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