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ltx23/lora/LTX 2.3 - I2V Better Breasts motion and jiggle/LTX2.3_Breasts_Jiggle_Physics-mid_2536021-vid_2850154.csv

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ltx23/lora/LTX 2.3 - I2V Better Breasts motion and jiggle/LTX2.3_Breasts_Jiggle_Physics-mid_2536021-vid_2850154.safetensors

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ltx23/lora/LTX 2.3 - I2V Better Breasts motion and jiggle/extra_data-vid_2850154/model_dict-mid_2536021-vid_2850154.json

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+{
+  "id": 2536021,
+  "name": "LTX 2.3 - I2V Better Breasts motion and jiggle",
+  "description": "<h3 id=\"tip-appreciated-if-you-enjoy-the-work-these-take-a-lot-of-time-to-create!-https:ko-fi.commisticrain69\"><span style=\"color:rgb(64, 247, 241)\">Tip appreciated if you enjoy the work, these take a lot of time to create!</span> <a target=\"_blank\" rel=\"ugc\" href=\"https://ko-fi.com/misticrain69\">https://ko-fi.com/misticrain69</a></h3><p></p><h2 id=\"a-lora-for-giving-breasts-bounce-and-jiggle-physics.-i-noticed-that-default-ltx-doesn't-have-a-lot-of-breast-movement-so-i-trained-this-lora.\"><span style=\"color:rgb(18, 184, 134)\"><strong>A lora for giving breasts bounce and jiggle physics. I noticed that default LTX doesn't have a lot of breast movement so I trained this lora.</strong></span></h2><h2></h2><h2 id=\"should-work-just-fine-on-non-furry-generations-as-well.\"><span style=\"color:rgb(18, 184, 134)\"><strong>Should work just fine on non furry generations as well.</strong></span></h2><p></p><h2 id=\"i-use-it-at-a-1.0-strength.\"><span style=\"color:rgb(18, 184, 134)\"><strong>I use it at a 1.0 strength.</strong></span></h2><p></p><h2 id=\"!-if-you-post-things-in-the-gallery-that-violate-the-tos-i-will-block-and-report-you.-don't-do-this-shit.\"><span style=\"color:rgb(250, 82, 82)\"><strong>! If you post things in the gallery that violate the TOS I will block and report you. Don't do this shit.</strong></span></h2><p></p><h2 id=\"for-18+-only.-this-is-not-to-be-used-for-any-illegal-or-unethical-purposes-or-on-any-real-person-or-likeness.-don't-be-fucking-sus.\"><strong>For 18+ only. This is NOT to be used for any illegal or unethical purposes or on any real person or likeness. Don't be fucking sus.</strong></h2><p></p>",
+  "allowNoCredit": false,
+  "allowCommercialUse": "{RentCivit,Rent}",
+  "allowDerivatives": true,
+  "allowDifferentLicense": false,
+  "type": "LORA",
+  "minor": false,
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+  "poi": false,
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+    "username": "MisticRain69",
+    "image": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7bfcc4ba-239f-4cfe-8ed6-4f17f797dc2d/width=96/MisticRain69.jpeg"
+  },
+  "tags": [
+    "concept",
+    "furry",
+    "nsfw",
+    "ltx",
+    "i2v",
+    "ltx 2.3"
+  ],
+  "modelVersions": [
+    {
+      "id": 2850154,
+      "index": 0,
+      "name": "v1.0 I2V",
+      "baseModel": "LTXV 2.3",
+      "baseModelType": "Standard",
+      "createdAt": "2026-04-11T23:30:32.304Z",
+      "publishedAt": "2026-04-11T23:36:23.924Z",
+      "status": "Published",
+      "availability": "Public",
+      "nsfwLevel": 60,
+      "covered": true,
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+          "name": "LTX2.3_Breasts_Jiggle_Physics.safetensors",
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+          "minor": false,
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+    }
+  ]
+}

ltx23/lora/LTX 2.3 - I2V Better Physics PhysLTX/extra_data-vid_2911845/model_dict-mid_2592090-vid_2911845.json

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-    "furry",
+    "ltx 2.3",
+    "physics",
     "realistic",
     "nsfw",
-    "physics",
-    "ltx 2.3"
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+    "animated",
+    "concept"
   ],
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ltx23/lora/LTX 2.3 - I2V T2V Video Reasoning lora VBVR/extra_data-vid_2848299/model_dict-mid_2497207-vid_2848299.json

@@ -1,7 +1,7 @@
 {
   "id": 2497207,
   "name": "LTX 2.3 - I2V T2V Video Reasoning lora VBVR",
-  "description": "<h2 id=\"this-was-the-first-vbvr-lora-trained-for-ltx-2.3\"><span style=\"color:rgb(156, 255, 185)\">This was the first VBVR lora trained for LTX 2.3</span></h2><h2 id=\"v3-has-been-optimized-for-motion.-expect-noticeably-livelier-more-dynamic-movement-compared-to-previous-versions.\"><span style=\"color:rgb(64, 192, 87)\">V3 has been optimized for motion. Expect noticeably livelier, more dynamic movement compared to previous versions.</span></h2><h3 id=\"what-changed-in-v3:-attention-only-layers.-the-feedforward-layers-have-been-stripped-leaving-only-the-attention-weights.-it-seems-like-the-prompt-following-and-reasoning-behavior-most-likely-live-in-the-attention-layers-while-the-feedforward-layers-were-potentially-interfering-with-natural-motion-likely-by-over-learning-features-like-textures-and-style-from-the-training-data.\"><span style=\"color:rgb(64, 192, 87)\"><strong>What changed in V3:</strong> Attention-only layers. The feedforward layers have been stripped, leaving only the attention weights. It seems like the prompt following and reasoning behavior most likely live in the attention layers, while the feedforward layers were potentially interfering with natural motion, likely by over-learning features like textures and style from the training data.</span></h3><h3 id=\"better-motion-smaller-file-size.\"><span style=\"color:rgb(64, 192, 87)\">Better motion, smaller file size.</span></h3><h2 id=\"a-lora-that-improves-prompt-following-temporal-consistency-and-motion-&quot;precision&quot;-for-ltx-2.3.