Initial mirror upload: weights + README + LICENSE + NOTICE (Apache 2.0; original by 麻雀 AI / fxj1131 on ModelScope)

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LTX2.3-IC-LORA-Dual-Character.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b6c3199e2c95eb0aad0cea4f3e8dfa33e8f6966d85bc79e3994faf5a57406103
+size 327287347

NOTICE

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+LTX2.3-IC-LoRA-Dual-Character (English mirror)
+
+This repository is an English-language mirror redistribution of the
+LTX2.3 IC-LoRA Dual-Character model originally published on ModelScope:
+
+  Original source:  https://www.modelscope.cn/models/fxj1131/LTX2.3-IC-LORA-Dual-Character
+  Original author:  麻雀 AI (Maque AI)
+  ModelScope user:  fxj1131
+  License:          Apache License 2.0
+
+All credit for the model weights belongs to the original author. This
+mirror exists to make the weights and documentation accessible to the
+English-speaking HuggingFace community, and to share field-tested usage
+notes from production deployment in a multi-shot Chinese drama video
+generation pipeline.
+
+The weights file (.safetensors) is unmodified and byte-identical to
+the original ModelScope upload. The README in this repository adds
+English documentation, integration notes, and production usage tips.
+
+Per Apache 2.0, attribution to the original author must be preserved
+in any further redistribution.

