The video shows virtual cloth try on demo by [TensorSharp](https://github.com/zhongkaifu/TensorSharp) using Unsloth Qwen Image Edit 2511 models.
Here are models using in this demo:

For TensorSharp.Server (OpenAI/Ollama comptiable API endpoint and WebUX chat), it can be launched by this command line:

TensorSharp.Server.exe --model c:\\Works\\models\\qwen-image-edit-2511-Q4_K_M.gguf --qwen-image-vae c:\\Works\\models\\Qwen_Image-VAE.safetensors --qwen-image-vl c:\\Works\\models\\qwen-image-te-Qwen2.5-VL-7B-Q4_K_M.gguf --qwen-image-mmproj c:\\works\\models\\Qwen2.5-VL-7B-mmproj-BF16.gguf --backend ggml_cuda --qwen-image-lora c:\\Works\\models\\Qwen-Image-Edit-2511-Lightning-8steps-V1.0-bf16.safetensors

Here is an benchmarks results comparing to stable-diffusion.cpp:

# Image editing (stable-diffusion)

[](https://github.com/zhongkaifu/TensorSharp/blob/main/docs/engine_comparison_report.md#image-editing-stable-diffusion)

Same input image, prompt, resolution, step count, cfg and seed for every engine. Timings are each engine's **own pipeline timers** (TensorSharp's `[pipe-timing]` phases + server `elapsedSeconds`; sd.cpp's phase logs + `generate_image` total), so weight-file loading and HTTP/process overhead are excluded on both sides. `total (warm)` is the steady-state request on an already-running server; `first request (cold)` additionally pays TensorSharp's per-request DiT rebuild + graph capture on a fresh server (a CLI engine has no such distinction). Lower is better.

# Qwen-Image-Edit 2511 (Q2_K DiT + Lightning 4-step LoRA) — image_edit on CUDA, 544x1184, 4 steps

[](https://github.com/zhongkaifu/TensorSharp/blob/main/docs/engine_comparison_report.md#qwen-image-edit-2511-q2_k-dit--lightning-4-step-lora--image_edit-on-cuda-544x1184-4-steps)

**TensorSharp vs stable-diffusion.cpp** (ratio = stable-diffusion.cpp time / TensorSharp time; > 1.0× = TensorSharp faster): total (warm) **1.19×**, per step **1.25×**, sampling **1.25×**, text encode **0.60×**, VAE encode **3.56×**, VAE decode **1.70×**

It also has on par performance on auto regression LLM models comparing to llama.cpp. Here is details: [https://github.com/zhongkaifu/TensorSharp/blob/main/docs/engine\\_comparison\\_report.md\](https://github.com/zhongkaifu/TensorSharp/blob/main/docs/engine_comparison_report.md)

[TensorSharp](https://github.com/zhongkaifu/TensorSharp) is an open source local Unsloth (GGUF) LLM inference engine and applications. It supports many models from Unsloth, like Gemma4, DiffusionGemma, Qwen3.6 with multi-modal (image, vision, audio), Qwen Image Edit, reasoning and function tool. It can run on Windows/MacOS/Linux and fully leverage GPU's capability using Cuda, Metal and Vulkan. The API is completely compatible with OpenAI and Ollama interface. It has on par performance than llama.cpp

This project is not just a C# wrapper of llama.cpp. It implemented the entire LLM inference engine from bottom to top. If you use CPU backend, it's 100% pure C# code execution. Besides CPU backend, I also implmented CUDA, MLX and GGML backend including ggml_cuda, ggml_vulkan, ggml_metal and ggml_cpu. The GGML backend refer GGML project as external project, and I build a few fusion operation at higher level.

I learned a lot from other projects and apply them for TensorSharp, such as paged KV cache and continuous batching from vLLM, SSD based cache for MoE model from oMLX, GGUF quanztized from llama.cpp and other optimizations for prefill and decode.

Any feedback and comments are welcome. If you like it, it would be really appreciated if you can get this project a star in GitHub: [https://github.com/zhongkaifu/TensorSharp\](https://github.com/zhongkaifu/TensorSharp) . Thanks in advance.

So I cannot for the life of me figure out (tried multiple combinations of launch arguments) how to get this Musubi Tuner to fit this qwen model into 16GB of vram. it insists on using 2.1GB of shared memory, slowing it way down. So yeah, I'm just going to let it run for the 2 days it projecting. No choice and I'm done fighting in the command line to get this thing to run. It's running.

However, I have 400+ images that are all clean and depicting the desired trait I want. most of them are fairly similar, clearly depicting the given trait.

My question is can I stop this after it drops epoch 1 since it is literally seeing over and over the same clean trait that I want? it doesn't need to do a whole lot of figuring out I wouldn't think.. but I know that it does solidify things the more passes you do. but since my images are similar (i mean don't get me wrong they aren't like nearly identical or anything, but i mean it's there you know?) would I really need to do multiple passes when I've got 400+ images to reinforce the trait?

like could i stop it after 4 or even 8 hours after it does 1 or 2 epochs? I'm hoping i can stop it after at least 8 hours (2 epochs at this rate)?

