Full-Stack AI Design & Drawing Hands-on Course: WebUI + ComfyUI + MJ + Flux Four Major Models, Static Illustration, Dynamic Video, Commercial Design Fully Implemented Tutorial

Course Overview
This zero-basis full-stack AI design & drawing course covers the mainstream tools WebUI, ComfyUI, MJ and Flux. It teaches software installation and deployment, prompt parameters, LoRA and ControlNet for precise image control, hands-on inpainting, outpainting, HD restoration and face swap; it teaches AnimateDiff, Deforum and other dynamic video production and video style transfer, paired with full commercial project flows for LOGO, IP illustration, poster packaging and interior design. No art foundation is needed; students can independently produce commercial static images and dynamic short videos, building a complete AI freelance commission creation system.

Target Learners

1. Total newcomers without art foundation who want to start AI drawing
2. Traditional designers who want AI to speed up image work
3. Self-media creators who need batch image-text and video assets
4. Students who can only do basic generation and whose work fails commercial standards
5. Side-hustle practitioners who want AI design commissions to add income

What You Gain

1. Skillfully deploy the four mainstream AI drawing tools locally and on the cloud
2. Master ControlNet/LoRA to precisely control composition and characters
3. Master outpainting, redraw and HD restoration to improve image quality
4. Independently make dynamic animations and video style transfers of various shorts
5. Fully implement commercial LOGO / IP / poster / interior design projects

Course Catalog

19-Faceswap 1

04-Using LORA model in webui

10-controlnet 3

16-WEBUI image upscale

18-webui three-view production

09-controlnet 2

12-controlnet example 2

02-basic text-to-image flow in webui

20-Faceswap 2

13-controlnet example 3

00-cloud system and cloud-host usage guide

22-animatediff animation making

01-webui install and basic config

14-controlnet example 4

25-webui vs comfyui comparison

23-deforum animation 1

21-video style transfer

03-img2img flow in webui

06-webui masked redraw

15-indoor design AI application example

17-webui model-to-real-person

03-img2img flow in webui

24.1-MJ intro and activation

25.2-MJ basics and homework

26.3-MJ prompts and model versions

27.07-webui outpainting

28.24-deforum animation 2

29.2-1-weight split tokens and artist style

30.2-2-Niji model and seed parameter

31.05-webui inpaint

32.08-webui controlnet

33.11-controlnet example 1

34.2-3-img2img and tile, iw params

35.00-ALGC design & drawing course intro

36.4-3-cartoon IP character and homework

37.04-comfyui img2img

38.06-basic flux model usage flow

39.02-comfyui interface

40.07-stylized prompts

41.4-1-film posters and product packaging

42.08-lora usage

43.10-controlnet usage

44.09-flux model lora

45.4-2-event posters and multi-character illustration

46.18-flux style transfer

47.01-comfyui basic install and model config

48.14-face refinement

49.3-2-operational illustration, icons, scenes, pop-up stores

50.3-3-creative products and homework

51.03-comfyui basic text-to-image

52.16-flux Alimama redraw and fi redraw

53.05-comfyui xy chart comparison

54.3-1-logo design

55.2-4-multi-image blend, blend command, cref and sref params, describe command

56.24-various background-removal tools - model-to-real

57.20-ipadapter group-photo case

58.29-mochi, pyramid video tools

59.21-instantID commercial portrait and upscale

60.27-character-consistency solutions

61.23-mask face-restore case

62.19-faceswap and ipadapter

63.30-cog video and Hunyuan video

64.11-xl model controlnet

65.28-animatediff animation, noodle-plant dance style transfer

66.13-outpainting case

67.22-layerstyle-1

68.15-background-removal redraw

69.31-catvton clothing-swap node

70.33-indoor design workflow breakdown, commercial workflow study

71.32-Ice real-time painting workflow

72.12-inpaint

73.26-Ketu Hunyuan sd3cascade model

74.17-latest flux controlnet tool

75.25-multiple large language models reverse-prompting