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README.md

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+---
+license: apache-2.0
+library_name: diffusers
+pipeline_tag: image-to-video
+tags:
+  - wan
+  - video-generation
+  - image-to-video
+  - diffusers
+base_model: alibaba-pai/Wan2.1-Fun-V1.1-1.3B-InP
+---
+
+# Wan2.1-Fun-V1.1-1.3B-InP (Diffusers)
+
+This is a diffusers-format conversion of [alibaba-pai/Wan2.1-Fun-V1.1-1.3B-InP](https://huggingface.co/alibaba-pai/Wan2.1-Fun-V1.1-1.3B-InP) (Wan-Fun Inpaint V1.1 1.3B) from VideoX-Fun format.
+
+## Model Details
+
+- **Architecture**: WanTransformer3DModel with `in_channels=36` (16 noise + 4 mask + 16 image latent)
+- **Parameters**: 1.3B
+- **Pipeline**: `WanImageToVideoPipeline` (standard diffusers, no patching required)
+- **Resolution**: 480x832 (480p) recommended
+- **Frames**: 49 frames at 16fps (~3 seconds)
+
+This model has the same I2V architecture as the official [Wan2.1-I2V-14B-480P](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P-Diffusers) (`in_channels=36`), but at 1.3B scale.
+
+## Usage
+
+```python
+import torch
+from diffusers import WanImageToVideoPipeline
+from PIL import Image
+
+pipe = WanImageToVideoPipeline.from_pretrained(
+    "engineerA314/Wan2.1-Fun-V1.1-1.3B-InP-Diffusers",
+    torch_dtype=torch.bfloat16,
+)
+pipe.enable_sequential_cpu_offload()
+
+image = Image.open("first_frame.png").convert("RGB")
+
+output = pipe(
+    image=image,
+    prompt="A person is talking naturally",
+    negative_prompt="static, blurred, low quality",
+    height=480,
+    width=832,
+    num_frames=49,
+    num_inference_steps=50,
+    guidance_scale=5.0,
+)
+
+from diffusers.utils import export_to_video
+export_to_video(output.frames[0], "output.mp4", fps=16)
+```
+
+## Conversion Details
+
+Converted from VideoX-Fun format using 1:1 weight key mapping (983 keys). No architectural modifications were needed -- the standard `WanImageToVideoPipeline` handles `in_channels=36` natively.
+
+### Components
+
+| Component | Source |
+|-----------|--------|
+| Transformer | Converted from `alibaba-pai/Wan2.1-Fun-V1.1-1.3B-InP` |
+| VAE | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` |
+| Text Encoder | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` (UMT5-XXL) |
+| Image Encoder | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` (CLIP ViT-H-14) |
+| Scheduler | UniPCMultistepScheduler (`flow_shift=3.0`) |
+
+### Comparison with TI2V variant
+
+| | This model (InP) | [TI2V](https://huggingface.co/engineerA314/Wan2.1-Fun-V1.1-1.3B-TI2V-Diffusers) |
+|---|---|---|
+| `in_channels` | 36 (noise + mask + image) | 32 (noise + image) |
+| Pipeline patches | None needed | `prepare_latents` override required |
+| Origin | Wan-Fun Inpaint | Wan-Fun Camera Control (adapter removed) |
+
+## Acknowledgements
+
+- [Alibaba PAI / VideoX-Fun](https://github.com/alibaba-pai/VideoX-Fun) for the original Wan-Fun models
+- [Wan-Video](https://github.com/Wan-Video/Wan2.1) for the Wan 2.1 architecture