<h1 id="cinematic-photorealism-turbo-checkpoint">Cinematic Photorealism Turbo Checkpoint</h1>Ultra-realistic generation optimized for speed, precision, and material fidelity. Delivers professional-grade photography results in as few as <code>9 steps</code>. Fully compatible with FP8 quantization.<hr /><h2 id="overview">📘 Overview</h2> This is a high-fidelity photorealism checkpoint built for creators who refuse to compromise between quality and generation speed. Fine-tuned on diverse real-world imagery, it excels at capturing natural skin textures, anatomically accurate human forms, complex material interactions, and cinematic lighting. Optimized for modern inference pipelines, it maintains stunning detail even at low step counts and minimal CFG.<hr /><h2 id="key-features">✨ Key Features</h2><ul><li>🧴 True-to-Life Skin &amp; Imperfections: Visible pores, peach fuzz, natural freckles, and subsurface scattering. Zero "plastic" or airbrushed look.</li><li>🤲 Reliable Anatomy &amp; Dynamics: Stable hands, facial features, and complex poses (jumping, dancing, object interaction). No fused fingers or distorted joints.</li><li>🧵 Material Mastery: Accurate rendering of silk, denim, leather, wet surfaces, metal, glass, and macro textures. Clear separation between contrasting materials.</li><li>💡 Advanced Lighting &amp; Color Grading: Handles golden hour, neon nights, volumetric light, and high-contrast scenes without banding, noise, or color shifts.</li><li>⚡ Turbo-Optimized Workflow: Performs exceptionally at <code>9–15 steps</code> with <code>CFG 1.0–1.5</code>, drastically reducing VRAM usage and generation time.</li><li>🔧 FP8 Ready: Official FP8 variant retains &gt;95% visual fidelity. Ideal for lower VRAM setups, batch processing, or real-time workflows.</li></ul><hr /><h2 id="recommended-settings">⚙️ Recommended Settings</h2>ParameterValueSampler<code>DPM++ 2s a RF</code> (or <code>DPM++ 2M Karras</code>)Steps<code>9</code> (Turbo) / <code>20–30</code> (High Detail)CFG Scale<code>1.0</code> (Turbo) / <code>5.0–7.0</code> (Standard)Scheduler<code>KL Optimal</code>Resolution<code>1024x1536</code> (or native aspect ratio)VAEBuilt-in / <code>vae-ft-mse-840000</code>Clip Skip<code>1</code>SeedFixed for consistency, or <code>-1</code> for variation<hr /><h2 id="prompting-guide">📝 Prompting Guide</h2>Style: Use photography-focused descriptors. The model responds best to clear, technical prompts rather than artistic/stylized keywords.✅ Positive Prompt Examples:<pre><code>text</code></pre>1<pre><code>text</code></pre>1🚫 Negative Prompt:<pre><code>text</code></pre>1<hr /><h2 id="fp8-quantization-notes">🔧 FP8 Quantization Notes</h2><ul><li>FP8 variant uses <code>float8_e4m3fn</code> per-tensor scaling.</li><li>Tested across macro, portrait, material, night-scene, and dynamic pose benchmarks with negligible quality loss.</li><li>Recommended for: VRAM-constrained GPUs, batch generation, turbo workflows.</li><li>Keep <code>CFG ≤ 1.5</code> and <code>Steps ≥ 9</code> for optimal FP8 stability.</li></ul><hr /><h2 id="validation-and-testing">🧪 Validation &amp; Testing</h2>Rigorously benchmarked across 10+ scenarios: ✅ Macro eye/portrait (skin, lashes, reflections) ✅ Hand-object interaction &amp; anatomy ✅ Mechanical macro (gears, metal, glass) ✅ Interior reflections &amp; wet surfaces ✅ Material contrast (denim, leather, wood) ✅ Night neon &amp; dynamic range ✅ Fashion editorial &amp; fabric dynamics ✅ Sports/action poses &amp; muscle definition ✅ Dance &amp; flowing fabric physics ✅ FP16 ↔ FP8 visual parity verification<hr /><h2 id="credits-and-license">📜 Credits &amp; License</h2><ul><li>Base Architecture: <code>[e.g., SDXL / Z-Image Turbo / Custom]</code></li><li>Trained/Fine-tuned by: <code>[Your Handle/Name]</code></li><li>License: <code>[e.g., CreativeML Open RAIL-M / CC BY-NC 4.0 / Custom]</code></li><li>⚠️ Disclaimer: This model is intended for creative, artistic, and research purposes. Users are responsible for complying with local laws and ethical guidelines. Generated content does not represent real individuals unless explicitly stated.</li></ul><hr />💡 Tip: If you experience minor contrast shifts in FP8, switch to <code>e5m2</code> dtype or increase steps to <code>12–15</code>. For maximum realism, keep CFG at <code>1.0</code> and let the model’s native priors guide composition.

FasciumZ-Image turbo

ZED IMAGE TURBO - Technical Specifications🎨 Generation ParametersModel Configuration:<ul><li>Model: FZIT_0605FP16 (cf727ea834)</li><li>Base Architecture: neo-2.22</li><li>Precision: float8-e5m2 (fp16 LoRA)</li><li>VAE: zImage_vae</li><li>Text Encoder: zImage_textEncoder</li></ul>Sampling Settings:<ul><li>Sampler: DPM++ 2s a RF</li><li>Schedule: KL Optimal</li><li>Steps: 9</li><li>CFG Scale: 1</li><li>Shift: 9</li><li>Denoising Strength: 0.3</li><li>RNG Source: CPU</li></ul>Resolution &amp; Upscaling:<ul><li>Base Size: 1024x1536</li><li>Hires Upscale: 2x (4x-UltraSharp)</li><li>Final Output: ~2048x3072</li><li>Hires Steps: 9</li><li>Hires Shift: 9</li><li>Hires CFG: 1</li></ul>⚡ Performance MetricsGeneration Time: 3 min. 45.1 secMemory Usage:<ul><li>Active VRAM: 17.37 GB</li><li>Reserved VRAM: 17.53 GB</li><li>System RAM: 19.1/24 GB (79.8%)</li></ul>🔧 Hardware RequirementsRecommended:<ul><li>GPU: 24GB VRAM minimum</li><li>RAM: 32GB system memory</li><li>Storage: SSD for model loading</li></ul>Optimization: Low-bit diffusion (float8) enables faster generation with minimal quality loss.Published with Fascium ZED IMAGE TURBO - Next-generation image synthesis

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