Add support for PixArt diffusers weights

PixArt/diffusers_convert.py

@@ -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

PixArt/loader.py

@@ -5,8 +5,7 @@ import comfy.model_base
 import comfy.utils
 import torch
 from comfy import model_management
-
+from .diffusers_convert import convert_pixart_state_dict
-from .models import PixArtMS
 
 class EXM_PixArt(comfy.supported_models_base.BASE):
 	unet_config = {}
@@ -27,18 +26,18 @@ class EXM_PixArt(comfy.supported_models_base.BASE):
 def load_pixart(model_path, model_conf):
 	state_dict = comfy.utils.load_torch_file(model_path)
 	state_dict = state_dict.get("model", state_dict)
+	if "caption_projection.y_embedding" in state_dict:
+		state_dict = convert_pixart_state_dict(state_dict) # Diffusers
 	parameters = comfy.utils.calculate_parameters(state_dict)
 	unet_dtype = model_management.unet_dtype(model_params=parameters)
 
 	model_conf = EXM_PixArt(model_conf) # convert to object
-
 	model = comfy.model_base.BaseModel(
 		model_conf,
 		model_type=comfy.model_base.ModelType.EPS,
 		device=model_management.get_torch_device()
 	)
 
-	model.pixart_config = model_conf
 	if model_conf.model_target == "PixArtMS":
 		from .models.PixArtMS import PixArtMS
 		model.diffusion_model = PixArtMS(**model_conf.unet_config)
@@ -48,7 +47,9 @@ def load_pixart(model_path, model_conf):
 	else:
 		raise NotImplementedError(f"Unknown model target '{model_conf.model_target}'")
 
-	model.diffusion_model.load_state_dict(state_dict)
+	m, u = model.diffusion_model.load_state_dict(state_dict, strict=False)
+	if len(m) > 0: print("Missing UNET keys", m)
+	if len(u) > 0: print("Leftover UNET keys", u)
 	model.diffusion_model.dtype = unet_dtype
 	model.diffusion_model.eval()
 	model.diffusion_model.to(unet_dtype)