Archive PoC (v0.1) release into _old/ ahead of v1.0 (#1)

- Archive PoC (v0.1) release into _old/ ahead of v1.0 (8f192c57453715373131a6e7f648fc0b19bc0884)
- Add v1.0 model card, workflow, license, demo videos, and 3 checkpoints (step 5000/7000/9000) (20d960a34f123e3a925c3a1736e92299b8c74c9b)
- Only reference Burgstall-VR-Outpaint.json as the workflow; drop duplicate filename (3578387f997a8cc349b66ae6022bec9a195348a7)

.gitattributes

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 samples/clip3-eq.mp4 filter=lfs diff=lfs merge=lfs -text
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 samples/clip3-sweep.mp4 filter=lfs diff=lfs merge=lfs -text
+_old/samples/clip1-eq.mp4 filter=lfs diff=lfs merge=lfs -text
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+jungle.mp4 filter=lfs diff=lfs merge=lfs -text
+junglePAN.mp4 filter=lfs diff=lfs merge=lfs -text
+jungleVR.mp4 filter=lfs diff=lfs merge=lfs -text
+office.mp4 filter=lfs diff=lfs merge=lfs -text
+officepan.mp4 filter=lfs diff=lfs merge=lfs -text
+officeVR.mp4 filter=lfs diff=lfs merge=lfs -text
+skate.mp4 filter=lfs diff=lfs merge=lfs -text
+skatepan.mp4 filter=lfs diff=lfs merge=lfs -text
+skateVR.mp4 filter=lfs diff=lfs merge=lfs -text

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         "id": 13743,
         "parentId": 63
+      },
+      {
+        "id": 13756,
+        "parentId": 9
       }
     ]
   },

LICENSE

@@ -0,0 +1,283 @@
+# VR-360-Outpaint 1.0 — License
+
+This model is released under **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)**.
+
+## Commercial licensing
+
+The CC BY-NC 4.0 license permits use for **non-commercial purposes only**. If you would like to use
+VR-360-Outpaint in a commercial product, service, or workflow, please contact me for a commercial
+license:
+
+> **Juho Alatalo**
+> 📧 juho.alatalo@gmail.com
+
+I'm happy to discuss reasonable terms for indie creators, small studios, and enterprise deployments alike.
+
+---
+
+## Creative Commons Attribution-NonCommercial 4.0 International
+
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README.md

@@ -1,144 +1,306 @@
 ---
 license: cc-by-nc-4.0
-base_model: Lightricks/LTX-2.3-22B
-library_name: diffusers
+library: diffusers
+pipeline_tag: video-to-video
+tags:
+  - ltx-2.3
+  - ic-lora
+  - lora
+  - "360"
+  - equirectangular
+  - vr
+  - outpainting
+  - video
+pretty_name: VR-360-Outpaint 1.0
 ---
 
-# LTX-2.3 — 360° Equirectangular Outpainting IC-LoRA · **v0.1**
+# VR-360-Outpaint 1.0 — LTX-2.3 IC-LoRA
 
-**Proof-of-concept** IC-LoRA adapter for [Lightricks/LTX-2.3-22B](https://huggingface.co/Lightricks) that
-outpaints standard widescreen footage into a full **360° equirectangular** projection for immersive/VR viewing.
+**360° equirectangular video outpainting.** Give it a flat (gnomonic) video crop on a black
+equirectangular canvas, and it fills the unknown regions to produce a full **360° equirectangular
+video** suitable for VR / immersive playback.
 
-> This is an **early v0.1 release**. Expect rough edges, limited subject variety, and inconsistent
-> coherence outside the sweet spot described below. A new version with a much larger, more diverse
-> dataset is planned.
+Built on `Lightricks/LTX-2.3-22B` via **IC-LoRA** (video-to-video conditioning). This is the production "v1.0" run — a fully retrained IC-LoRA, multi-reference augmentation, and mixed-resolution buckets.
 
 ---
 
-## Samples
+## How it works: the gnomonic projection trick
 
-Three clips from the model. `sweep` is a rendered 2D camera pan through the
-360° output — the easiest way to judge results without a VR player.
-`fl-eq` shows the flat source and the equirect output side-by-side.
+The core insight behind VR-360-Outpaint — first developed in **April 2026** by yours truly — is that a video diffusion
+model can struggle with equirectangular geometry. Asking it to "extend this flat crop into 360°" without geometric guidance is a near-impossible task: the model must simultaneously understand both the
+spherical projection mapping *and* the outpainting task.
 
