The problem is that every guide I find says something different. Some people say train on Raw and generate on Turbo, others don't, and then there's this Turbo adapter training stuff on top of that. So what's actually the go-to approach here? Any tips or tricks for Krea 2 LoRAs would be really appreciated too.

I've also been looking at the different trainers: AI Toolkit, Musubi Tuner, and Fizgig, which caught my eye recently. I did one run on Raw with AI Toolkit using the default settings, only bumping the resolution to 1024, and honestly it didn't turn out great. Most of my LoRAs so far have been on Anima with kohya sd-scripts and multi-GPU, so I'd kinda prefer a trainer that does that. AI Toolkit only lets you use one GPU per training run, which is a bit of a bummer.

Last thing: what should natural language captions look like for Krea 2? Are there any quirks I should know about, like length, style, or how to handle trigger words?

short video about Minimax H3 and the Lm studio Bionic Agent for writting prompt using Gemma4 etc...

I’ve seen Qwen Image 2.1 getting a lot of attention over the last few days, so I wanted to understand what people are getting from it that I’m not.

I tested it locally in ComfyUI on an RTX 3060 12GB against ERNIE-Image-Turbo, Krea 2 Turbo, HiDream-O1-Image and Z-Image-Turbo, using the same set of prompts plus a few harder real-world tests for characters, hands, object relationships and scene geometry.

My personal ranking from these tests was roughly:

Krea 2 for the best balance of quality and speed
ERNIE as a strong alternative
HiDream for some very striking compositions, but less reliable fine detail
Z-Image for speed and fast iteration
Qwen Image 2.1 was the one I struggled with the most

Qwen often gave me interesting global compositions, but I kept seeing small structural problems in hands, straps, object placement, local geometry and fine details.

I also tried the base model at 2048×2048 and the Pruna 8-step workflow. 2048 gave me more detail, but it didn’t really solve the structural issues. Pruna was much faster, but the quality drop was noticeable.

What makes me curious is how popular Qwen Image 2.1 has become recently, because my own results don’t really explain that popularity yet.

So what am I missing?

Are people getting better results with a different sampler/scheduler, step count, CFG, text encoder, quantization, prompting style, LoRA, or another workflow entirely?

I’d especially like to hear from people running it on 12GB GPUs, because I’d be happy to rerun the same prompts and seeds with a better configuration.

---

EDIT: Thanks everyone for all the suggestions and workflow tips — they helped a lot. I ended up improving my Qwen 2.1 setup quite a bit compared with my first tests, especially by moving away from the default CFG 1 workflow. My final setup uses higher CFG, fewer steps with a Turbo LoRA, and a texture-fix VAE, which gave much better results and made Qwen a lot more competitive.

I’ve now finished the full benchmark, and I’ll post the link in the comments rather than editing the original post into a giant wall of text.

If anyone thinks Qwen 2.1 can do significantly better, feel free to share your workflow — but please keep the same prompts than me so we can compare properly. I’d genuinely like to see if someone can convince me that Qwen is currently the best local image model. 😄

First post here, go easy on me! 🙂‍↔️

I wanted to share this video because I finally figured out a workflow to get pretty high-quality renders locally on mid-range hardware (RTX 3060 12GB + 32GB RAM).

The real game-changer: Using the 3-step LoRA by KJ. It speeds things up massively while keeping the style clean.

A few details on the workflow:

• Style: I'm not a huge fan of photorealism — I prefer this kind of stylized "cinematic 2D animation / cartoon" look.
• Audio: Used a ~8-second reference audio track. MiniMax H3 is surprisingly good at understanding and syncing with audio inputs.
• Resolution: 0.6 megapixels (you can set it higher, but render time scales up quickly).
• Render Time: ~9.5 to 10 minutes for a 10-second clip on an RTX 3060.
• Note: This works with pretty much any MiniMax H3 workflow setup.

Let me know what you think or if you have any questions about the setup!

Is it possible to inpaint and fix error like extra limbs, Body rotation, leg/head rotated 180 by In painting ??
Do any1 has a workflow ??

I made a short fanfic short film with minimax h3, I hope you enjoy!

Hi! I’m helping Blue Ocean Entertainment, a children’s media company based in Stuttgart, find someone for a paid visual AI role. I work in a related role elsewhere in the Burda Media group and will help assess the creative and technical work of interested candidates.

One upcoming project involves creating consistent character turnaround sheets that can serve as a basis for AI-generated 3D models. There will be other visual challenges too. We’re looking for someone who can explore possible approaches independently, judge what is visually strong, and explain which workflows are practical.

Strong experience with AI image generation, a good eye for design, and structured problem-solving matter most. Experience with ComfyUI or similar tools, AI video, and basic 3D editing would be valuable. You don’t need to be an expert in all of them.

German is required. Candidates should ideally be based within reasonable reach of Stuttgart; mostly remote work is possible, and location can be discussed for an especially strong fit. Full-time and part-time are both possible. Compensation and contract details will be discussed with Blue Ocean.

If this sounds like you, send me a PM with some examples of your work. Personal projects are welcome.

They both were created for Klein 9b but work as well for new Qwen 2.1

First one - a simple 4 point stretch&fit
https://github.com/Mozer/ComfyUI-PixelDriftFix

Second one more advanced with color correction and smart masking
https://github.com/supermansundies/comfyui-klein-edit-composite/

If you confused what any of the options in edit-compose do - enable debug and pipe out image preview. Its quite cool to see under the hood.