-reduces-the-floaty-drifty-motion-that-ltx-tends-to-add-to-scenes.-things-that-should-move-move-with-purpose.-things-that-shouldn't-move-move-less.-also-works-on-non-nsfw-non-furry-realistic-animated-etc.-it-responds-well-to-detailed-prompts.\"><span style=\"color:rgb(228, 245, 73)\">A LoRA that improves prompt following, temporal consistency, and motion \"precision\" for LTX 2.3. Reduces the floaty, drifty motion that LTX tends to add to scenes. Things that should move, move with purpose. Things that shouldn't move, move less. Also works on non-NSFW, non-Furry, realistic, animated etc. It responds well to detailed prompts.</span></h2><h2 id=\"in-comfyui-or-wan2gp-lowering-image-strength-to-0.85-can-improve-motion-in-general-if-you-want-more-motion\"><span style=\"color:rgb(228, 245, 73)\">In comfyui or wan2gp lowering image strength to 0.85 can improve motion in general if you want more motion</span></h2><h3 id=\"feedback-and-a-b-comparisons-welcome.-v2-and-v1-was-trained-on-4800-videos.\"><span style=\"color:rgb(73, 245, 216)\"><strong>Feedback and A-B comparisons welcome. V2 and V1 was trained on 4800 videos.</strong></span></h3><h3 id=\"recommended-to-run-at-strength-1.0-0.7-but-experiment-to-find-what-works-best-for-your-setup.-if-you-want-stronger-prompt-adherence-try-strength-1.5-2.0.-i-have-noticed-the-only-side-effect-i-have-gotten-from-a-high-strength-is-the-video-looking-like-its-16fps.-i-have-not-seen-the-choppiness-issues-on-i2v-unless-the-lora-is-cranked-to-1.5-2.0-so-it-may-be-a-t2v-thing.\"><span style=\"color:rgb(73, 245, 216)\"><strong>Recommended to run at strength 1.0-0.7 <u>but experiment to find what works best for your setup</u>. If you want stronger prompt adherence try strength 1.5-2.0. I have noticed the only side effect I have gotten from a high strength is the video looking like its 16fps. I have not seen the choppiness issues on i2v unless the lora is cranked to 1.5-2.0 so it may be a t2v thing.</strong></span></h3><p></p><h2 id=\"prompting-tips-in-non-nsfw-terms-so-its-less-confusing-just-adapt-it-to-nsfw:\"><span style=\"color:rgb(64, 192, 87)\"><strong>Prompting tips in non-nsfw terms so its less confusing just adapt it to nsfw:</strong></span></h2><h2 id=\"be-specific-and-literal.-describe-what-happens-in-what-order-step-by-step.\"><strong>Be specific and literal. Describe what happens, in what order, step by step.</strong></h2><h2 id=\"instead-of-&quot;a-ball-bouncing-around&quot;-&quot;a-red-ball-moves-to-the-right-bounces-off-the-wall-and-returns-to-the-center&quot;\"><strong>Instead of \"a ball bouncing around\" → \"A red ball moves to the right, bounces off the wall, and returns to the center\"</strong></h2><h2 id=\"instead-of-&quot;fluid-pouring&quot;-&quot;water-flows-from-the-left-container-through-the-connecting-tube-into-the-right-container-until-both-levels-are-equal&quot;\"><strong>Instead of \"fluid pouring\" → \"Water flows from the left container through the connecting tube into the right container until both levels are equal\"</strong></h2><h2 id=\"describe-the-starting-state-the-action-and-the-end-state\"><strong><u>Describe the starting state, the action, and the end state</u></strong></h2><h2 id=\"the-lora-follows-prompts-more-literally-than-base-ltx-precise-prompts-will-give-much-better-results\"><strong>The LoRA follows prompts more literally than base LTX — precise prompts will give much better results</strong></h2><h2 id=\"how-was-it-made\"><span style=\"color:rgb(92, 242, 195)\"><strong>How was it made?</strong></span></h2><h2 id=\"v0.1-and-v0.2-were-trained-on-360-videos-from-the-vbvr-(very-big-video-reasoning)-dataset-synthetic-task-videos-where-every-motion-is-precise-and-intentional.-no-concept-bleed-no-style-change-just-tighter-control.\"><span style=\"color:rgb(92, 242, 195)\">v0.1 and v0.2 were trained on 360 videos from the VBVR (Very Big Video Reasoning) dataset synthetic task videos where every motion is precise and intentional. No concept bleed, no style change, just tighter control.</span></h2><h2 id=\"based-on-the-paper-&quot;a-very-big-video-reasoning-suite&quot;-which-demonstrated-this-approach-on-wan-2.2.-i-noticed-that-lora-helped-prompt-following-and-temporal-consistency-a-ton-with-wan-so-i-am-training-this-version-for-ltx.\"><span style=\"color:rgb(92, 242, 195)\">Based on the paper</span><span style=\"color:rgb(92, 255, 87)\"><strong><u> </u></strong></span><a target=\"_blank\" rel=\"ugc\" href=\"https://arxiv.org/abs/2602.20159\"><span style=\"color:rgb(92, 255, 87)\"><strong><u>\"A Very Big Video Reasoning Suite\"</u></strong></span></a><span style=\"color:rgb(92, 242, 195)\"> which demonstrated this approach on Wan 2.2. I noticed that lora helped prompt following and temporal consistency a ton with wan so I am training this version for LTX.</span></h2><h2 id=\"what-does-it-actually-do\"><span style=\"color:rgb(92, 242, 195)\"><strong>What does it actually do?