-### Clip 1 — sweep
+The solution that I came up with was to provide the model with a **forward gnomonic (rectilinear) projection** of the input
+video, composited onto the equirectangular canvas at the correct angular position and field of view. This
+gives the model an explicit geometric nudge — it can see exactly where and how the known pixels sit on
+the sphere, and what the local rectilinear perspective looks like. The outpainting task then becomes:
+*"given this correctly-projected window into the sphere, complete the rest of the equirectangular frame
+in a way that is consistent with it."*
 
-<video controls loop muted playsinline src="https://huggingface.co/TheBurgstall/VR-360-Outpaint-LTX2.3-IC-LoRA/resolve/main/samples/clip1-sweep.mp4"></video>
+This geometric conditioning is applied identically in **both training and inference** — every training
+pair was a 360° video paired with a gnomonic crop of a specific FOV and aspect ratio
+on the canvas, and the model learned to recover the full equirectangular frame from that partial view.
+At inference time, the **[ComfyUI-VR-Outpaint-Tools](https://github.com/Burgstall-labs/ComfyUI-VR-Outpaint-Tools)**
+node pack performs the same projection from your flat footage, now also including automatic FOV estimation via
+the **GeoCalib** node.
 
-Side-by-side (flat input · equirect output):
+Without this geometric scaffolding, the model would have to discover spherical projection rules from
+its core training — although LTX2.3 obviously has some equirectangular material in its core training data, this still presents a vastly harder learning problem. The gnomonic projection provides the inductive bias that
+makes VR outpainting tractable.
 
-<video controls loop muted playsinline src="https://huggingface.co/TheBurgstall/VR-360-Outpaint-LTX2.3-IC-LoRA/resolve/main/samples/clip1-fl-eq.mp4"></video>
+---
 
-### Clip 2 — sweep
+## Quick start
 
-<video controls loop muted playsinline src="https://huggingface.co/TheBurgstall/VR-360-Outpaint-LTX2.3-IC-LoRA/resolve/main/samples/clip2-sweep.mp4"></video>
+1. **Base model:** `Lightricks/LTX-2.3-22B` (dev safetensors)
+2. **Load this LoRA** on top via the LTX-2 `video_to_video` pipeline (ComfyUI recommended)
+3. **Trigger word:** `equirectangular`
+4. **Caption (the only one the model knows):**
+   > `360 degree equirectangular panorama, high quality, detailed`
+5. **Resolution:** 1920×960, 121 frames @ 24 fps
+6. **Workflow:** Use the included [`Burgstall-VR-Outpaint.json`](./Burgstall-VR-Outpaint.json) in ComfyUI
+7. **Companion tools:** [ComfyUI-VR-Outpaint-Tools](https://github.com/Burgstall-labs/ComfyUI-VR-Outpaint-Tools)
+   provides the gnomonic projection nodes and the **GeoCalib FOV estimation** node for automatic
+   field-of-view detection from your flat footage — no manual FOV entry needed.
 
-Side-by-side:
+---
 
-<video controls loop muted playsinline src="https://huggingface.co/TheBurgstall/VR-360-Outpaint-LTX2.3-IC-LoRA/resolve/main/samples/clip2-fl-eq.mp4"></video>
+## Examples
+
+Each example shows the flat input video, a slow gnomonic pan across the outpainted 360° result
+(a virtual camera sweeping through the scene), and the raw equirectangular output.
+
+<table>
+<tr>
+<td align="center"><b>Flat input</b></td>
+<td align="center"><b>Pan across the result</b></td>
+<td align="center"><b>Raw equirectangular output</b></td>
+</tr>
+<tr>
+<td><video src="jungle.mp4" width="260" controls muted loop></video></td>
+<td><video src="junglePAN.mp4" width="260" controls muted loop></video></td>
+<td><video src="jungleVR.mp4" width="260" controls muted loop></video></td>
+</tr>
+<tr>
+<td><video src="office.mp4" width="260" controls muted loop></video></td>
+<td><video src="officepan.mp4" width="260" controls muted loop></video></td>
+<td><video src="officeVR.mp4" width="260" controls muted loop></video></td>
+</tr>
+<tr>
+<td><video src="skate.mp4" width="260" controls muted loop></video></td>
+<td><video src="skatepan.mp4" width="260" controls muted loop></video></td>
+<td><video src="skateVR.mp4" width="260" controls muted loop></video></td>
+</tr>
+</table>
 
-### Clip 3 — sweep
+---
 
-<video controls loop muted playsinline src="https://huggingface.co/TheBurgstall/VR-360-Outpaint-LTX2.3-IC-LoRA/resolve/main/samples/clip3-sweep.mp4"></video>
+## Which checkpoint?
 
