content.raw: I have done an extensive multi-GPU FLUX Full Fine Tuning / DreamBooth training experimentation on RunPod by using 2x A100–80 GB GPUs (PCIe) since this was commonly asked of me.
content.type: text
content.value: I have done an extensive multi-GPU FLUX Full Fine Tuning / DreamBooth training experimentation on RunPod by using 2x A100–80 GB GPUs (PCIe) since this was commonly asked of me.
content.type: new_line
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content.raw: Full article here : 
content.type: text
content.value: Full article here : 
content.href: https://medium.com/@furkangozukara/multi-gpu-flux-fu
content.raw: https://medium.com/@furkangozukara/multi-gpu-flux-fu
content.type: link
content.type: new_line
content.type: new_line
content.raw: Image 1
content.type: text
content.value: Image 1
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content.raw: Image 1 shows that only first part of installation of Kohya GUI took 30 minutes on a such powerful machine on a very expensive Secure Cloud pod — 3.28 USD per hour
content.type: text
content.value: Image 1 shows that only first part of installation of Kohya GUI took 30 minutes on a such powerful machine on a very expensive Secure Cloud pod — 3.28 USD per hour
content.type: new_line
content.raw: There was also part 2, so just installation took super time
content.type: text
content.value: There was also part 2, so just installation took super time
content.type: new_line
content.raw: On Massed Compute, it would take like 2–3 minutes
content.type: text
content.value: On Massed Compute, it would take like 2–3 minutes
content.type: new_line
content.raw: This is why I suggest you to use Massed Compute over RunPod, RunPod machines have terrible hard disk speeds and they are like lottery to get good ones
content.type: text
content.value: This is why I suggest you to use Massed Compute over RunPod, RunPod machines have terrible hard disk speeds and they are like lottery to get good ones
content.type: new_line
content.type: new_line
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content.raw: Image 2, 3 and 4
content.type: text
content.value: Image 2, 3 and 4
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content.raw: Image 2 shows speed of our very best config FLUX Fine Tuning training shared below when doing 2x Multi GPU training
content.type: text
content.value: Image 2 shows speed of our very best config FLUX Fine Tuning training shared below when doing 2x Multi GPU training
content.type: new_line
content.href: https://www.patreon.com/posts/kohya-flux-fine-112099700
content.raw: https://www.patreon.com/posts/kohya-flux-fine-112099700
content.type: link
content.type: new_line
content.raw: Used config name is : Quality_1_27500MB_6_26_Second_IT.json
content.type: text
content.value: Used config name is : Quality_1_27500MB_6_26_Second_IT.json
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content.raw: Image 3 shows VRAM usage of this config when doing 2x Multi GPU training
content.type: text
content.value: Image 3 shows VRAM usage of this config when doing 2x Multi GPU training
content.type: new_line
content.raw: Image 4 shows the GPUs of the Pod
content.type: text
content.value: Image 4 shows the GPUs of the Pod
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content.raw: Image 5 and 6
content.type: text
content.value: Image 5 and 6
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content.raw: Image 5 shows speed of our very best config FLUX Fine Tuning training shared below when doing a single GPU training
content.type: text
content.value: Image 5 shows speed of our very best config FLUX Fine Tuning training shared below when doing a single GPU training
content.type: new_line
content.href: https://www.patreon.com/posts/kohya-flux-fine-112099700
content.raw: https://www.patreon.com/posts/kohya-flux-fine-112099700
content.type: link
content.type: new_line
content.raw: Used config name is : Quality_1_27500MB_6_26_Second_IT.json
content.type: text
content.value: Used config name is : Quality_1_27500MB_6_26_Second_IT.json
content.type: new_line
content.raw: Image 6 shows this setup used VRAM amount
content.type: text
content.value: Image 6 shows this setup used VRAM amount
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content.type: new_line
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content.raw: Image 7 and 8
content.type: text
content.value: Image 7 and 8
content.type: new_line
content.raw: Image 7 shows speed of our very best config FLUX Fine Tuning training shared below when doing a single GPU training and Gradient Checkpointing is disabled
content.type: text
content.value: Image 7 shows speed of our very best config FLUX Fine Tuning training shared below when doing a single GPU training and Gradient Checkpointing is disabled
content.type: new_line
content.href: https://www.patreon.com/posts/kohya-flux-fine-112099700
content.raw: https://www.patreon.com/posts/kohya-flux-fine-112099700
content.type: link
content.type: new_line
content.raw: Used config name is : Quality_1_27500MB_6_26_Second_IT.json
content.type: text
content.value: Used config name is : Quality_1_27500MB_6_26_Second_IT.json
content.type: new_line
content.raw: Image 8 shows this setup used VRAM amount
content.type: text
content.value: Image 8 shows this setup used VRAM amount
content.type: new_line
content.type: new_line
content.type: new_line
content.raw: ....
content.type: text
content.value: ....
rawContent: I have done an extensive multi-GPU FLUX Full Fine Tuning / DreamBooth training experimentation on RunPod by using 2x A100–80 GB GPUs (PCIe) since this was commonly asked of me.
  
  Full article here : https://medium.com/@furkangozukara/multi-gpu-flux-fu
  
  Image 1
  Image 1 shows that only first part of installation of Kohya GUI took 30 minutes on a such powerful machine on a very expensive Secure Cloud pod — 3.28 USD per hour
  There was also part 2, so just installation took super time
  On Massed Compute, it would take like 2–3 minutes
  This is why I suggest you to use Massed Compute over RunPod, RunPod machines have terrible hard disk speeds and they are like lottery to get good ones
  
  
  
  Image 2, 3 and 4
  Image 2 shows speed of our very best config FLUX Fine Tuning training shared below when doing 2x Multi GPU training
  https://www.patreon.com/posts/kohya-flux-fine-112099700
  Used config name is : Quality_1_27500MB_6_26_Second_IT.json
  Image 3 shows VRAM usage of this config when doing 2x Multi GPU training
  Image 4 shows the GPUs of the Pod
  
  
  Image 5 and 6
  Image 5 shows speed of our very best config FLUX Fine Tuning training shared below when doing a single GPU training
  https://www.patreon.com/posts/kohya-flux-fine-112099700
  Used config name is : Quality_1_27500MB_6_26_Second_IT.json
  Image 6 shows this setup used VRAM amount
  
  
  Image 7 and 8
  Image 7 shows speed of our very best config FLUX Fine Tuning training shared below when doing a single GPU training and Gradient Checkpointing is disabled
  https://www.patreon.com/posts/kohya-flux-fine-112099700
  Used config name is : Quality_1_27500MB_6_26_Second_IT.json
  Image 8 shows this setup used VRAM amount
  
  
  ....
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