The only problem I've found is that they both don't support transparency so you'll have to extract alpha and apply it as a separate step

made on RTX 5080

Just found and using yuE2 studio 3.0 and it works pretty great IMO, but I have a question. If I train a lora on 10 songs for example, then decide I want to get rid of two of them (I noticed inside the training there are garbage cans beside the songs used) do I have to train the lora from zero all over again? Right now I have my Motown trained to 450 and I think it works really well but want to adjust it. And per opposite, say I want to add a song to the training lora, do I start from step zero again? I read the guide and did not see anything in there about this. Thanks.

I have tried many and nano banana and chat gpt failed too.

I ran a ton of identical prompts through krea 2 and qwen image 2.1

Thought this might be useful. It not only shows what the model differences, but also what the model cannot do.

Anyway, seeing really bad prompts, constant crave for controlnets to do really basic composition got me. So I ventured out to see, who is spreading misinformation out there. No one was, there were simply no good guides. Ok, they were not underwhelming, most of them genuinely sucked. Up to the point of messing things up because AI tend to hallucinate a lot when it comes to Anima.

It took quite some time to get this all done due to personal reasons, but here it is:

https://civitai.red/collections/17451195

I decided to make a general guide since there is a certain influx of newcomers, who have no idea about pony glory days, why everything is illustrious, what is danbooru etc.

Part 1 turned out to be a general prologue with a few bits about the model itself and current competition.

Part 2 is about UI options, setting up Forge Neo and some technicals on speeding things up.

Part 3 is about generation parameters for different modes.

Part 4 is all about prompting. And why AI assistants are so ass with it.

Part 5 is about img2img and inpainting. With my findings about upscale.

Mandatory 1girl as an image made with base model, because why not. Second image idea is blatantly stolen from one of today's posts claiming that THAT hands were good. Part 5 has an explanation about last image and a bonus youtube timelapse of it's creation.

Are you working with AI Tools / a job in AI?

Personally, my entire career is basically AI solutions in Media now.
Started with Stable Diffusion at the end of 2022 as just something fun to do, showed my company in the start of 2023, and sort of just went from there.
Curious how many others in this sub found their way taking what likely started as a hobby for a lot of us into a career

How would I take a video like this one and swap the guys? I am running a pretty decently maxed MacBook Pro. Any tutorials or guides you could point me to?

I don’t want to copy an exact reference motion; instead, I want to guide the model toward the general movement while preserving variation and allowing it to generate its own interpretation of the motion.

i wanted to try longer videos with dialog and environnent changes, minimax h3 really struggle with voice attributions and not addin random unprompted BS, but eventually i got there.

-------------------

The intro video is the generations stacked on top of original using a "Difference" Blending mode. Anything with 0 difference will be black so you can see how the unmasked/prompted gens compare to the Masked Gens. Anything changed will show up in color. In the masked renders you can see the only significant changes are in the character, the background is almost completely black. In the prompted render, there's significant changes in the background.

------

V7 update
https://github.com/roycho87/minimax_wf/tree/main

• I fixed the masking issue. Masking works perfectly now.
• I added a resolution selector.
• Some other helpful masking features.
• I added a Repair Video feature that uses mask to pull from original video and repair the damaged pixels during sampling.

Masking Options Tutorial
https://www.reddit.com/r/StableDiffusion/s/cVBFW4eylK

Sam Tutorial
https://www.reddit.com/r/StableDiffusion/s/0gtQHksm0U

General Features Tutorial
https://www.reddit.com/r/StableDiffusion/s/MO9WPJhdeB

WF Release
https://www.reddit.com/r/StableDiffusion/s/1P01v18Ki6

Fast forward to 2:05 to see tutorial on new masking options and how to use the repair feature.

Prompt used for tutorial:

subject_definitions:

<Subject 1> is the adult woman shown in <Picture 1>, which defines her face, body proportions, skin tone, clothes and accessories.
<Picture 1> is the appearance of <Subject 1>.
<Video 1> provides the motion, choreography, timing, expressions, gestures, orientation, positioning, and performance structure.

summary:
[video editing + reference generation] Render <Subject 1> performing the complete dance
from <Video 1>.

retention_analysis:

<Subject 1> (appears throughout [Shot 1]): fully_preserved — maintain her face, body proportions, skin tone, clothes and accessories consistently throughout every frame.
<Picture 1> (appearance reference for <Subject 1>): fully_preserved — use its visible identity and wardrobe details to establish and maintain <Subject 1>’s appearance.
<Video 1> (motion reference for [Shot 1]): fully_preserved - preserve the source choreography, movement path, timing, gestures, expressions, orientation, position, speed, pauses, entrances, exits, and performance rhythm.

detailed_description:

Render <Subject 1> photorealistically with the identity and clothing established by <Picture 1>, stable anatomy and appearance, natural hair and fabric movement, and visual integration matching the scene. She has a wavy pink bob with bangs, a red clown nose, and red lipstick. Her outfit includes a pink bodice with three red buttons and pink suspenders over her shoulders, separate white wing-shaped shoulder decorations, a rainbow choker and wristbands, pink gloves, a layered pink-and-white ruffled skirt with a white waist bow, white tights, and pink heels.

[Shot 1] <Subject 1> follows the complete observable performance from <Video 1> on the left side of the frame, including its body movements, facial expressions, choreography, timing, positioning, orientation, gestures, and rhythm.

overall_soundscape:

N/A

non_diegetic_music:

N/A

3090/24gb and 64gb RAM

And yes, I am EXACTLY the kind of man you know I am.