-Side-by-side:
+Three checkpoints are provided (`.safetensors`, ~1.3 GB each). They all produce valid equirectangular
+output; pick based on your priorities:
 
-<video controls loop muted playsinline src="https://huggingface.co/TheBurgstall/VR-360-Outpaint-LTX2.3-IC-LoRA/resolve/main/samples/clip3-fl-eq.mp4"></video>
+| Checkpoint | Step | Character |
+|------------|------|-----------|
+| **`360vroutpaint_v2_step07000.safetensors`** | 7000 | **Recommended.** Best balance of equirectangular consistency and scene variety |
+| `360vroutpaint_v2_step05000.safetensors` | 5000 | Slightly more creative outpainting, but the 360° wrap can be less seamless |
+| `360vroutpaint_v2_step09000.safetensors` | 9000 | Tightest equirectangular consistency, but tends toward more uniform / monotonic scenery |
 
-Raw equirectangular outputs (load in a 360° player for VR):
-[clip1-eq.mp4](./samples/clip1-eq.mp4) ·
-[clip2-eq.mp4](./samples/clip2-eq.mp4) ·
-[clip3-eq.mp4](./samples/clip3-eq.mp4)
+All three are fully converged. The
+differences are subtle; if unsure, start with step 7000.
 
-## What it does
+---
 
-- **Input**: a flat 2.39:1 (cinemascope) clip and a matching equirectangular *reference* (the input
-  projected into the equirect canvas, with the unknown regions left masked/black)
-- **Output**: the model fills in the masked regions, turning the flat shot into a plausible
-  360° equirectangular video that can be viewed in a VR/360 player
+## Training
 
-Intended for transforming existing live-action or cinematic footage into immersive content.
+| Parameter | Value |
+|-----------|-------|
+| **Base model** | LTX-2.3-22B (dev) |
+| **Strategy** | IC-LoRA (`video_to_video`, `first_frame_conditioning_p: 0.0`) |
+| **LoRA rank / alpha** | 128 / 128 |
+| **Target modules** | Self-attn (q, k, v, out) + cross-attn (q, k, v, out) + FFN (`ff.net.0.proj`, `ff.net.2`) |
+| **Trainable params** | 654M |
+| **Resolution** | Mixed buckets: 1024×512×121 + 1920×960×49 |
+| **Frame rate** | 24 fps |
+| **Optimizer** | Prodigy (D-Adaptation), lr=1.0, decoupled weight decay, **constant** schedule |
+| **Precision** | bf16 (weights + Gemma text encoder), gradient checkpointing ON |
+| **Batch / accum** | 1 / 1 |
+| **Steps trained** | 9000 (converged by ~3000, stopped on plateau) |
+| **Hardware** | 1× RTX PRO 6000 Blackwell (96 GB), compute sponsored by [Lightricks](https://www.lightricks.com/) |
+| **Caption** | `360 degree equirectangular panorama, high quality, detailed` |
 
-## Sweet spot (v0.1)
+### Dataset
 
-The v0.1 model was tuned toward a deliberately narrow domain to validate the approach:
+The model was trained on **13,281 pairs**, sampled from a master dataset of **53,124 augmented
+video pairs** built from **4,427 unique 360° clips** sourced from 51 CC-BY YouTube channels.
 
-- **Semi-static establishing city / urban scenes** (no heavy camera motion)
-- **~100° horizontal field of view** in the source clip
-- **2.39:1 source aspect** (standard cinemascope)
+Each of the 4,427 clips was rendered into **12 reference variants** — the combination of:
 
-It will **generalize poorly** outside these conditions — fast action, extreme close-ups, heavily
-stylised imagery, or very different FOVs are not reliably handled yet.
+- **4 fields of view:** 70°, 90°, 110°, 130°
+- **3 aspect ratios:** 1.33 (4:3), 1.78 (16:9), 2.39 (cinemascope)
 
-## Files
+From these 12 variants per clip, **3 distinct FOVs** were assigned per target (globally balanced across
+all 12 FOV×aspect cells, ~1,107 pairs each), and the trainer randomly sampled one of the 3 at each step.
+A per-clip random yaw shift (0–1920 px) exploits equirectangular rotational symmetry, effectively
+multiplying each scene into many independent viewpoints and keeping operators / tripods off a fixed azimuth.
 
