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offload-never and offload-always per-module and new highvram profile

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-07-31 11:40:24 -04:00
parent 1b3f5405a3
commit fa44521ea3
28 changed files with 144 additions and 91 deletions
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modules/model_quant.py
+7 -7
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@@ -436,7 +436,7 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
else:
getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
if do_gc:
devices.torch_gc(force=True)
devices.torch_gc(force=True, reason='sdnq')
if shared.cmd_opts.medvram or shared.cmd_opts.lowvram or shared.opts.diffusers_offload_mode != "none":
quant_last_model_name = op
quant_last_model_device = model.device
@@ -447,7 +447,7 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
elif shared.opts.diffusers_offload_mode != "none":
model = model.to(devices.cpu)
if do_gc:
devices.torch_gc(force=True)
devices.torch_gc(force=True, reason='sdnq')
return model
@@ -465,7 +465,7 @@ def sdnq_quantize_weights(sd_model):
getattr(getattr(sd_model, last_model_names[0]), last_model_names[1]).to(quant_last_model_device)
else:
getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
devices.torch_gc(force=True)
devices.torch_gc(force=True, reason='sdnq')
quant_last_model_name = None
quant_last_model_device = None
@@ -510,7 +510,7 @@ def optimum_quanto_model(model, op=None, sd_model=None, weights=None, activation
getattr(getattr(sd_model, last_model_names[0]), last_model_names[1]).to(quant_last_model_device)
else:
getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
devices.torch_gc(force=True)
devices.torch_gc(force=True, reason='quanto')
if shared.cmd_opts.medvram or shared.cmd_opts.lowvram or shared.opts.diffusers_offload_mode != "none":
quant_last_model_name = op
quant_last_model_device = model.device
@@ -518,7 +518,7 @@ def optimum_quanto_model(model, op=None, sd_model=None, weights=None, activation
quant_last_model_name = None
quant_last_model_device = None
model.to(devices.device)
devices.torch_gc(force=True)
devices.torch_gc(force=True, reason='quanto')
return model
@@ -540,7 +540,7 @@ def optimum_quanto_weights(sd_model):
getattr(getattr(sd_model, last_model_names[0]), last_model_names[1]).to(quant_last_model_device)
else:
getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
devices.torch_gc(force=True)
devices.torch_gc(force=True, reason='quanto')
quant_last_model_name = None
quant_last_model_device = None
@@ -572,7 +572,7 @@ def optimum_quanto_weights(sd_model):
sd_models.move_model(sd_model, devices.cpu)
if hasattr(sd_model, "encode_prompt"):
sd_model.encode_prompt = original_encode_prompt
devices.torch_gc(force=True)
devices.torch_gc(force=True, reason='quanto')
t1 = time.time()
log.info(f"Quantization: type=Optimum.quanto time={t1-t0:.2f}")