import torch from einops import rearrange from .svd_unet import TemporalTimesteps from .tiler import TileWorker class RMSNorm(torch.nn.Module): def __init__(self, dim, eps, elementwise_affine=True): super().__init__() self.eps = eps if elementwise_affine: self.weight = torch.nn.Parameter(torch.ones((dim,))) else: self.weight = None def forward(self, hidden_states): input_dtype = hidden_states.dtype variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.eps) hidden_states = hidden_states.to(input_dtype) if self.weight is not None: hidden_states = hidden_states * self.weight return hidden_states class PatchEmbed(torch.nn.Module): def __init__(self, patch_size=2, in_channels=16, embed_dim=1536, pos_embed_max_size=192): super().__init__() self.pos_embed_max_size = pos_embed_max_size self.patch_size = patch_size self.proj = torch.nn.Conv2d(in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size) self.pos_embed = torch.nn.Parameter(torch.zeros(1, self.pos_embed_max_size, self.pos_embed_max_size, embed_dim)) def cropped_pos_embed(self, height, width): height = height // self.patch_size width = width // self.patch_size top = (self.pos_embed_max_size - height) // 2 left = (self.pos_embed_max_size - width) // 2 spatial_pos_embed = self.pos_embed[:, top : top + height, left : left + width, :].flatten(1, 2) return spatial_pos_embed def forward(self, latent): height, width = latent.shape[-2:] latent = self.proj(latent) latent = latent.flatten(2).transpose(1, 2) pos_embed = self.cropped_pos_embed(height, width) return latent + pos_embed class TimestepEmbeddings(torch.nn.Module): def __init__(self, dim_in, dim_out, computation_device=None): super().__init__() self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device) self.timestep_embedder = torch.nn.Sequential( torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out) ) def forward(self, timestep, dtype): time_emb = self.time_proj(timestep).to(dtype) time_emb = self.timestep_embedder(time_emb) return time_emb class AdaLayerNorm(torch.nn.Module): def __init__(self, dim, single=False, dual=False): super().__init__() self.single = single self.dual = dual self.linear = torch.nn.Linear(dim, dim * [[6, 2][single], 9][dual]) self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) def forward(self, x, emb): emb = self.linear(torch.nn.functional.silu(emb)) if self.single: scale, shift = emb.unsqueeze(1).chunk(2, dim=2) x = self.norm(x) * (1 + scale) + shift return x elif self.dual: shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_msa2, scale_msa2, gate_msa2 = emb.unsqueeze(1).chunk(9, dim=2) norm_x = self.norm(x) x = norm_x * (1 + scale_msa) + shift_msa norm_x2 = norm_x * (1 + scale_msa2) + shift_msa2 return x, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_x2, gate_msa2 else: shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.unsqueeze(1).chunk(6, dim=2) x = self.norm(x) * (1 + scale_msa) + shift_msa return x, gate_msa, shift_mlp, scale_mlp, gate_mlp class JointAttention(torch.nn.Module): def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False, use_rms_norm=False): super().__init__() self.num_heads = num_heads self.head_dim = head_dim self.only_out_a = only_out_a self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3) self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3) self.a_to_out = torch.nn.Linear(dim_a, dim_a) if not only_out_a: self.b_to_out = torch.nn.Linear(dim_b, dim_b) if use_rms_norm: self.norm_q_a = RMSNorm(head_dim, eps=1e-6) self.norm_k_a = RMSNorm(head_dim, eps=1e-6) self.norm_q_b = RMSNorm(head_dim, eps=1e-6) self.norm_k_b = RMSNorm(head_dim, eps=1e-6) else: self.norm_q_a = None self.norm_k_a = None self.norm_q_b = None self.norm_k_b = None def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k): batch_size = hidden_states.shape[0] qkv = to_qkv(hidden_states) qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) q, k, v = qkv.chunk(3, dim=1) if norm_q is not None: q = norm_q(q) if norm_k is not None: k = norm_k(k) return q, k, v def forward(self, hidden_states_a, hidden_states_b): batch_size = hidden_states_a.shape[0] qa, ka, va = self.process_qkv(hidden_states_a, self.a_to_qkv, self.norm_q_a, self.norm_k_a) qb, kb, vb = self.process_qkv(hidden_states_b, self.b_to_qkv, self.norm_q_b, self.norm_k_b) q = torch.concat([qa, qb], dim=2) k = torch.concat([ka, kb], dim=2) v = torch.concat([va, vb], dim=2) hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) hidden_states = hidden_states.to(q.dtype) hidden_states_a, hidden_states_b = hidden_states[:, :hidden_states_a.shape[1]], hidden_states[:, hidden_states_a.shape[1]:] hidden_states_a = self.a_to_out(hidden_states_a) if self.only_out_a: return hidden_states_a else: hidden_states_b = self.b_to_out(hidden_states_b) return hidden_states_a, hidden_states_b class SingleAttention(torch.nn.Module): def __init__(self, dim_a, num_heads, head_dim, use_rms_norm=False): super().__init__() self.num_heads = num_heads self.head_dim = head_dim self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3) self.a_to_out = torch.nn.Linear(dim_a, dim_a) if use_rms_norm: self.norm_q_a = RMSNorm(head_dim, eps=1e-6) self.norm_k_a = RMSNorm(head_dim, eps=1e-6) else: self.norm_q_a = None self.norm_k_a = None def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k): batch_size = hidden_states.shape[0] qkv = to_qkv(hidden_states) qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) q, k, v = qkv.chunk(3, dim=1) if norm_q is not None: q = norm_q(q) if norm_k is not None: k = norm_k(k) return q, k, v def forward(self, hidden_states_a): batch_size = hidden_states_a.shape[0] q, k, v = self.process_qkv(hidden_states_a, self.a_to_qkv, self.norm_q_a, self.norm_k_a) hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) hidden_states = hidden_states.to(q.dtype) hidden_states = self.a_to_out(hidden_states) return hidden_states class DualTransformerBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads, use_rms_norm=False): super().__init__() self.norm1_a = AdaLayerNorm(dim, dual=True) self.norm1_b = AdaLayerNorm(dim) self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm) self.attn2 = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm) self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_a = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_b = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) def forward(self, hidden_states_a, hidden_states_b, temb): norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a, norm_hidden_states_a_2, gate_msa_a_2 = self.norm1_a(hidden_states_a, emb=temb) norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb) # Attention attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b) # Part A hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a hidden_states_a = hidden_states_a + gate_msa_a_2 * self.attn2(norm_hidden_states_a_2) norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) # Part B hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b) return hidden_states_a, hidden_states_b class JointTransformerBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads, use_rms_norm=False, dual=False): super().__init__() self.norm1_a = AdaLayerNorm(dim, dual=dual) self.norm1_b = AdaLayerNorm(dim) self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm) if dual: self.attn2 = SingleAttention(dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm) self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_a = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_b = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) def forward(self, hidden_states_a, hidden_states_b, temb): if self.norm1_a.dual: norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a, norm_hidden_states_a_2, gate_msa_a_2 = self.norm1_a(hidden_states_a, emb=temb) else: norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb) norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb) # Attention attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b) # Part A hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a if self.norm1_a.dual: hidden_states_a = hidden_states_a + gate_msa_a_2 * self.attn2(norm_hidden_states_a_2) norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) # Part B hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b) return hidden_states_a, hidden_states_b class JointTransformerFinalBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads, use_rms_norm=False): super().