-| File | What it is |
-|---|---|
-| `ltx-2.3-22b-ic-lora-360-equirect-poc-step3500.safetensors` | The LoRA weights (final step 3500 checkpoint, ~1.3 GB) |
-| `Equirect-Outpaint.json` | Reference ComfyUI workflow wired end-to-end for this LoRA |
-| `samples/clipN-fl-eq.mp4` | Flat input + equirect output side-by-side (3626×960) |
-| `samples/clipN-eq.mp4` | Raw equirectangular output (1920×960) — load in a 360° player |
-| `samples/clipN-sweep.mp4` | 2D camera sweep through the 360° output (1920×1080) for quick preview without a VR player |
+All clips in the master dataset were normalized to 1920×960 at 24 fps, 121 frames (~5 seconds).
 
-Three sample clips (`clip1`, `clip2`, `clip3`) are included under `samples/`.
+The training dataset is **private** and not distributed with these weights.
 
-## Usage
+### Validation
 
-Load on top of `ltx-2.3-22b-dev.safetensors` with the LTX-2 `video_to_video` pipeline and pass:
+153 held-out clips from 6 diverse non-CC sources (urban day/night, indoor, nature, action, aurora).
+Eval-only — never trained on. Validation is sample-generation only (no held-out val-loss exists).
 
-- **Trigger word**: `equirectangular` (works without any trigger word or prompt too, but with a descriptive prompt you can direct the content of outpainted part)
-- **Reference video**: your source clip projected into the equirect canvas with unknown regions masked
-- **Resolution**: 1920x960, 121 frames, 24 fps
+---
 
-Only tested in ComfyUI with the workflow available in this repo. Please note that the workflow's padding node crops your input footage to 2.39:1, you can select whether it's cut from center, top or bottom. Other aspect ratios will work poorly in this early version.
+## Convergence evidence
 
-### Companion tooling
+Loss is a poor signal for IC-LoRA. I tracked three independent metrics; all agree the model converged
+by step 3000 and plateaued through 9000:
 
-A small ComfyUI helper pack — **[ComfyUI-EquirectProjector](https://github.com/Burgstall-labs/ComfyUI-EquirectProjector)** —
-was written alongside this LoRA to produce the masked equirectangular reference from a flat clip.
-Pair it with the standard LTX-2 video-to-video nodes. The `Equirect-Outpaint.json` workflow in this
-repo shows the exact wiring.
+**1. 360° seam continuity** (`seam_ratio` — wrap-edge MSE ÷ mean adjacent-column MSE, lower is better):
 
-## Training (v0.1)
+| Step | Seam ratio |
+|------|-----------|
+| 1000 | 59.7 |
+| 2000 | 33.4 |
+| **3000** | **6.1** ← converged |
+| 5000 | 9.3 |
+| 7000 | 5.1 |
+| 9000 | 5.6 |
 
-| | |
-|--|--|
-| Base model | LTX-2.3-22B (dev) |
-| Strategy | IC-LoRA (`video_to_video`) |
-| Rank / alpha | 128 / 128 |
-| Target modules | video self+cross attention + FFN |
-| Resolution | 1024×512, 41 frames @ 24 fps |
-| Optimizer | Prodigy (D-Adaptation), lr=1.0, constant |
-| Precision | bf16, gradient checkpointing |
-| Steps | 3500 |
-| Hardware | 1× NVIDIA H100 80GB |
-| Dataset | Small curated POC set (not released) — semi-static city establishing clips |
+The wrap seam dropped from ~60 to ~6 in 3000 steps and stayed
+in the 5–9 band (noise floor) through 9000. The residual ~5–6 is a data/config limit, not fixable
+by more steps.
 
-The final **step 3500** checkpoint is shipped here. Intermediate checkpoints were used for
-validation during training but aren't included in this release.
+**2. Weight delta** (relative ‖Wₙ − Wₙ₋₅₀₀‖):
+- Early: 0.15–0.17 (high, noisy learning)
+- Step 5000: 0.137
+- Step 7500+: 0.126–0.135 (stable plateau — no continued learning)
 
-## What's next
+**3. Visual inspection:** Side-by-side comparisons at steps 5000, 7000, and 9000 are
+visually near-identical for most scenes, with mostly just subtle sharpening differences as well as negligible differences in seam.
 
