This workflow is designed for Z-Image Base 2603 acceleration LoRA comparison, focusing on 2-step, 4-step, and 8-step generation routes. Its main purpose is to help creators compare how different Z-Image acceleration LoRAs affect speed, image quality, prompt adherence, composition stability, and final visual detail under the same base model and prompt structure.

The workflow uses z_image_bf16.safetensors as the main Z-Image Base model, qwen_3_4b.safetensors as the text encoder, and ae.safetensors as the VAE. The generation canvas is set to 1280 x 720, which is a practical landscape format for cinematic images, comparison previews, YouTube thumbnails, Bilibili covers, Civitai showcase examples, and RunningHub workflow demonstrations.

The core design is a multi-route comparison structure. One route uses a more standard Z-Image Base generation setup with a higher step count, while the other routes apply different Z-Image Fun Distill LoRA versions for accelerated generation. The workflow includes 2602 and 2603 acceleration LoRA paths, including 8-step, 4-step, and 2-step versions. Each route uses its own model-loading and KSampler structure, so users can directly compare how the same prompt behaves when sampling time is reduced.

This is useful because acceleration is not only about speed. A 2-step route may be extremely fast, but it may lose fine detail, texture richness, or composition stability. A 4-step route may provide a better balance between speed and quality. An 8-step route usually gives the model more room to build lighting, structure, and detail. By placing these routes in one workflow, creators can judge which version is best for preview generation, batch testing, online publishing, or final image output.

The prompt used in the workflow describes a dramatic silhouetted warrior standing in a field of red spider lilies against a huge blazing orange sun. This is a strong test prompt because it contains high-contrast lighting, atmosphere, silhouette structure, foreground elements, color intensity, and cinematic composition. The same kind of prompt makes it easier to observe whether each LoRA route preserves the image’s core visual impact.

The workflow also uses shared negative prompt logic to reduce common issues such as bad lighting, overexposure, underexposure, low contrast, sketch-like output, cartoon drift, blur, and ugly artifacts. This keeps the comparison more controlled and makes the difference between the LoRA routes easier to evaluate.

In short, this is a practical Z-Image Base acceleration benchmark workflow. It helps users decide whether 2-step, 4-step, or 8-step 2603 LoRA generation is the best choice for their own production needs. If you want to see how the routes are connected, how the LoRA versions compare, and which setting gives the best speed-quality balance, watch the full tutorial from the YouTube link above.

⚙️ Try the Workflow Online

👉 Workflow: https://www.runninghub.ai/post/2029447461651091457?inviteCode=rh-v1111

Open the link above to run the workflow directly online and view the generation results in real time.

If the results meet your expectations, you can also deploy it locally for further customization.

🎁 Fan Benefits: Register now to get 1000 points, plus 100 daily login points — enjoy 4090-level performance and 48 GB of powerful compute!

📺 Bilibili Updates (Mainland China & Asia-Pacific)

If you are in Mainland China or the Asia-Pacific region, you can watch the video below for workflow demos and a detailed creative breakdown.

📺 Bilibili Video: https://www.bilibili.com/video/BV141Pkz4E95/

I will continue updating model resources on Quark Drive:

👉 https://pan.quark.cn/s/20c6f6f8d87b

These resources are mainly prepared for local users, making creation and learning more convenient.

⚙️ 在线体验工作流

👉 工作流： https://www.runninghub.ai/post/2029447461651091457?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。

🎁 粉丝福利： 注册即送 1000 积分，每日登录 100 积分，畅玩 4090 体验 48 G 超级性能！

📺 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/BV141Pkz4E95/

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。