Support loading unet files in diffusers format.

comfy/diffusers_load.py

@@ -8,7 +8,8 @@ import os.path as osp
 import re
 import torch
 from safetensors.torch import load_file, save_file
-import diffusers_convert
+from . import diffusers_convert
+
 
 def load_diffusers(model_path, fp16=True, output_vae=True, output_clip=True, embedding_directory=None):
     diffusers_unet_conf = json.load(open(osp.join(model_path, "unet/config.json")))

comfy/model_detection.py

@@ -108,11 +108,13 @@ def detect_unet_config(state_dict, key_prefix, use_fp16):
     unet_config["context_dim"] = context_dim
     return unet_config
 
-
-def model_config_from_unet(state_dict, unet_key_prefix, use_fp16):
-    unet_config = detect_unet_config(state_dict, unet_key_prefix, use_fp16)
+def model_config_from_unet_config(unet_config):
     for model_config in supported_models.models:
         if model_config.matches(unet_config):
             return model_config(unet_config)
 
     return None
+
+def model_config_from_unet(state_dict, unet_key_prefix, use_fp16):
+    unet_config = detect_unet_config(state_dict, unet_key_prefix, use_fp16)
+    return model_config_from_unet_config(unet_config)

comfy/sd.py

@@ -1049,7 +1049,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
             clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
 
     offload_device = model_management.unet_offload_device()
-    model = model_config.get_model(sd)
+    model = model_config.get_model(sd, "model.diffusion_model.")
     model = model.to(offload_device)
     model.load_model_weights(sd, "model.diffusion_model.")
 
@@ -1073,6 +1073,73 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
 
     return (ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae, clipvision)
 
+
+def load_unet(unet_path): #load unet in diffusers format
+    sd = utils.load_torch_file(unet_path)
+    parameters = calculate_parameters(sd, "")
+    fp16 = model_management.should_use_fp16(model_params=parameters)
+
+    match = {}
+    match["context_dim"] = sd["down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_k.weight"].shape[1]
+    match["model_channels"] = sd["conv_in.weight"].shape[0]
+    match["in_channels"] = sd["conv_in.weight"].shape[1]
+    match["adm_in_channels"] = None
+    if "class_embedding.linear_1.weight" in sd:
+        match["adm_in_channels"] = sd["class_embedding.linear_1.weight"].shape[1]
+
+    SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+            'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': fp16, 'in_channels': 4, 'model_channels': 320,
+            'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 2, 10], 'channel_mult': [1, 2, 4],
+            'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048}
+
+    SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+                    'num_classes': 'sequential', 'adm_in_channels': 2560, 'use_fp16': fp16, 'in_channels': 4, 'model_channels': 384,
+                    'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 4, 4, 0], 'channel_mult': [1, 2, 4, 4],
+                    'transformer_depth_middle': 4, 'use_linear_in_transformer': True, 'context_dim': 1280}
+
+    SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+            'adm_in_channels': None, 'use_fp16': fp16, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
+            'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
+            'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024}
+
+    SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+                    'num_classes': 'sequential', 'adm_in_channels': 2048, 'use_fp16': True, 'in_channels': 4, 'model_channels': 320,
+                    'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
+                    'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024}
+
+    SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+                    'num_classes': 'sequential', 'adm_in_channels': 1536, 'use_fp16': True, 'in_channels': 4, 'model_channels': 320,
+                    'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
+                    'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024}
+
+    SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+            'adm_in_channels': None, 'use_fp16': True, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
+            'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
+            'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768}
+
+    supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl]
+    print("match", match)
+    for unet_config in supported_models:
+        matches = True
+        for k in match:
+            if match[k] != unet_config[k]:
+                matches = False
+                break
+        if matches:
+            diffusers_keys = utils.unet_to_diffusers(unet_config)
+            new_sd = {}
+            for k in diffusers_keys:
+                if k in sd:
+                    new_sd[diffusers_keys[k]] = sd.pop(k)
+                else:
+                    print(diffusers_keys[k], k)
+            offload_device = model_management.unet_offload_device()
+            model_config = model_detection.model_config_from_unet_config(unet_config)
+            model = model_config.get_model(new_sd, "")
+            model = model.to(offload_device)
+            model.load_model_weights(new_sd, "")
+            return ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device)
+
 def save_checkpoint(output_path, model, clip, vae, metadata=None):
     try:
         model.patch_model()

comfy/supported_models.py

@@ -53,9 +53,9 @@ class SD20(supported_models_base.BASE):
 
     latent_format = latent_formats.SD15
 
-    def v_prediction(self, state_dict):
+    def v_prediction(self, state_dict, prefix=""):
         if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction
-            k = "model.diffusion_model.output_blocks.11.1.transformer_blocks.0.norm1.bias"
+            k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix)
             out = state_dict[k]
             if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
                 return True
@@ -109,7 +109,7 @@ class SDXLRefiner(supported_models_base.BASE):
 
     latent_format = latent_formats.SDXL
 
-    def get_model(self, state_dict):
+    def get_model(self, state_dict, prefix=""):
         return model_base.SDXLRefiner(self)
 
