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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
inference working but SLOW
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v0xie
parent
ec718f76b5
commit
1c6efdbba7
@ -12,6 +12,7 @@ class ModuleTypeOFT(network.ModuleType):
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# adapted from https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
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class NetworkModuleOFT(network.NetworkModule):
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def __init__(self, net: network.Network, weights: network.NetworkWeights):
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super().__init__(net, weights)
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self.oft_blocks = weights.w["oft_blocks"]
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@ -20,24 +21,29 @@ class NetworkModuleOFT(network.NetworkModule):
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self.dim = self.oft_blocks.shape[0]
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self.num_blocks = self.dim
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#if type(self.alpha) == torch.Tensor:
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# self.alpha = self.alpha.detach().numpy()
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if "Linear" in self.sd_module.__class__.__name__:
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self.out_dim = self.sd_module.out_features
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elif "Conv" in self.sd_module.__class__.__name__:
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self.out_dim = self.sd_module.out_channels
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self.constraint = self.alpha * self.out_dim
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self.constraint = self.alpha
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#self.constraint = self.alpha * self.out_dim
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self.block_size = self.out_dim // self.num_blocks
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self.oft_multiplier = self.multiplier()
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self.org_module: list[torch.Module] = [self.sd_module]
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self.R = self.get_weight()
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self.apply_to()
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# replace forward method of original linear rather than replacing the module
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# self.org_forward = self.sd_module.forward
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# self.sd_module.forward = self.forward
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def apply_to(self):
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self.org_forward = self.org_module[0].forward
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self.org_module[0].forward = self.forward
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def get_weight(self):
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def get_weight(self, multiplier=None):
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if not multiplier:
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multiplier = self.multiplier()
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block_Q = self.oft_blocks - self.oft_blocks.transpose(1, 2)
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norm_Q = torch.norm(block_Q.flatten())
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new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
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@ -45,38 +51,31 @@ class NetworkModuleOFT(network.NetworkModule):
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I = torch.eye(self.block_size, device=self.oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
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block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())
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block_R_weighted = self.oft_multiplier * block_R + (1 - self.oft_multiplier) * I
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block_R_weighted = multiplier * block_R + (1 - multiplier) * I
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R = torch.block_diag(*block_R_weighted)
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return R
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def calc_updown(self, orig_weight):
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oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
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block_Q = oft_blocks - oft_blocks.transpose(1, 2)
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norm_Q = torch.norm(block_Q.flatten())
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new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
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block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
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I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
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block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())
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block_R_weighted = self.oft_multiplier * block_R + (1 - self.oft_multiplier) * I
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R = torch.block_diag(*block_R_weighted)
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#R = self.get_weight().to(orig_weight.device, dtype=orig_weight.dtype)
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# W = R*W_0
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updown = orig_weight + R
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output_shape = [R.size(0), orig_weight.size(1)]
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R = self.R
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if orig_weight.dim() == 4:
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weight = torch.einsum("oihw, op -> pihw", orig_weight, R)
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else:
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weight = torch.einsum("oi, op -> pi", orig_weight, R)
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updown = orig_weight @ R
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output_shape = [orig_weight.size(0), R.size(1)]
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#output_shape = [R.size(0), orig_weight.size(1)]
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return self.finalize_updown(updown, orig_weight, output_shape)
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# def forward(self, x, y=None):
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# x = self.org_forward(x)
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# if self.oft_multiplier == 0.0:
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# return x
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# R = self.get_weight().to(x.device, dtype=x.dtype)
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# if x.dim() == 4:
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# x = x.permute(0, 2, 3, 1)
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# x = torch.matmul(x, R)
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# x = x.permute(0, 3, 1, 2)
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# else:
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# x = torch.matmul(x, R)
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# return x
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def forward(self, x, y=None):
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x = self.org_forward(x)
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if self.multiplier() == 0.0:
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return x
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R = self.get_weight().to(x.device, dtype=x.dtype)
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if x.dim() == 4:
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x = x.permute(0, 2, 3, 1)
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x = torch.matmul(x, R)
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x = x.permute(0, 3, 1, 2)
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else:
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x = torch.matmul(x, R)
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return x
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@ -170,6 +170,10 @@ def load_network(name, network_on_disk):
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emb_dict[vec_name] = weight
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bundle_embeddings[emb_name] = emb_dict
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#if key_network_without_network_parts == "oft_unet":
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# print(key_network_without_network_parts)
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# pass
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key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
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sd_module = shared.sd_model.network_layer_mapping.get(key, None)
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@ -185,15 +189,39 @@ def load_network(name, network_on_disk):
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elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts:
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key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
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sd_module = shared.sd_model.network_layer_mapping.get(key, None)
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elif sd_module is None and "oft_unet" in key_network_without_network_parts:
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key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
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sd_module = shared.sd_model.network_layer_mapping.get(key, None)
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# some SD1 Loras also have correct compvis keys
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if sd_module is None:
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key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
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sd_module = shared.sd_model.network_layer_mapping.get(key, None)
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elif sd_module is None and "oft_unet" in key_network_without_network_parts:
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# UNET_TARGET_REPLACE_MODULE_ALL_LINEAR = ["Transformer2DModel"]
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# UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
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UNET_TARGET_REPLACE_MODULE_ATTN_ONLY = ["CrossAttention"]
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# TODO: Change matchedm odules based on whether all linear, conv, etc
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key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
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sd_module = shared.sd_model.network_layer_mapping.get(key, None)
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#key_no_suffix = key.rsplit("_to_", 1)[0]
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## Match all modules of class CrossAttention
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#replace_module_list = []
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#for module_type in UNET_TARGET_REPLACE_MODULE_ATTN_ONLY:
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# replace_module_list += [module for k, module in shared.sd_model.network_layer_mapping.items() if module_type in module.__class__.__name__]
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#matched_module = replace_module_list.get(key_no_suffix, None)
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#if key.endswith('to_q'):
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# sd_module = matched_module.to_q or None
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#if key.endswith('to_k'):
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# sd_module = matched_module.to_k or None
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#if key.endswith('to_v'):
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# sd_module = matched_module.to_v or None
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#if key.endswith('to_out_0'):
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# sd_module = matched_module.to_out[0] or None
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#if key.endswith('to_out_1'):
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# sd_module = matched_module.to_out[1] or None
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if sd_module is None:
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keys_failed_to_match[key_network] = key
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continue
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@ -215,6 +243,14 @@ def load_network(name, network_on_disk):
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net.modules[key] = net_module
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# replaces forward method of original Linear
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# applied_to_count = 0
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#for key, created_module in net.modules.items():
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# if isinstance(created_module, network_oft.NetworkModuleOFT):
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# net_module.apply_to()
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#applied_to_count += 1
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# print(f'Applied OFT modules: {applied_to_count}')
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embeddings = {}
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for emb_name, data in bundle_embeddings.items():
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embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)
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