__init__() self.norm1_a = AdaLayerNorm(dim) self.norm1_b = AdaLayerNorm(dim, single=True) self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, only_out_a=True, use_rms_norm=use_rms_norm) self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_a = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) def forward(self, hidden_states_a, hidden_states_b, temb): norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb) norm_hidden_states_b = self.norm1_b(hidden_states_b, emb=temb) # Attention attn_output_a = self.attn(norm_hidden_states_a, norm_hidden_states_b) # Part A hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) return hidden_states_a, hidden_states_b class SD3DiT(torch.nn.Module): def __init__(self, embed_dim=1536, num_layers=24, use_rms_norm=False, num_dual_blocks=0, pos_embed_max_size=192): super().__init__() self.pos_embedder = PatchEmbed(patch_size=2, in_channels=16, embed_dim=embed_dim, pos_embed_max_size=pos_embed_max_size) self.time_embedder = TimestepEmbeddings(256, embed_dim) self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(2048, embed_dim), torch.nn.SiLU(), torch.nn.Linear(embed_dim, embed_dim)) self.context_embedder = torch.nn.Linear(4096, embed_dim) self.blocks = torch.nn.ModuleList([JointTransformerBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm, dual=True) for _ in range(num_dual_blocks)] + [JointTransformerBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm) for _ in range(num_layers-1-num_dual_blocks)] + [JointTransformerFinalBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm)]) self.norm_out = AdaLayerNorm(embed_dim, single=True) self.proj_out = torch.nn.Linear(embed_dim, 64) def tiled_forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size=128, tile_stride=64): # Due to the global positional embedding, we cannot implement layer-wise tiled forward. hidden_states = TileWorker().tiled_forward( lambda x: self.forward(x, timestep, prompt_emb, pooled_prompt_emb), hidden_states, tile_size, tile_stride, tile_device=hidden_states.device, tile_dtype=hidden_states.dtype ) return hidden_states def forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tiled=False, tile_size=128, tile_stride=64, use_gradient_checkpointing=False): if tiled: return self.tiled_forward(hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size, tile_stride) conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb) prompt_emb = self.context_embedder(prompt_emb) height, width = hidden_states.shape[-2:] hidden_states = self.pos_embedder(hidden_states) def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward for block in self.blocks: if self.training and use_gradient_checkpointing: hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, prompt_emb, conditioning, use_reentrant=False, ) else: hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning) hidden_states = self.norm_out(hidden_states, conditioning) hidden_states = self.proj_out(hidden_states) hidden_states = rearrange(hidden_states, "B (H W) (P Q C) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2) return hidden_states @staticmethod def state_dict_converter(): return SD3DiTStateDictConverter() class SD3DiTStateDictConverter: def __init__(self): pass def infer_architecture(self, state_dict): embed_dim = state_dict["blocks.0.ff_a.0.weight"].shape[1] num_layers = 100 while num_layers > 0 and f"blocks.{num_layers-1}.ff_a.0.bias" not in state_dict: num_layers -= 1 use_rms_norm = "blocks.0.attn.norm_q_a.weight" in state_dict num_dual_blocks = 0 while f"blocks.