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@@ -349,3 +349,6 @@ klein-9b/lora/Ultra[[:space:]]Real[[:space:]]-[[:space:]]Klein[[:space:]]9b/extr
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klein-9b/lora/BFS - Best Face Swap/extra_data-vid_2610018/model_dict-mid_2027766-vid_2610018.json

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-      "description": "<p>This model is the result of 3 weeks of non-stop R&amp;D and infinite testing of <strong>IC-LoRA</strong> for LTX-2. To achieve this, I <strong>heavily modified the official LTX-2 training scripts</strong>, adding novel conditioning methods and a complete overhaul of the noise scheduling logic (**custom High-Noise Power Law distribution**) to force the model to prioritize target identity over the guide video's context. I went through dozens of iterations, resulting in over 300GB of experimental files and 60+ hours of training on <strong>NVIDIA RTX PRO 6000 Blackwell</strong> GPUs.</p><p><strong>Dataset Specifications:</strong></p><p>* <strong>Data:</strong> 300 high-quality head swap video pairs.</p><p>* <strong>Base Resolution:</strong> Trained on <strong>512x512</strong> buckets.</p><p>* <strong>Aspect Ratio:</strong> Primarily <strong>Landscape</strong> data. Horizontal videos will likely yield significantly better results than portrait ones.</p><p>* <strong>Framing:</strong> Optimized for <strong>Close-ups</strong> (face near the camera). Wide shots may result in lower identity fidelity.</p><p><strong>Inference &amp; Tuning Tips:</strong></p><p>* <strong>Custom Workflow Required:</strong> You <strong>MUST</strong> use my custom workflow. It expects a video where <strong>Frame 0</strong> has already been face-swapped (even a rough cutout works) to act as the identity anchor. I suggest using my previous <strong>BFS Image Models</strong> to prepare Frame 0.</p><p>* <strong>LoRA Strength Tuning:</strong></p><p>* <strong>Strength 1.0:</strong> Best for motion fidelity. Keeps the movement as close as possible to the guide video.</p><p>* <strong>Strength &gt; 1.0:</strong> Improves hair capture and identity from Frame 0 but causes the model to deviate from the guide video's original motion.</p><p>* <strong>Multi-pass Workflows:</strong> You can try to improve results by creating <strong>multi-pass workflows</strong>, running the LoRA at different strengths to refine the output.</p><p>* <strong>Prompting:</strong> Detailed prompts currently have <strong>no effect</strong>. The LoRA was trained with a single unique trigger and does not respond to scene or subject descriptions yet.</p><p><strong>Known Issues (Alpha Release):</strong></p><p>* <strong>Identity Leakage:</strong> The model may occasionally default back to the original hair from the guide video.</p><p>* <strong>Hard Cuts:</strong> Jump cuts break the temporal attention chain. If a character disappears and reappears, the model might \"reset\" to the original guide video features.</p><p>Workflow Link: <a target=\"_blank\" rel=\"ugc\" href=\"https://huggingface.co/Alissonerdx/BFS-Best-Face-Swap-Video/blob/main/workflows/workflow_ltx2_head_swap_drag_and_drop.json\">workflows/workflow_ltx2_head_swap_drag_and_drop.json · Alissonerdx/BFS-Best-Face-Swap-Video at main</a></p><p><strong>Future &amp; Support:</strong></p><p>I still have many other trained versions to test. If I find an iteration that performs better than this one, I will release it here. Maintaining R&amp;D and renting Blackwell GPUs is expensive—if you find this LoRA useful, please consider a donation. Every bit helps!</p><p>👉 <strong>Donate here:</strong> <a target=\"_blank\" rel=\"ugc\" href=\"https://buymeacoffee.com/nrdx\">https://buymeacoffee.com/nrdx</a></p>",
+      "description": "<p>V3 introduces a new <strong>persistent-template conditioning workflow</strong>.</p><p>Unlike previous versions, which relied primarily on the identity being established from <strong>Frame 0 only</strong>, V3 uses a <strong>custom guide-video construction step</strong> that keeps the new face visible throughout the entire guide sequence.</p><p>This results in a much stronger and more persistent identity signal during inference.</p><p>You can choose between the two models I released and see which one you think is better.</p><p>Youtube Video 40 minutes in Portuguese:</p><p><a target=\"_blank\" rel=\"ugc\" href=\"https://www.youtube.com/watch?v=HBp03iu7wLA\">LTX-2.3 Head Swap (IC LoRA)</a></p><p>Models:</p><p><a target=\"_blank\" rel=\"ugc\" href=\"https://huggingface.co/Alissonerdx/BFS-Best-Face-Swap-Video/tree/main/ltx-2.3\">Alissonerdx/BFS-Best-Face-Swap-Video at main</a></p><p>Workflow:</p><p><a target=\"_blank\" rel=\"ugc\" href=\"https://huggingface.co/Alissonerdx/BFS-Best-Face-Swap-Video/blob/main/workflows/workflow_ltx2_head_swap_drag_and_drop_v3.0.json\">workflows/workflow_ltx2_head_swap_drag_and_drop_v3.0.json · Alissonerdx/BFS-Best-Face-Swap-Video at main</a><br /></p><p>🙏 Acknowledgements<br /><br />Special thanks to <a target=\"_blank\" rel=\"ugc\" href=\"http://facy.ai\"><strong>facy.ai</strong></a> for sponsoring the GPU used to train this model.<br /><br />If you want to check their platform, you can use my referral link:<br /><br /><a target=\"_blank\" rel=\"ugc\" href=\"https://facy.ai/a/headswap\">https://facy.ai/a/headswap</a></p><p><br />Because the identity reference stays visible during the full guide sequence, V3 gives the model a much more stable conditioning signal across time.</p><p>In practice, this can improve:</p><ul><li><p>Identity consistency</p></li><li><p>Temporal stability</p></li><li><p>Resistance to identity drift</p></li><li><p>Facial motion continuity</p></li><li><p>Lip sync behavior</p></li><li><p>Expressive facial movement preservation</p></li></ul><p>This version is especially useful for shots where the face remains visible for longer periods, or where dialogue, mouth movement, and facial acting matter more.</p><p>V3 is not just a refinement of the first-frame method. It changes the conditioning logic by giving the model access to a persistent identity template across the entire inference sequence.</p>",
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+  "remixOfId": null
+}

