<p>This is a subject oriented json experimentation meant to allow better control with json capacity to plain English. High probability that this variation faulted in one way or another. Likely some worse than others. It'll definitely produce some highly interesting and impressively unique liminal images due to the method trained.</p><h1 id="talking-to-the-models">Talking to the models</h1><p>Plain English is your best bet, or multilingual communication - if the model knows it. Use words as usual and then things will.. happen. Just ask the model what you want to see, and the model will create it for you. The more surreal, the more likely this model can accommodate than standard versions of this model. No guarantees.</p><h1 id="training">Training</h1><p>Each image was issued a json aligned caption organized by Qwen 3.5 0.8b finetuned to create json aligned structure.</p><h1 id="learned-what">Learned what?</h1><p>Json. The model learned how to think in Json. You don't have to understand why it does this, you simply have to understand the importance of subject association. In this case, the json links tokens together through subject association pathways.</p><p>Many of the pathways are interesting or useful, many of them corrupted or caused causal collapse. Which doesn't matter, because in a liminal training you WANT corruption and causal collapse.</p><h1 id="so-how-do-i-use-it">So how do I use it?</h1><p>Describe in plain text what you want to see. The model learned like this;</p><pre><code>{"subjects":[{"name":"housees","attributes":["yellow","rowed"]},{"name":"street","attributes":["asphalt"]},{"name":"grassy lanes","attributes":[]},{"name":"sidewalk","attributes":[]},{"name":"sky","attributes":["light"]}],"actions":["rowed in a line","street extends into the distance"],"setting":"outdoor"}</code></pre><p>What you say is;</p><p>A suburban neighborhood street extending into the distance, rows of houses along each sides with sidewalks and mailboxes per house.</p><h2 id="qwen-model">Qwen model</h2><p>This is the v2 lora trained for QWEN 3.5</p><p><a target="_blank" rel="ugc" href="https://huggingface.co/AbstractPhil/qwen3.5-0.8b-task_1-lora-v2">https://huggingface.co/AbstractPhil/qwen3.5-0.8b-task_1-lora-v2</a></p><p>Entire purpose is to convert english to json.</p><p></p><h2 id="larger-dataset-incoming">Larger dataset incoming</h2><p><a target="_blank" rel="ugc" href="https://huggingface.co/datasets/AbstractPhil/diffusion-pretrain-set-ft1">https://huggingface.co/datasets/AbstractPhil/diffusion-pretrain-set-ft1</a></p><p>A much larger pretrain dataset is preparing.</p><p></p><p></p>

BigLiminal - JSON

PY2SYARRWMNZBA3Q9WHDQD3ZE0.safetensors

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