{num_dual_blocks}.attn2.a_to_out.bias" in state_dict: num_dual_blocks += 1 pos_embed_max_size = state_dict["pos_embedder.pos_embed"].shape[1] return { "embed_dim": embed_dim, "num_layers": num_layers, "use_rms_norm": use_rms_norm, "num_dual_blocks": num_dual_blocks, "pos_embed_max_size": pos_embed_max_size } def from_diffusers(self, state_dict): rename_dict = { "context_embedder": "context_embedder", "pos_embed.pos_embed": "pos_embedder.pos_embed", "pos_embed.proj": "pos_embedder.proj", "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0", "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2", "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0", "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2", "norm_out.linear": "norm_out.linear", "proj_out": "proj_out", "norm1.linear": "norm1_a.linear", "norm1_context.linear": "norm1_b.linear", "attn.to_q": "attn.a_to_q", "attn.to_k": "attn.a_to_k", "attn.to_v": "attn.a_to_v", "attn.to_out.0": "attn.a_to_out", "attn.add_q_proj": "attn.b_to_q", "attn.add_k_proj": "attn.b_to_k", "attn.add_v_proj": "attn.b_to_v", "attn.to_add_out": "attn.b_to_out", "ff.net.0.proj": "ff_a.0", "ff.net.2": "ff_a.2", "ff_context.net.0.proj": "ff_b.0", "ff_context.net.2": "ff_b.2", "attn.norm_q": "attn.norm_q_a", "attn.norm_k": "attn.norm_k_a", "attn.norm_added_q": "attn.norm_q_b", "attn.norm_added_k": "attn.norm_k_b", } state_dict_ = {} for name, param in state_dict.items(): if name in rename_dict: if name == "pos_embed.pos_embed": param = param.reshape((1, 192, 192, param.shape[-1])) state_dict_[rename_dict[name]] = param elif name.endswith(".weight") or name.endswith(".bias"): suffix = ".weight" if name.endswith(".weight") else ".bias" prefix = name[:-len(suffix)] if prefix in rename_dict: state_dict_[rename_dict[prefix] + suffix] = param elif prefix.startswith("transformer_blocks."): names = prefix.split(".") names[0] = "blocks" middle = ".".join(names[2:]) if middle in rename_dict: name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]]) state_dict_[name_] = param merged_keys = [name for name in state_dict_ if ".a_to_q." in name or ".b_to_q." in name] for key in merged_keys: param = torch.concat([ state_dict_[key.replace("to_q", "to_q")], state_dict_[key.replace("to_q", "to_k")], state_dict_[key.replace("to_q", "to_v")], ], dim=0) name = key.replace("to_q", "to_qkv") state_dict_.pop(key.replace("to_q", "to_q")) state_dict_.pop(key.replace("to_q", "to_k")) state_dict_.pop(key.replace("to_q", "to_v")) state_dict_[name] = param return state_dict_, self.infer_architecture(state_dict_) def from_civitai(self, state_dict): rename_dict = { "model.diffusion_model.context_embedder.bias": "context_embedder.bias", "model.diffusion_model.context_embedder.weight": "context_embedder.weight", "model.diffusion_model.final_layer.linear.bias": "proj_out.bias", "model.diffusion_model.final_layer.linear.weight": "proj_out.weight", "model.diffusion_model.pos_embed": "pos_embedder.pos_embed", "model.diffusion_model.t_embedder.mlp.0.bias": "time_embedder.timestep_embedder.0.bias", "model.diffusion_model.t_embedder.mlp.0.weight": "time_embedder.timestep_embedder.0.weight", "model.diffusion_model.t_embedder.mlp.2.bias": "time_embedder.timestep_embedder.2.bias", "model.diffusion_model.t_embedder.mlp.2.weight": "time_embedder.timestep_embedder.2.weight", "model.diffusion_model.x_embedder.proj.bias": "pos_embedder.proj.bias", "model.diffusion_model.x_embedder.proj.weight": "pos_embedder.proj.weight", "model.diffusion_model.y_embedder.mlp.0.bias": "pooled_text_embedder.0.bias", "model.diffusion_model.y_embedder.mlp.0.weight": "pooled_text_embedder.0.weight", "model.diffusion_model.y_embedder.mlp.2.bias": "pooled_text_embedder.2.bias", "model.diffusion_model.y_embedder.mlp.2.weight": "pooled_text_embedder.2.weight", "model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1.weight": "blocks.23.norm1_b.linear.weight", "model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1.bias": "blocks.23.norm1_b.linear.bias", "model.diffusion_model.final_layer.adaLN_modulation.1.weight": "norm_out.linear.weight", "model.diffusion_model.final_layer.adaLN_modulation.1.bias": "norm_out.linear.bias", } for i in range(40): rename_dict.update({ f"model.diffusion_model.joint_blocks.{i}.context_block.adaLN_modulation.1.bias": f"blocks.