Full Training Tutorial and Guide and Research For a FLUX Style
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Furkan Gözükara
MonsterMMORPG
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Simple prompt 2x latent upscaled FLUX - Fine Tuning / DreamBooth Images - Can be trained on as low as 6 GB GPUs - Each image 2048x2048 pixels
AI Photos of Yourself - Workflow Guide
Step 1: Initial Setup
Follow any standard FLUX Fine-Tuning / DreamBooth tutorial of your choice
You can also follow mine step by step : https://youtu.be/FvpWy1x5etM
Step 2: Data Collection
Gather high-quality photos of yourself
I used a Poco X6 Pro (mid-tier phone) with good results
Ensure good variety in poses and lighting
Step 3: Training
Use "ohwx man" as the only caption for all images
Keep it simple - no complex descriptions needed
Step 4: Testing & Optimization
Use SwarmUI grid to find the optimal checkpoint
Test different variations to find what works best
Step 5: Generation Settings
Upscale Parameters:
Scale: 2x
Refiner Control: 0.6
Model: RealESRGAN_x4plus.pth
Prompt Used:
photograph of ohwx man wearing an amazing ultra expensive suit on a luxury studio<segment:yolo-face_yolov9c.pt-1,0.7,0.5>photograph of ohwx man
Note: The model naturally generated smiling expressions since the training dataset included many smiling photos.
Note: yolo-face_yolov9c.pt used to mask face and auto inpaint face to improve distant shot face quality
AI Photos of Yourself - Workflow Guide
Step 1: Initial Setup
Follow any standard FLUX Fine-Tuning / DreamBooth tutorial of your choice
You can also follow mine step by step : https://youtu.be/FvpWy1x5etM
Step 2: Data Collection
Gather high-quality photos of yourself
I used a Poco X6 Pro (mid-tier phone) with good results
Ensure good variety in poses and lighting
Step 3: Training
Use "ohwx man" as the only caption for all images
Keep it simple - no complex descriptions needed
Step 4: Testing & Optimization
Use SwarmUI grid to find the optimal checkpoint
Test different variations to find what works best
Step 5: Generation Settings
Upscale Parameters:
Scale: 2x
Refiner Control: 0.6
Model: RealESRGAN_x4plus.pth
Prompt Used:
photograph of ohwx man wearing an amazing ultra expensive suit on a luxury studio<segment:yolo-face_yolov9c.pt-1,0.7,0.5>photograph of ohwx man
Note: The model naturally generated smiling expressions since the training dataset included many smiling photos.
Note: yolo-face_yolov9c.pt used to mask face and auto inpaint face to improve distant shot face quality
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13 days ago
Post
2550
Simple prompt 2x latent upscaled FLUX - Fine Tuning / DreamBooth Images - Can be trained on as low as 6 GB GPUs - Each image 2048x2048 pixels
AI Photos of Yourself - Workflow Guide
Step 1: Initial Setup
Follow any standard FLUX Fine-Tuning / DreamBooth tutorial of your choice
You can also follow mine step by step : https://youtu.be/FvpWy1x5etM
Step 2: Data Collection
Gather high-quality photos of yourself
I used a Poco X6 Pro (mid-tier phone) with good results
Ensure good variety in poses and lighting
Step 3: Training
Use "ohwx man" as the only caption for all images
Keep it simple - no complex descriptions needed
Step 4: Testing & Optimization
Use SwarmUI grid to find the optimal checkpoint
Test different variations to find what works best
Step 5: Generation Settings
Upscale Parameters:
Scale: 2x
Refiner Control: 0.6
Model: RealESRGAN_x4plus.pth
Prompt Used:
photograph of ohwx man wearing an amazing ultra expensive suit on a luxury studio<segment:yolo-face_yolov9c.pt-1,0.7,0.5>photograph of ohwx man
Note: The model naturally generated smiling expressions since the training dataset included many smiling photos.
