| 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) |
|
|
| |
| attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b) |
|
|
| |
| 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) |
|
|
| |
| 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) |
|
|
| |
| attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b) |
|
|
| |
| 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) |
|
|
| |
| 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) |
|
|
| |
| attn_output_a = self.attn(norm_hidden_states_a, norm_hidden_states_b) |
|
|
| |
| 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): |
| |
| 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_) |
|
|