</strong></span></h2><h3 id=\"prompt-following-is-more-faithful-the-model-does-more-of-what-you-asked-instead-of-improvising\"><span style=\"color:rgb(92, 242, 195)\">Prompt following is more faithful — the model does more of what you asked instead of improvising</span></h3><h3 id=\"motion-is-more-deliberate-and-less-erratic\"><span style=\"color:rgb(92, 242, 195)\">Motion is more deliberate and less erratic</span></h3><h3 id=\"reduces-random-drift-and-wobble-in-scenes\"><span style=\"color:rgb(92, 242, 195)\">Reduces random drift and wobble in scenes</span></h3><h3 id=\"temporal-consistency-improved-actions-follow-logical-sequences\"><span style=\"color:rgb(92, 242, 195)\">Temporal consistency improved — actions follow logical sequences</span></h3><h2 id=\"what-it-doesn't-do:\"><span style=\"color:rgb(92, 242, 195)\"><strong>What it doesn't do:</strong></span></h2><h3 id=\"doesn't-change-visual-style\"><span style=\"color:rgb(92, 242, 195)\">Doesn't change visual style</span></h3><h3 id=\"doesn't-add-or-remove-capabilities-ltx-doesn't-already-have\"><span style=\"color:rgb(92, 242, 195)\">Doesn't add or remove capabilities LTX doesn't already have</span></h3><h3 id=\"not-a-motion-lora-stacks-with-motion-lora's\"><span style=\"color:rgb(92, 242, 195)\">Not a motion LoRA — stacks with motion LoRA's</span></h3><h2 id=\"training-details-for-v0.1-and-v0.2-(if-you-give-a-shit)\"><span style=\"color:rgb(92, 242, 195)\"><strong>Training details for v0.1 and v0.2 (if you give a shit)</strong></span></h2><h3 id=\"rank-32\"><span style=\"color:rgb(92, 242, 195)\">Rank 32</span></h3><h3 id=\"360-vbvr-synthetic-videos-at-512x512-81-frames-less-alot-less-than-1-million-but-still-a-shitload-to-train-on-this-is-very-slow-to-train-locally.\"><span style=\"color:rgb(92, 242, 195)\">360 VBVR synthetic videos at 512x512, 81 frames &lt;------Alot less than 1 million but still a shitload to train on this is very slow to train locally.</span></h3><h3 id=\"lr-1e-4-adamw8bit\"><span style=\"color:rgb(92, 242, 195)\">LR 1e-4, adamw8bit</span></h3><h3 id=\"early-release-still-training-and-evaluating\"><span style=\"color:rgb(92, 242, 195)\">Early release — still training and evaluating</span></h3><h2 id=\"training-details-for-v1\"><span style=\"color:rgb(92, 242, 195)\"><strong>Training details for V1</strong></span></h2><h3 id=\"training-videos-were-increased-to-4800\"><span style=\"color:rgb(92, 242, 195)\"><strong>Training videos were increased to 4800</strong></span></h3><h3 id=\"resolution-is-the-same-but-frames-were-increased-to-121\"><span style=\"color:rgb(92, 242, 195)\"><strong>Resolution is the same but frames were increased to 121</strong></span></h3><h3 id=\"every-other-setting-the-same-as-v0.1-and-v0.2\"><span style=\"color:rgb(92, 242, 195)\"><strong>Every other setting the same as v0.1 and v0.2</strong></span></h3><h2 id=\"more-training-data-from-the-vbvr-dataset-was-added-to-v1\"><span style=\"color:rgb(92, 242, 195)\">More training data from the VBVR dataset was added to v1</span></h2><h2 id=\"below-is-the-new-dataset-i-trained-on's-data-composition-if-your-curious\"><span style=\"color:rgb(21, 170, 191)\"><u>Below is the new dataset I trained on's data composition if your curious</u></span></h2><h2 id=\"tier-1-physics-and-motion-(3400-samples)\"><span style=\"color:rgb(21, 170, 191)\"><strong>Tier 1 — Physics and Motion (3,400 samples)</strong></span></h2><h2 id=\"core-generators-at-300-each:-g-11-(object-reappearance)-has-a-shape-move-off-screen-in-a-direction-and-return-along-the-same-path-teaches-trajectory-and-object-persistence.-g-25-(separate-object-spinning)-is-a-shape-that-rotates-in-place-then-translates-horizontally-to-a-target-position-multi-step-motion-sequencing.-g-33-(visual-jenga)-is-a-stack-of-objects-that-get-removed-one-by-one-from-top-to-bottom-sequential-extraction-with-implicit-physics-ordering.-o-29-(ballcolor)-is-ball-tracking-tasks-with-color-motion-following-plus-identity-preservation.-o-52-(traffic-light)-is-discrete-state-transitions-lights-switching-onoff-between-green-and-gray-teaches-the-model-that-state-changes-are-crisp-not-gradual.-o-75-(communicating-vessels)-is-fluid-equalizing-between-connected-tubes-based-on-pressure-continuous-physics-simulation-over-time.-o-87-(fluid-diffusion)-is-ink-spreading-in-water-another-continuous-physical-transformation-but-with-expansion-rather-than-equalization.\"><span style=\"color:rgb(21, 170, 191)\"><strong>Core generators at 300 each: </strong></span><code>G-11</code><span style=\"color:rgb(21, 170, 191)\"><strong> (object reappearance) has a shape move off-screen in a direction and return along the same path — teaches trajectory and object persistence. </strong></span><code>G-25</code><span style=\"color:rgb(21, 170, 191)\"><strong> (separate object spinning) is a shape that rotates in place then translates horizontally to a target position — multi-step motion sequencing. </strong></span><code>G-33</code><span style=\"color:rgb(21, 170, 191)\"><strong> (visual jenga) is a stack of objects that get removed one by one from top to bottom — sequential extraction with implicit physics ordering. </strong></span><code>O-29</code><span style=\"color:rgb(21, 170, 191)\"><strong> (ballcolor) is ball tracking tasks with color — motion following plus identity preservation. </strong></span><code>O-52</code><span style=\"color:rgb(21, 170, 191)\"><strong> (traffic light) is discrete state transitions, lights switching on/off between green and gray — teaches the model that state changes are crisp, not gradual. </strong></span><code>O-75</code><span style=\"color:rgb(21, 170, 191)\"><strong> (communicating vessels) is fluid equalizing between connected tubes based on pressure — continuous physics simulation over time. </strong></span><code>O-87</code><span style=\"color:rgb(21, 170, 191)\"><strong> (fluid diffusion) is ink spreading in water — another continuous physical transformation but with expansion rather than equalization.</strong></span></h2><h2 id=\"new-additions-at-250-each:-g-35-(hit-target-after-bounce)-is-a-ball-with-an-initial-direction-that-bounces-off-walls-following-reflection-laws-to-hit-a-target-pure-trajectory-prediction-with-physics-constraints.-o-30-(bookshelf)-is-book-rearrangement-on-shelves-the-specific-task-vbvr-highlighted-where-their-model-beat-sora-2.