{i}.norm1_b.linear.bias", f"model.diffusion_model.joint_blocks.{i}.context_block.adaLN_modulation.1.weight": f"blocks.{i}.norm1_b.linear.weight", f"model.diffusion_model.joint_blocks.{i}.context_block.attn.proj.bias": f"blocks.{i}.attn.b_to_out.bias", f"model.diffusion_model.joint_blocks.{i}.context_block.attn.proj.weight": f"blocks.{i}.attn.b_to_out.weight", f"model.diffusion_model.joint_blocks.{i}.context_block.attn.qkv.bias": [f'blocks.{i}.attn.b_to_q.bias', f'blocks.{i}.attn.b_to_k.bias', f'blocks.{i}.attn.b_to_v.bias'], f"model.diffusion_model.joint_blocks.{i}.context_block.attn.qkv.weight": [f'blocks.{i}.attn.b_to_q.weight', f'blocks.{i}.attn.b_to_k.weight', f'blocks.{i}.attn.b_to_v.weight'], f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc1.bias": f"blocks.{i}.ff_b.0.bias", f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc1.weight": f"blocks.{i}.ff_b.0.weight", f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc2.bias": f"blocks.{i}.ff_b.2.bias", f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc2.weight": f"blocks.{i}.ff_b.2.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.adaLN_modulation.1.bias": f"blocks.{i}.norm1_a.linear.bias", f"model.diffusion_model.joint_blocks.{i}.x_block.adaLN_modulation.1.weight": f"blocks.{i}.norm1_a.linear.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn.proj.bias": f"blocks.{i}.attn.a_to_out.bias", f"model.diffusion_model.joint_blocks.{i}.x_block.attn.proj.weight": f"blocks.{i}.attn.a_to_out.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn.qkv.bias": [f'blocks.{i}.attn.a_to_q.bias', f'blocks.{i}.attn.a_to_k.bias', f'blocks.{i}.attn.a_to_v.bias'], f"model.diffusion_model.joint_blocks.{i}.x_block.attn.qkv.weight": [f'blocks.{i}.attn.a_to_q.weight', f'blocks.{i}.attn.a_to_k.weight', f'blocks.{i}.attn.a_to_v.weight'], f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc1.bias": f"blocks.{i}.ff_a.0.bias", f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc1.weight": f"blocks.{i}.ff_a.0.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc2.bias": f"blocks.{i}.ff_a.2.bias", f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc2.weight": f"blocks.{i}.ff_a.2.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn.ln_q.weight": f"blocks.{i}.attn.norm_q_a.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn.ln_k.weight": f"blocks.{i}.attn.norm_k_a.weight", f"model.diffusion_model.joint_blocks.{i}.context_block.attn.ln_q.weight": f"blocks.{i}.attn.norm_q_b.weight", f"model.diffusion_model.joint_blocks.{i}.context_block.attn.ln_k.weight": f"blocks.{i}.attn.norm_k_b.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.ln_q.weight": f"blocks.{i}.attn2.norm_q_a.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.ln_k.weight": f"blocks.{i}.attn2.norm_k_a.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.qkv.weight": f"blocks.{i}.attn2.a_to_qkv.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.qkv.bias": f"blocks.{i}.attn2.a_to_qkv.bias", f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.proj.weight": f"blocks.{i}.attn2.a_to_out.weight", f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.proj.bias": f"blocks.{i}.attn2.a_to_out.bias", }) state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "model.diffusion_model.pos_embed": pos_embed_max_size = int(param.shape[1] ** 0.5 + 0.4) param = param.reshape((1, pos_embed_max_size, pos_embed_max_size, param.shape[-1])) if isinstance(rename_dict[name], str): state_dict_[rename_dict[name]] = param else: name_ = rename_dict[name][0].replace(".a_to_q.", ".a_to_qkv.").replace(".b_to_q.", ".b_to_qkv.") state_dict_[name_] = param extra_kwargs = self.infer_architecture(state_dict_) num_layers = extra_kwargs["num_layers"] for name in [ f"blocks.{num_layers-1}.norm1_b.linear.weight", f"blocks.{num_layers-1}.norm1_b.linear.bias", "norm_out.linear.weight", "norm_out.linear.bias", ]: param = state_dict_[name] dim = param.shape[0] // 2 param = torch.concat([param[dim:], param[:dim]], axis=0) state_dict_[name] = param return state_dict_, self.infer_architecture(state_dict_)