Note: yolo-face_yolov9c.pt used to mask face and auto inpaint face to improve distant shot face quality
AI Photos of Yourself - Workflow Guide
Step 1: Initial Setup
Follow any standard FLUX Fine-Tuning / DreamBooth tutorial of your choice
You can also follow mine step by step : https://youtu.be/FvpWy1x5etM
Step 2: Data Collection
Gather high-quality photos of yourself
I used a Poco X6 Pro (mid-tier phone) with good results
Ensure good variety in poses and lighting
Step 3: Training
Use "ohwx man" as the only caption for all images
Keep it simple - no complex descriptions needed
Step 4: Testing & Optimization
Use SwarmUI grid to find the optimal checkpoint
Test different variations to find what works best
Step 5: Generation Settings
Upscale Parameters:
Scale: 2x
Refiner Control: 0.6
Model: RealESRGAN_x4plus.pth
Prompt Used:
photograph of ohwx man wearing an amazing ultra expensive suit on a luxury studio<segment:yolo-face_yolov9c.pt-1,0.7,0.5>photograph of ohwx man
Note: The model naturally generated smiling expressions since the training dataset included many smiling photos.
Note: yolo-face_yolov9c.pt used to mask face and auto inpaint face to improve distant shot face quality
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FLUX Tools Complete Tutorial with SwarmUI (as easy as Automatic1111 or Forge) : Outpainting, Inpainting, Redux Style Transfer + Re-Imagine + Combine Multiple Images, Depth and Canny - More info at the oldest comment - No-paywall : https://youtu.be/hewDdVJEqOQ
FLUX.1 Tools by BlackForestLabs changed the #AI field forever. They became the number 1 Open Source community provider after this massive release. In this tutorial, I will show you step by step how use FLUX.1 Fill model (inpainting model) to do perfect outpainting (yes this model used for outpainting) and inpainting. Moreover, I will show all features of FLUX Redux model to do style transfer / re-imagine 1 and more than 1 images combination. Furthermore, I will show you step by step how to convert input image into Depth or Canny maps and then how to use them on #FLUX Depth and Canny models. Both LoRA and full checkpoints of FLUX Depth and Canny.
🔗 Full Instructions, Configs, Installers, Information and Links Shared Post (the one used in the tutorial - Public - no-paywall) ⤵️
▶️ https://www.patreon.com/posts/tutorial-instructions-links-public-post-106135985
Preparation of this tutorial took more than 1 week and this will be the very best and easiest to follow tutorial since it is made with famous #SwarmUI. SwarmUI is as easy and as advanced as Automatic1111 SD Web UI. Biggest advantage of SwarmUI is that, it uses ComfyUI as a back-end. Therefore, It is extremely fast, VRAM optimized and supports all of the newest SOTA models as soon as they are published.
So in this tutorial I will show you how to setup SwarmUI and FLUX Dev tools on your Windows Computer, Massed Compute, RunPod and Kaggle. I will step by step explanatin and show you every tips and tricks that you need to properly do style transfer, re-imagine, inpaint, outpaint, depth and canny with FLUX.
Video Chapters first image
FLUX.1 Tools by BlackForestLabs changed the #AI field forever. They became the number 1 Open Source community provider after this massive release. In this tutorial, I will show you step by step how use FLUX.1 Fill model (inpainting model) to do perfect outpainting (yes this model used for outpainting) and inpainting. Moreover, I will show all features of FLUX Redux model to do style transfer / re-imagine 1 and more than 1 images combination. Furthermore, I will show you step by step how to convert input image into Depth or Canny maps and then how to use them on #FLUX Depth and Canny models. Both LoRA and full checkpoints of FLUX Depth and Canny.
🔗 Full Instructions, Configs, Installers, Information and Links Shared Post (the one used in the tutorial - Public - no-paywall) ⤵️
▶️ https://www.patreon.com/posts/tutorial-instructions-links-public-post-106135985
Preparation of this tutorial took more than 1 week and this will be the very best and easiest to follow tutorial since it is made with famous #SwarmUI. SwarmUI is as easy and as advanced as Automatic1111 SD Web UI. Biggest advantage of SwarmUI is that, it uses ComfyUI as a back-end. Therefore, It is extremely fast, VRAM optimized and supports all of the newest SOTA models as soon as they are published.
So in this tutorial I will show you how to setup SwarmUI and FLUX Dev tools on your Windows Computer, Massed Compute, RunPod and Kaggle. I will step by step explanatin and show you every tips and tricks that you need to properly do style transfer, re-imagine, inpaint, outpaint, depth and canny with FLUX.
Video Chapters first image