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Refactor sampling code for more advanced sampler nodes.

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comfyanonymous
2024-04-03 16:34:19 -04:00
parent 6c6a39251f
commit 57753c964a
2 changed files with 68 additions and 44 deletions
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38
comfy/sample.py
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@@ -52,9 +52,16 @@ def convert_cond(cond):
out.append(temp)
return out
def get_additional_models(positive, negative, dtype):
"""loads additional models in positive and negative conditioning"""
control_nets = set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control"))
def get_additional_models(conds, dtype):
"""loads additional models in conditioning"""
cnets = []
gligen = []
for i in range(len(conds)):
cnets += get_models_from_cond(conds[i], "control")
gligen += get_models_from_cond(conds[i], "gligen")
control_nets = set(cnets)
inference_memory = 0
control_models = []
@@ -62,7 +69,6 @@ def get_additional_models(positive, negative, dtype):
control_models += m.get_models()
inference_memory += m.inference_memory_requirements(dtype)
gligen = get_models_from_cond(positive, "gligen") + get_models_from_cond(negative, "gligen")
gligen = [x[1] for x in gligen]
models = control_models + gligen
return models, inference_memory
@@ -73,24 +79,25 @@ def cleanup_additional_models(models):
if hasattr(m, 'cleanup'):
m.cleanup()
def prepare_sampling(model, noise_shape, positive, negative, noise_mask):
def prepare_sampling(model, noise_shape, conds, noise_mask):
device = model.load_device
positive = convert_cond(positive)
negative = convert_cond(negative)
for i in range(len(conds)):
conds[i] = convert_cond(conds[i])
if noise_mask is not None:
noise_mask = prepare_mask(noise_mask, noise_shape, device)
real_model = None
models, inference_memory = get_additional_models(positive, negative, model.model_dtype())
models, inference_memory = get_additional_models(conds, model.model_dtype())
comfy.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory)
real_model = model.model
return real_model, positive, negative, noise_mask, models
return real_model, conds, noise_mask, models
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):
real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask)
real_model, conds_copy, noise_mask, models = prepare_sampling(model, noise.shape, [positive, negative], noise_mask)
positive_copy, negative_copy = conds_copy
noise = noise.to(model.load_device)
latent_image = latent_image.to(model.load_device)
@@ -105,14 +112,19 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
return samples
def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None):
real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask)
real_model, conds, noise_mask, models = prepare_sampling(model, noise.shape, [positive, negative], noise_mask)
noise = noise.to(model.load_device)
latent_image = latent_image.to(model.load_device)
sigmas = sigmas.to(model.load_device)
samples = comfy.samplers.sample(real_model, noise, positive_copy, negative_copy, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
samples = comfy.samplers.sample(real_model, noise, conds[0], conds[1], cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
samples = samples.to(comfy.model_management.intermediate_device())
cleanup_additional_models(models)
cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control")))
control_cleanup = []
for i in range(len(conds)):
control_cleanup += get_models_from_cond(conds[i], "control")
cleanup_additional_models(set(control_cleanup))
return samples