I've been conducting an extensive evaluation of Leonardo AI for potential use in generating architectural and infrastructure visualization concepts. While the tool is impressive for many styles, I've hit a consistent roadblock when attempting to generate photorealistic images of people, environments, or objects: the output persistently exhibits telltale signs of poor CGI. This manifests as a "plastic" skin texture, unnaturally perfect symmetry in facial features, a lack of authentic subsurface scattering, lighting that doesn't interact correctly with materials, and an overall synthetic "cleanliness" that fails to capture the inherent imperfection of reality.
My objective is to achieve a level of realism suitable for pre-visualization in client presentations, where believability is paramount. I suspect the issue lies not in the base model selection alone, but in a nuanced interplay of prompt engineering, model choice, and post-processing workflows. I am methodically testing variables, but the solution space is large.
**My standard test parameters are as follows:**
```
Model: Leonardo Diffusion XL
Prompt: "photorealistic portrait of a middle-aged architect, sharp focus, studio lighting, detailed skin pores, slight stubble, professional headshot"
Negative Prompt: "3d render, cgi, cartoon, anime, drawing, painting, digital art, smooth skin"
Dimensions: 1024x1024
Guidance Scale: 7
Alchemy: Refined (v2.1)
Public Prompt: Off
Tiling: Off
```
The results, while high-quality as digital art, consistently fall into the uncanny valley. The skin lacks the complex, layered texture of real human skin—it appears as a uniform, slightly waxy surface. Lighting, even when specified as "studio lighting," doesn't cast believable shadows or create accurate specular highlights on the skin and eyes.
From an infrastructure perspective, I approach this as a pipeline bottleneck analysis. The "rendering" pipeline here is the AI's generative process. Potential failure points I've identified:
* **Model Bias:** The foundational models may be trained on a dataset containing a high proportion of actual CGI/3D render art, making it difficult for them to disentangle "photograph" from "high-quality render."
* **Prompt Contamination:** Certain keywords, even in a positive prompt, may have stronger associations to synthetic media within the model's latent space. For example, "sharp focus" and "detailed skin pores" might be more prevalent in high-end CGI than in natural photography.
* **Lack of Authentic Noise:** Real photographs contain grain, lens artifacts, slight chromatic aberration, and minute imperfections. AI-generated images often have a mathematically "perfect" but artificial noise pattern, or are too clean.
**My current troubleshooting matrix includes:**
* **Model Switches:** Testing Photoreal, DreamShaper v7, and Absolute Reality v1.6 against the same prompt set.
* **Prompt Engineering:** Incorporating specific photographic terms (e.g., "shot on Canon EOS R5, 85mm f/1.8," "film grain," "natural lighting," "environmental portraiture").
* **Negative Prompt Reinforcement:** Aggressively adding terms like "waxy skin," "plastic skin," "airbrushed," "soft focus," "blender render," "unreal engine."
* **Post-Processing Workflow:** Exporting to external tools for adding subtle noise, adjusting micro-contrast, and applying lens simulation filters.
The core question for the community is: what is the critical component I am missing? Is there a specific model fine-tuned on genuine photography datasets? Are there particular negative prompt tags that have proven effective in stripping away that CGI "sheen"? Has anyone successfully established a reproducible pipeline for outputs that can pass as authentic photographs, and if so, what are the key architectural steps in that pipeline beyond simple prompt crafting?
Plan the exit before entry.