| | |
| | import torch |
| | import gc |
| | from diffusers import DiffusionPipeline |
| |
|
| | shape = (30_000, 30_000) |
| |
|
| | input = torch.randn(shape, device="cuda") |
| |
|
| |
|
| | def clear_memory(model): |
| | model.to('cpu') |
| | gc.collect() |
| | torch.cuda.empty_cache() |
| | torch.cuda.ipc_collect() |
| | torch.clear_autocast_cache() |
| |
|
| | for _ids in ["runwayml/stable-diffusion-v1-5", "CompVis/stable-diffusion-v1-4", "runwayml/stable-diffusion-v1-5", "CompVis/stable-diffusion-v1-4", "runwayml/stable-diffusion-v1-5"]: |
| | pipe = DiffusionPipeline.from_pretrained(_ids, use_safetensors=True).to("cuda") |
| | pipe("hey", num_inference_steps=1) |
| | print("finished...") |
| |
|
| | clear_memory(pipe) |
| |
|
