Updated: May 9, 2026
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This ComfyUI workflow is designed for fast Qwen Image 2512 generation with an ultra-low-step Turbo LoRA route. The main purpose of this workflow is to let creators generate or refine images much faster than a standard high-step Qwen Image setup, while still keeping strong prompt understanding, detailed visual structure, and a polished final result.
The workflow is built around Qwen Image 2512, using qwen_image_2512_bf16.safetensors as the main diffusion model, qwen_2.5_vl_7b_fp8_scaled.safetensors as the text encoder, and qwen_image_vae.safetensors as the VAE. The key acceleration module is Wuli-Qwen-Image-2512-Turbo-LoRA-2steps-V1.0-bf16.safetensors, which is loaded through LoraLoaderModelOnly and applied directly to the Qwen Image 2512 model. This makes the workflow suitable for fast testing, prompt exploration, quick image drafts, and efficient online generation.
Unlike a heavy full-step generation workflow, this graph focuses on speed. The workflow uses a compact scheduler setup, low CFG, image latent initialization, DetailDaemon sampling, and SamplerCustomAdvanced to produce fast results with reduced sampling cost. It is especially useful when creators need to quickly test multiple ideas, compare prompts, generate visual drafts, or produce fast preview images before moving into a heavier final refinement workflow.
The workflow can work as an image-to-image generation or enhancement route. The ImageLoader node imports a source image, then ImageResizeKJv2 resizes it into a controlled working resolution. In the included setup, the resize node targets a large working size while keeping the image structure stable. The resized image is then encoded into latent space through VAEEncode, giving the sampler an image latent to work from rather than starting from pure empty noise only. This makes the workflow useful for fast redraw, image enhancement, concept variation, and quick visual polishing.
The prompt section uses PrimitiveStringMultiline and CLIPTextEncode. The included example prompt describes a magical fantasy illustration involving a fey-like weaver, moonlight, lunar moths, glowing forest atmosphere, and decorative art influences. This shows that the workflow is not limited to simple prompts. It can handle rich descriptive prompts with subject, scene, lighting, atmosphere, artistic style, and detailed material description.
The positive prompt controls the main subject, composition, lighting, and atmosphere. The negative prompt suppresses common visual problems such as bad lighting, dark or gloomy results, overexposure, underexposure, low contrast, grayscale output, monochrome output, draft-like rendering, sketch artifacts, crayon texture, comic style, or unwanted cartoon effects when the target is a more polished or realistic result.
The Qwen 2.5 VL text encoder route is important because Qwen Image workflows often benefit from structured and detailed prompt writing. This workflow is suitable for creators who like writing long descriptive prompts, Chinese prompts, cinematic prompts, fantasy prompts, product prompts, or story-based visual instructions. The text encoder helps convert these prompts into model conditioning for the generation stage.
The sampling section uses RandomNoise, CFGGuider, BasicScheduler, SamplerEulerAncestral, DetailDaemonSamplerNode, and SamplerCustomAdvanced. This is a more advanced sampling chain than a simple KSampler setup. CFGGuider controls how strongly the model follows the prompt. In the included setup, the CFG value is low, which is suitable for a fast Turbo-style route because accelerated LoRA workflows often do not need very high guidance.
BasicScheduler controls the scheduler and step count. The workflow uses a very low-step generation structure for fast output, matching the design idea of the 2-step Turbo LoRA route. This makes it suitable for quick iteration. The goal is not to spend many steps slowly refining the image, but to produce a usable image quickly and allow the user to test more ideas in less time.
DetailDaemonSamplerNode is included to improve detail behavior during the low-step sampling process. Low-step generation can sometimes lose texture, edge clarity, or fine detail. DetailDaemon helps compensate by adding stronger detail control during sampling. This can improve fabric texture, fantasy light effects, character details, background structure, decorative elements, and the overall visual density of the result.
SamplerCustomAdvanced receives the noise, guider, sampler, sigmas, and latent image, then performs the main generation process. The denoised output is passed into VAEDecode, then previewed and saved through PreviewImage and SaveImage. This gives users a simple final output route while still keeping a more advanced internal sampling structure.
This workflow is especially useful for prompt testing. When exploring a new model or LoRA, creators often need to run many prompt variations. A normal high-step workflow can be slow, especially when testing long prompts or high resolutions. This fast Qwen 2512 route makes it easier to quickly evaluate whether a prompt idea works before spending more time on final rendering.
It is also useful for RunningHub online workflows. Fast workflows are easier for users to try online because they reduce waiting time and resource pressure. A creator can upload an image, adjust the prompt, run a quick generation, and immediately decide whether the result is worth refining further.
This workflow is not only for fantasy illustration. It can also be used for AI cover images, concept art, stylized portraits, product-style visual drafts, cinematic scenes, surreal images, character design, decorative illustration, poster concepts, and social media visuals. The key advantage is that it allows fast ideation with Qwen Image 2512 rather than forcing every test to run as a heavy full-quality render.