\"><span style=\"color:rgb(21, 170, 191)\"><strong>New additions at 250 each: </strong></span><code>G-35</code><span style=\"color:rgb(21, 170, 191)\"><strong> (hit target after bounce) is a ball with an initial direction that bounces off walls following reflection laws to hit a target — pure trajectory prediction with physics constraints. </strong></span><code>O-30</code><span style=\"color:rgb(21, 170, 191)\"><strong> (bookshelf) is book rearrangement on shelves — the specific task VBVR highlighted where their model beat Sora 2.</strong></span></h2><h2 id=\"multi-step-transforms-at-160-each:-o-7-(shape-color-change)-is-a-single-transformation-shape-changes-from-one-color-to-another.-o-8-(shape-rotation)-is-a-shape-rotating-by-a-specific-angle.-o-13-(outline-then-move)-is-two-sequential-steps:-change-a-shape's-outline-style-then-move-it-to-a-new-position.-o-14-(scale-then-outline)-is-also-two-steps:-scale-a-shape-up-or-down-then-change-its-outline.-these-four-together-teach-the-model-that-instructions-are-ordered-and-each-step-completes-before-the-next-begins.\"><span style=\"color:rgb(21, 170, 191)\"><strong>Multi-step transforms at 160 each: </strong></span><code>O-7</code><span style=\"color:rgb(21, 170, 191)\"><strong> (shape color change) is a single transformation — shape changes from one color to another. </strong></span><code>O-8</code><span style=\"color:rgb(21, 170, 191)\"><strong> (shape rotation) is a shape rotating by a specific angle. </strong></span><code>O-13</code><span style=\"color:rgb(21, 170, 191)\"><strong> (outline then move) is two sequential steps: change a shape's outline style, then move it to a new position. </strong></span><code>O-14</code><span style=\"color:rgb(21, 170, 191)\"><strong> (scale then outline) is also two steps: scale a shape up or down, then change its outline. These four together teach the model that instructions are ordered and each step completes before the next begins.</strong></span></h2><h2 id=\"tier-2-spatial-and-reasoning-(1420-samples)\"><span style=\"color:rgb(21, 170, 191)\"><strong>Tier 2 — Spatial and Reasoning (1,420 samples)</strong></span></h2><h2 id=\"proven-generators-at-100-each:-g-13-(grid-number-sequence)-is-filling-in-number-patterns-on-a-grid.-g-17-(grid-avoid-red-block)-is-pathfinding-on-a-grid-while-avoiding-obstacles.-g-31-(directed-graph-navigation)-is-finding-the-shortest-path-through-a-directed-graph.-g-41-(grid-highest-cost)-is-evaluating-spatial-values-on-a-grid-to-find-the-optimal-path.-o-24-(domino-chain)-is-a-sequential-cascade-where-dominoes-fall-until-they-hit-a-gap-teaches-causal-chains-and-stopping-conditions.-o-34-(dot-to-dot)-is-connecting-numbered-dots-in-sequence-ordered-drawing.-o-47-(sliding-puzzle)-is-tile-rearrangement-under-constraints-like-a-15-puzzle.-o-83-(planar-warp)-is-warping-a-grid-to-align-with-a-target-quadrilateral-geometric-transformation.\"><span style=\"color:rgb(21, 170, 191)\"><strong>Proven generators at 100 each: </strong></span><code>G-13</code><span style=\"color:rgb(21, 170, 191)\"><strong> (grid number sequence) is filling in number patterns on a grid. </strong></span><code>G-17</code><span style=\"color:rgb(21, 170, 191)\"><strong> (grid avoid red block) is pathfinding on a grid while avoiding obstacles. </strong></span><code>G-31</code><span style=\"color:rgb(21, 170, 191)\"><strong> (directed graph navigation) is finding the shortest path through a directed graph. </strong></span><code>G-41</code><span style=\"color:rgb(21, 170, 191)\"><strong> (grid highest cost) is evaluating spatial values on a grid to find the optimal path. </strong></span><code>O-24</code><span style=\"color:rgb(21, 170, 191)\"><strong> (domino chain) is a sequential cascade where dominoes fall until they hit a gap — teaches causal chains and stopping conditions. </strong></span><code>O-34</code><span style=\"color:rgb(21, 170, 191)\"><strong> (dot to dot) is connecting numbered dots in sequence — ordered drawing. </strong></span><code>O-47</code><span style=\"color:rgb(21, 170, 191)\"><strong> (sliding puzzle) is tile rearrangement under constraints, like a 15-puzzle. </strong></span><code>O-83</code><span style=\"color:rgb(21, 170, 191)\"><strong> (planar warp) is warping a grid to align with a target quadrilateral — geometric transformation.</strong></span></h2><h2 id=\"new-reasoning-diversity-at-130-each:-o-1-(color-mixing)-is-rgb-additive-mixing-where-two-light-sources-combine-and-the-result-fills-a-target-zone-rule-based-continuous-process.-o-33-(counting-objects)-is-exactly-what-it-sounds-like-count-things-correctly.-g-3-(stable-sort)-is-arranging-objects-by-a-rule-while-preserving-relative-order.-g-37-(symmetry-random)-is-completing-a-pattern-by-mirroring-across-an-axis.-o-21-(construction-blueprint)-is-fitting-a-correct-puzzle-piece-into-a-gap-in-a-structure.-g-44-(bfs)-is-breadth-first-search-traversal-of-a-graph-systematic-layer-by-layer-exploration.\"><span style=\"color:rgb(21, 170, 191)\"><strong>New reasoning diversity at 130 each: </strong></span><code>O-1</code><span style=\"color:rgb(21, 170, 191)\"><strong> (color mixing) is RGB additive mixing where two light sources combine and the result fills a target zone — rule-based continuous process. </strong></span><code>O-33</code><span style=\"color:rgb(21, 170, 191)\"><strong> (counting objects) is exactly what it sounds like — count things correctly. </strong></span><code>G-3</code><span style=\"color:rgb(21, 170, 191)\"><strong> (stable sort) is arranging objects by a rule while preserving relative order. </strong></span><code>G-37</code><span style=\"color:rgb(21, 170, 191)\"><strong> (symmetry random) is completing a pattern by mirroring across an axis. </strong></span><code>O-21</code><span style=\"color:rgb(21, 170, 191)\"><strong> (construction blueprint) is fitting a correct puzzle piece into a gap in a structure. </strong></span><code>G-44</code><span style=\"color:rgb(21, 170, 191)\"><strong> (BFS) is breadth-first search traversal of a graph — systematic layer-by-layer exploration.