README.md

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+---
+license: apache-2.0
+base_model:
+  - Lightricks/LTX-2.3
+tags:
+  - video-generation
+  - lora
+  - ic-lora
+  - ltx-video
+  - dual-character
+  - dialogue
+  - cinematic
+  - chinese-drama
+pipeline_tag: image-to-video
+language:
+  - en
+  - zh
+---
+
+# LTX-Video 2.3 IC-LoRA: Dual-Character (English mirror)
+
+> ⚠️ **This is an English-language mirror of [fxj1131's LTX2.3 IC-LoRA Dual-Character on ModelScope](https://www.modelscope.cn/models/fxj1131/LTX2.3-IC-LORA-Dual-Character).**
+> All credit for the model weights belongs to the original author, **麻雀 AI (Maque AI)**.
+> This mirror exists to make the model + documentation accessible to HuggingFace users who cannot easily access ModelScope, and to share field-tested usage notes from a production deployment.
+> **The `.safetensors` weights file is unmodified and byte-identical to the ModelScope upload.**
+
+---
+
+## What this LoRA does
+
+An **In-Context LoRA (IC-LoRA)** trained on top of `Lightricks/LTX-2.3` (22B distilled), specifically tuned for:
+
+1. **Two-character dialogue scenes** — significantly reduces character drift when two people appear in the same frame
+2. **Cinematic shot composition** — reinforced for dialogue-driven framing (close-up ↔ medium ↔ wide)
+3. **Multi-shot narrative continuity** — better understanding of multi-segment prompts (storyboard-style descriptions)
+4. **Style compatibility** — works well across 古风仙侠 (ancient Chinese fantasy), 现代都市 (modern urban), and 3D 动漫 styles
+
+This is an **IC-LoRA** (in-context LoRA), so it expects reference images to be passed through the parallel-canvas conditioning mechanism, NOT as pixel-pinned frames. See the [Lightricks `ltx_pipelines.ic_lora.ICLoraPipeline`](https://github.com/Lightricks/LTX-2) for the upstream pipeline.
+
+---
+
+## Model card
+
+| Field | Value |
+|---|---|
+| Base model | `Lightricks/LTX-2.3` (22B distilled) |
+| LoRA type | IC-LoRA (video-to-video conditioning) |
+| File | `LTX2.3-IC-LORA-Dual-Character.safetensors` (~313 MB) |
+| License | Apache 2.0 |
+| Original author | 麻雀 AI / fxj1131 (on ModelScope) |
+| Trigger word | None — no special token required |
+
+---
+
+## Field-tested production usage
+
+The notes below are from running this LoRA in production as part of a multi-shot Chinese drama video generation pipeline. They go beyond what's in the original model card.
+
+### Recommended LoRA stack
+
+For cinematic Chinese-drama output we found this stack works well, totalling 1.4 strength (under the 1.5 over-baking ceiling):
+
+| LoRA | Strength | Role |
+|---|---|---|
+| **LTX2.3-IC-LORA-Dual-Character** (this) | **0.6** | Multi-character identity from refs |
+| `LTX2.3_Fantasy_Realism` (vrgamedevgirl84) | 0.4 | Cinematic style |
+| `Motion_Stability` | 0.4 | Temporal coherence helper |
+
+If using standalone: 0.7–0.9 works well. If stacking with multiple other LoRAs: drop to 0.3–0.5.
+
+### Resolution
+
+- **Recommended:** 1280×704 (16:9, native LTX-2.3 distilled training resolution)
+- **Faster preview:** 960×544 (~40% faster, slightly less detail)
+- **Avoid portrait (9:16)** — this LoRA was trained on landscape; identity quality degrades noticeably in portrait orientation
+- Width and height must each be divisible by 64
+
+### Number of frames
+
+LTX-2.3 requires `num_frames` to satisfy `8k + 1` (e.g., 121, 145, 193, 241, 361). At 24 fps:
+- 5s shot = 121 frames
+- 8s shot = 193 frames
+- 15s shot = 361 frames
+
+### Prompt structure that works well
+
+Use a 3-block structure: `[场景] / [角色] / [镜头与情节]`.
+
+```text
+[场景] 古风皇宫御花园桃花径，午后金色阳光透过盛开桃花斜射，
+粉色花瓣随风飘落，朱红宫墙翠竹环绕。
+
+[角色] 沈月华：年轻女子，长黑发半扎绿玉簪，鬓边一朵小白花，
+柔和圆润大眼，肤色白皙。身穿浅蓝色丝绸汉服宫装，白色云鹤刺绣。
+萧云霄：年轻男子，黑发束起金冠玉饰，剑眉星目。身穿深红色丝绸金线龙纹宫袍。
+
+[镜头与情节] 中景两人画面。沈月华手中持翠绿玉佩，萧云霄从右侧朱红
+宫墙转角缓步走出停下，拱手轻施一礼，目光温和注视沈月华手中玉佩。
+电影级布光，浅景深虚化，35mm 双人中景，温暖色调。
+```
+
+### Production tips earned the hard way
+
+These are quirks of this LoRA + the LTX-2.3 distilled backbone that aren't documented in the original model card but matter in practice:
+
+#### 1. Static-image reference: use a SHORT video wrap (≤ 8 frames)
+
+If you wrap a single PNG character ref into a video for IC-LoRA conditioning, **use 8 frames (≈ 0.33 s) — NOT 30 frames**. Longer static wraps cause a "first second stuck on the ref image" beat at the start of every clip. The IC-LoRA's per-frame matching dominates motion onset when the static wrap is too long.
+
+#### 2. Repeat color tokens for dark-clothed characters
+
+This LoRA + Fantasy_Realism stack has a light-wuxia-robe bias. Dark outfits drift toward white at low ref-image-strength. Recipe: **repeat the color token glued to each clothing noun**:
+
+```text
+BAD:  black fedora and black suit
+GOOD: BLACK fedora, white shirt, BLACK suit jacket, BLACK trousers,
+      ... BLACK suit, BLACK trousers throughout
+```
+
+Also bump `ref_image_strength` to 0.55 (action) or 0.85 (medium-slow) for color fidelity.
+
+#### 3. **Never use quoted dialogue in prompts**
+
