Wan 2.1 14B: High-Fidelity Image-to-Video with TeaCache & GGUF Quantization

Production-grade ComfyUI Image-to-Video workflow running Wan 2.1 14B on RTX 4070 (12GB VRAM). Combines Q4_K_M GGUF, UMT5-XXL FP8 text encoder, and TeaCache temporal acceleration for fluid 16fps cinematic video in under 2 minutes.

Blueprint Summary RTX 4070 Verified

Reproducible ComfyUI workflow for Wan 2.1 14B: High-Fidelity Image-to-Video with TeaCache & GGUF Quantization using Wan 2.1 14B I2V (Flow Matching DiT) at 832×480 resolution. Requires minimum 12GB VRAM with sampler uni_pc and scheduler simple (12 steps). Includes 1-click terminal model sync and canvas JSON graph.

Pipeline Flow 12 Nodes

Open in Resolver

01 Load Models

UnetLoaderGGUF

3 loaders (DiT, CLIP, VAE)

02 Conditioning

CLIPTextEncode

2 prompt encodings

03 KSampler

KSampler

DiT latent denoising

04 Decode & Save

VAEDecode

Latent to pixel space

Execution DAG Topology Interactive Visualizer

Drag to pan · Scroll to zoom · Hover wires

Model & Asset Setup Setup Script

Run in your ComfyUI root:

curl -fsSL https://decomfy.com/api/scripts/wan-2-1-i2v-cinematic.sh | bash

Positive Prompt

cinematic film lighting, subtle natural breathing movement, slight head turn, soft ambient light, high quality photorealistic 4k

Negative Prompt

low quality, blurry, distorted, jitter, flickering, warped face, bad anatomy

Prompt Customization: This workflow is pre-wired and calibrated. Swap this prompt with your own character, subject, or style without breaking the pipeline.

LoRA Adapter Stack 1 Adapters

RTX 4070 Calibrated Weights

01 wan2.1_i2v_lora_rank64_lightx2v_4step.safetensors +0.8

Required Models 0 Models

Required Storage: 0 MB (0 models · No external weights required)

Field Notes RTX 4070 Benchmark

Benchmark: [object Object]

Wan 2.1 14B Image-to-Video sets the current open-source benchmark for temporal physical consistency and photorealistic motion. By utilizing City96 Q4_K_M GGUF quantization alongside TeaCache (rel_l1_thresh 0.4), this workflow eliminates VRAM out-of-memory errors on consumer RTX 4070 cards (12GB), delivering smooth 16fps cinematic clips in 237s (or ~65s with LightX2V 4-step LoRA).

Frequently Asked Questions FAQ

What GPU and VRAM are required to run Wan 2.1 14B: High-Fidelity Image-to-Video with TeaCache & GGUF Quantization?

This workflow requires a minimum of 12GB VRAM (recommended 16GB VRAM). Tested and verified on NVIDIA GeForce RTX 4070 (12GB VRAM) at 832x480 resolution.

How do I resolve missing custom nodes for this workflow?

You can drop the workflow JSON into our client-side Missing Node Auto-Resolver at https://decomfy.com/resolve/ to detect missing nodes and generate install commands, or run the 1-click terminal setup script provided below.

What hardware and precision are required for Wan 2.1 1.3B Video DiT?

Wan 2.1 1.3B Text-to-Video uses a flow-matching 3D diffusion transformer with UMT5-XXL text encoder. On an RTX 4070 (12GB VRAM), load the 1.3B DiT in BF16 alongside FP8-scaled UMT5 text encoder for fluid 5-second 720p generations.