<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/02d5bebf-9c38-47e4-99f7-146070f4f46c/width=525/02d5bebf-9c38-47e4-99f7-146070f4f46c.jpeg" /><h2 id="heading-4">🗡️ FFusionAI's Knife LoRA Model Demonstrations</h2>This demonstration dives deep into the intricacies of three distinct LoRA trainings. Each model has been meticulously trained on a unique dataset of 200 knives, captured in the professional environment of our partner, NoramePhotography Studio. The visuals span from pure white studio shots to rustic wood settings, glinting coins, and fantasy-inspired indoor decor.<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/5ee23d0a-230e-4f06-9980-c3469442ce4e/width=525/5ee23d0a-230e-4f06-9980-c3469442ce4e.jpeg" />🔍 Dataset Insight: The dataset, although rich in variety, was curated with a fast and informal tagging approach, mainly for demonstration purposes. If you're intrigued by the knife photo session and wish for a more in-depth training, do let us know!While depth variations are in the pipeline, our current focus revolves around evaluating the distinct LoRA variations.<h1 id="heading-570"><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/e477e50f-63c1-40dd-b88f-067795e1921f/width=525/e477e50f-63c1-40dd-b88f-067795e1921f.jpeg" /></h1><h2 id="heading-947">🎯 Models at a Glance:</h2><h3 id="heading-948">1. CivitAI's Quick LoRA Training (Lora1)</h3>📌 Highlights:<ul><li>Powered by CivitAI's new LoRA trainer.</li><li>Swift 10-epoch run, completed in a breezy 20-30 minutes.</li><li>Quality may vary with default settings, but hey, time is essence!</li></ul>📊 Specifications:<ul><li>Date: 2023-09-19T14:36:14</li><li>Resolution: 1024x1024</li><li>Architecture: stable-diffusion-xl-v1-base/lora</li><li>Network Dim/Rank: 32.0</li><li>Alpha: 16.0</li></ul><pre><code>Knife_XL_FFusion.safetensors
Date: 2023-09-19T14:36:14 Title: Knife_XL_FFusion
Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
Network Dim/Rank: 32.0 Alpha: 16.0
Module: networks.lora
Learning Rate (LR): 0.0005 UNet LR: 0.0005 TE LR: 5e-05
Optimizer: bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)
Scheduler: cosine_with_restarts Warmup steps: 0
Epoch: 10 Batches per epoch: 74 Gradient accumulation steps: 1
Train images: 282 Regularization images: 0
Multires noise iterations: 6.0 Multires noise discount: 0.3
Min SNR gamma: 5.0 Zero terminal SNR: True Max grad norm: 1.0 Clip skip: 1
Dataset dirs: 1
 [img] 282 images
UNet weight average magnitude: 2.634092236933176
UNet weight average strength: 0.009947009810559605
Text Encoder (1) weight average magnitude: 1.696394163771355
Text Encoder (1) weight average strength: 0.008538951936953606
Text Encoder (2) weight average magnitude: 1.720911101275907
Text Encoder (2) weight average strength: 0.006699097931942388</code></pre><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/a2c53a35-2609-4f29-8b89-c6d01d1625e7/width=525/a2c53a35-2609-4f29-8b89-c6d01d1625e7.jpeg" /><h3 id="heading-949">2. LoRA FA with Text Encoder Only (Lora2)</h3>📌 Highlights:<ul><li>Exclusive training on text encoder.</li><li>Absence of UNet in this LoRA variant.</li></ul>📊 Specifications:<ul><li>Date: 2023-09-19T20:04:24</li><li>Resolution: 1024x1024</li><li>Architecture: stable-diffusion-xl-v1-base/lora</li><li>Network Dim/Rank: 32.0</li><li>Alpha: 32.0</li></ul><pre><code>Knife-FFusion-LoRA-FA.safetensors

Date: 2023-09-19T20:04:24 Title: Knife-FFusion-LoRA-FA
Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
Network Dim/Rank: 32.0 Alpha: 32.0
Module: networks.lora_fa

