Z-Image Base 提示词实验：构图与环境测试

提示词可在此修改后复制或实测，不会改动源内容

Experimental Setup:
- Model: Z-Image Base INT8
- Text Encoder: Qwen3 4B
- VAE: AE VAE
- Resolution: 768 × 1368 (9:16)
- Steps: 50
- CFG Scale: 4
- Negative Prompt: Specific anatomic cleanup tags
- Seeds: 5, 50, 100

Prompt Structure:
The prompt is structured into functional scene blocks, keeping the core character description stable and modifying only the variable under test.

Experiment 1 - Composition:
Test how explicitly describing the character's spatial placement and scale affects the generated layout. Using the same subject and baseline environment, vary explicit spatial instructions such as:
- Centered in the frame
- Positioned to the left/right
- Positioned lower in the frame
- Larger/smaller relative scale
- Extreme edge placement

Experiment 2 - Environment:
Keep the character description and general composition consistent while swapping only the environment block. Test three distinct environments (Library, Forest, Town Square) across three seeds (Seed 5, Seed 50, Seed 100).

Key Takeaways:
- Explicit spatial language works: Directly describing position and scale in plain English is more reliable than relying on generic framing buzzwords.
- Environment blocks are modular: Separating the character description from the environment block allows for recontextualizing the same character across different scenes.
- Seeds control execution, prompts control structure: Seeds determine pose variation, expression, and micro-details, while prompt blocks lock in layout, lighting, and narrative context.
- Natural language worked better than disconnected tag lists: Descriptive, coherent sentences provide clearer spatial and conceptual control.

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用法与适用条件

根据需要修改角色描述、环境模块和构图指令，生成不同场景的角色图像。

适用工具: stable-diffusion comfyui
变量: character_description environment composition seeds
来源: r/StableDiffusion ↗

Z-Image Base 提示词实验：构图与环境测试

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用法

变量

适用工具

来源