Unload sd_model before loading the other
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@ -38,13 +38,18 @@ def setup_for_low_vram(sd_model, use_medvram):
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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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def first_stage_model_encode_wrap(self, encoder, x):
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send_me_to_gpu(self, None)
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return encoder(x)
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def first_stage_model_decode_wrap(self, decoder, z):
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send_me_to_gpu(self, None)
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return decoder(z)
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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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# remove three big modules, cond, first_stage, 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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@ -56,8 +61,8 @@ def setup_for_low_vram(sd_model, use_medvram):
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# register hooks for those the first two 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 = lambda x, en=sd_model.first_stage_model.encode: first_stage_model_encode_wrap(sd_model.first_stage_model, en, x)
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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)
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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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parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
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if use_medvram:
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@ -597,6 +597,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if p.scripts is not None:
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p.scripts.postprocess(p, res)
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p.sd_model = None
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p.sampler = None
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return res
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@ -94,6 +94,10 @@ class StableDiffusionModelHijack:
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if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
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model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
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self.layers = None
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self.circular_enabled = False
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self.clip = None
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def apply_circular(self, enable):
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if self.circular_enabled == enable:
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return
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@ -1,6 +1,7 @@
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import collections
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import os.path
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import sys
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import gc
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from collections import namedtuple
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import torch
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import re
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@ -220,6 +221,12 @@ def load_model(checkpoint_info=None):
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if checkpoint_info.config != shared.cmd_opts.config:
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print(f"Loading config from: {checkpoint_info.config}")
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if shared.sd_model:
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sd_hijack.model_hijack.undo_hijack(shared.sd_model)
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shared.sd_model = None
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gc.collect()
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devices.torch_gc()
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sd_config = OmegaConf.load(checkpoint_info.config)
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if should_hijack_inpainting(checkpoint_info):
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@ -233,6 +240,7 @@ def load_model(checkpoint_info=None):
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checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
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do_inpainting_hijack()
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sd_model = instantiate_from_config(sd_config.model)
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load_model_weights(sd_model, checkpoint_info)
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@ -252,14 +260,18 @@ def load_model(checkpoint_info=None):
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return sd_model
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def reload_model_weights(sd_model, info=None):
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def reload_model_weights(sd_model=None, info=None):
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from modules import lowvram, devices, sd_hijack
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checkpoint_info = info or select_checkpoint()
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if not sd_model:
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sd_model = shared.sd_model
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if sd_model.sd_model_checkpoint == checkpoint_info.filename:
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return
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if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
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del sd_model
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checkpoints_loaded.clear()
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load_model(checkpoint_info)
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return shared.sd_model
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2
webui.py
2
webui.py
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@ -77,7 +77,7 @@ def initialize():
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modules.scripts.load_scripts()
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modules.sd_models.load_model()
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shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
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shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
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shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
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shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
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