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Smarter memory management.

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Try to keep models on the vram when possible.

Better lowvram mode for controlnets.
This commit is contained in:
comfyanonymous
2023-08-17 01:06:34 -04:00
parent 2c97c30256
commit 89a0767abf
6 changed files with 230 additions and 168 deletions
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19
comfy/sample.py
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@@ -51,19 +51,24 @@ def get_models_from_cond(cond, model_type):
models += [c[1][model_type]]
return models
def load_additional_models(positive, negative, dtype):
def get_additional_models(positive, negative):
"""loads additional models in positive and negative conditioning"""
control_nets = get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")
control_models = []
for m in control_nets:
control_models += m.get_models()
gligen = get_models_from_cond(positive, "gligen") + get_models_from_cond(negative, "gligen")
gligen = [x[1].to(dtype) for x in gligen]
models = control_nets + gligen
comfy.model_management.load_controlnet_gpu(models)
gligen = [x[1] for x in gligen]
models = control_models + gligen
return models
def cleanup_additional_models(models):
"""cleanup additional models that were loaded"""
for m in models:
m.cleanup()
if hasattr(m, 'cleanup'):
m.cleanup()
def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
device = comfy.model_management.get_torch_device()
@@ -72,7 +77,8 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
noise_mask = prepare_mask(noise_mask, noise.shape, device)
real_model = None
comfy.model_management.load_model_gpu(model)
models = get_additional_models(positive, negative)
comfy.model_management.load_models_gpu([model] + models, comfy.model_management.batch_area_memory(noise.shape[2] * noise.shape[3]))
real_model = model.model
noise = noise.to(device)
@@ -81,7 +87,6 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
positive_copy = broadcast_cond(positive, noise.shape[0], device)
negative_copy = broadcast_cond(negative, noise.shape[0], device)
models = load_additional_models(positive, negative, model.model_dtype())
sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)