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import logging |
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from typing import Optional |
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import torch |
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import comfy.model_management |
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from .base import WeightAdapterBase, weight_decompose |
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class GLoRAAdapter(WeightAdapterBase): |
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name = "glora" |
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def __init__(self, loaded_keys, weights): |
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self.loaded_keys = loaded_keys |
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self.weights = weights |
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@classmethod |
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def load( |
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cls, |
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x: str, |
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lora: dict[str, torch.Tensor], |
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alpha: float, |
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dora_scale: torch.Tensor, |
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loaded_keys: set[str] = None, |
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) -> Optional["GLoRAAdapter"]: |
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if loaded_keys is None: |
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loaded_keys = set() |
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a1_name = "{}.a1.weight".format(x) |
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a2_name = "{}.a2.weight".format(x) |
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b1_name = "{}.b1.weight".format(x) |
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b2_name = "{}.b2.weight".format(x) |
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if a1_name in lora: |
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weights = (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale) |
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loaded_keys.add(a1_name) |
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loaded_keys.add(a2_name) |
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loaded_keys.add(b1_name) |
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loaded_keys.add(b2_name) |
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return cls(loaded_keys, weights) |
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else: |
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return None |
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def calculate_weight( |
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self, |
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weight, |
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key, |
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strength, |
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strength_model, |
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offset, |
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function, |
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intermediate_dtype=torch.float32, |
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original_weight=None, |
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): |
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v = self.weights |
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dora_scale = v[5] |
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old_glora = False |
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if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]: |
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rank = v[0].shape[0] |
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old_glora = True |
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if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]: |
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if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]: |
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pass |
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else: |
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old_glora = False |
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rank = v[1].shape[0] |
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a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype) |
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a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype) |
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b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype) |
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b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype) |
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if v[4] is not None: |
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alpha = v[4] / rank |
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else: |
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alpha = 1.0 |
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try: |
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if old_glora: |
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lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) |
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else: |
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if weight.dim() > 2: |
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lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) |
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else: |
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lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) |
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lora_diff += torch.mm(b1, b2).reshape(weight.shape) |
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if dora_scale is not None: |
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weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) |
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else: |
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weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) |
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except Exception as e: |
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logging.error("ERROR {} {} {}".format(self.name, key, e)) |
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return weight |
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