128 lines
4.6 KiB
Python
128 lines
4.6 KiB
Python
import torch
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from modules import devices
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module_in_gpu = None
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cpu = torch.device("cpu")
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def send_everything_to_cpu():
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global module_in_gpu
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if module_in_gpu is not None:
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module_in_gpu.to(cpu)
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module_in_gpu = None
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def setup_for_low_vram(sd_model, use_medvram):
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sd_model.lowvram = True
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parents = {}
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def send_me_to_gpu(module, _):
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"""send this module to GPU; send whatever tracked module was previous in GPU to CPU;
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we add this as forward_pre_hook to a lot of modules and this way all but one of them will
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be in CPU
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"""
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global module_in_gpu
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module = parents.get(module, module)
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if module_in_gpu == module:
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return
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if module_in_gpu is not None:
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module_in_gpu.to(cpu)
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module.to(devices.device)
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module_in_gpu = module
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# see below for register_forward_pre_hook;
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# first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
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# useless here, and we just replace those methods
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first_stage_model = sd_model.first_stage_model
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first_stage_model_encode = sd_model.first_stage_model.encode
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first_stage_model_decode = sd_model.first_stage_model.decode
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def first_stage_model_encode_wrap(x):
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send_me_to_gpu(first_stage_model, None)
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return first_stage_model_encode(x)
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def first_stage_model_decode_wrap(z):
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send_me_to_gpu(first_stage_model, None)
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return first_stage_model_decode(z)
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to_remain_in_cpu = [
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(sd_model, 'first_stage_model'),
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(sd_model, 'depth_model'),
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(sd_model, 'embedder'),
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(sd_model, 'model'),
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(sd_model, 'embedder'),
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]
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is_sdxl = hasattr(sd_model, 'conditioner')
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is_sd2 = not is_sdxl and hasattr(sd_model.cond_stage_model, 'model')
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if is_sdxl:
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to_remain_in_cpu.append((sd_model, 'conditioner'))
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elif is_sd2:
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to_remain_in_cpu.append((sd_model.cond_stage_model, 'model'))
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else:
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to_remain_in_cpu.append((sd_model.cond_stage_model, 'transformer'))
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# remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model
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stored = []
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for obj, field in to_remain_in_cpu:
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module = getattr(obj, field, None)
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stored.append(module)
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setattr(obj, field, None)
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# send the model to GPU.
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sd_model.to(devices.device)
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# put modules back. the modules will be in CPU.
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for (obj, field), module in zip(to_remain_in_cpu, stored):
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setattr(obj, field, module)
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# register hooks for those the first three models
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if is_sdxl:
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sd_model.conditioner.register_forward_pre_hook(send_me_to_gpu)
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elif is_sd2:
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sd_model.cond_stage_model.model.register_forward_pre_hook(send_me_to_gpu)
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else:
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sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
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sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
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sd_model.first_stage_model.encode = first_stage_model_encode_wrap
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sd_model.first_stage_model.decode = first_stage_model_decode_wrap
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if sd_model.depth_model:
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sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
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if sd_model.embedder:
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sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
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parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
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if use_medvram:
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sd_model.model.register_forward_pre_hook(send_me_to_gpu)
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else:
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diff_model = sd_model.model.diffusion_model
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# the third remaining model is still too big for 4 GB, so we also do the same for its submodules
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# so that only one of them is in GPU at a time
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stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed
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diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None
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sd_model.model.to(devices.device)
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diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored
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# install hooks for bits of third model
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diff_model.time_embed.register_forward_pre_hook(send_me_to_gpu)
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for block in diff_model.input_blocks:
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block.register_forward_pre_hook(send_me_to_gpu)
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diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
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for block in diff_model.output_blocks:
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block.register_forward_pre_hook(send_me_to_gpu)
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def is_enabled(sd_model):
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return getattr(sd_model, 'lowvram', False)
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