105 lines
4.5 KiB
Python
105 lines
4.5 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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# 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
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if hasattr(sd_model.cond_stage_model, 'model'):
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sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
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# remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then
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# send the model to GPU. Then put modules back. the modules will be in CPU.
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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
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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
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sd_model.to(devices.device)
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sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored
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# register hooks for those the first three models
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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 hasattr(sd_model.cond_stage_model, 'model'):
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sd_model.cond_stage_model.model = sd_model.cond_stage_model.transformer
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del sd_model.cond_stage_model.transformer
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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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