Running large models like Wan 2.2 on Fedora 43 requires precise hardware tuning. Beginners often struggle with AMD ROCm installation errors during the setup process.
Eliminate Site Wide Python Dependency Conflicts
The first mistake is using the site wide Python installation. This leads to massive dependency conflicts within the Fedora ecosystem.
Fedora updates frequently change system packages and libraries. Always use a dedicated virtual environment for every ComfyUI project.
Create a clean virtual environment using the venv module. Run python -m venv comfy-venv to initialize the folder.
Activate the environment before installing any pip requirements. Use source comfy-venv/bin/activate to isolate your AI workspace.
Configure Virtual Memory and Linux Swap Files
The second mistake is ignoring your limited system RAM. Your 32GB of physical memory is not enough for 14B models.
The Ryzen 5600GT iGPU reserves 4GB for video processing. This leaves only 28GB for the entire Fedora operating system.
Loading the Wan 2.2 model weights will spike memory usage. Create a 32GB swap file to prevent the OOM killer.
Use btrfs filesystem mkswapfile for modern Fedora installations. Add the entry to /etc/fstab to enable it on boot.
Resolve ROCm DNF No Match for Argument Errors
The third mistake involves the ROCm 6.4 repository configuration. DNF will return no match errors if the path is wrong.
Fedora 43 requires a specific repository file in /etc/yum.repos.d/. Check that your baseurl points to the correct ROCm version.
Isolate the Instinct Mi60 from the Ryzen iGPU
Hardware conflicts between the iGPU and Mi60 cause crashes. The integrated graphics can confuse the ROCm compiler scripts.
Set the HIP_VISIBLE_DEVICES="0" variable in your launch script. This forces ComfyUI to ignore the 5600GT graphics unit.
Why You Must Use GGUF Quantization
Standard fp16 models are too large for 32GB VRAM. A 14B model in fp16 requires nearly 60GB of memory.
Even fp8 models can cause fragmentation on Vega 20. The Mi60 architecture handles GGUF quants with much better stability.
Select the Q5_K_M GGUF quantization for the best quality. This format allows the model to fit inside your VRAM.
Advanced Startup Tweak and Custom Nodes
Add the HSA_OVERRIDE_GFX_VERSION="9.0.6" variable to your environment. This tells ROCm to treat the Mi60 as a Vega card.
Use the --lowvram and --force-fp16-vae flags for ComfyUI. These arguments prevent the VAE from exceeding your VRAM limit.
Install the Sage Attention custom node for optimized sampling. This node reduces the memory footprint during the video generation.
Use the Model Sampling Flux/Wan node for motion control. Set the shift value to 1.1 for smoother video results.
Monitor your progress using the rocminfo command in terminal. This tool verifies that Fedora sees your hardware correctly.
Screenshot
Web Browser ComfyUI Displaying Wan 2.2 14B ModelWeb Browser ComfyUI Running Low Noise Wan 2.2 14B ModelSystem Monitor Displaying Resources Used For Wan 2.2 14 Model
Live Screencast
Screencast Of AI Generated Video Using Wan 2.2 I2V 14B And ComfyUI On AMD GPU
Procedural Landscapes with Python in Blender: Infinite World Generation Tutorial
Creating vast digital worlds does not require manual sculpting. You can generate infinite procedural landscapes using Blender’s Python API. This method allows you to build unique terrains algorithmically.
Procedural generation uses math to create complex geometry. This script will automate the creation of a terrain mesh. It then applies a noise-based displacement to create mountains.
The Procedural Blender Script
Open the Scripting tab in Blender. Create a new text file and paste this code. This script creates a high-resolution grid and manipulates its height.
import bpy
import bmesh
import random
import math
# Clear existing objects in the scene
bpy.ops.object.select_all(action='SELECT')
bpy.ops.object.delete()
# Create a high-resolution grid for the landscape
size = 10
subdiv = 100
bpy.ops.mesh.primitive_grid_add(size=size, x_subdivisions=subdiv, y_subdivisions=subdiv)
obj = bpy.context.active_object
# Access mesh data to manipulate vertices
mesh = obj.data
bm = bmesh.new()
bm.from_mesh(mesh)
# Apply simple procedural noise for mountains
for vert in bm.verts:
# Use math functions to create organic waves
noise = math.sin(vert.co.x * 1.5) * math.cos(vert.co.y * 1.5)
vert.co.z = (noise * 2.0) + (random.uniform(0, 0.3))
# Update mesh and cleanup
bm.to_mesh(mesh)
bm.free()
mesh.update()
# Automatic Export to glTF for Web
export_path = "C:/path/to/your/project/landscape.glb"
bpy.ops.export_scene.gltf(filepath=export_path, export_format='GLB')
Viewing Your World in HTML5
Export your generated landscape as a glTF file. You can then view it in any browser using HTML5. Use the following code to load your landscape with Three.js.
<script type="module">
import * as THREE from 'https://unpkg.com/three@0.160.0/build/three.module.js';
import { GLTFLoader } from 'https://unpkg.com/three@0.160.0/examples/jsm/loaders/GLTFLoader.js';
const scene = new THREE.Scene();
const camera = new THREE.PerspectiveCamera(75, window.innerWidth / window.innerHeight, 0.1, 1000);
const renderer = new THREE.WebGLRenderer({ antialias: true });
renderer.setSize(window.innerWidth, window.innerHeight);
document.body.appendChild(renderer.domElement);
const loader = new GLTFLoader();
loader.load('landscape.glb', (gltf) => {
scene.add(gltf.scene);
});
const light = new THREE.DirectionalLight(0xffffff, 2);
light.position.set(2, 5, 5);
scene.add(light);
scene.add(new THREE.AmbientLight(0x404040));
camera.position.set(0, 5, 10);
camera.lookAt(0, 0, 0);
function animate() {
requestAnimationFrame(animate);
renderer.render(scene, camera);
}
animate();
</script>
Using Python scripts makes world-building fast. You can tweak parameters to generate entirely new terrains. This workflow is perfect for developers building 3D web games.
LocalSend is a free tool for sharing files. It works on Linux Android and many others.
You can visit https://localsend.org to download it. This website hosts all the official setup files.
Understanding the Apache License 2.0
The software uses the secure Apache License 2.0. This license allows you to use the code freely.
You can modify the software for your own needs. The license protects you from certain patent claims too.
Open Source Benefits
Open source code ensures your data stays private. The code is public for any developer to see.
This license requires users to keep the original notices. It builds trust within the global programming community today.
Installation on Fedora Linux
Open the terminal on your Fedora Linux computer. Type flatpak install flathub org.localsend.localsend to start.
Confirm the setup and launch it from GNOME. The interface looks clean on the Wayland desktop.
Setup for Android Devices
Visit https://localsend.org on your Android device too. Connect both devices to your home Wi-Fi signal.
The application will find your desktop automatically. You can now transfer files between your devices easily.
📷 Screenshots
Dashboard Of Mobile And Desktop LocalSend ApplicaitonSending From Mobile To Desktop LocalSend ApplicationReceiving On Desktop From Mobile LocalSend Application
🎬 Live YouTube Screencast
Video Displaying The Installation And Use Of LocalSend