Low vram mode for controlnets.
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220a72d36b
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@ -102,6 +102,12 @@ def load_model_gpu(model):
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def load_controlnet_gpu(models):
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global current_gpu_controlnets
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global vram_state
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if vram_state == LOW_VRAM or vram_state == NO_VRAM:
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#don't load controlnets like this if low vram because they will be loaded right before running and unloaded right after
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return
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for m in current_gpu_controlnets:
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if m not in models:
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m.cpu()
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@ -111,6 +117,19 @@ def load_controlnet_gpu(models):
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current_gpu_controlnets.append(m.cuda())
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def load_if_low_vram(model):
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global vram_state
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if vram_state == LOW_VRAM or vram_state == NO_VRAM:
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return model.cuda()
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return model
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def unload_if_low_vram(model):
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global vram_state
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if vram_state == LOW_VRAM or vram_state == NO_VRAM:
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return model.cpu()
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return model
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def get_free_memory():
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dev = torch.cuda.current_device()
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stats = torch.cuda.memory_stats(dev)
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@ -349,7 +349,9 @@ class ControlNet:
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precision_scope = contextlib.nullcontext
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with precision_scope(self.device):
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self.control_model = model_management.load_if_low_vram(self.control_model)
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control = self.control_model(x=x_noisy, hint=self.cond_hint, timesteps=t, context=cond_txt)
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self.control_model = model_management.unload_if_low_vram(self.control_model)
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out = []
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autocast_enabled = torch.is_autocast_enabled()
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for x in control:
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