Wan 2.2 14B Fun Inp
Generate videos from start and end frames using Wan 2.2 Fun Inp.
About
This ComfyUI workflow, titled 'Wan 2.2 14B Fun Inp', is designed to generate videos by interpolating between start and end frames using the Wan 2.2 model. The workflow leverages a series of nodes including CLIPLoader, VAELoader, and ModelSamplingSD3 to load and process the input images, while the WanFunInpaintToVideo node plays a crucial role in creating the video by applying inpainting techniques.
The workflow is particularly useful for creating seamless transitions in videos, making it ideal for artistic video projects or animations where smooth morphing between frames is desired. The use of advanced nodes like KSamplerAdvanced and VAEDecode ensures high-quality video output, while the inclusion of LoraLoaderModelOnly allows for flexible model adjustments.
What you provide
- Reading the workflow
Runs in ComfyUI. This workflow can't run on this page. Open it in Comfy Cloud with everything loaded, or download it for your own ComfyUI.
Try in CloudInside the workflow
A read-only preview of the node graph. Open it in Comfy Cloud or download it to edit the nodes.
Loading the graph
Open full-size workflow preview- Author
- ComfyUI
Requires custom nodes
- Reading the workflow
Use this workflow from code
Generated from the workflow graph and inputs when possible; stored examples are shown otherwise. Review credentials and endpoints before running code, which may incur charges.
What you need
- Runs on
- Comfy Cloud
- Key
- COMFY_API_KEY
Needs a paid Cloud plan and credits.
# Python 3.10+: pip install comfy-sdk==0.4.0
import os
from comfy_sdk import Comfy
api_key = os.environ.get("COMFY_API_KEY", "").strip()
if not api_key: raise RuntimeError("Set COMFY_API_KEY")
os.environ.setdefault("COMFY_BASE_URL", "https://cloud.comfy.org")
client = Comfy(api_key=api_key)
workflow = client.workflows.from_json({
"90": {
"class_type": "CLIPLoader",
"inputs": {
"clip_name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
"device": "default",
"type": "wan"
},
"_meta": {
"title": "Load CLIP"
}
},
"91": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"90",
0
],
"text": "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
},
"_meta": {
"title": "CLIP Text Encode (Negative Prompt)"
}
},
"92": {
"class_type": "VAELoader",
"inputs": {
"vae_name": "wan_2.1_vae.safetensors"
},
"_meta": {
"title": "Load VAE"
}
},
"93": {
"class_type": "ModelSamplingSD3",
"inputs": {
"model": [
"116",
0
],
"shift": 8
},
"_meta": {
"title": "ModelSamplingSD3"
}
},
"94": {
"class_type": "ModelSamplingSD3",
"inputs": {
"model": [
"117",
0
],
"shift": 8
},
"_meta": {
"title": "ModelSamplingSD3"
}
},
"95": {
"class_type": "KSamplerAdvanced",
"inputs": {
"add_noise": "disable",
"cfg": 1,
"end_at_step": 4,
"latent_image": [
"96",
0
],
"model": [
"94",
0
],
"negative": [
"111",
1
],
"noise_seed": 0,
"positive": [
"111",
0
],
"return_with_leftover_noise": "disable",
"sampler_name": "euler",
"scheduler": "simple",
"start_at_step": 2,
"steps": 4
},
"_meta": {
"title": "KSampler (Advanced)"
}
},
"96": {
"class_type": "KSamplerAdvanced",
"inputs": {
"add_noise": "enable",
"cfg": 1,
"end_at_step": 2,
"latent_image": [
"111",
2
],
"model": [
"93",
0
],
"negative": [
"111",
1
],
"noise_seed": 221824495232956,
"positive": [
"111",
0
],
"return_with_leftover_noise": "enable",
"sampler_name": "euler",
"scheduler": "simple",
"start_at_step": 0,
"steps": 4
},
"_meta": {
"title": "KSampler (Advanced)"
}
},
"97": {
"class_type": "VAEDecode",
"inputs": {
"samples": [
"95",
0
],
"vae": [
"92",
0
]
},
"_meta": {
"title": "VAE Decode"
}
},
"99": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"90",
0
],
"text": "A dreamy scene where a little cat is sleeping. Zoom in, and the cat opens its eyes, looks up, and blinks. In Q-style, with ice crystals."
},
"_meta": {
"title": "CLIP Text Encode (Positive Prompt)"
}
},
"100": {
"class_type": "CreateVideo",
"inputs": {
"bit_depth": "auto",
"codec": "none",
"color_space": "sRGB",
"fps": 16,
"images": [
"97",
0
]
},
"_meta": {
"title": "Create Video"
}
},
"101": {
"class_type": "UNETLoader",
"inputs": {
"unet_name": "wan2.2_fun_inpaint_high_noise_14B_fp8_scaled.safetensors",
"weight_dtype": "default"
},
"_meta": {
"title": "Load Diffusion Model"
}
},
"102": {
"class_type": "UNETLoader",
"inputs": {
"unet_name": "wan2.2_fun_inpaint_low_noise_14B_fp8_scaled.safetensors",
"weight_dtype": "default"
},
"_meta": {
"title": "Load Diffusion Model"
}
},
"110": {
"class_type": "LoadImage",
"inputs": {
"image": "video_wan2_2_14B_fun_inpaint_start_image.png"
},
"_meta": {
"title": "Load Image"
}
},
"111": {
"class_type": "WanFunInpaintToVideo",
"inputs": {
"batch_size": 1,
"end_image": [
"112",
0
],
"height": 640,
"length": 81,
"negative": [
"91",
0
],
"positive": [
"99",
0
],
"start_image": [
"110",
0
],
"vae": [
"92",
0
],
"width": 640
},
"_meta": {
"title": "WanFunInpaintToVideo"
}
},
"112": {
"class_type": "LoadImage",
"inputs": {
"image": "video_wan2_2_14B_fun_inpaint_end_image.png"
},
"_meta": {
"title": "Load Image"
}
},
"116": {
"class_type": "LoraLoaderModelOnly",
"inputs": {
"lora_name": "wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors",
"model": [
"101",
0
],
"strength_model": 1
},
"_meta": {
"title": "Load LoRA"
}
},
"117": {
"class_type": "LoraLoaderModelOnly",
"inputs": {
"lora_name": "wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors",
"model": [
"102",
0
],
"strength_model": 1
},
"_meta": {
"title": "Load LoRA"
}
},
"158": {
"class_type": "SaveVideo",
"inputs": {
"filename_prefix": "video/ComfyUI",
"format": "auto",
"format.codec": "auto",
"video": [
"100",
0
]
},
"_meta": {
"title": "Save Video"
}
}
})
# run() also passes api_key to the partner nodes in this workflow.
job = client.run(workflow, api_key=api_key)Questions & answers
The workflow uses the Wan2.2 model along with nodes like CLIPLoader and VAELoader for image processing and video generation.
Yes, the workflow includes nodes for configuring video size and length to suit your VRAM capacity and project needs.
Ensure that your input frames are of high quality and use the KSamplerAdvanced and VAEDecode nodes for optimal video processing.
Yes, you can input various prompts in the CLIPTextEncode node to guide the video style and appearance according to your preferences.