base version
Laufzeitprofil
Quellenbeschreibung
✨ WAN2.1 — Text to image — Simple Workflow
A clean, all-in-one WAN text-to-image workflow built entirely with the UmeAiRT Toolkit for ComfyUI. Only 10 nodes . No spaghetti wires. Just load your model, write your prompt, and hit generate.
⚠️ IMPORTANT — Nodes 2.0 Required
This workflow is built for the Nodes 2.0 (Vue) interface of ComfyUI. If you don't enable it, the workflow may have display problems.
How to activate Nodes 2.0:
Open ComfyUI
Go to Settings (⚙️ icon, bottom-left)
KI-generierter Kommentar
KI-generierte Erklärung auf Grundlage von Quellen- und Konfigurationsdetails. Vorschläge sind klar gekennzeichnet.
【WAN】TXT to IMAGE — v1.0 is a ComfyUI workflow for generating images from written prompts; it takes a text prompt, a Wan Video 2.2 T2V-A14B model, and optional LoRA settings, then produces images with optional SeedVR2 tiled upscaling and embedded generation parameters.
Load the model and prompt to generate an image; the built-in SeedVR2 tiled upscaler can be toggled on or off, and generation parameters are embedded in saved images.
The main inputs are a written prompt and a loaded model.
Three LoRA slots provide individual on/off toggles and strength controls, with support for connecting additional LoRA modules.
The workflow saves images with generation parameters in their metadata and can apply tiled upscaling through SeedVR2.
In ComfyUI, turn on “Use Nodes V2 (Vue)” in Settings, refresh, and then load the workflow; without it, the workflow may have display problems.
Install ComfyUI-UmeAiRT-Toolkit through ComfyUI Manager by searching “UmeAiRT,” or use the UmeAiRT Auto-Installer.
Named node packs include: comfyui-custom-scripts, comfyui-easy-use, comfyui-image-saver, comfyui-kjnodes, comfyui-mxtoolkit, rgthree-comfy, wanblockswap, and ComfyUI-GGUF.
Named model files include: umt5_xxl_fp8_e4m3fn_scaled.safetensors; wan_2.1_vae.safetensors; 4x-ClearRealityV1.pth; wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors; wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors; Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf; and Wan2.2-T2V-A14B-HighNoise−
The instructions also name lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank64_bf16.safetensors, RealESRGAN_x4plus.pth, and RealESRGAN_x4plus_anime_6B.pth.
Choose either fp16 or fp8 T2V files for the base version, or Q8, Q5, or Q3 T2V quant files for the GGUF version.
Vorschlag
For manual setup, place T2V model files in models/diffusion_models, the CLIP file in models/clip, the VAE in models/vae, the Speed LoRA in models/loras, and an upscaler in models/upscale_models.
Before running, verify the node packs required by your installation: the instructions call out ComfyUI-UmeAiRT-Toolkit, while the named node-pack list includes other names as well.
Vorschlag
Check applicable usage terms before publishing or remixing images; no license is specified in the listing.
Vorschlag
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Quellen
1 QuelleQuellenauszüge
1 AuszugSource context: 1658 downloads · Type Workflows · Base model Wan Video 2.2 T2V-A14B
Find "Use Nodes V2 (Vue)" and toggle it ON
Refresh the page
Load the workflow
If you prefer the classic interface, check out my Legacy version of this workflow instead ( link ).
🎯 Features
Text-to-Image generation
Automatic download of models in auto version
Built-in SeedVR2 upscaler — high-quality tiled upscaling (toggleable on/off) Slower than a classic upscaler, but significantly better quality
Full metadata embedding — your images are saved with all generation parameters, ready for online publishing and remixing
3 LoRA slots — with individual on/off toggles and strength control and you can connect as many other lora modules to each other for as many LoRA as you want.
📦 Custom Node Required
Only one custom node to install:
👉 ComfyUI-UmeAiRT-Toolkit
Install via ComfyUI Manager (search "UmeAiRT") or use the UmeAiRT Auto-Installer . The Toolkit packages everything internally — upscaler, face detailer, metadata saver. No other custom nodes needed.
📂 Files you need (in manual version)
For base version T2V Model: fp16 , fp8 In models/diffusion_models
For GGUF version T2V Quant Model: Q8 , Q5 , Q3 In models/diffusion_models
Common files : CLIP: umt5_xxl_fp8_e4m3fn_scaled.safetensors in models/clip
VAE: wan_2.1_vae.safetensors in models/vae
Speed LoRA: lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank64_bf16.safetensors in models/loras
ANY upscale model:
Realistic : RealESRGAN_x4plus.pth
Anime : RealESRGAN_x4plus_anime_6B.pth
in models/upscale_models
Geschätzter VRAM Bedarf
Schätzung nicht verfügbar
33,1 GB über 4 von 5 Modell-Dateien. Gesamtmodell-Dateien + 25% Ladeaufwand + 2 GB Ausführungs-Puffer, aufgerundet.
Anforderungen
12 Anforderungen4x-ClearRealityV1.pth
Nicht aufgelöstUpscaler · Unknown
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