Text Encoder (1) weight average magnitude: 3.986337637923385
Text Encoder (1) weight average strength: 0.018590648076750333
Text Encoder (2) weight average magnitude: 4.043434837883338
Text Encoder (2) weight average strength: 0.014620680042179104
No UNet found in this LoRA</code></pre><h3 id="heading-950">3. General LoRA Training</h3>📌 Highlights:<ul><li>Comprehensive LoRA training with diverse specifications.</li><li>Trained on an extensive dataset of 485 knife images.</li></ul>📊 Specifications:<ul><li>Date: 2023-08-26T23:08:56</li><li>Resolution: 1024x1024</li><li>Architecture: stable-diffusion-xl-v1-base/lora</li><li>Network Dim/Rank: 32.0</li><li>Alpha: 16.0 <pre><code>FF-Minecraft-XL
Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
Network Dim/Rank: 32.0 Alpha: 16.0
Module: networks.lora
Learning Rate (LR): 0.0005 UNet LR: 0.0005 TE LR: 5e-05
Optimizer: bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)
Scheduler: cosine_with_restarts Warmup steps: 0
Epoch: 10 Batches per epoch: 121 Gradient accumulation steps: 1
Train images: 458 Regularization images: 0
Multires noise iterations: 6.0 Multires noise discount: 0.3
Min SNR gamma: 5.0 Zero terminal SNR: True Max grad norm: 1.0 Clip skip: 1
Dataset dirs: 1
 [img] 458 images
UNet weight average magnitude: 2.9987627096874507
UNet weight average strength: 0.011098071585284945
Text Encoder (1) weight average magnitude: 1.729993708156961
Text Encoder (1) weight average strength: 0.008685239007756952
Text Encoder (2) weight average magnitude: 1.7630326984758309
Text Encoder (2) weight average strength: 0.0068346636309082635</code></pre></li></ul><h1 id="heading-571"><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f4ebdaaa-e364-4ce3-bb99-64d6ea32c387/width=525/f4ebdaaa-e364-4ce3-bb99-64d6ea32c387.jpeg" /></h1><h2 id="heading-951">🎨 Readme Crafted by: 🤖 &amp; FFusionAI 🚀 </h2><h3 id="heading-952">🌐 Contact Information</h3>The <a target="_blank" rel="ugc" href="http://FFusion.ai">FFusion.ai</a> project is proudly maintained by Source Code Bulgaria Ltd &amp; Black Swan Technologies.📧 Reach us at <a target="_blank" rel="ugc" href="mailto:di@ffusion.ai">di@ffusion.ai</a> for any inquiries or support.🌌 Find us on:<ul><li>🐙 <a target="_blank" rel="ugc" href="https://github.com/1e-2">GitHub</a></li><li>😊 <a target="_blank" rel="ugc" href="https://huggingface.co/FFusion/">Hugging Face</a></li><li>💡 <a target="_blank" rel="ugc" href="https://civarchive.com/user/idle/models">Civitai</a></li></ul><a target="_blank" rel="ugc" href="mailto:enquiries@ffusion.ai"><img src="https://img.shields.io/badge/Email-enquiries%40ffusion.ai-blue?style=for-the-badge&amp;logo=gmail" alt="Email" /></a>🌍 Sofia Istanbul London

Knife XL FFusion - CivitaI / LoRA + FA Text Encoder

FF-Minecraft-XLResolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/loraNetwork Dim/Rank: 32.0 Alpha: 16.0Module: networks.loraLearning Rate (LR): 0.0005 UNet LR: 0.0005 TE LR: 5e-05Optimizer: bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)Scheduler: cosine_with_restarts Warmup steps: 0Epoch: 10 Batches per epoch: 121 Gradient accumulation steps: 1Train images: 458 Regularization images: 0Multires noise iterations: 6.0 Multires noise discount: 0.3Min SNR gamma: 5.0 Zero terminal SNR: True Max grad norm: 1.0 Clip skip: 1Dataset dirs: 1 [img] 458 imagesUNet weight average magnitude: 2.9987627096874507UNet weight average strength: 0.011098071585284945Text Encoder (1) weight average magnitude: 1.729993708156961Text Encoder (1) weight average strength: 0.008685239007756952Text Encoder (2) weight average magnitude: 1.7630326984758309Text Encoder (2) weight average strength: 0.0068346636309082635

