<h3 id="first-test:-photorealism-model">First Test: Photorealism Model</h3>Sharing my first practical test in training a model focused on transforming images into photorealism. Technical Specifications<ul><li>Software: Ostris (ai-toolkit)</li><li>Dataset: 172 high-quality images (2K resolution), with all "before and after" pairs manually aligned.</li><li>Trigger Word: <code>transform to photorealism</code></li><li>Train Steps: 250 (currently; planning to extend training to 2,500–5,000 steps).</li></ul>MethodologyTo ensure that the final render quality is grounded in real-world details rather than synthetic approximations, I used a reverse workflow: real photographs were used as the AFTER state, which were then converted into vector art using the Qwen Image 2.1 model to serve as the BEFORE state. This guarantees that the learning baseline is anchored in genuine realism. Recommended Settings<ul><li>Sampler / Scheduler: Euler / Simple</li><li>CFG: 4</li><li>Steps: 25–40</li></ul><blockquote>Important Note regarding ComfyUI:The official text encoder currently has incorrect standard multiples. To achieve zero pixel drift and maintain maximum fidelity, it is recommended to use: <ul><li>Multiples of 28 for the VL</li><li>Multiples of 16 for the VAE</li></ul></blockquote>Since this is my initial test with a relatively low step count, I appreciate your understanding if some outputs are not yet fully refined. Feedback is always welcome!

Transform To photorealism

Transform2Photorealism_Qwen_21_v1_000000250.safetensors

v1.0

<h3 id="first-test:-photorealism-model">First Test: Photorealism Model</h3>Sharing my first practical test in training a model focused on transforming images into photorealism.Technical Specifications<ul><li>Software: Ostris (ai-toolkit)</li><li>Dataset: 172 high-quality images (2K resolution), with all "before and after" pairs manually aligned.</li><li>Trigger Word: <code>transform to photorealism</code></li><li>Train Steps: 250 (currently; planning to extend training to 2,500–5,000 steps).</li></ul>MethodologyTo ensure that the final render quality is grounded in real-world details rather than synthetic approximations, I used a reverse workflow: real photographs were used as the AFTER state, which were then converted into vector art using the Qwen Image 2.1 model to serve as the BEFORE state. This guarantees that the learning baseline is anchored in genuine realism. Recommended Settings<ul><li>Sampler / Scheduler: Euler / Simple</li><li>CFG: 4</li><li>Steps: 25–40</li></ul><blockquote>Important Note regarding ComfyUI:The official text encoder currently has incorrect standard multiples. To achieve zero pixel drift and maintain maximum fidelity, it is recommended to use: <ul><li>Multiples of 28 for the VL</li><li>Multiples of 16 for the VAE</li></ul></blockquote>Since this is my initial test with a relatively low step count, I appreciate your understanding if some outputs are not yet fully refined. Feedback is always welcome!

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