-Next version is planned on a significantly larger and more diverse dataset covering:
+---
 
-- Broader subject matter (interiors, landscapes, crowds, vehicles, …)
-- Varied input FOVs and focal lengths
-- A wider range of camera motion — not just static establishing shots
-- Better handling of the polar regions (top/bottom caps of the equirect canvas)
+## Sweet spot
+
+The model was tuned on and performs best with:
+
+- **Content:** Semi-static establishing shots — cityscapes, urban scenes, landscapes
+- **FOV:** ~90–110° horizontal (the training distribution center)
+- **Aspect ratio:** 1.78–2.39 (standard widescreen to cinemascope)
+- **Camera motion:** Slow pans, gentle dolly, static tripod
+
+It will **generalize** beyond these conditions — that's why I trained across 4 FOVs and 3 aspect
+ratios — but quality degrades gracefully as you move further from the training distribution.
+
+---
 
 ## Limitations
 
-- Does not model the top/bottom caps of the sphere well — expect stretching or repetition
-- Struggles with busy motion and fast cuts
-- Prompt adherence is weak; conditioning is dominated by the reference video
-- Outputs are not a substitute for natively captured 360 footage — this is a creative
-  re-projection, not a reconstruction
+- **Top & bottom caps** (zenith / nadir of the sphere) are the hardest regions; the model has less
+  signal there. Avoid critical detail near the poles.
+- **Busy motion** (fast cuts, whip pans, handheld shake) easily break the wrap seam.
+- **Prompt adherence** — conditioning is dominated by the reference video, not text. The base caption / prompt is good to keep there, but you can describe the elements that you want there to be in the outpainted area to guide it. It will outpaint with the base prompt only, too.
+- **Narrow domain** — trained on outdoor / architectural scenes. Interiors, crowds, close-ups,
+  and organic subjects might work less reliably, although these draw strongly from the model's base training.
+
+
+---
+
+## Companion tools
+
+- **[ComfyUI-VR-Outpaint-Tools](https://github.com/Burgstall-labs/ComfyUI-VR-Outpaint-Tools)** —
+  A ComfyUI node pack providing:
+  - **Gnomonic projection nodes** for converting flat footage into the masked equirectangular
+    reference this LoRA expects (FOV selection, projection, canvas compositing)
+  - **GeoCalib FOV estimation node** for automatic field-of-view detection from your footage —
+    no manual measurement needed
+  - Formerly named *ComfyUI-EquirectProjector*; renamed after expanding scope beyond the original idea of just forward gnomonic projection.
+- **`Equirect-Outpaint.json`** (included in this repo) — End-to-end ComfyUI workflow wiring the
+  tools → LTX-2.3 + this LoRA → output.
+
+> ⚠️ **Repo re-star request:** The node pack was renamed from `ComfyUI-EquirectProjector` to
+> `ComfyUI-VR-Outpaint-Tools`, and in the process I accidentally turned the repo private for a
+> moment — which resets the star count to zero. If you starred it before, I'd really appreciate
+> it if you could **drop by and re-star it** if you appreciate this work. Every one helps! ⭐
+
+---
+
+## What's next
+
+I am currently in the process of developing a seamless equirectangular video generation method. Stay tuned.
+
+---
 
 ## Acknowledgements
 
-- First part of the training done at ADOS Paris event in collaboration with Cseti (https://huggingface.co/Cseti), NebSH (https://huggingface.co/Nebsh) and S4f3ty_Marc (https://huggingface.co/s4f3tymarc). Good times, thank you guys.
-- Advice from oumoumad (https://huggingface.co/oumoumad) in IC-LoRA training altogether has been very valuable to me. The workflow included in this release is modified from oumoumad's IC-LoRA workflow.
+- **Compute sponsorship** by [Lightricks](https://www.lightricks.com/) — the LTX team graciously
+  provided the GPU hardware for this training run. Special thanks to **Ofir Bibi** for technical
+  guidance and advice throughout the project.
+- Initial PoC training done at ADOS Paris 2026 with collaborators **Cseti**, **NebSH**, and **S4f3ty_Marc**. Thank you for your collaboration and support.
+- IC-LoRA training methodologies and multi-reference dataset design advised by **oumoumad**.
+- Built on the [Lightricks LTX-2.3](https://huggingface.co/Lightricks/LTX-2.3-22B) foundation model.
 
