diff --git "a/diffsynth/models/sdxl_unet.py" "b/diffsynth/models/sdxl_unet.py" new file mode 100644--- /dev/null +++ "b/diffsynth/models/sdxl_unet.py" @@ -0,0 +1,1892 @@ +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": "blocks.0.time_emb_proj.bias", + "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", + "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", + "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", + "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", + 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