Add support for PixArt diffusers weights

2023-12-15 20:43:32 +01:00
parent 3782b16606
commit a457da3f12
2 changed files with 101 additions and 5 deletions
@@ -0,0 +1,95 @@
+# For using the diffusers format weights
+#  Based on the original ComfyUI function + 
+#  https://github.com/PixArt-alpha/PixArt-alpha/blob/master/tools/convert_pixart_alpha_to_diffusers.py
+import torch
+
+conversion_map = [ # main SD conversion map (PixArt reference, HF Diffusers)
+	# Patch embeddings
+	("x_embedder.proj.weight", "pos_embed.proj.weight"),
+	("x_embedder.proj.bias",   "pos_embed.proj.bias"),
+	# Caption projection
+	("y_embedder.y_embedding", "caption_projection.y_embedding"),
+	("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"),
+	("y_embedder.y_proj.fc1.bias",   "caption_projection.linear_1.bias"),
+	("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"),
+	("y_embedder.y_proj.fc2.bias",   "caption_projection.linear_2.bias"),
+	# AdaLN-single LN
+	("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"),
+	("t_embedder.mlp.0.bias",   "adaln_single.emb.timestep_embedder.linear_1.bias"),
+	("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"),
+	("t_embedder.mlp.2.bias",   "adaln_single.emb.timestep_embedder.linear_2.bias"),
+	# Shared norm
+	("t_block.1.weight", "adaln_single.linear.weight"),
+	("t_block.1.bias",   "adaln_single.linear.bias"),
+	# Final block
+	("final_layer.linear.weight", "proj_out.weight"),
+	("final_layer.linear.bias",   "proj_out.bias"),
+	("final_layer.scale_shift_table", "scale_shift_table"),
+]
+
+conversion_map_ms = [ # for multi_scale_train (MS)
+	# Resolution
+	("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"),
+	("csize_embedder.mlp.0.bias",   "adaln_single.emb.resolution_embedder.linear_1.bias"),
+	("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"),
+	("csize_embedder.mlp.2.bias",   "adaln_single.emb.resolution_embedder.linear_2.bias"),
+	# Aspect ratio
+	("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"),
+	("ar_embedder.mlp.0.bias",   "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"),
+	("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"),
+	("ar_embedder.mlp.2.bias",   "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"),
+]
+
+# Add actual transformer blocks
+for depth in range(28):
+	# Transformer blocks
+	conversion_map += [
+		(f"blocks.{depth}.scale_shift_table", f"transformer_blocks.{depth}.scale_shift_table"),
+		# Projection
+		(f"blocks.{depth}.attn.proj.weight", f"transformer_blocks.{depth}.attn1.to_out.0.weight"),
+		(f"blocks.{depth}.attn.proj.bias",   f"transformer_blocks.{depth}.attn1.to_out.0.bias"),
+		# Feed-forward
+		(f"blocks.{depth}.mlp.fc1.weight", f"transformer_blocks.{depth}.ff.net.0.proj.weight"),
+		(f"blocks.{depth}.mlp.fc1.bias",   f"transformer_blocks.{depth}.ff.net.0.proj.bias"),
+		(f"blocks.{depth}.mlp.fc2.weight", f"transformer_blocks.{depth}.ff.net.2.weight"),
+		(f"blocks.{depth}.mlp.fc2.bias",   f"transformer_blocks.{depth}.ff.net.2.bias"),
+		# Cross-attention (proj)
+		(f"blocks.{depth}.cross_attn.proj.weight" ,f"transformer_blocks.{depth}.attn2.to_out.0.weight"),
+		(f"blocks.{depth}.cross_attn.proj.bias"   ,f"transformer_blocks.{depth}.attn2.to_out.0.bias"),
+	]
+
+def convert_pixart_state_dict(unet_state_dict):
+	if "adaln_single.emb.resolution_embedder.linear_1.weight" in unet_state_dict.keys():
+		cmap = conversion_map + conversion_map_ms
+	else:
+		cmap = conversion_map
+
+	new_state_dict = {k: unet_state_dict.pop(v) for k,v in cmap}
+	
+	for depth in range(28):
+		# Self Attention
+		q = unet_state_dict.pop(f"transformer_blocks.{depth}.attn1.to_q.weight")
+		k = unet_state_dict.pop(f"transformer_blocks.{depth}.attn1.to_k.weight")
+		v = unet_state_dict.pop(f"transformer_blocks.{depth}.attn1.to_v.weight")
+		new_state_dict[f"blocks.{depth}.attn.qkv.weight"] = torch.cat((q,k,v), dim=0)
+		qb = unet_state_dict.pop(f"transformer_blocks.{depth}.attn1.to_q.bias")
+		kb = unet_state_dict.pop(f"transformer_blocks.{depth}.attn1.to_k.bias")
+		vb = unet_state_dict.pop(f"transformer_blocks.{depth}.attn1.to_v.bias")
+		new_state_dict[f"blocks.{depth}.attn.qkv.bias"] = torch.cat((qb,kb,vb), dim=0)
+
+		# Cross-attention (linear)
+		q = unet_state_dict.pop(f"transformer_blocks.{depth}.attn2.to_q.weight")
+		k = unet_state_dict.pop(f"transformer_blocks.{depth}.attn2.to_k.weight")
+		v = unet_state_dict.pop(f"transformer_blocks.{depth}.attn2.to_v.weight")
+		new_state_dict[f"blocks.{depth}.cross_attn.q_linear.weight"] = q
+		new_state_dict[f"blocks.{depth}.cross_attn.kv_linear.weight"] = torch.cat((k,v), dim=0)
+		qb = unet_state_dict.pop(f"transformer_blocks.{depth}.attn2.to_q.bias")
+		kb = unet_state_dict.pop(f"transformer_blocks.{depth}.attn2.to_k.bias")
+		vb = unet_state_dict.pop(f"transformer_blocks.{depth}.attn2.to_v.bias")
+		new_state_dict[f"blocks.{depth}.cross_attn.q_linear.bias"] = qb
+		new_state_dict[f"blocks.{depth}.cross_attn.kv_linear.bias"] = torch.cat((kb,vb), dim=0)
+
+	if len(unet_state_dict.keys()) > 0:
+		print(f"PixArt: UNET conversion has leftover keys!:\n{unet_state_dict.keys()}")
+	
+	return new_state_dict