-## License
+---
 
-CC BY-NC 4.0
+## Training data attribution
+
+The model was trained on 360° video clips from the following YouTube creators, all shared under
+**Creative Commons Attribution (CC-BY)** license. Thank you to each of them for making their work
+available for reuse.
+
+| Creator | Clips | Channel |
+|---------|-------|---------|
+| Luxury Travel VR | 5 | [@LuxuryTravelVR](https://www.youtube.com/@LuxuryTravelVR) |
+| Watchers Club | 4 | [@watchersclub](https://www.youtube.com/@watchersclub) |
+| Naomi Wu | 3 | [@Naomi-Wu](https://www.youtube.com/@Naomi-Wu) |
+| whereisnoa | 3 | [@whereisnoa](https://www.youtube.com/@whereisnoa) |
+| Self-following 360° shooting [SNOWBIRD] | 2 | [@360snowbird](https://www.youtube.com/@360snowbird) |
+| Travel Smart Seniors | 2 | [@travelsmartseniors](https://www.youtube.com/@travelsmartseniors) |
+| HaoRyu32 | 2 | [@haoryu3233](https://www.youtube.com/@haoryu3233) |
+| VR Tours 360 | 2 | [@VRTours360](https://www.youtube.com/@VRTours360) |
+| 360VR Tours | 2 | [@360vr_Tours](https://www.youtube.com/@360vr_Tours) |
+| PAV360 VR tour | 2 | [@PAV360](https://www.youtube.com/@PAV360) |
+| Snicel 69 | 2 | [@Snicel69](https://www.youtube.com/@Snicel69) |
+| Heyman Chan | 1 | [@heymanchan7829](https://www.youtube.com/@heymanchan7829) |
+| Hike360 | 1 | [@Hike360](https://www.youtube.com/@Hike360) |
+| TokyoNoob | 1 | [@TokyoNoob](https://www.youtube.com/@TokyoNoob) |
+| Switchflick Productions | 1 | [@SwitchflickProductions](https://www.youtube.com/@SwitchflickProductions) |
+| Dan Lockhart | 1 | [@DanLockhart1](https://www.youtube.com/@DanLockhart1) |
+| 360° Attractions — Immersive Experiences | 1 | [@FUNFAIRRIDES360](https://www.youtube.com/@FUNFAIRRIDES360) |
+| Tucson Arizona Real Estate | 1 | [@tucsonarizonarealestate](https://www.youtube.com/@tucsonarizonarealestate) |
+| LearningNowHere sg | 1 | [@learningnowheresg4316](https://www.youtube.com/@learningnowheresg4316) |
+| VR 360 Vídeo | 1 | [@vr360video2](https://www.youtube.com/@vr360video2) |
+| Captain Steve Plays Vlogs Talks | 1 | [@CaptainStevePlaysVlogsTalks](https://www.youtube.com/@CaptainStevePlaysVlogsTalks) |
+| Eduard Moraru | 1 | [@Enygma2002](https://www.youtube.com/@Enygma2002) |
+| Day1 Walks | 1 | [@day1_walks](https://www.youtube.com/@day1_walks) |
+| Alliks82 | 1 | [@Alliks82](https://www.youtube.com/@Alliks82) |
+| BigT 360 | 1 | [@BigT_360](https://www.youtube.com/@BigT_360) |
+| PIXEO VR | 1 | [@pixeovr](https://www.youtube.com/@pixeovr) |
+| Real Madrid | 1 | [@realmadrid](https://www.youtube.com/@realmadrid) |
+| Jonathan Sadler | 1 | [@jonnysadler4064](https://www.youtube.com/@jonnysadler4064) |
+| N Flight Video | 1 | [@nflightvideo7787](https://www.youtube.com/@nflightvideo7787) |
+| This Way Up Travel | 1 | [@ThisWayUpTravel](https://www.youtube.com/@ThisWayUpTravel) |
+| Top Virtual Tours | 1 | [@topvirtualtours](https://www.youtube.com/@topvirtualtours) |
+| Train H | 1 | [@Train_H](https://www.youtube.com/@Train_H) |
+| He Spoke Style | 1 | [@hespokestyle](https://www.youtube.com/@hespokestyle) |
+| 3D-VR-360 Videos | 1 | [@D-VR-VIDEOSREALIDADVIRTUAL](https://www.youtube.com/@D-VR-VIDEOSREALIDADVIRTUAL) |
+| Virtual Trip | 1 | [@virtualtrip4316](https://www.youtube.com/@virtualtrip4316) |
+| MsBlackoreanlady | 1 | [@MsBlackoreanlady](https://www.youtube.com/@MsBlackoreanlady) |
+| Frontline Ulster | 1 | [@frontlineulster](https://www.youtube.com/@frontlineulster) |
+| Radu Ciocan | 1 | [@RaduCiocan](https://www.youtube.com/@RaduCiocan) |
+| Pogiboy Productions | 1 | [@PogiboyProductions](https://www.youtube.com/@PogiboyProductions) |
+| AlexTravel360 | 1 | [@AlexTravel360](https://www.youtube.com/@AlexTravel360) |
+| Glen Park | 1 | [@o2warp](https://www.youtube.com/@o2warp) |
+| Joni Gritzner | 1 | [@jonigr](https://www.youtube.com/@jonigr) |
+| IDEAS STUDIO | 1 | [@IDEASVRTV](https://www.youtube.com/@IDEASVRTV) |
+| DroneTourism507 | 1 | [@dronetourism507](https://www.youtube.com/@dronetourism507) |
+| Duncan Schieb | 1 | [@Haveabeerwithduncan](https://www.youtube.com/@Haveabeerwithduncan) |
+| Tony McIlwain | 1 | [@TonyMcIlwain](https://www.youtube.com/@TonyMcIlwain) |
+
+All clips were obtained under CC-BY as indicated by YouTube at the time of download. License proof
+(`.info.json` sidecar files) is archived.

