Rohit-Katkar2003/qwen3-vl-3b-unsloth-bnb-4bit-lora
Updated
Rohit katkar
Rohit-Katkar2003
AI & ML interests
Transformers 🤗
Recent Activity
updated
a model
24 days ago
Rohit-Katkar2003/qwen3-vl-3b-unsloth-bnb-4bit-lora
published
a model
24 days ago
Rohit-Katkar2003/qwen3-vl-3b-unsloth-bnb-4bit-lora
updated
a model
about 1 month ago
Rohit-Katkar2003/qwen2-vl-8b-lora-finetune
Organizations
updated
a
model
24 days ago
published
a
model
24 days ago
updated
a
model
about 1 month ago
published
a
model
about 1 month ago
reacted to
MonsterMMORPG's
post with 👍
about 1 month ago
Post
3645
Wan 2.2 Complete Training Tutorial - Text to Image, Text to Video, Image to Video, Windows & Cloud : https://youtu.be/ocEkhAsPOs4
Wan 2.2 training is now so easy. I have done over 64 different unique Wan 2.2 trainings to prepare the very best working training configurations for you. The configurations are fully working locally with as low as 6 GB GPUs. So you will be able to train your awesome Wan 2.2 image or video generation LoRAs on your Windows computer with easiness. Moreover, I have shown how to train on cloud platforms RunPod and Massed Compute so even if you have no GPU or you want faster training, you can train on cloud for very cheap prices fully privately.
Full step by step tutorial : https://youtu.be/ocEkhAsPOs4
⏱️ Video Chapters:
0:00 Introduction to Wan 2.2 Training & Capabilities
0:56 Installing & Updating Musubi Tuner Locally
2:20 Explanation of Optimized Presets & Research Logic
4:00 Differences Between T2I, T2V, and I2V Configs
5:36 Extracting Files & Running Update Batch File
6:14 Downloading Wan 2.2 Training Models via Script
7:30 Loading Configs: Selecting GPU & VRAM Options
9:33 Using nvitop to Monitor RAM & VRAM Usage
10:28 Preparing Image Dataset & Trigger Words
11:17 Generating Dataset Config & Resolution Logic
12:55 Calculating Epochs & Checkpoint Save Frequency
13:40 Troubleshooting: Fixing Missing VAE Path Error
15:12 VRAM Cache Behavior & Training Speed Analysis
15:51 Trade-offs: Learning Rate vs Resolution vs Epochs
16:29 Installing SwarmUI & Updating ComfyUI Backend
18:13 Importing Latest Presets into SwarmUI
19:25 Downloading Inference Models via Script
20:33 Generating Images with Trained Low Noise LoRA
22:22 Upscaling Workflow for High-Fidelity Results
24:15 Increasing Base Resolution to 1280x1280
27:26 Text-to-Video Generation with Lightning LoRA
30:12 Image-to-Video Generation Workflow & Settings
31:35 Restarting Backend to Clear VRAM for Model Switching
33:45 Fixing RAM Crashes with Cache-None Argument
....
Wan 2.2 training is now so easy. I have done over 64 different unique Wan 2.2 trainings to prepare the very best working training configurations for you. The configurations are fully working locally with as low as 6 GB GPUs. So you will be able to train your awesome Wan 2.2 image or video generation LoRAs on your Windows computer with easiness. Moreover, I have shown how to train on cloud platforms RunPod and Massed Compute so even if you have no GPU or you want faster training, you can train on cloud for very cheap prices fully privately.
Full step by step tutorial : https://youtu.be/ocEkhAsPOs4
⏱️ Video Chapters:
0:00 Introduction to Wan 2.2 Training & Capabilities
0:56 Installing & Updating Musubi Tuner Locally
2:20 Explanation of Optimized Presets & Research Logic
4:00 Differences Between T2I, T2V, and I2V Configs
5:36 Extracting Files & Running Update Batch File
6:14 Downloading Wan 2.2 Training Models via Script
7:30 Loading Configs: Selecting GPU & VRAM Options
9:33 Using nvitop to Monitor RAM & VRAM Usage
10:28 Preparing Image Dataset & Trigger Words
11:17 Generating Dataset Config & Resolution Logic
12:55 Calculating Epochs & Checkpoint Save Frequency
13:40 Troubleshooting: Fixing Missing VAE Path Error
15:12 VRAM Cache Behavior & Training Speed Analysis
15:51 Trade-offs: Learning Rate vs Resolution vs Epochs
16:29 Installing SwarmUI & Updating ComfyUI Backend
18:13 Importing Latest Presets into SwarmUI
19:25 Downloading Inference Models via Script
20:33 Generating Images with Trained Low Noise LoRA
22:22 Upscaling Workflow for High-Fidelity Results
24:15 Increasing Base Resolution to 1280x1280
27:26 Text-to-Video Generation with Lightning LoRA
30:12 Image-to-Video Generation Workflow & Settings
31:35 Restarting Backend to Clear VRAM for Model Switching
33:45 Fixing RAM Crashes with Cache-None Argument
....
- 3 replies
reacted to
prithivMLmods's
post with 🔥🤗
about 1 month ago
Post
2094
Introducing TRELLIS.2 Text-to-3D. The demo for the TRELLIS.2-4B (Image-to-3D) model is streamlined with the Z-Image Turbo image generation model to enable Text-to-3D functionality. There is no need for input assets, making a small leap forward for ideation. Optionally, it also includes default support for Image-to-3D inference using direct image assets. Find the demo and related collections below... 🤗🔥
✨ TRELLIS.2-Text-to-3D [Demo]: prithivMLmods/TRELLIS.2-Text-to-3D
✨ Multimodal Collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
✨ Github: https://github.com/PRITHIVSAKTHIUR/TRELLIS.2-Text-to-3D
To know more about it, visit the app page or the respective model page!
✨ TRELLIS.2-Text-to-3D [Demo]: prithivMLmods/TRELLIS.2-Text-to-3D
✨ Multimodal Collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
✨ Github: https://github.com/PRITHIVSAKTHIUR/TRELLIS.2-Text-to-3D
To know more about it, visit the app page or the respective model page!
liked
a
model
2 months ago
updated
a
Space
2 months ago
published
a
Space
2 months ago
liked
a
Space
2 months ago
updated
a
model
3 months ago
published
a
dataset
3 months ago
published
a
model
3 months ago
updated
a
model
3 months ago
published
a
model
3 months ago
updated
a
Space
3 months ago
published
a
Space
3 months ago
updated
a
Space
3 months ago
liked
a
model
3 months ago