+This LoRA was trained on Chinese drama clips with burned-in Chinese subtitles. **Any quoted dialogue (`「…」` or `"…"`) in the prompt causes the LoRA to hallucinate subtitle characters at the bottom of the frame.** This is the single biggest gotcha.
+
+```text
+BAD:  低声警告 「此茶不可饮！」    ← produces fake on-screen subtitles
+GOOD: 低声急切警告她茶水有毒        ← clean output, indirect narration
+```
+
+If your application needs actual subtitles, burn them post-hoc via `ffmpeg drawtext`, not via the prompt.
+
+#### 4. Avoid "object detaches" prompts during action
+
+At high motion intensity (cas/ris ≤ 0.40), the model loses object tracking. A directive like "fedora flies off mid-spin and tumbles to the floor" produces broken output — the hat dematerialises. Either:
+- Keep the object attached and say so explicitly ("the fedora STAYS ON his head throughout the spin")
+- Or render attach + detach as two clips and concat
+
+#### 5. Cross-shot identity drift
+
+For multi-shot dialogue scenes, character identity drifts across cuts. Workaround: chain shots by passing a **12-frame tail clip** of shot N as `ref_videos=[(tail.mp4, 0.7)]` for shot N+1. Significantly improves continuity.
+
+### Render performance
+
+- **Resolution:** 1280×704, 121 frames @ 24 fps (~5 s output)
+- **Stack:** the production stack above (3 LoRAs, total 1.4)
+- **Hardware:** NVIDIA A800 80GB
+- **Time:** ~70 s per shot (8-step distilled + 3-step spatial upscaler + audio decode)
+- **Output:** mp4 with ambient audio track (no TTS)
+
+On consumer hardware (RTX 4090 24GB), expect ~3–4 minutes per shot due to memory pressure from the 22B model.
+
+---
+
+## Limitations
+
+From the original author + our field testing:
+
+1. **Subtitle hallucination** with quoted dialogue (see tip #3 above)
+2. **Complex physical interactions** (wrestling, hugging, intricate hand-on-hand) can deform
+3. **Tail-frame artifact** of LTX-2.3 — last 6–8 frames may smear; trim post-hoc if needed
+4. **Action complexity ceiling** — the 8-step distilled budget caps motion complexity at action peaks
+5. **Portrait orientation** degrades identity (LoRA trained on landscape only)
+
+---
+
+## Original Chinese README (preserved)
+
+The original Chinese model card from ModelScope is reproduced below for users who want the unmodified original documentation.
+
+<details>
+<summary>点击展开原版中文模型卡片 (click to expand original Chinese README)</summary>
+
+### LTX-Video (2.3) IC-LoRA: 双人分镜头对话增强模型
+
+本模型是基于 Lightricks LTX-2.3 底模训练的 IC-LoRA，专为双人同框对话、角色互动及分镜头视频生成场景深度优化。
+
+**一、模型核心提升**
+
+1. 角色参考稳定性：显著提升双人同框时的人物特征一致性，减少角色漂移。
+2. 分镜构图稳定性：针对影视化对话构图进行了加固，支持更精准的镜头控制。
+3. 叙事连贯性：增强了对多段描述的理解力，使分镜间的过渡衔接更自然。
+4. 风格兼容性：完美支持古风仙侠、现代都市、3D 动漫等主流视觉风格。
+
+**二、模型基本信息**
+
+1. 基础模型：Lightricks/LTX-2.3
+2. 许可证：Apache-2.0
+3. 管道标签：image-to-video, text-to-video
+4. 模型用途：仅供学习交流使用
+5. 开发者：麻雀 AI
+
+**三、运行指南**
+
+1. 推荐平台：ComfyUI
+2. 支持工作流：ComfyUI 官方 LTX 工作流、KJ-LTX 插件工作流
+3. 生成模式：文生视频 (T2V) 与 图生视频 (I2V) 均支持
+4. 硬件参考：RTX 5090 显卡在 720P 分辨率下，单条视频生成耗时约 2 分钟
+
+**四、推荐参数配置**
+
+1. 分辨率：建议使用 16:9 (如 1280x720)
+2. 时长与帧率：建议时长 ≥10 秒，帧率设定为 24 FPS
+3. LoRA 权重设定：
+   - 独立使用建议：0.6 - 1.0
+   - 叠加其他 LoRA 使用时建议：0.3 - 0.5
+
+**五、Prompt 编写规范**
+
+1. 编写逻辑：需包含完整的场景描述 + 角色设定 + 分镜设计 + 镜头语言，强化双人对话互动逻辑。
+2. 触发词说明：无需特定触发词。
+
+**六、效果说明与局限性**
+
+1. 优势风格：在古风、现代、3D 动漫类双人对话场景中表现最佳。
+2. 已知限制：受限于 LTX-2.3 底模性能，极其复杂的双人肢体互动（如缠绕、打斗）可能出现形变。
+3. 运动幅度：建议以对话和微动作为主，大动态动作的连贯性仍有提升空间。
+
+</details>
+
+---
+
+## How to use
+
+### With the upstream Lightricks pipeline
+
+```python
+from ltx_pipelines.ic_lora import ICLoraPipeline
+from ltx_core.loader import LoraPathStrengthAndSDOps
+from ltx_core.loader import sd_ops as _sd_ops_mod
+import torch
+
+# Use the IC-LoRA's standard SDOps mapping
+lora = LoraPathStrengthAndSDOps(
+    "LTX2.3-IC-LORA-Dual-Character.safetensors",
+    0.6,                                      # strength
+    _sd_ops_mod.LTXV_LORA_COMFY_RENAMING_MAP,
+)
+
+pipe = ICLoraPipeline(
+    distilled_checkpoint_path="ltx-2.3-22b-distilled-1.1.safetensors",
+    spatial_upsampler_path="ltx-2.3-spatial-upscaler-x2-1.1.safetensors",
+    gemma_root="google/gemma-3-12b-it-qat-q4_0-unquantized",
+    loras=[lora],
+    device=torch.device("cuda:0"),
+)
+
+video, audio = pipe(
+    prompt="...",                # your structured 3-block prompt
+    seed=42,
+    height=704, width=1280,
+    num_frames=121,              # 5s @ 24fps, satisfies 8k+1
+    frame_rate=24,
+    video_conditioning=[("char_ref.mp4", 0.85)],   # 8-frame static wrap of the character portrait
+    enhance_prompt=False,
+    conditioning_attention_strength=0.85,
+)
+```
+
+### Hardware requirements
+
+| GPU | VRAM | Works? |
+|---|---|---|
+| A100 / A800 80GB | 80 GB | ✅ ~70 s per 5s shot |
+| RTX 4090 / 3090 | 24 GB | ✅ ~3–4 min per 5s shot |
+| RTX 4080 / 4070 Ti Super | 16 GB | ❌ won't fit 22B in bf16 |
+| anything < 24 GB | — | ❌ no |
+
+---
+
+## Acknowledgements
+
+- **麻雀 AI (Maque AI)** — original author of this LoRA, [original ModelScope repository](https://www.modelscope.cn/models/fxj1131/LTX2.3-IC-LORA-Dual-Character)
+- **[Lightricks](https://www.lightricks.com/)** — for the LTX-Video 2.3 base model and the IC-LoRA framework
+
+---
+
+## License
+
+Apache License 2.0 — same as the original. See `LICENSE` and `NOTICE`.