</strong></span></h2><h2 id=\"the-overall-dataset-is-weighted-roughly-7030-toward-physical-motion-and-transformation-tasks-over-abstract-spatial-reasoning-all-of-these-are-taken-from-the-vbvr-dataset-i-am-not-the-creator-of-the-dataset.-i'm-pretty-new-to-lora-training-so-if-you-have-tips-let-me-know.\"><span style=\"color:rgb(21, 170, 191)\"><strong>The overall dataset is weighted roughly 70/30 toward physical motion and transformation tasks over abstract spatial reasoning, All of these are taken from the VBVR dataset I am not the creator of the dataset. I'm pretty new to lora training so if you have tips let me know.</strong></span></h2><h2></h2><h2 id=\"remember-its-not-x-its-y.\"><span style=\"color:rgb(141, 171, 51)\">REMEMBER its not X, its Y.</span></h2><p></p><h2 id=\"!-if-you-post-things-in-the-gallery-that-violate-the-tos-i-will-block-and-report-you.-don't-do-this-shit.\"><span style=\"color:rgb(250, 82, 82)\"><strong>! If you post things in the gallery that violate the TOS I will block and report you. Don't do this shit.</strong></span></h2><p></p><h2 id=\"for-18+-only.-this-is-not-to-be-used-for-any-illegal-or-unethical-purposes-or-on-any-real-person.-don't-be-fucking-sus.\"><strong>For 18+ only. This is NOT to be used for any illegal or unethical purposes or on any real person. Don't be fucking sus.</strong></h2><p></p>",
+  "description": "<h2 id=\"this-was-the-first-vbvr-lora-trained-for-ltx-2.3\"><span style=\"color:rgb(156, 255, 185)\">This was the first VBVR lora trained for LTX 2.3</span></h2><h3 id=\"tip-appreciated-if-you-enjoy-the-work-these-take-a-lot-of-time-to-create!-https:ko-fi.commisticrain69\"><span style=\"color:rgb(64, 247, 241)\">Tip appreciated if you enjoy the work, these take a lot of time to create!</span> <a target=\"_blank\" rel=\"ugc\" href=\"https://ko-fi.com/misticrain69\">https://ko-fi.com/misticrain69</a></h3><h2 id=\"v3-has-been-optimized-for-motion.-expect-noticeably-livelier-more-dynamic-movement-compared-to-previous-versions.-i-generated-the-example-videos-using-the-int8fp8-distilled-model-using-8-steps-in-wan2gp.-for-on-site-generation-the-dev-model-has-been-reported-to-be-broken-if-using-on-site-gen-try-the-distilled-one-instead!-i-can-highly-recommend-ltx-2.3-sulphur-2-base-if-you-are-using-comfyui-and-if-your-using-wan2gp-use-the-sulphur-2-base-rank-768-lora-https:huggingface.cosulphuraisulphur-2-basetreemain\"><span style=\"color:rgb(64, 192, 87)\">V3 has been optimized for motion. Expect noticeably livelier, more dynamic movement compared to previous versions.</span><span style=\"color:rgb(130, 201, 30)\"> </span><span style=\"color:rgb(0, 224, 22)\">I generated the example videos using the int8/fp8 distilled model using 8 steps in wan2gp. </span><span style=\"color:rgb(230, 73, 128)\">For on site generation the dev model has been reported to be broken, if using on site gen try the distilled one instead! </span><span style=\"color:rgb(0, 224, 22)\">I can highly recommend LTX 2.3 Sulphur 2 base if you are using comfyui and if your using wan2gp use the Sulphur 2 base rank 768 lora </span><a target=\"_blank\" rel=\"ugc\" href=\"https://huggingface.co/SulphurAI/Sulphur-2-base/tree/main\"><span style=\"color:rgb(0, 224, 22)\">https://huggingface.co/SulphurAI/Sulphur-2-base/tree/main</span></a></h2><h3 id=\"what-changed-in-v3:-attention-only-layers.-the-feedforward-layers-have-been-stripped-leaving-only-the-attention-weights.-it-seems-like-the-prompt-following-and-reasoning-behavior-most-likely-live-in-the-attention-layers-while-the-feedforward-layers-were-potentially-interfering-with-natural-motion-likely-by-over-learning-features-like-textures-and-style-from-the-training-data.\"><span style=\"color:rgb(64, 192, 87)\"><strong>What changed in V3:</strong> Attention-only layers. The feedforward layers have been stripped, leaving only the attention weights. It seems like the prompt following and reasoning behavior most likely live in the attention layers, while the feedforward layers were potentially interfering with natural motion, likely by over-learning features like textures and style from the training data.</span></h3><h3 id=\"better-motion-smaller-file-size.\"><span style=\"color:rgb(64, 192, 87)\">Better motion, smaller file size.</span></h3><h2 id=\"a-lora-that-improves-prompt-following-temporal-consistency-and-motion-&quot;precision&quot;-for-ltx-2.3.-reduces-the-floaty-drifty-motion-that-ltx-tends-to-add-to-scenes.-things-that-should-move-move-with-purpose.-things-that-shouldn't-move-move-less.-also-works-on-non-nsfw-non-furry-realistic-animated-etc.-it-responds-well-to-detailed-prompts.\"><span style=\"color:rgb(228, 245, 73)\">A LoRA that improves prompt following, temporal consistency, and motion \"precision\" for LTX 2.3. Reduces the floaty, drifty motion that LTX tends to add to scenes. Things that should move, move with purpose. Things that shouldn't move, move less. Also works on non-NSFW, non-Furry, realistic, animated etc. It responds well to detailed prompts.</span></h2><h2 id=\"in-comfyui-or-wan2gp-lowering-image-strength-to-0.85-can-improve-motion-in-general-if-you-want-more-motion\"><span style=\"color:rgb(228, 245, 73)\">In comfyui or wan2gp lowering image strength to 0.85 can improve motion in general if you want more motion</span></h2><h3 id=\"feedback-and-a-b-comparisons-welcome.-v2-and-v1-was-trained-on-4800-videos.\"><span style=\"color:rgb(73, 245, 216)\"><strong>Feedback and A-B comparisons welcome. V2 and V1 was trained on 4800 videos.</strong></span></h3><h3 id=\"recommended-to-run-at-strength-1.0-0.7-but-experiment-to-find-what-works-best-for-your-setup.-if-you-want-stronger-prompt-adherence-try-strength-1.5-2.0.