import torch from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock, DownSampler, UpSampler class SDXLUNet(torch.nn.Module): def __init__(self, is_kolors=False): super().__init__() self.time_proj = Timesteps(320) self.time_embedding = torch.nn.Sequential( torch.nn.Linear(320, 1280), torch.nn.SiLU(), torch.nn.Linear(1280, 1280) ) self.add_time_proj = Timesteps(256) self.add_time_embedding = torch.nn.Sequential( torch.nn.Linear(5632 if is_kolors else 2816, 1280), torch.nn.SiLU(), torch.nn.Linear(1280, 1280) ) self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) self.text_intermediate_proj = torch.nn.Linear(4096, 2048) if is_kolors else None self.blocks = torch.nn.ModuleList([ # DownBlock2D ResnetBlock(320, 320, 1280), PushBlock(), ResnetBlock(320, 320, 1280), PushBlock(), DownSampler(320), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(320, 640, 1280), AttentionBlock(10, 64, 640, 2, 2048), PushBlock(), ResnetBlock(640, 640, 1280), AttentionBlock(10, 64, 640, 2, 2048), PushBlock(), DownSampler(640), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(640, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), PushBlock(), ResnetBlock(1280, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), PushBlock(), # UNetMidBlock2DCrossAttn ResnetBlock(1280, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), ResnetBlock(1280, 1280, 1280), # CrossAttnUpBlock2D PopBlock(), ResnetBlock(2560, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), PopBlock(), ResnetBlock(2560, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), PopBlock(), ResnetBlock(1920, 1280, 1280), AttentionBlock(20, 64, 1280, 10, 2048), UpSampler(1280), # CrossAttnUpBlock2D PopBlock(), ResnetBlock(1920, 640, 1280), AttentionBlock(10, 64, 640, 2, 2048), PopBlock(), ResnetBlock(1280, 640, 1280), AttentionBlock(10, 64, 640, 2, 2048), PopBlock(), ResnetBlock(960, 640, 1280), AttentionBlock(10, 64, 640, 2, 2048), UpSampler(640), # UpBlock2D PopBlock(), ResnetBlock(960, 320, 1280), PopBlock(), ResnetBlock(640, 320, 1280), PopBlock(), ResnetBlock(640, 320, 1280) ]) self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5) self.conv_act = torch.nn.SiLU() self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1) def forward( self, sample, timestep, encoder_hidden_states, add_time_id, add_text_embeds, tiled=False, tile_size=64, tile_stride=8, use_gradient_checkpointing=False, **kwargs ): # 1. time t_emb = self.time_proj(timestep[None]).to(sample.dtype) t_emb = self.time_embedding(t_emb) time_embeds = self.add_time_proj(add_time_id) time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1)) add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1) add_embeds = add_embeds.to(sample.dtype) add_embeds = self.add_time_embedding(add_embeds) time_emb = t_emb + add_embeds # 2. pre-process height, width = sample.shape[2], sample.shape[3] hidden_states = self.conv_in(sample) text_emb = encoder_hidden_states if self.text_intermediate_proj is None else self.text_intermediate_proj(encoder_hidden_states) res_stack = [hidden_states] # 3. blocks def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward for i, block in enumerate(self.blocks): if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock)): hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, time_emb, text_emb, res_stack, use_reentrant=False, ) else: hidden_states, time_emb, text_emb, res_stack = block( hidden_states, time_emb, text_emb, res_stack, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride ) # 4. output hidden_states = self.conv_norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) return hidden_states def state_dict_converter(self): return SDXLUNetStateDictConverter() class SDXLUNetStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): # architecture block_types = [ 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock' ] # Rename each parameter name_list = sorted([name for name in state_dict]) rename_dict = {} block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} for name in name_list: names = name.split(".") if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: pass elif names[0] in ["encoder_hid_proj"]: names[0] = "text_intermediate_proj" elif names[0] in ["time_embedding", "add_embedding"]: if names[0] == "add_embedding": names[0] = "add_time_embedding" names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: if names[0] == "mid_block": names.insert(1, "0") block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] block_type_with_id = ".".join(names[:4]) if block_type_with_id != last_block_type_with_id[block_type]: block_id[block_type] += 1 last_block_type_with_id[block_type] = block_type_with_id while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: block_id[block_type] += 1 block_type_with_id = ".".join(names[:4]) names = ["blocks", str(block_id[block_type])] + names[4:] if "ff" in names: ff_index = names.index("ff") component = ".".join(names[ff_index:ff_index+3]) component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] names = names[:ff_index] + [component] + names[ff_index+3:] if "to_out" in names: names.pop(names.index("to_out") + 1) else: raise ValueError(f"Unknown parameters: {name}") rename_dict[name] = ".".join(names) # Convert state_dict state_dict_ = {} for name, param in state_dict.items(): if ".proj_in." in name or ".proj_out." in name: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_ def from_civitai(self, state_dict): rename_dict = { "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias", "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight", "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": 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