79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
import torch
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import comfy.model_management
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import comfy.conds
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def prepare_mask(noise_mask, shape, device):
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"""ensures noise mask is of proper dimensions"""
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noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
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noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
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noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0])
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noise_mask = noise_mask.to(device)
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return noise_mask
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def get_models_from_cond(cond, model_type):
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models = []
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for c in cond:
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if model_type in c:
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models += [c[model_type]]
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return models
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def convert_cond(cond):
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out = []
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for c in cond:
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temp = c[1].copy()
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model_conds = temp.get("model_conds", {})
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if c[0] is not None:
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model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove
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temp["cross_attn"] = c[0]
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temp["model_conds"] = model_conds
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out.append(temp)
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return out
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def get_additional_models(conds, dtype):
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"""loads additional models in conditioning"""
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cnets = []
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gligen = []
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for k in conds:
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cnets += get_models_from_cond(conds[k], "control")
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gligen += get_models_from_cond(conds[k], "gligen")
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control_nets = set(cnets)
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inference_memory = 0
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control_models = []
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for m in control_nets:
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control_models += m.get_models()
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inference_memory += m.inference_memory_requirements(dtype)
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gligen = [x[1] for x in gligen]
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models = control_models + gligen
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return models, inference_memory
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def cleanup_additional_models(models):
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"""cleanup additional models that were loaded"""
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for m in models:
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if hasattr(m, 'cleanup'):
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m.cleanup()
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def prepare_sampling(model, noise_shape, conds):
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device = model.load_device
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real_model = None
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models, inference_memory = get_additional_models(conds, model.model_dtype())
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memory_required = model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory
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minimum_memory_required = model.memory_required([noise_shape[0]] + list(noise_shape[1:])) + inference_memory
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comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required)
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real_model = model.model
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return real_model, conds, models
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def cleanup_models(conds, models):
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cleanup_additional_models(models)
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control_cleanup = []
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for k in conds:
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control_cleanup += get_models_from_cond(conds[k], "control")
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cleanup_additional_models(set(control_cleanup))
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