Description:⚠️ EXPERIMENTAL MERGE / TEST BUILDThis model is a direct weight merge of Baidu's ERNIE-Image and Alibaba's Z-Image Turbo.This is a proof of concept to test the compatibility of DiT architectures. Due to technical nuances of merging different DiT checkpoints, the model exhibits specific behaviors. It is not a polished final release.🔥 Key Features:<ul><li>⚡ Lightning Fast: Runs perfectly on 8 Steps (Turbo mode).</li><li>📝 Incredible Text Rendering: Inherits ERNIE's ability to render legible text (Cyrillic, Latin, Kanji) and follow complex instructions.</li><li>🎨 High Fidelity: Combines Z-Image Turbo's photorealism and lighting with ERNIE's composition logic.</li></ul>⚙️ Recommended Settings:<ul><li>Sampler: Euler</li><li>Scheduler: Beta / Simple (Beta schedule alpha: 0.6, beta: 0.6)</li><li>Steps: 8 - 10</li><li>CFG Scale: 1.0 (CRITICAL: Do not increase, or the image will degrade/burn)</li><li>Resolution: 1024x1024 or 1024x1536</li></ul>⚠️ Known Limitations &amp; Quirks:<ol><li>Facial Bias: The model inherits a strong Asian/East Asian prior from the ERNIE base weights.<ul><li>Solution: To generate non-Asian faces, you MUST use explicit physical descriptors in your prompt (e.g., <code>Caucasian man, blue eyes</code>, <code>African woman, dark skin</code>, <code>European features</code>). Generic terms like "man" or "woman" will likely result in Asian faces.</li></ul></li><li>Architecture Mismatch: This is a direct merge of weights, not a trained hybrid. Some layer alignments may be imperfect, leading to occasional inconsistencies in complex scenes.</li></ol>🚀 Future Roadmap: We are currently analyzing the latent space alignment. The next version will likely utilize LoRA-based merging or adapter training to fix the architectural inconsistencies and neutralize the facial bias for a more universal model.Please generate and share your results! We are looking for feedback on prompt adherence and image quality.

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