Generate stunning photorealistic portraits with SDXL. Covers lighting setups, camera simulation, skin texture, and professional photography techniques.
## SDXL Photorealistic Portrait Prompt Framework ### Base Prompt Structure "[Shot type] portrait of [subject description], [lighting], [camera settings], [style modifiers]" ### Shot Types - Headshot: tight crop, face fills frame - Half-body: waist up, environmental context - Three-quarter: knees up, dynamic pose - Full-body: complete figure, setting visible ### Lighting Setups - Rembrandt: triangular shadow on cheek, dramatic - Butterfly: overhead, glamour, defined cheekbones - Split: half-face illuminated, moody - Rim: backlit edge glow, separation from background - Natural: golden hour, window light, overcast softbox ### Camera Simulation - Lens: 85mm f/1.4 (bokeh), 50mm f/1.8 (natural), 135mm f/2 (compressed) - Film stock: Portra 400 (warm skin), Tri-X (B&W grain), Ektar (vivid) - Settings: shallow DOF, sharp eyes, soft background ### Quality Tags masterpiece, best quality, ultra-detailed, RAW photo, 8k UHD, high resolution, photorealistic, hyperrealistic ### Negative Prompt (worst quality, low quality:1.4), (deformed, distorted:1.3), extra fingers, bad anatomy, blurry, oversaturated, artificial, plastic skin, CGI look
Design and optimize ComfyUI node workflows for Stable Diffusion. Covers ControlNet, IP-Adapter, inpainting, upscaling, and multi-pass generation pipelines.
Comprehensive prompt engineering guide covering subject description, style keywords, quality modifiers, negative prompts, prompt weighting syntax, and SDXL-specific techniques. The most frequently referenced SD prompt resource online.
Detailed guide for crafting textual descriptions specifically for SDXL image generation, covering the dual-encoder system, optimal prompt lengths, and style-specific formulas for photorealism, illustration, and concept art.
Technical deep-dive into prompt engineering covering token limits, attention mechanisms, prompt weighting with parentheses and numerical values, embedding manipulation, and A/B testing different prompt structures with reproducible experiments.
Covers the full prompt engineering workflow including subject specification, style references, quality boosters, camera and lighting terminology, negative prompt strategies, and CFG scale tuning for different prompt styles.
Exhaustive guide to negative prompts covering common negative terms, negative embeddings (EasyNegative, bad_prompt), weighting strategies, model-specific negative prompts, and common mistakes like over-weighting.
Workflows from the Neura Market marketplace related to this Stable Diffusion resource