A lora model trained on the Engram dataset, 5 songs split, converted, captioned and trained. use the genres.txt and instruments.txt for the identifiers - <a target="_blank" rel="ugc" href="https://github.com/wedgeewoo/Riffusion-Textual-Inversion-template">wedgeewoo/Riffusion-Textual-Inversion-template: Templates for musical textual inversion for riffusion (</a><a target="_blank" rel="ugc" href="http://github.com">github.com</a><a target="_blank" rel="ugc" href="https://github.com/wedgeewoo/Riffusion-Textual-Inversion-template">)</a> Example - <a target="_blank" rel="ugc" href="https://soundcloud.com/wedgeewoo/sprouts-11">Stream Sprouts #11 by Wedgeewoo | Listen online for free on SoundCloud</a> Tips and Tricks - Use a weight of .8, the results come out much more cohesive when using the genres.txt identifiers.Example 2 - <a target="_blank" rel="ugc" href="https://soundcloud.com/wedgeewoo/sprouts-14">Stream Sprouts #14 by Wedgeewoo | Listen online for free on SoundCloud</a> Prompt - Complextro Tropical house song , reese bass, neuro bass, tuba, drop &lt;lora:Engram:.75&gt; Steps: 30, Sampler: Euler a, CFG scale: 8.5, Seed: 3779202002, Size: 512x512, Model hash: 99a6eb51c1, Variation seed: 1752705851, Variation seed strength: 0.25, Tile X: True, Tile Y: False, Start Tiling From Step: 15, Stop Tiling After Step: -1, Wildcard prompt: "__genres__, reese bass, neuro bass, tuba, drop &lt;lora:Engram:.75&gt;" <a target="_blank" rel="ugc" href="https://www.youtube.com/shorts/rTje4L8gGAA">Wedgewoo - Engram : LoRa model for Riffusion. - YouTube</a>

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