     def process_clip_state_dict(self, state_dict):
@@ -144,7 +144,7 @@ class SDXL(supported_models_base.BASE):
 
     latent_format = latent_formats.SDXL
 
-    def get_model(self, state_dict):
+    def get_model(self, state_dict, prefix=""):
         return model_base.SDXL(self)
 
     def process_clip_state_dict(self, state_dict):

comfy/supported_models_base.py

@@ -41,7 +41,7 @@ class BASE:
                 return False
         return True
 
-    def v_prediction(self, state_dict):
+    def v_prediction(self, state_dict, prefix=""):
         return False
 
     def inpaint_model(self):
@@ -53,13 +53,13 @@ class BASE:
         for x in self.unet_extra_config:
             self.unet_config[x] = self.unet_extra_config[x]
 
-    def get_model(self, state_dict):
+    def get_model(self, state_dict, prefix=""):
         if self.inpaint_model():
-            return model_base.SDInpaint(self, v_prediction=self.v_prediction(state_dict))
+            return model_base.SDInpaint(self, v_prediction=self.v_prediction(state_dict, prefix))
         elif self.noise_aug_config is not None:
-            return model_base.SD21UNCLIP(self, self.noise_aug_config, v_prediction=self.v_prediction(state_dict))
+            return model_base.SD21UNCLIP(self, self.noise_aug_config, v_prediction=self.v_prediction(state_dict, prefix))
         else:
-            return model_base.BaseModel(self, v_prediction=self.v_prediction(state_dict))
+            return model_base.BaseModel(self, v_prediction=self.v_prediction(state_dict, prefix))
 
     def process_clip_state_dict(self, state_dict):
         return state_dict

comfy/utils.py

@@ -117,6 +117,23 @@ UNET_MAP_RESNET = {
     "out_layers.0.bias": "norm2.bias",
 }
 
+UNET_MAP_BASIC = {
+    "label_emb.0.0.weight": "class_embedding.linear_1.weight",
+    "label_emb.0.0.bias": "class_embedding.linear_1.bias",
+    "label_emb.0.2.weight": "class_embedding.linear_2.weight",
+    "label_emb.0.2.bias": "class_embedding.linear_2.bias",
+    "input_blocks.0.0.weight": "conv_in.weight",
+    "input_blocks.0.0.bias": "conv_in.bias",
+    "out.0.weight": "conv_norm_out.weight",
+    "out.0.bias": "conv_norm_out.bias",
+    "out.2.weight": "conv_out.weight",
+    "out.2.bias": "conv_out.bias",
+    "time_embed.0.weight": "time_embedding.linear_1.weight",
+    "time_embed.0.bias": "time_embedding.linear_1.bias",
+    "time_embed.2.weight": "time_embedding.linear_2.weight",
+    "time_embed.2.bias": "time_embedding.linear_2.bias"
+}
+
 def unet_to_diffusers(unet_config):
     num_res_blocks = unet_config["num_res_blocks"]
     attention_resolutions = unet_config["attention_resolutions"]
@@ -185,6 +202,10 @@ def unet_to_diffusers(unet_config):
                 for k in ["weight", "bias"]:
                     diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k)
             n += 1
+
+    for k in UNET_MAP_BASIC:
+        diffusers_unet_map[UNET_MAP_BASIC[k]] = k
+
     return diffusers_unet_map
 
 def convert_sd_to(state_dict, dtype):

folder_paths.py

@@ -14,6 +14,7 @@ folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".y
 folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions)
 folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions)
 folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions)
+folder_names_and_paths["unet"] = ([os.path.join(models_dir, "unet")], supported_pt_extensions)
 folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions)
 folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions)
 folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions)

nodes.py

@@ -397,7 +397,7 @@ class DiffusersLoader:
     RETURN_TYPES = ("MODEL", "CLIP", "VAE")
     FUNCTION = "load_checkpoint"
 
-    CATEGORY = "advanced/loaders"
+    CATEGORY = "advanced/loaders/deprecated"
 
     def load_checkpoint(self, model_path, output_vae=True, output_clip=True):
         for search_path in folder_paths.get_folder_paths("diffusers"):
@@ -552,6 +552,21 @@ class ControlNetApply:
             c.append(n)
         return (c, )
 
+class UNETLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "unet_name": (folder_paths.get_filename_list("unet"), ),
+                             }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_unet"
+
+    CATEGORY = "advanced/loaders"
+
+    def load_unet(self, unet_name):
+        unet_path = folder_paths.get_full_path("unet", unet_name)
+        model = comfy.sd.load_unet(unet_path)
+        return (model,)
+
 class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -1371,6 +1386,7 @@ NODE_CLASS_MAPPINGS = {
     "LatentCrop": LatentCrop,
     "LoraLoader": LoraLoader,
     "CLIPLoader": CLIPLoader,
+    "UNETLoader": UNETLoader,
     "DualCLIPLoader": DualCLIPLoader,
     "CLIPVisionEncode": CLIPVisionEncode,
     "StyleModelApply": StyleModelApply,