FLUX Local and Cloud Tutorial With SwarmUI - FLUX: The First Ever Open Source Truly Beats Midjourney
🔗 Complete Tutorial Video Link ▶️ https://youtu.be/bupRePUOA18
FLUX represents a groundbreaking achievement in open-source txt2img technology, surpassing the image quality and prompt adherence of renowned platforms such as #Midjourney, Adobe Firefly, Leonardo Ai, Playground Ai, Stable Diffusion, SDXL, SD3, and Dall E3. Developed by Black Forest Labs, FLUX's team primarily consists of the original #StableDiffusion creators, resulting in astounding quality. This tutorial will guide you through the straightforward process of downloading and utilizing FLUX models on your personal computer and cloud services like Massed Compute, RunPod, and a complimentary Kaggle account.
🔗 FLUX Instructions Post (accessible without login) ⤵️
▶️ https://www.patreon.com/posts/106135985
🔗 FLUX Models One-Click Robust Automatic Downloader Scripts ⤵️
▶️ https://www.patreon.com/posts/109289967
🔗 Primary Windows SwarmUI Tutorial (Essential for Usage Instructions) ⤵️
▶️ https://youtu.be/HKX8_F1Er_w
🔗 Cloud SwarmUI Tutorial (Massed Compute - RunPod - Kaggle) ⤵️
▶️ https://youtu.be/XFUZof6Skkw
🔗 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 Segments
0:00 Introduction to the truly state-of-the-art txt2img model FLUX, which is Open Source
5:01 Our approach to installing the FLUX model into SwarmUI for usage
5:33 Accurate manual download process for FLUX models
5:54 Automated one-click download for FP16 and optimized FP8 FLUX models
6:45 Determining the ideal precision and type of FLUX models for your needs and their differences
7:56 Correct folder placement for FLUX models
8:07 Updating SwarmUI to the latest version for FLUX compatibility
8:58 Utilizing FLUX models after SwarmUI initialization
9:44 Implementing CFG scale for the FLUX model
10:23 Monitoring real-time server debug logs
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 using the development model
11:53 Employing FLUX model in FP16 instead of default FP8 precision on SwarmUI
12:31 Distinctions between development and turbo FLUX models
13:05 Generating native 1536x1536 images and evaluating FLUX's high-resolution capabilities and VRAM usage
13:41 1536x1536 resolution FLUX image generation speed on RTX 3090 Ti GPU with SwarmUI
13:56 Verifying shared VRAM usage - a potential cause of reduced generation speed
14:35 Utilizing SwarmUI and FLUX on cloud services - no local PC or GPU required
14:48 Using pre-installed SwarmUI on Massed Compute's 48 GB GPU for $0.31 per hour with FLUX dev FP16 model
16:05 Downloading FLUX models on Massed Compute instance
17:15 FLUX models 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 Comparing Midjourney image to open-source FLUX using identical prompts
22:02 Configuring DType to FP16 for enhanced image quality on Massed Compute with FLUX
22:12 Juxtaposing FLUX-generated image with Midjourney-generated image using the same prompt
23:00 Installing SwarmUI and downloading FLUX models on RunPod
25:01 Comparing step speed and VRAM usage of Turbo model vs Dev model of FLUX
26:04 Downloading FLUX models on RunPod post-SwarmUI installation
26:55 Initiating SwarmUI after pod restart or power cycle
27:42 Resolving visibility issues with SwarmUI's CFG scale panel
27:54 Comparing FLUX quality with top-tier Stable Diffusion XL (SDXL) models via popular CivitAI image
29:20 FLUX image generation speed on L40S GPU - FP16 precision
29:43 Contrasting FLUX image with 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 assessing 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 Joining SECourses discord channel for support and AI discussions
FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from textual descriptions. For more information, please refer to our blog post.
Key Attributes
State-of-the-art output quality, second only to our cutting-edge model FLUX.1 [pro].
Competitive prompt adherence, matching closed-source alternatives' performance.
Trained using guidance distillation for improved efficiency.
Open weights to foster 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 unparalleled 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 similar quality and prompt adherence capabilities while being more efficient than a standard model of equivalent 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 accessible 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 utilize a hybrid architecture of multimodal and parallel diffusion transformer blocks, scaled to 12B parameters. FLUX 1 enhances previous state-of-the-art diffusion models by incorporating flow matching, a versatile and conceptually straightforward method for training generative models, which encompasses diffusion as a special case.
Furthermore, FLUX 1 boosts model performance and hardware efficiency by integrating 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] outperform popular models like Midjourney v6.0, DALL·E 3 (HD), and SD3-Ultra in Visual Quality, Prompt Following, Size/Aspect Variability, Typography, and Output Diversity.
FLUX.1 [schnell] is the most advanced few-step model to date, surpassing 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 enhanced possibilities.