Comments (4)
Hi @jyoung105,
DeepCache is a dynamic model inference algorithm, which makes it incompatible with torch.compile(). One possible solution (though I'm not certain) is to split the entire pipeline into two models: one for the whole network inference and the other for partial network inference (the shallow on). This way, both of these models can be converted into static models, thus supporting torch.compile().
from deepcache.
I think the one you say is on the project 'onediff'.
They enable both torch.compile() and your awesome project. And they ask you to set their way to deal with memory named oneflow, too. Thanks for your kindness!
from deepcache.
Related Issues (20)
- Does it support SD-Inpaint model? HOT 4
- Even more loss-less HOT 1
- is it compatible with Faster-Diffusion?
- Is it useful in the LCM model OpenVino? Can image to image accelerate on CPU
- Base
- It is not work when using the base model in safetensors format. HOT 5
- How much GPU memory is needed to complete the SVD experiment? HOT 3
- deepcache support DPM \DPM etc ?
- Controlnet support?
- pipe.enable_model_cpu_offload() makes every image after first distorted HOT 1
- No Speed Gains HOT 1
- Diffusers 0.26.3 HOT 5
- When i enable Deep Cache my image start being generated as siginficantly more distorted HOT 2
- Complimentary combination with FreeU HOT 1
- Does DeepCache support controlnet? HOT 2
- How to use Actually in local pc HOT 1
- Does DeepCache support LCM? HOT 1
- Use of cache_block_id HOT 4
- Stable Diffusion- WebUI Implementation HOT 2
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from deepcache.