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_old/README.md

@@ -0,0 +1,144 @@
+---
+license: cc-by-nc-4.0
+base_model: Lightricks/LTX-2.3-22B
+library_name: diffusers
+---
+
+# LTX-2.3 — 360° Equirectangular Outpainting IC-LoRA · **v0.1**
+
+**Proof-of-concept** IC-LoRA adapter for [Lightricks/LTX-2.3-22B](https://huggingface.co/Lightricks) that
+outpaints standard widescreen footage into a full **360° equirectangular** projection for immersive/VR viewing.
+
+> This is an **early v0.1 release**. Expect rough edges, limited subject variety, and inconsistent
+> coherence outside the sweet spot described below. A new version with a much larger, more diverse
+> dataset is planned.
+
+---
+
+## Samples
+
+Three clips from the model. `sweep` is a rendered 2D camera pan through the
+360° output — the easiest way to judge results without a VR player.
+`fl-eq` shows the flat source and the equirect output side-by-side.
+
+### Clip 1 — sweep
+
+<video controls loop muted playsinline src="https://huggingface.co/TheBurgstall/VR-360-Outpaint-LTX2.3-IC-LoRA/resolve/main/samples/clip1-sweep.mp4"></video>
+
+Side-by-side (flat input · equirect output):
+
+<video controls loop muted playsinline src="https://huggingface.co/TheBurgstall/VR-360-Outpaint-LTX2.3-IC-LoRA/resolve/main/samples/clip1-fl-eq.mp4"></video>
+
+### Clip 2 — sweep
+
+<video controls loop muted playsinline src="https://huggingface.co/TheBurgstall/VR-360-Outpaint-LTX2.3-IC-LoRA/resolve/main/samples/clip2-sweep.mp4"></video>
+
+Side-by-side:
+
+<video controls loop muted playsinline src="https://huggingface.co/TheBurgstall/VR-360-Outpaint-LTX2.3-IC-LoRA/resolve/main/samples/clip2-fl-eq.mp4"></video>
+
+### Clip 3 — sweep
+
+<video controls loop muted playsinline src="https://huggingface.co/TheBurgstall/VR-360-Outpaint-LTX2.3-IC-LoRA/resolve/main/samples/clip3-sweep.mp4"></video>
+
+Side-by-side:
+
+<video controls loop muted playsinline src="https://huggingface.co/TheBurgstall/VR-360-Outpaint-LTX2.3-IC-LoRA/resolve/main/samples/clip3-fl-eq.mp4"></video>
+
+Raw equirectangular outputs (load in a 360° player for VR):
+[clip1-eq.mp4](./samples/clip1-eq.mp4) ·
+[clip2-eq.mp4](./samples/clip2-eq.mp4) ·
+[clip3-eq.mp4](./samples/clip3-eq.mp4)
+
+## What it does
+
+- **Input**: a flat 2.39:1 (cinemascope) clip and a matching equirectangular *reference* (the input
+  projected into the equirect canvas, with the unknown regions left masked/black)
+- **Output**: the model fills in the masked regions, turning the flat shot into a plausible
+  360° equirectangular video that can be viewed in a VR/360 player
+
+Intended for transforming existing live-action or cinematic footage into immersive content.
+
+## Sweet spot (v0.1)
+
+The v0.1 model was tuned toward a deliberately narrow domain to validate the approach:
+
+- **Semi-static establishing city / urban scenes** (no heavy camera motion)
+- **~100° horizontal field of view** in the source clip
+- **2.39:1 source aspect** (standard cinemascope)
+
+It will **generalize poorly** outside these conditions — fast action, extreme close-ups, heavily
+stylised imagery, or very different FOVs are not reliably handled yet.
+
+## Files
+
+| File | What it is |
+|---|---|
+| `ltx-2.3-22b-ic-lora-360-equirect-poc-step3500.safetensors` | The LoRA weights (final step 3500 checkpoint, ~1.3 GB) |
+| `Equirect-Outpaint.json` | Reference ComfyUI workflow wired end-to-end for this LoRA |