klein-9b/lora/SamsungCam UltraReal/extra_data-vid_2777498/124345026.json

@@ -0,0 +1,28 @@
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+  "onSite": false,
+  "remixOfId": null
+}

klein-9b/lora/SamsungCam UltraReal/extra_data-vid_2777498/124354245.json

@@ -0,0 +1,29 @@
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klein-9b/lora/SamsungCam UltraReal/extra_data-vid_2777498/model_dict-mid_1551668-vid_2777498.json

@@ -0,0 +1,1016 @@
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+  "name": "SamsungCam UltraReal",
+  "description": "<p>--Qwen2512<br /><span style=\"background-color:rgb(26, 27, 30);color:rgb(193, 194, 197);font-family:-apple-system, BlinkMacSystemFont, &quot;Segoe UI&quot;, Roboto, Helvetica, Arial, sans-serif, &quot;Apple Color Emoji&quot;, &quot;Segoe UI Emoji&quot;;font-size:16px\">Trained with the best my technique. Best use with NiceGirls lora (if u need better female). Also works good with other loras + works good with some nsfw loras </span><br /><br />--Qwen<br />I have prepared a version for Qwen. It looks quite promising; however, work on its enhancement is still ongoing. Examples with girls i generated with my lora NiceGirls</p><p>P.S: yeah, I changed epoch in models manager, cause 7k steps gave overtrained result, and in process of testing I understood that 3k is enough <br /><br /><br />🗺️ Backstory</p><p>Lately I kept getting DMs like “When will you drop an iPhone‑style LoRA?” Problem: iPhone LoRA already lives on Civitai—and, well, I shoot on Samsung. SamsungCam UltraReal captures the S‑series look you can’t fake with generic “phone” prompts</p><p>After some tests i noticed that the best to use with UltraReal Fine-tune or UltraReal Abliterated with 60 steps (yeah, so much steps gives pretty good details, also text usually looks much better).<br />Use 0.7-1.0 weight (depends on artifacts in scene)</p><hr /><p>🎨 What it brings</p><ul><li><p><strong>Vivid, high‑contrast colors</strong></p></li><li><p><strong>Smartphone softness</strong> – just enough in‑camera smoothing to feel “real phone,” not plastic CGI.</p></li><li><p><strong>Highlight‑happy HDR</strong> – clipped whites around street lamps for that over‑exposed‑yet‑cinematic pop.</p></li><li><p><strong>Less bokeh, more context</strong></p></li></ul><hr /><p>🤝 NSFWloras-friendly<br />Tested with <strong>NippleDiffusion</strong> and <strong>MysticXXX (For some reasons is not visible in Suggested Resources)</strong><br /><br />You can try to generate in 2MP for more realism and details, but be ready that in some scenes hands will be broken. But still looks very good imo in usual 1MP.<br />In most scenes removes blurred background (sometimes it could be still blurred). Also improves details a lot even on default <a target=\"_blank\" rel=\"ugc\" href=\"http://flux.dev\">flux.dev</a> (cause mostly i train all loras for my <a target=\"_blank\" rel=\"ugc\" href=\"https://civitai.com/models/978314?modelVersionId=1413133\"><strong>ultrareal fine-tune checkpoint</strong></a><br /><br /><strong>P.S: yeah, first version is slightly overtrained, but i glad with a result. v2.0 already WIP</strong></p>",
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