While my primary focus remains on benchmarking text-based LLM APIs and coding assistants, I was engaged by a client to evaluate the potential of image generation models for rapid asset prototyping in their indie game development pipeline. The specific task was to generate a consistent character sheet for a key NPC—a "Crystal Warden" mage—across multiple poses and angles. This presented a significant challenge in prompt engineering and model consistency, areas I approached with a systematic methodology.
I utilized Leonardo AI for this task, treating it as a black-box API system to quantify its performance against the client's requirements. The goal was not a single image, but a coherent set: front view, side view, battle stance, and idle animation pose, all maintaining rigid uniformity in armor design, crystal color, and facial features.
**Workflow & Prompt Engineering Strategy:**
1. **Base Image Generation:** I began with a highly detailed prompt to establish the canonical character.
```
A full-body portrait of a "Crystal Warden" human mage, male, early 30s. Intricate leather armor with embedded blue crystals glowing on the chestplate, left pauldron, and gauntlet. Short brown hair, stern expression, holding a staff topped with a large, uncut blue crystal. Fantasy art style, highly detailed, sharp focus, character reference sheet, white background.
```
I generated 20 variants at 1024x1024 using the Leonardo Diffusion XL model. I selected the optimal output based on adherence to the description and clarity of design elements.
2. **Iteration for Consistency:** This base image was then used as an image prompt for subsequent generations. The text prompt was reduced to the specific pose instruction, leveraging the visual input to carry the design consistency.
```
Same character, side view, 90-degree rotation, looking forward, staff held vertically.
```
This process was repeated for the "battle stance" and "casting pose" requirements.
**Quantitative Results & Observations:**
* **Success Rate:** Out of 80 total generations (20 per pose), only 3 images across all sets were deemed "coherent enough" with the base design without manual editing. The primary failure modes were:
* Inconsistent crystal placement and count.
* Drastic changes in facial structure despite the image prompt.
* Alterations in armor style and texture.
* **Latency:** Average generation time per 4-image batch was 22.4 seconds, which is competitive for image generation but irrelevant if the outputs require significant manual correction.
* **Cost Implication:** Using a token-based system, the cost of generating the 80 images was equivalent to approximately 850 credits. The effective "cost per usable image" was therefore extremely high, as only 3 of the 80 were directly usable.
**Conclusion on Viability:**
For the specific use case of generating a *consistent* multi-pose character sheet from a single text description, the current state of Leonardo AI—or, I hypothesize, any current image generation model—is not sufficiently reliable. The stochastic nature of the generation process introduces too much variance in critical, specified details. The technology excels at producing inspirational concept art or standalone assets. However, for a true, production-ready character sheet, it served only as a preliminary mood board. The client's artist ultimately used the best single output as a reference sketch and manually drew the subsequent poses, which was more time-efficient than attempting to correct the AI-generated variants.
This case study highlights that benchmark metrics for image generation must move beyond simple latency and cost-per-image. A critical KPI for asset pipeline applications is "Design Consistency Accuracy," which would require a rigorous, automated comparison of specified features across a generated set—a benchmarking challenge I may explore formally in the future.
benchmarks or bust
That prompt engineering workflow is super interesting, especially treating the image gen like a black-box API. I'm used to tracing system calls, but the concept of "benchmarking" a model's consistency across generations is weirdly similar to profiling a kernel path.
Did you run into any issues with the "seed" or generation parameters behaving non-deterministically between your reference and variation prompts? I'd be curious if there's a measurable latency/throughput tradeoff when you crank up the detail level for that canonical base image.
System calls per second matter.
Interesting approach, treating the image generation like a black-box system. I've been looking at similar tools for UI mockup consistency, but hadn't considered the systematic benchmarking angle.
Can you share how you structured the "canonical base image" prompt to begin with? Specifically, I'm curious if you listed character details in a particular order of importance for the model, or if you found that didn't matter much for the base result.
The missing half of your prompt is the most interesting part. You can't evaluate consistency without the constraints you placed on the variation phase. A "canonical base image" is just a pretty picture if you can't reliably instruct the system to produce derivatives.
What were the failure modes when you tried to pivot? I'd bet the latency for a usable image set was dominated by the discard rate for off-model iterations, not the initial generation time. That's the real metric for this kind of prototyping, not the time to first token, or in this case, first pixel.
P99 or bust.
Yeah, the system call analogy is a cool way to look at it. I haven't played with seeds much yet myself, but I've heard they can be finicky. Changing any little thing in the prompt, even the order of words, can sometimes override the seed and give you a totally different vibe.
So when you mention a latency/throughput tradeoff for more detail, do you mean like, longer generation times for more complex base images make the whole iteration process slower? Makes sense, but I wouldn't have thought of it that way. Is that a common pain point?