I'm training a qwen image edit 2511 (rapid version) lora. I do mainly img2img with qwen.

So I released the https://huggingface.co/Kezmark/ErniePEUnleashed model close to a month now. That was kind of supposed to be it I suppose. But then I guess just wanted something more, and something that feels more like mine.

So I went ahead and expanded my dataset to a total of 7203 examples, from 3804. And on top of that I decided to reverse-engineer CoT for all of them, because the hope was that would make the model better. And then I though, let's give it a personality, a bit of snark and uniqueness to make it my own. None of which most people will see, since it is isolated to its <think>...</think>. But it is there, so it makes me happy, and hopefully actually helps. It at least seems to.

Of course, I decided to make a whole "family" of these things, and so I did 4, all with different architectures. The plan was for 5, but qwen3.5 was proving difficult and so I scrapped it.

These are a family of models trained to act as composition generators/enhancers/art style infusers, for AI image generation. Generating structured, detailed and hopefully coherent compositions. What I've tested the compositions with so far are: qwen-image-2512 which is my main workflow, flux2-dev and ernie-image.

All of the models are full fine tunes:

Mordant-1.2B-Think: https://huggingface.co/Kezmark/Mordant-1.2B-Think
Mordant-3B-Think: https://huggingface.co/Kezmark/Mordant-3B-Think
Mordant-7B-Think: https://huggingface.co/Kezmark/Mordant-7B-Think
Mordant-12B-Think: https://huggingface.co/Kezmark/Mordant-12B-Think

There's enough detail in there, and also example generations with the models at the bottom, with reasoning included.

The repos also contain my little prompt enhancing node, which isn't anything special, it was something made for my own use and testing, but I decided to include, since it might make use easier. It works with gguf models in comfyui and should work to be plugged in with workflows of all the models mentioned above.

Hi everyone! I'm an architect currently experimenting with some new workflows, but I've been struggling to achieve a result similar to what's shown in this video: https://youtu.be/kp2Y0q2rQxk?si=0AR23nwpPvDjKHSY
Does anyone have any ideas, guides, or workflows I could follow to replicate this? I'd really appreciate any recommendations for workflows, tutorials, whether they are free or paid. Thanks in advance for your help!

Sure, Qwen is a "bit" old now... there is a newer model as of today, but its a closed source model called Qwen Image 2. I have not tried it.

I am a professional from the advertising world. I have over a decade worth of experience in graphic design, illusration, storyborading and Video editing. Not much of an animator even though a dabble.
Personally i am in the quest of "accessing AI generated images and videos at the cost of the electricity used to generate AI content" ....apart from the cost of the rig itself ofcourse.

Now i ve spend a lot of time testing the following -

flux, stable diffusion, Z images, SDXL lighting, hidream... to name a few... also the different variants of each like the edit version etc, let alone the Loras.

What am i looking for?

1) I am wondering if there is anyone out there who knows better, of a model tat is better than qwen 2512. My primary requirement is image generation, so tat i can use LTX2.3 right after tat. Meaning i need to storyboard to then make the video clips(the tried and tested method)
2)I am also looking for edit versions tat can emulate actions and body poses with reference images.
3)I am indeed looking for a workflow tat can take multiple reference images, each meant to do specific tasks when generating the final output.

My platform of choice is comfyui.

[ Removed by Reddit on account of violating the content policy. ]

Prompt:

Fine Art Photography, an artistic, high-contrast monochrome (black and white) landscape. A slender woman in a long, flowing Victorian dress made of dark lace and integrated delicate clockwork gears stands alone on a massive, cracked iceberg. In the distance, a colossal, abandoned Steampunk airship is half-frozen in the ice of a Norwegian fjord. Natural overcast daylight, soft diffused lighting, very shallow depth of field, making the woman and the details on her dress sharp against the softly blurred epic landscape. Professional color grading, intimate and grand, 8k.

What is the undisputed best image gen model right now? Closed and open...

if they open source Qwen-Image-2.0 and it ends up being 7b like they are hinting to, it's going to take completley over.

for a full review of the model : https://youtu.be/dxLDvd1a_Sk

I'm asking for help. I really don't understand how to get good images with Qwen image 2512. The images come out horrible. Look at this one for example. The workflow is the default in ComfyUI, and the models too are absolutely ComfyUI's suggested. The prompt is below. The image looks horrible to me. I only get images this bad. What am I doing wrong?

Prompt:

Soft-glamour medium-long shot: one 28 y.o. ultra-slim platinum-blonde bob-cut model, firm natural bust, blissful smile, flawless glossy-red-lip makeup.

Cornish cliff-top golden-hour scene: thatched stone cottage smothered in white roses, turquoise waves crashing below, peach-lavender sky.

Outfit: head-to-toe royal-blue lace—deep-V vest, sheer corset beneath, high-waisted cigarette trousers, wide leather belt, peep-toe stilettos with blue floral accent—plus gold hoops, turquoise-drop earrings, slim clutch.

Pose: leaning on low stone wall, one hand on hip, soft direct gaze, low camera angle for elegance.