FF-Knives-XL-Cr2-v0163.safetensors

Blade Art

FF-Minecraft-XL-Normal

Knife-FFusion-LoRA-FA

Knife XL FFusion CivitAI

<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/02d5bebf-9c38-47e4-99f7-146070f4f46c/width=525/02d5bebf-9c38-47e4-99f7-146070f4f46c.jpeg" /><h2 id="heading-4">🗡️ FFusionAI's Knife LoRA Model Demonstrations</h2>This demonstration dives deep into the intricacies of three distinct LoRA trainings. Each model has been meticulously trained on a unique dataset of 200 knives, captured in the professional environment of our partner, NoramePhotography Studio. The visuals span from pure white studio shots to rustic wood settings, glinting coins, and fantasy-inspired indoor decor.<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/5ee23d0a-230e-4f06-9980-c3469442ce4e/width=525/5ee23d0a-230e-4f06-9980-c3469442ce4e.jpeg" />🔍 Dataset Insight: The dataset, although rich in variety, was curated with a fast and informal tagging approach, mainly for demonstration purposes. If you're intrigued by the knife photo session and wish for a more in-depth training, do let us know!While depth variations are in the pipeline, our current focus revolves around evaluating the distinct LoRA variations.<h1 id="heading-570"><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/e477e50f-63c1-40dd-b88f-067795e1921f/width=525/e477e50f-63c1-40dd-b88f-067795e1921f.jpeg" /></h1><h2 id="heading-947">🎯 Models at a Glance:</h2><h3 id="heading-948">1. CivitAI's Quick LoRA Training (Lora1)</h3>📌 Highlights:<ul><li>Powered by CivitAI's new LoRA trainer.</li><li>Swift 10-epoch run, completed in a breezy 20-30 minutes.</li><li>Quality may vary with default settings, but hey, time is essence!</li></ul>📊 Specifications:<ul><li>Date: 2023-09-19T14:36:14</li><li>Resolution: 1024x1024</li><li>Architecture: stable-diffusion-xl-v1-base/lora</li><li>Network Dim/Rank: 32.0</li><li>Alpha: 16.0</li></ul><pre><code>Knife_XL_FFusion.safetensors
Date: 2023-09-19T14:36:14 Title: Knife_XL_FFusion
Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
Network Dim/Rank: 32.0 Alpha: 16.0
Module: networks.lora
Learning Rate (LR): 0.0005 UNet LR: 0.0005 TE LR: 5e-05
Optimizer: bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)
Scheduler: cosine_with_restarts Warmup steps: 0
Epoch: 10 Batches per epoch: 74 Gradient accumulation steps: 1
Train images: 282 Regularization images: 0
Multires noise iterations: 6.0 Multires noise discount: 0.3
Min SNR gamma: 5.0 Zero terminal SNR: True Max grad norm: 1.0 Clip skip: 1
Dataset dirs: 1
 [img] 282 images
UNet weight average magnitude: 2.634092236933176
UNet weight average strength: 0.009947009810559605
Text Encoder (1) weight average magnitude: 1.696394163771355
Text Encoder (1) weight average strength: 0.008538951936953606
Text Encoder (2) weight average magnitude: 1.720911101275907
Text Encoder (2) weight average strength: 0.006699097931942388</code></pre><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/a2c53a35-2609-4f29-8b89-c6d01d1625e7/width=525/a2c53a35-2609-4f29-8b89-c6d01d1625e7.jpeg" /><h3 id="heading-949">2. LoRA FA with Text Encoder Only (Lora2)</h3>📌 Highlights:<ul><li>Exclusive training on text encoder.</li><li>Absence of UNet in this LoRA variant.</li></ul>📊 Specifications:<ul><li>Date: 2023-09-19T20:04:24</li><li>Resolution: 1024x1024</li><li>Architecture: stable-diffusion-xl-v1-base/lora</li><li>Network Dim/Rank: 32.0</li><li>Alpha: 32.0</li></ul><pre><code>Knife-FFusion-LoRA-FA.safetensors

Date: 2023-09-19T20:04:24 Title: Knife-FFusion-LoRA-FA
Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
Network Dim/Rank: 32.0 Alpha: 32.0
Module: networks.lora_fa

