Furkan Gözükara's picture

Furkan Gözükara

MonsterMMORPG

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https://www.youtube.com/@SECourses

AI & ML interests

Check out my youtube page SECourses for Stable Diffusion tutorials. They will help you tremendously in every topic

Recent Activity

new activity about 6 hours ago

MonsterMMORPG/Generative-AI:images

reacted to their post with 🤯 about 6 hours ago

Best open source Image to Video CogVideoX1.5-5B-I2V is pretty decent and optimized for low VRAM machines with high resolution - native resolution is 1360px and up to 10 seconds 161 frames - audios generated with new open source audio model Full YouTube tutorial for CogVideoX1.5-5B-I2V : https://youtu.be/5UCkMzP2VLE 1-Click Windows, RunPod and Massed Compute installers : https://www.patreon.com/posts/112848192 https://www.patreon.com/posts/112848192 - installs into Python 3.11 VENV Official Hugging Face repo of CogVideoX1.5-5B-I2V : https://huggingface.co./THUDM/CogVideoX1.5-5B-I2V Official github repo : https://github.com/THUDM/CogVideo Used prompts to generate videos txt file : https://gist.github.com/FurkanGozukara/471db7b987ab8d9877790358c126ac05 Demo images shared in : https://www.patreon.com/posts/112848192 I used 1360x768px images at 16 FPS and 81 frames = 5 seconds +1 frame coming from initial image Also I have enabled all the optimizations shared on Hugging Face pipe.enable_sequential_cpu_offload() pipe.vae.enable_slicing() pipe.vae.enable_tiling() quantization = int8_weight_only - you need TorchAO and DeepSpeed works great on Windows with Python 3.11 VENV Used audio model : https://github.com/hkchengrex/MMAudio 1-Click Windows, RunPod and Massed Compute Installers for MMAudio : https://www.patreon.com/posts/117990364 https://www.patreon.com/posts/117990364 - Installs into Python 3.10 VENV Used very simple prompts - it fails when there is human in input video so use text to audio in such cases I also tested some VRAM usages for CogVideoX1.5-5B-I2V Resolutions and here their VRAM requirements - may work on lower VRAM GPUs too but slower 512x288 - 41 frames : 7700 MB , 576x320 - 41 frames : 7900 MB 576x320 - 81 frames : 8850 MB , 704x384 - 81 frames : 8950 MB 768x432 - 81 frames : 10600 MB , 896x496 - 81 frames : 12050 MB 896x496 - 81 frames : 12050 MB , 960x528 - 81 frames : 12850 MB

reacted to their post with 🤝 about 6 hours ago

Best open source Image to Video CogVideoX1.5-5B-I2V is pretty decent and optimized for low VRAM machines with high resolution - native resolution is 1360px and up to 10 seconds 161 frames - audios generated with new open source audio model Full YouTube tutorial for CogVideoX1.5-5B-I2V : https://youtu.be/5UCkMzP2VLE 1-Click Windows, RunPod and Massed Compute installers : https://www.patreon.com/posts/112848192 https://www.patreon.com/posts/112848192 - installs into Python 3.11 VENV Official Hugging Face repo of CogVideoX1.5-5B-I2V : https://huggingface.co./THUDM/CogVideoX1.5-5B-I2V Official github repo : https://github.com/THUDM/CogVideo Used prompts to generate videos txt file : https://gist.github.com/FurkanGozukara/471db7b987ab8d9877790358c126ac05 Demo images shared in : https://www.patreon.com/posts/112848192 I used 1360x768px images at 16 FPS and 81 frames = 5 seconds +1 frame coming from initial image Also I have enabled all the optimizations shared on Hugging Face pipe.enable_sequential_cpu_offload() pipe.vae.enable_slicing() pipe.vae.enable_tiling() quantization = int8_weight_only - you need TorchAO and DeepSpeed works great on Windows with Python 3.11 VENV Used audio model : https://github.com/hkchengrex/MMAudio 1-Click Windows, RunPod and Massed Compute Installers for MMAudio : https://www.patreon.com/posts/117990364 https://www.patreon.com/posts/117990364 - Installs into Python 3.10 VENV Used very simple prompts - it fails when there is human in input video so use text to audio in such cases I also tested some VRAM usages for CogVideoX1.5-5B-I2V Resolutions and here their VRAM requirements - may work on lower VRAM GPUs too but slower 512x288 - 41 frames : 7700 MB , 576x320 - 41 frames : 7900 MB 576x320 - 81 frames : 8850 MB , 704x384 - 81 frames : 8950 MB 768x432 - 81 frames : 10600 MB , 896x496 - 81 frames : 12050 MB 896x496 - 81 frames : 12050 MB , 960x528 - 81 frames : 12850 MB

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Articles

Full Training Tutorial and Guide and Research For a FLUX Style

20 New SDXL Fine Tuning Tests and Their Results (Better Workflow Obtained and Published)

Batch size 30 AdamW vs Batch Size 1 Adafactor SDXL Training Comparison

Expert-Level Tutorials on Stable Diffusion & SDXL: Master Advanced Techniques and Strategies

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MonsterMMORPG's activity

upvoted an article 4 months ago

Article

20 New SDXL Fine Tuning Tests and Their Results (Better Workflow Obtained and Published)

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Aug 13

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upvoted an article 5 months ago

Article

Batch size 30 AdamW vs Batch Size 1 Adafactor SDXL Training Comparison

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Aug 8

• 2

upvoted an article 7 months ago

Article

Expert-Level Tutorials on Stable Diffusion & SDXL: Master Advanced Techniques and Strategies

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Jun 3

• 3

upvoted a paper about 1 year ago

PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Paper • 2310.00426 • Published Sep 30, 2023 • 61