+| `samples/clipN-fl-eq.mp4` | Flat input + equirect output side-by-side (3626×960) |
+| `samples/clipN-eq.mp4` | Raw equirectangular output (1920×960) — load in a 360° player |
+| `samples/clipN-sweep.mp4` | 2D camera sweep through the 360° output (1920×1080) for quick preview without a VR player |
+
+Three sample clips (`clip1`, `clip2`, `clip3`) are included under `samples/`.
+
+## Usage
+
+Load on top of `ltx-2.3-22b-dev.safetensors` with the LTX-2 `video_to_video` pipeline and pass:
+
+- **Trigger word**: `equirectangular` (works without any trigger word or prompt too, but with a descriptive prompt you can direct the content of outpainted part)
+- **Reference video**: your source clip projected into the equirect canvas with unknown regions masked
+- **Resolution**: 1920x960, 121 frames, 24 fps
+
+Only tested in ComfyUI with the workflow available in this repo. Please note that the workflow's padding node crops your input footage to 2.39:1, you can select whether it's cut from center, top or bottom. Other aspect ratios will work poorly in this early version.
+
+### Companion tooling
+
+A small ComfyUI helper pack — **[ComfyUI-EquirectProjector](https://github.com/Burgstall-labs/ComfyUI-EquirectProjector)** —
+was written alongside this LoRA to produce the masked equirectangular reference from a flat clip.
+Pair it with the standard LTX-2 video-to-video nodes. The `Equirect-Outpaint.json` workflow in this
+repo shows the exact wiring.
+
+## Training (v0.1)
+
+| | |
+|--|--|
+| Base model | LTX-2.3-22B (dev) |
+| Strategy | IC-LoRA (`video_to_video`) |
+| Rank / alpha | 128 / 128 |
+| Target modules | video self+cross attention + FFN |
+| Resolution | 1024×512, 41 frames @ 24 fps |
+| Optimizer | Prodigy (D-Adaptation), lr=1.0, constant |
+| Precision | bf16, gradient checkpointing |
+| Steps | 3500 |
+| Hardware | 1× NVIDIA H100 80GB |
+| Dataset | Small curated POC set (not released) — semi-static city establishing clips |
+
+The final **step 3500** checkpoint is shipped here. Intermediate checkpoints were used for
+validation during training but aren't included in this release.
+
+## What's next
+
+Next version is planned on a significantly larger and more diverse dataset covering:
+
+- Broader subject matter (interiors, landscapes, crowds, vehicles, …)
+- Varied input FOVs and focal lengths
+- A wider range of camera motion — not just static establishing shots
+- Better handling of the polar regions (top/bottom caps of the equirect canvas)
+
+## Limitations
+
+- Does not model the top/bottom caps of the sphere well — expect stretching or repetition
+- Struggles with busy motion and fast cuts
+- Prompt adherence is weak; conditioning is dominated by the reference video
+- Outputs are not a substitute for natively captured 360 footage — this is a creative
+  re-projection, not a reconstruction
+
+## Acknowledgements
+
+- First part of the training done at ADOS Paris event in collaboration with Cseti (https://huggingface.co/Cseti), NebSH (https://huggingface.co/Nebsh) and S4f3ty_Marc (https://huggingface.co/s4f3tymarc). Good times, thank you guys.
+- Advice from oumoumad (https://huggingface.co/oumoumad) in IC-LoRA training altogether has been very valuable to me. The workflow included in this release is modified from oumoumad's IC-LoRA workflow.
+
+## License
+
+CC BY-NC 4.0

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