-i-have-noticed-the-only-side-effect-i-have-gotten-from-a-high-strength-is-the-video-looking-like-its-16fps.-i-have-not-seen-the-choppiness-issues-on-i2v-unless-the-lora-is-cranked-to-1.5-2.0-so-it-may-be-a-t2v-thing.\"><span style=\"color:rgb(73, 245, 216)\"><strong>Recommended to run at strength 1.0-0.7 <u>but experiment to find what works best for your setup</u>. If you want stronger prompt adherence try strength 1.5-2.0. I have noticed the only side effect I have gotten from a high strength is the video looking like its 16fps. I have not seen the choppiness issues on i2v unless the lora is cranked to 1.5-2.0 so it may be a t2v thing.</strong></span></h3><p></p><h2 id=\"prompting-tips-in-non-nsfw-terms-so-its-less-confusing-just-adapt-it-to-nsfw:\"><span style=\"color:rgb(64, 192, 87)\"><strong>Prompting tips in non-nsfw terms so its less confusing just adapt it to nsfw:</strong></span></h2><h2 id=\"be-specific-and-literal.-describe-what-happens-in-what-order-step-by-step.\"><strong>Be specific and literal. Describe what happens, in what order, step by step.</strong></h2><h2 id=\"instead-of-&quot;a-ball-bouncing-around&quot;-&quot;a-red-ball-moves-to-the-right-bounces-off-the-wall-and-returns-to-the-center&quot;\"><strong>Instead of \"a ball bouncing around\" → \"A red ball moves to the right, bounces off the wall, and returns to the center\"</strong></h2><h2 id=\"instead-of-&quot;fluid-pouring&quot;-&quot;water-flows-from-the-left-container-through-the-connecting-tube-into-the-right-container-until-both-levels-are-equal&quot;\"><strong>Instead of \"fluid pouring\" → \"Water flows from the left container through the connecting tube into the right container until both levels are equal\"</strong></h2><h2 id=\"describe-the-starting-state-the-action-and-the-end-state\"><strong><u>Describe the starting state, the action, and the end state</u></strong></h2><h2 id=\"the-lora-follows-prompts-more-literally-than-base-ltx-precise-prompts-will-give-much-better-results\"><strong>The LoRA follows prompts more literally than base LTX — precise prompts will give much better results</strong></h2><h2 id=\"how-was-it-made\"><span style=\"color:rgb(92, 242, 195)\"><strong>How was it made?</strong></span></h2><h2 id=\"v0.1-and-v0.2-were-trained-on-360-videos-from-the-vbvr-(very-big-video-reasoning)-dataset-synthetic-task-videos-where-every-motion-is-precise-and-intentional.-no-concept-bleed-no-style-change-just-tighter-control.\"><span style=\"color:rgb(92, 242, 195)\">v0.1 and v0.2 were trained on 360 videos from the VBVR (Very Big Video Reasoning) dataset synthetic task videos where every motion is precise and intentional. No concept bleed, no style change, just tighter control.</span></h2><h2 id=\"based-on-the-paper-&quot;a-very-big-video-reasoning-suite&quot;-which-demonstrated-this-approach-on-wan-2.2.-i-noticed-that-lora-helped-prompt-following-and-temporal-consistency-a-ton-with-wan-so-i-am-training-this-version-for-ltx.\"><span style=\"color:rgb(92, 242, 195)\">Based on the paper</span><span style=\"color:rgb(92, 255, 87)\"><strong><u> </u></strong></span><a target=\"_blank\" rel=\"ugc\" href=\"https://arxiv.org/abs/2602.20159\"><span style=\"color:rgb(92, 255, 87)\"><strong><u>\"A Very Big Video Reasoning Suite\"</u></strong></span></a><span style=\"color:rgb(92, 242, 195)\"> which demonstrated this approach on Wan 2.2. I noticed that lora helped prompt following and temporal consistency a ton with wan so I am training this version for LTX.</span></h2><h2 id=\"what-does-it-actually-do\"><span style=\"color:rgb(92, 242, 195)\"><strong>What does it actually do?</strong></span></h2><h3 id=\"prompt-following-is-more-faithful-the-model-does-more-of-what-you-asked-instead-of-improvising\"><span style=\"color:rgb(92, 242, 195)\">Prompt following is more faithful — the model does more of what you asked instead of improvising</span></h3><h3 id=\"motion-is-more-deliberate-and-less-erratic\"><span style=\"color:rgb(92, 242, 195)\">Motion is more deliberate and less erratic</span></h3><h3 id=\"reduces-random-drift-and-wobble-in-scenes\"><span style=\"color:rgb(92, 242, 195)\">Reduces random drift and wobble in scenes</span></h3><h3 id=\"temporal-consistency-improved-actions-follow-logical-sequences\"><span style=\"color:rgb(92, 242, 195)\">Temporal consistency improved — actions follow logical sequences</span></h3><h2 id=\"what-it-doesn't-do:\"><span style=\"color:rgb(92, 242, 195)\"><strong>What it doesn't do:</strong></span></h2><h3 id=\"doesn't-change-visual-style\"><span style=\"color:rgb(92, 242, 195)\">Doesn't change visual style</span></h3><h3 id=\"doesn't-add-or-remove-capabilities-ltx-doesn't-already-have\"><span style=\"color:rgb(92, 242, 195)\">Doesn't add or remove capabilities LTX doesn't already have</span></h3><h3 id=\"not-a-motion-lora-stacks-with-motion-lora's\"><span style=\"color:rgb(92, 242, 195)\">Not a motion LoRA — stacks with motion LoRA's</span></h3><h2 id=\"training-details-for-v0.1-and-v0.2-(if-you-give-a-shit)\"><span style=\"color:rgb(92, 242, 195)\"><strong>Training details for v0.1 and v0.2 (if you give a shit)</strong></span></h2><h3 id=\"rank-32\"><span style=\"color:rgb(92, 242, 195)\">Rank 32</span></h3><h3 id=\"360-vbvr-synthetic-videos-at-512x512-81-frames-less-alot-less-than-1-million-but-still-a-shitload-to-train-on-this-is-very-slow-to-train-locally.\"><span style=\"color:rgb(92, 242, 195)\">360 VBVR synthetic videos at 512x512, 81 frames &lt;------Alot less than 1 million but still a shitload to train on this is very slow to train locally.