FLUX Local & Cloud Tutorial With SwarmUI - FLUX: The Pioneering Open Source txt2img Model Outperforming Midjourney & Others
FLUX: The Anticipated Successor to SD3
🔗 Comprehensive Tutorial Video Link ▶️
FLUX marks a significant milestone as the first open-source txt2img model to genuinely surpass and produce superior quality images with better prompt adherence compared to #Midjourney, Adobe Firefly, Leonardo Ai, Playground Ai, Stable Diffusion, SDXL, SD3, and Dall E3. #FLUX, developed by Black Forest Labs, boasts a team primarily consisting of the original #StableDiffusion creators, and its quality is truly remarkable. This statement is not an exaggeration, as you'll discover upon watching the tutorial. This guide will demonstrate how to effortlessly download and utilize FLUX models on your personal computer and cloud services such as Massed Compute, RunPod, and a complimentary Kaggle account.
🔗 FLUX Instructions Post (publicly accessible, no login required) ⤵️
▶️ https://www.patreon.com/posts/106135985
🔗 FLUX Models 1-Click Robust Auto Downloader Scripts ⤵️
▶️ https://www.patreon.com/posts/109289967
🔗 Primary Windows SwarmUI Tutorial (Watch for Usage Instructions) ⤵️
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🔗 Cloud SwarmUI Tutorial (Massed Compute - RunPod - Kaggle) ⤵️
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🔗 SECourses Discord Channel for Comprehensive Support ⤵️
▶️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
🔗 SECourses Reddit ⤵️
▶️ https://www.reddit.com/r/SECourses/
🔗 SECourses GitHub ⤵️
▶️ https://github.com/FurkanGozukara/Stable-Diffusion
🔗 FLUX 1 Official Blog Post Announcement ⤵️
▶️ https://blackforestlabs.ai/announcing-black-forest-labs/
Video Chapters
0:00 Introduction to the groundbreaking open-source txt2img model FLUX
5:01 Process of installing FLUX model into SwarmUI for usage
5:33 Guide to accurately downloading FLUX models manually
5:54 Automatic 1-click download method for FP16 and optimized FP8 FLUX models
6:45 Explanation of different FLUX model precisions and types, and their suitability
7:56 Correct folder placement for FLUX models
8:07 Instructions for updating SwarmUI to the latest FLUX-supported version
8:58 Using FLUX models post-SwarmUI launch
9:44 Applying CFG scale to FLUX model
10:23 Monitoring server debug logs in real-time
10:49 Turbo model image generation speed on RTX 3090 Ti GPU
10:59 Potential blurriness in some turbo model outputs
11:30 Image generation with the development model
11:53 Utilizing FLUX model in FP16 instead of default FP8 precision on SwarmUI
12:31 Differences between FLUX development and turbo models
13:05 Testing high-resolution capabilities of FLUX with 1536x1536 native generation and VRAM usage
13:41 1536x1536 resolution FLUX image generation speed on RTX 3090 Ti GPU with SwarmUI
13:56 Verifying shared VRAM usage and its impact on generation speed
14:35 Cloud service implementation of SwarmUI and FLUX - no personal PC or GPU required
14:48 Using pre-installed SwarmUI on Massed Compute's 48 GB GPU with FLUX dev FP16 model at $0.31/hour
16:05 Downloading FLUX models on Massed Compute instance
17:15 FLUX model download speed on Massed Compute
18:19 Time required to download all premium FP16 FLUX and T5 models on Massed Compute
18:52 One-click update and launch of SwarmUI on Massed Compute
19:33 Accessing Massed Compute's SwarmUI from your PC's browser via ngrok - mobile compatibility included
21:08 Comparison between Midjourney and open-source FLUX images using identical prompts
22:02 Setting DType to FP16 for enhanced image quality on Massed Compute with FLUX
22:12 Side-by-side comparison of FLUX and Midjourney generated images from the same prompt
23:00 SwarmUI installation and FLUX model download guide for RunPod
25:01 Step speed and VRAM usage comparison between FLUX Turbo and Dev models
26:04 FLUX model download process on RunPod post-SwarmUI installation
26:55 Restarting SwarmUI after pod reboot or power cycle
27:42 Troubleshooting invisible CFG scale panel in SwarmUI
27:54 Quality comparison between FLUX and top-tier Stable Diffusion XL (SDXL) models via popular CivitAI image
29:20 FLUX image generation speed on L40S GPU with FP16 precision
29:43 Comparative analysis of FLUX image vs popular CivitAI SDXL image
30:05 Impact of increased step count on image quality
30:33 Generating larger 1536x1536 pixel images
30:45 Installing nvitop and checking VRAM usage for 1536px resolution and FP16 DType
31:25 Speed reduction when increasing image resolution from 1024px to 1536px
31:42 Implementing SwarmUI and FLUX models on a free Kaggle account, mirroring local PC usage
32:29 Instructions for joining SECourses discord channel and contacting for assistance and AI discussions
FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from textual descriptions.
Key Features
State-of-the-art output quality, second only to our cutting-edge model FLUX.1 [pro].
Competitive prompt adherence, matching the performance of proprietary alternatives.
Trained using guidance distillation for improved efficiency.
Open weights to facilitate new scientific research and empower artists to develop innovative workflows.
The FLUX.1 suite of text-to-image models establishes a new benchmark in image detail, prompt adherence, style diversity, and scene complexity for text-to-image synthesis.
To balance accessibility and model capabilities, FLUX.1 is available in three variants: FLUX.1 [pro], FLUX.1 [dev], and FLUX.1 [schnell]:
FLUX.1 [pro]: The pinnacle of FLUX.1, offering state-of-the-art performance in image generation with superior prompt following, visual quality, image detail, and output diversity.
FLUX.1 [dev]: An open-weight, guidance-distilled model for non-commercial applications. Directly derived from FLUX.1 [pro], it achieves comparable quality and prompt adherence capabilities while being more efficient than a standard model of the same size. FLUX.1 [dev] weights are accessible on HuggingFace.
FLUX.1 [schnell]: Our fastest model, optimized for local development and personal use. FLUX.1 [schnell] is openly available under an Apache2.0 license. Like FLUX.1 [dev], weights are available on Hugging Face, and inference code can be found on GitHub and in HuggingFace's Diffusers.
Transformer-powered Flow Models at Scale
All public FLUX.1 models are based on a hybrid architecture of multimodal and parallel diffusion transformer blocks, scaled to 12B parameters. FLUX 1 improves upon previous state-of-the-art diffusion models by leveraging flow matching, a versatile and conceptually straightforward method for training generative models, which encompasses diffusion as a special case.
Furthermore, FLUX 1 enhances model performance and hardware efficiency by incorporating rotary positional embeddings and parallel attention layers.
A New Benchmark for Image Synthesis
FLUX.1 sets a new standard in image synthesis. FLUX.1 [pro] and [dev] surpass popular models like Midjourney v6.0, DALL·E 3 (HD), and SD3-Ultra in various aspects: Visual Quality, Prompt Following, Size/Aspect Variability, Typography, and Output Diversity.
FLUX.1 [schnell] stands as the most advanced few-step model to date, outperforming not only its in-class competitors but also robust non-distilled models like Midjourney v6.0 and DALL·E 3 (HD).
FLUX models are specifically fine-tuned to preserve the entire output diversity from pretraining. Compared to the current state-of-the-art, they offer significantly improved possibilities.