Main features:
- Qwen Image 2512 fast generation workflow
- Uses qwen_image_2512_bf16.safetensors
- Uses Wuli-Qwen-Image-2512-Turbo-LoRA-2steps-V1.0-bf16
- Ultra-low-step / Turbo-style acceleration route
- Qwen 2.5 VL 7B FP8 text encoder support
- Qwen Image VAE support
- ImageLoader source image input
- ImageResizeKJv2 for controlled input resizing
- VAEEncode image latent initialization
- BasicScheduler low-step scheduling
- CFGGuider prompt guidance control
- DetailDaemonSamplerNode for detail enhancement
- SamplerEulerAncestral sampling route
- SamplerCustomAdvanced generation pipeline
- VAEDecode, PreviewImage, and SaveImage output
- Suitable for fast prompt testing and online generation
Recommended use cases:
Qwen Image 2512 fast generation, two-step Turbo LoRA testing, image-to-image enhancement, rapid prompt exploration, fantasy illustration drafts, concept art generation, AI cover creation, character design tests, cinematic scene generation, social media visual production, RunningHub online workflow publishing, Civitai showcase examples, and fast AIGC content prototyping.
Suggested workflow:
Start by loading a source image if you want to use the image-to-image route. The source image should have a clear structure and subject. The workflow can redraw and enhance the image quickly, but a better source image will usually produce a better result. If the image is too blurry, too compressed, or too chaotic, the model may need stronger denoise or a cleaner prompt to produce a useful output.
Use ImageResizeKJv2 to control the working size. The included workflow uses a large target size and keeps the image structure stable. If your GPU or cloud environment has limited resources, lower the resolution first. Fast generation does not help much if the resolution is too large for the available VRAM.
Write a detailed but focused prompt. Qwen Image 2512 can understand rich prompt descriptions, but the prompt should still have a clear structure. Describe the main subject, scene, lighting, style, camera angle, color palette, and desired atmosphere. For fantasy or cinematic images, describe the visual focus first, then add secondary details.
Use the negative prompt to suppress unwanted output styles. If you want a realistic or cinematic result, avoid sketch, draft, cartoon, grayscale, low contrast, bad lighting, and similar problems. If you want anime or illustration, adjust the negative prompt so it does not fight against your target style.
Use the low-step Turbo route for fast testing. This workflow is designed for speed, so it is best used to test many prompt directions quickly. Once you find a strong composition or style, you can reuse the prompt in a heavier refinement workflow if needed.
Pay attention to DetailDaemon settings. DetailDaemon can improve texture and detail density, but too much detail can make the image noisy or over-rendered. For clean covers and product-style visuals, use moderate detail. For fantasy illustration or highly decorative scenes, stronger detail can help create a richer result.
Use seed control for repeatable testing. If you get a promising result, keep the seed fixed and adjust the prompt gradually. If the composition is not good, randomize the seed and run several quick tests. This is one of the main advantages of the fast workflow: you can test multiple seeds quickly instead of waiting for a slow full render.
When evaluating results, check more than sharpness. Look at prompt adherence, composition, subject accuracy, lighting, color harmony, edge quality, texture, and whether the image feels coherent. A low-step workflow is strongest when it gives a good fast draft; for final production, you may still choose to upscale or refine the best result later.
This workflow is designed for creators who want a faster Qwen Image 2512 pipeline inside ComfyUI. It combines Qwen Image 2512, a 2-step Turbo LoRA route, Qwen 2.5 VL text encoding, image latent initialization, DetailDaemon sampling, and advanced scheduler control into one practical graph. It is especially useful for quick visual testing, online workflow publishing, prompt research, and fast Civitai example generation.
🎥 YouTube Video Tutorial
Want to know what this workflow actually does and how to start fast?
This video explains what the tool is, how to launch the workflow instantly, and shares my core design logic — no local setup, no complicated environment.
Everything starts directly on RunningHub, so you can experience it in action first.
👉 YouTube Tutorial: https://youtu.be/Y6L5qkA8ZYs
Before you begin, I recommend watching the video thoroughly — getting the full context helps you understand the tool faster and avoid common detours.
⚙️ RunningHub Workflow
Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2017532037657272322/?inviteCode=rh-v1111
If the results meet your expectations, you can later deploy it locally for customization.
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📺 Bilibili Updates (Mainland China & Asia-Pacific)
If you’re in the Asia-Pacific region, you can watch the video below to see the workflow demonstration and creative breakdown.
📺 Bilibili Video: https://www.bilibili.com/video/BV1DQ61B1Eix/
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🎥 YouTube 视频教程
想了解这个工作流到底是怎样的工具，以及如何快速启动？
视频主要介绍 工具定位、快速启动方法 和 我的构筑思路。
我们会直接在 RunningHub 上进行演示，让你第一时间看到实际效果。
👉 YouTube 教程： https://youtu.be/Y6L5qkA8ZYs
开始前建议尽量完整地观看视频 —— 把握整体思路会更快上手，也能少走常见弯路。
⚙️ 在线体验工作流
现在就可以在线体验，无需安装。
👉 工作流： https://www.runninghub.ai/post/2017532037657272322/?inviteCode=rh-v1111
打开上方链接即可直接运行该工作流，实时查看生成效果。
如果觉得效果理想，你也可以在本地进行自定义部署。
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📺 Bilibili 更新（中国大陆及南亚太地区）
如果你在中国大陆或南亚太地区，可以通过下方视频查看该工作流的实测效果与构思讲解。
📺 B站视频： https://www.bilibili.com/video/BV1DQ61B1Eix/
我会在 夸克网盘 持续更新模型资源：
👉 https://pan.quark.cn/s/20c6f6f8d87b
这些资源主要面向本地用户，方便进行创作与学习。