</span></h3><h3 id=\"lr-1e-4-adamw8bit\"><span style=\"color:rgb(92, 242, 195)\">LR 1e-4, adamw8bit</span></h3><h2 id=\"training-details-for-v1\"><span style=\"color:rgb(92, 242, 195)\"><strong>Training details for V1</strong></span></h2><h3 id=\"training-videos-were-increased-to-4800\"><span style=\"color:rgb(92, 242, 195)\"><strong>Training videos were increased to 4800</strong></span></h3><h3 id=\"resolution-is-the-same-but-frames-were-increased-to-121\"><span style=\"color:rgb(92, 242, 195)\"><strong>Resolution is the same but frames were increased to 121</strong></span></h3><h3 id=\"every-other-setting-the-same-as-v0.1-and-v0.2\"><span style=\"color:rgb(92, 242, 195)\"><strong>Every other setting the same as v0.1 and v0.2</strong></span></h3><h2 id=\"more-training-data-from-the-vbvr-dataset-was-added-to-v1\"><span style=\"color:rgb(92, 242, 195)\">More training data from the VBVR dataset was added to v1</span></h2><h2 id=\"below-is-the-new-dataset-i-trained-on's-data-composition-if-your-curious\"><span style=\"color:rgb(21, 170, 191)\"><u>Below is the new dataset I trained on's data composition if your curious</u></span></h2><h2 id=\"tier-1-physics-and-motion-(3400-samples)\"><span style=\"color:rgb(21, 170, 191)\"><strong>Tier 1 — Physics and Motion (3,400 samples)</strong></span></h2><h2 id=\"core-generators-at-300-each:-g-11-(object-reappearance)-has-a-shape-move-off-screen-in-a-direction-and-return-along-the-same-path-teaches-trajectory-and-object-persistence.-g-25-(separate-object-spinning)-is-a-shape-that-rotates-in-place-then-translates-horizontally-to-a-target-position-multi-step-motion-sequencing.-g-33-(visual-jenga)-is-a-stack-of-objects-that-get-removed-one-by-one-from-top-to-bottom-sequential-extraction-with-implicit-physics-ordering.-o-29-(ballcolor)-is-ball-tracking-tasks-with-color-motion-following-plus-identity-preservation.-o-52-(traffic-light)-is-discrete-state-transitions-lights-switching-onoff-between-green-and-gray-teaches-the-model-that-state-changes-are-crisp-not-gradual.-o-75-(communicating-vessels)-is-fluid-equalizing-between-connected-tubes-based-on-pressure-continuous-physics-simulation-over-time.-o-87-(fluid-diffusion)-is-ink-spreading-in-water-another-continuous-physical-transformation-but-with-expansion-rather-than-equalization.\"><span style=\"color:rgb(21, 170, 191)\"><strong>Core generators at 300 each: </strong></span><code>G-11</code><span style=\"color:rgb(21, 170, 191)\"><strong> (object reappearance) has a shape move off-screen in a direction and return along the same path — teaches trajectory and object persistence. </strong></span><code>G-25</code><span style=\"color:rgb(21, 170, 191)\"><strong> (separate object spinning) is a shape that rotates in place then translates horizontally to a target position — multi-step motion sequencing. </strong></span><code>G-33</code><span style=\"color:rgb(21, 170, 191)\"><strong> (visual jenga) is a stack of objects that get removed one by one from top to bottom — sequential extraction with implicit physics ordering. </strong></span><code>O-29</code><span style=\"color:rgb(21, 170, 191)\"><strong> (ballcolor) is ball tracking tasks with color — motion following plus identity preservation. </strong></span><code>O-52</code><span style=\"color:rgb(21, 170, 191)\"><strong> (traffic light) is discrete state transitions, lights switching on/off between green and gray — teaches the model that state changes are crisp, not gradual. </strong></span><code>O-75</code><span style=\"color:rgb(21, 170, 191)\"><strong> (communicating vessels) is fluid equalizing between connected tubes based on pressure — continuous physics simulation over time. </strong></span><code>O-87</code><span style=\"color:rgb(21, 170, 191)\"><strong> (fluid diffusion) is ink spreading in water — another continuous physical transformation but with expansion rather than equalization.</strong></span></h2><h2 id=\"new-additions-at-250-each:-g-35-(hit-target-after-bounce)-is-a-ball-with-an-initial-direction-that-bounces-off-walls-following-reflection-laws-to-hit-a-target-pure-trajectory-prediction-with-physics-constraints.-o-30-(bookshelf)-is-book-rearrangement-on-shelves-the-specific-task-vbvr-highlighted-where-their-model-beat-sora-2.\"><span style=\"color:rgb(21, 170, 191)\"><strong>New additions at 250 each: </strong></span><code>G-35</code><span style=\"color:rgb(21, 170, 191)\"><strong> (hit target after bounce) is a ball with an initial direction that bounces off walls following reflection laws to hit a target — pure trajectory prediction with physics constraints. </strong></span><code>O-30</code><span style=\"color:rgb(21, 170, 191)\"><strong> (bookshelf) is book rearrangement on shelves — the specific task VBVR highlighted where their model beat Sora 2.</strong></span></h2><h2 id=\"multi-step-transforms-at-160-each:-o-7-(shape-color-change)-is-a-single-transformation-shape-changes-from-one-color-to-another.-o-8-(shape-rotation)-is-a-shape-rotating-by-a-specific-angle.-o-13-(outline-then-move)-is-two-sequential-steps:-change-a-shape's-outline-style-then-move-it-to-a-new-position.-o-14-(scale-then-outline)-is-also-two-steps:-scale-a-shape-up-or-down-then-change-its-outline.-these-four-together-teach-the-model-that-instructions-are-ordered-and-each-step-completes-before-the-next-begins.\"><span style=\"color:rgb(21, 170, 191)\"><strong>Multi-step transforms at 160 each: </strong></span><code>O-7</code><span style=\"color:rgb(21, 170, 191)\"><strong> (shape color change) is a single transformation — shape changes from one color to another. </strong></span><code>O-8</code><span style=\"color:rgb(21, 170, 191)\"><strong> (shape rotation) is a shape rotating by a specific angle. </strong></span><code>O-13</code><span style=\"color:rgb(21, 170, 191)\"><strong> (outline then move) is two sequential steps: change a shape's outline style, then move it to a new position. </strong></span><code>O-14</code><span style=\"color:rgb(21, 170, 191)\"><strong> (scale then outline) is also two steps: scale a shape up or down, then change its outline. These four together teach the model that instructions are ordered and each step completes before the next begins.