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76 lines
2.4 KiB
76 lines
2.4 KiB
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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comfy.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory) |
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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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