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| import torch | |
| from modules.devices import get_optimal_device | |
| module_in_gpu = None | |
| cpu = torch.device("cpu") | |
| device = gpu = get_optimal_device() | |
| def send_everything_to_cpu(): | |
| global module_in_gpu | |
| if module_in_gpu is not None: | |
| module_in_gpu.to(cpu) | |
| module_in_gpu = None | |
| def setup_for_low_vram(sd_model, use_medvram): | |
| parents = {} | |
| def send_me_to_gpu(module, _): | |
| """send this module to GPU; send whatever tracked module was previous in GPU to CPU; | |
| we add this as forward_pre_hook to a lot of modules and this way all but one of them will | |
| be in CPU | |
| """ | |
| global module_in_gpu | |
| module = parents.get(module, module) | |
| if module_in_gpu == module: | |
| return | |
| if module_in_gpu is not None: | |
| module_in_gpu.to(cpu) | |
| module.to(gpu) | |
| module_in_gpu = module | |
| # see below for register_forward_pre_hook; | |
| # first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is | |
| # useless here, and we just replace those methods | |
| def first_stage_model_encode_wrap(self, encoder, x): | |
| send_me_to_gpu(self, None) | |
| return encoder(x) | |
| def first_stage_model_decode_wrap(self, decoder, z): | |
| send_me_to_gpu(self, None) | |
| return decoder(z) | |
| # remove three big modules, cond, first_stage, and unet from the model and then | |
| # send the model to GPU. Then put modules back. the modules will be in CPU. | |
| stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model | |
| sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None | |
| sd_model.to(device) | |
| sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored | |
| # register hooks for those the first two models | |
| sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu) | |
| sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu) | |
| sd_model.first_stage_model.encode = lambda x, en=sd_model.first_stage_model.encode: first_stage_model_encode_wrap(sd_model.first_stage_model, en, x) | |
| sd_model.first_stage_model.decode = lambda z, de=sd_model.first_stage_model.decode: first_stage_model_decode_wrap(sd_model.first_stage_model, de, z) | |
| parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model | |
| if use_medvram: | |
| sd_model.model.register_forward_pre_hook(send_me_to_gpu) | |
| else: | |
| diff_model = sd_model.model.diffusion_model | |
| # the third remaining model is still too big for 4 GB, so we also do the same for its submodules | |
| # so that only one of them is in GPU at a time | |
| stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed | |
| diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None | |
| sd_model.model.to(device) | |
| diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored | |
| # install hooks for bits of third model | |
| diff_model.time_embed.register_forward_pre_hook(send_me_to_gpu) | |
| for block in diff_model.input_blocks: | |
| block.register_forward_pre_hook(send_me_to_gpu) | |
| diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu) | |
| for block in diff_model.output_blocks: | |
| block.register_forward_pre_hook(send_me_to_gpu) | |