</strong></span></h2><h2 id=\"tier-2-spatial-and-reasoning-(1420-samples)\"><span style=\"color:rgb(21, 170, 191)\"><strong>Tier 2 — Spatial and Reasoning (1,420 samples)</strong></span></h2><h2 id=\"proven-generators-at-100-each:-g-13-(grid-number-sequence)-is-filling-in-number-patterns-on-a-grid.-g-17-(grid-avoid-red-block)-is-pathfinding-on-a-grid-while-avoiding-obstacles.-g-31-(directed-graph-navigation)-is-finding-the-shortest-path-through-a-directed-graph.-g-41-(grid-highest-cost)-is-evaluating-spatial-values-on-a-grid-to-find-the-optimal-path.-o-24-(domino-chain)-is-a-sequential-cascade-where-dominoes-fall-until-they-hit-a-gap-teaches-causal-chains-and-stopping-conditions.-o-34-(dot-to-dot)-is-connecting-numbered-dots-in-sequence-ordered-drawing.-o-47-(sliding-puzzle)-is-tile-rearrangement-under-constraints-like-a-15-puzzle.-o-83-(planar-warp)-is-warping-a-grid-to-align-with-a-target-quadrilateral-geometric-transformation.\"><span style=\"color:rgb(21, 170, 191)\"><strong>Proven generators at 100 each: </strong></span><code>G-13</code><span style=\"color:rgb(21, 170, 191)\"><strong> (grid number sequence) is filling in number patterns on a grid. </strong></span><code>G-17</code><span style=\"color:rgb(21, 170, 191)\"><strong> (grid avoid red block) is pathfinding on a grid while avoiding obstacles. </strong></span><code>G-31</code><span style=\"color:rgb(21, 170, 191)\"><strong> (directed graph navigation) is finding the shortest path through a directed graph. </strong></span><code>G-41</code><span style=\"color:rgb(21, 170, 191)\"><strong> (grid highest cost) is evaluating spatial values on a grid to find the optimal path. </strong></span><code>O-24</code><span style=\"color:rgb(21, 170, 191)\"><strong> (domino chain) is a sequential cascade where dominoes fall until they hit a gap — teaches causal chains and stopping conditions. </strong></span><code>O-34</code><span style=\"color:rgb(21, 170, 191)\"><strong> (dot to dot) is connecting numbered dots in sequence — ordered drawing. </strong></span><code>O-47</code><span style=\"color:rgb(21, 170, 191)\"><strong> (sliding puzzle) is tile rearrangement under constraints, like a 15-puzzle. </strong></span><code>O-83</code><span style=\"color:rgb(21, 170, 191)\"><strong> (planar warp) is warping a grid to align with a target quadrilateral — geometric transformation.</strong></span></h2><h2 id=\"new-reasoning-diversity-at-130-each:-o-1-(color-mixing)-is-rgb-additive-mixing-where-two-light-sources-combine-and-the-result-fills-a-target-zone-rule-based-continuous-process.-o-33-(counting-objects)-is-exactly-what-it-sounds-like-count-things-correctly.-g-3-(stable-sort)-is-arranging-objects-by-a-rule-while-preserving-relative-order.-g-37-(symmetry-random)-is-completing-a-pattern-by-mirroring-across-an-axis.-o-21-(construction-blueprint)-is-fitting-a-correct-puzzle-piece-into-a-gap-in-a-structure.-g-44-(bfs)-is-breadth-first-search-traversal-of-a-graph-systematic-layer-by-layer-exploration.\"><span style=\"color:rgb(21, 170, 191)\"><strong>New reasoning diversity at 130 each: </strong></span><code>O-1</code><span style=\"color:rgb(21, 170, 191)\"><strong> (color mixing) is RGB additive mixing where two light sources combine and the result fills a target zone — rule-based continuous process. </strong></span><code>O-33</code><span style=\"color:rgb(21, 170, 191)\"><strong> (counting objects) is exactly what it sounds like — count things correctly. </strong></span><code>G-3</code><span style=\"color:rgb(21, 170, 191)\"><strong> (stable sort) is arranging objects by a rule while preserving relative order. </strong></span><code>G-37</code><span style=\"color:rgb(21, 170, 191)\"><strong> (symmetry random) is completing a pattern by mirroring across an axis. </strong></span><code>O-21</code><span style=\"color:rgb(21, 170, 191)\"><strong> (construction blueprint) is fitting a correct puzzle piece into a gap in a structure. </strong></span><code>G-44</code><span style=\"color:rgb(21, 170, 191)\"><strong> (BFS) is breadth-first search traversal of a graph — systematic layer-by-layer exploration.</strong></span></h2><h2 id=\"the-overall-dataset-is-weighted-roughly-7030-toward-physical-motion-and-transformation-tasks-over-abstract-spatial-reasoning-all-of-these-are-taken-from-the-vbvr-dataset-i-am-not-the-creator-of-the-dataset.-i'm-pretty-new-to-lora-training-so-if-you-have-tips-let-me-know.\"><span style=\"color:rgb(21, 170, 191)\"><strong>The overall dataset is weighted roughly 70/30 toward physical motion and transformation tasks over abstract spatial reasoning, All of these are taken from the VBVR dataset I am not the creator of the dataset. I'm pretty new to lora training so if you have tips let me know.</strong></span></h2><h2></h2><h2 id=\"remember-its-not-x-its-y.\"><span style=\"color:rgb(141, 171, 51)\">REMEMBER its not X, its Y.</span></h2><p></p><h2 id=\"!-if-you-post-things-in-the-gallery-that-violate-the-tos-i-will-block-and-report-you.-don't-do-this-shit.\"><span style=\"color:rgb(250, 82, 82)\"><strong>! If you post things in the gallery that violate the TOS I will block and report you. Don't do this shit.</strong></span></h2><p></p><h2 id=\"for-18+-only.-this-is-not-to-be-used-for-any-illegal-or-unethical-purposes-or-on-any-real-person-or-likeness.-don't-be-fucking-sus.\"><strong>For 18+ only. This is NOT to be used for any illegal or unethical purposes or on any real person or likeness. Don't be fucking sus.</strong></h2><p></p><p></p>",
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