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| import torch | |
| from modules import devices | |
| module_in_gpu = None | |
| cpu = torch.device("cpu") | |
| def send_everything_to_cpu(): | |
| global module_in_gpu # pylint: disable=global-statement | |
| 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 # pylint: disable=global-statement | |
| 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(devices.device) | |
| 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 | |
| first_stage_model = sd_model.first_stage_model | |
| first_stage_model_encode = sd_model.first_stage_model.encode | |
| first_stage_model_decode = sd_model.first_stage_model.decode | |
| def first_stage_model_encode_wrap(x): | |
| send_me_to_gpu(first_stage_model, None) | |
| return first_stage_model_encode(x) | |
| def first_stage_model_decode_wrap(z): | |
| send_me_to_gpu(first_stage_model, None) | |
| return first_stage_model_decode(z) | |
| # for SD1, cond_stage_model is CLIP and its NN is in the tranformer frield, but for SD2, it's open clip, and it's in model field | |
| if hasattr(sd_model, 'cond_stage_model') and hasattr(sd_model.cond_stage_model, 'model'): | |
| sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model | |
| # remove several big modules: cond, first_stage, depth/embedder (if applicable), 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, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model | |
| sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None | |
| sd_model.to(devices.device) | |
| sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored | |
| # register hooks for those the first three 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 = first_stage_model_encode_wrap | |
| sd_model.first_stage_model.decode = first_stage_model_decode_wrap | |
| if sd_model.depth_model: | |
| sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu) | |
| if sd_model.embedder: | |
| sd_model.embedder.register_forward_pre_hook(send_me_to_gpu) | |
| parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model | |
| if hasattr(sd_model, 'cond_stage_model') and hasattr(sd_model.cond_stage_model, 'model'): | |
| sd_model.cond_stage_model.model = sd_model.cond_stage_model.transformer | |
| del sd_model.cond_stage_model.transformer | |
| 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(devices.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) | |