Forum Replies Created
-
Posts
-
Like many A1111 extensions, deforum is not compatible with forge. Even the forge version of deforum is broken. You can only use it on A1111.
Thanks for the feedback! It’s great that you find good value in the membership.
I aim to spread the knowledge while getting somewhat compensated (and justified) for my time. I’m glad to find a solution that works both ways.
I agree it’s not entirely straightforward. I usually deal with memory issues as they arise. This can be done using a more memory-efficient version of the model (fp8, fp4), using a smaller image size, and unloading models from memory, etc.
The HiRes fix function often results in memory issues. I am not sure what’s wrong with the implementation. Maybe a flux workflow with increasing batch size or image size is a better way to test.
Welcome, Jessica! Thank you for sharing your beautiful work. It is a great remix of “What is life”!
I’m sorry I didn’t notice your message awaiting my moderation.
This is a good suggestion. Instructions for A1111 and Forge should be interchangeable. I will probably add new ComfyUI ones.
Thanks for the suggestions.
When I wrote the course, I tried to keep it agnostic to how to use A1111 (local/colab/online service). So I didn’t write much about instructions specific to the Colab notebook. I will add them to lessons that require additional extensions.
Several factors determine the relationship between model size and the required VRAM.
- Not all model parts need to be in the memory simultaneously. For example, The CLIP model can be unloaded after processing the prompt, and the VAE is required only after sampling. So, the VRAM required is smaller than the model size.
-
A model’s size is measured by the number of parameters. A parameter can be represented in different precisions in a GPU, e.g., FP32 (high), FP16, and FP8 (low). The lower the precision, the smaller the size of a GPU, but the quality may be reduced.
Optimizations like these enable fixing large models in limited VRAMs.
I use all cloud services in my day job. There’s no hardware maintenance and no noise when running a heavy job. I am fortunate enough that I was allowed to keep the VM running 24/7. I won’t enjoy it as much if I need to shut it down every day.
Some online services, like Think Diffusion, auto-shutdown after a specific amount of time. This is not a bad way to control costs.