Text Encoder (1) weight average magnitude: 3.986337637923385
Text Encoder (1) weight average strength: 0.018590648076750333
Text Encoder (2) weight average magnitude: 4.043434837883338
Text Encoder (2) weight average strength: 0.014620680042179104
No UNet found in this LoRA</code></pre><h3 id="heading-950">3. General LoRA Training</h3>📌 Highlights:<ul><li>Comprehensive LoRA training with diverse specifications.</li><li>Trained on an extensive dataset of 485 knife images.</li></ul>📊 Specifications:<ul><li>Date: 2023-08-26T23:08:56</li><li>Resolution: 1024x1024</li><li>Architecture: stable-diffusion-xl-v1-base/lora</li><li>Network Dim/Rank: 32.0</li><li>Alpha: 16.0 <pre><code>FF-Minecraft-XL
Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
Network Dim/Rank: 32.0 Alpha: 16.0
Module: networks.lora
Learning Rate (LR): 0.0005 UNet LR: 0.0005 TE LR: 5e-05
Optimizer: bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)
Scheduler: cosine_with_restarts Warmup steps: 0
Epoch: 10 Batches per epoch: 121 Gradient accumulation steps: 1
Train images: 458 Regularization images: 0
Multires noise iterations: 6.0 Multires noise discount: 0.3
Min SNR gamma: 5.0 Zero terminal SNR: True Max grad norm: 1.0 Clip skip: 1
Dataset dirs: 1
 [img] 458 images
UNet weight average magnitude: 2.9987627096874507
UNet weight average strength: 0.011098071585284945
Text Encoder (1) weight average magnitude: 1.729993708156961
Text Encoder (1) weight average strength: 0.008685239007756952
Text Encoder (2) weight average magnitude: 1.7630326984758309
Text Encoder (2) weight average strength: 0.0068346636309082635</code></pre></li></ul><h1 id="heading-571"><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f4ebdaaa-e364-4ce3-bb99-64d6ea32c387/width=525/f4ebdaaa-e364-4ce3-bb99-64d6ea32c387.jpeg" /></h1><h2 id="heading-951">🎨 Readme Crafted by: 🤖 &amp; FFusionAI 🚀 </h2><h3 id="heading-952">🌐 Contact Information</h3>The <a target="_blank" rel="ugc" href="http://FFusion.ai">FFusion.ai</a> project is proudly maintained by Source Code Bulgaria Ltd &amp; Black Swan Technologies.📧 Reach us at <a target="_blank" rel="ugc" href="mailto:di@ffusion.ai">di@ffusion.ai</a> for any inquiries or support.🌌 Find us on:<ul><li>🐙 <a target="_blank" rel="ugc" href="https://github.com/1e-2">GitHub</a></li><li>😊 <a target="_blank" rel="ugc" href="https://huggingface.co/FFusion/">Hugging Face</a></li><li>💡 <a target="_blank" rel="ugc" href="https://civitai.com/user/idle/models">Civitai</a></li></ul><a target="_blank" rel="ugc" href="mailto:enquiries@ffusion.ai"><img src="https://img.shields.io/badge/Email-enquiries%40ffusion.ai-blue?style=for-the-badge&amp;logo=gmail" alt="Email" /></a>🌍 Sofia Istanbul London

&lt;p&gt;FF-Minecraft-XL&lt;/p&gt;&lt;p&gt;Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora&lt;/p&gt;&lt;p&gt;Network Dim/Rank: 32.0 Alpha: 16.0&lt;/p&gt;&lt;p&gt;Module: networks.lora&lt;/p&gt;&lt;p&gt;Learning Rate (LR): 0.0005 UNet LR: 0.0005 TE LR: 5e-05&lt;/p&gt;&lt;p&gt;Optimizer: bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)&lt;/p&gt;&lt;p&gt;Scheduler: cosine_with_restarts  Warmup steps: 0&lt;/p&gt;&lt;p&gt;Epoch: 10 Batches per epoch: 121 Gradient accumulation steps: 1&lt;/p&gt;&lt;p&gt;Train images: 458 Regularization images: 0&lt;/p&gt;&lt;p&gt;Multires noise iterations: 6.0 Multires noise discount: 0.3&lt;/p&gt;&lt;p&gt;Min SNR gamma: 5.0 Zero terminal SNR: True Max grad norm: 1.0  Clip skip: 1&lt;/p&gt;&lt;p&gt;Dataset dirs: 1&lt;/p&gt;&lt;p&gt;        [img] 458 images&lt;/p&gt;&lt;p&gt;UNet weight average magnitude: 2.9987627096874507&lt;/p&gt;&lt;p&gt;UNet weight average strength: 0.011098071585284945&lt;/p&gt;&lt;p&gt;Text Encoder (1) weight average magnitude: 1.729993708156961&lt;/p&gt;&lt;p&gt;Text Encoder (1) weight average strength: 0.008685239007756952&lt;/p&gt;&lt;p&gt;Text Encoder (2) weight average magnitude: 1.7630326984758309&lt;/p&gt;&lt;p&gt;Text Encoder (2) weight average strength: 0.0068346636309082635&lt;/p&gt;

Files with SHA256: 6c6c5e4b37f696c1c632562a34eef55b1de089f84c04c2c0d1c7d4bbe79acb2d