Add experimental photomaker nodes.

Put the model file in models/photomaker and use PhotoMakerLoader.

Then use PhotoMakerEncode with the keyword "photomaker" to apply the image

comfy_extras/nodes_photomaker.py

+import torch
+import torch.nn as nn
+import folder_paths
+import comfy.clip_model
+import comfy.clip_vision
+import comfy.ops
+
+# code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0
+VISION_CONFIG_DICT = {
+    "hidden_size": 1024,
+    "image_size": 224,
+    "intermediate_size": 4096,
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 24,
+    "patch_size": 14,
+    "projection_dim": 768,
+    "hidden_act": "quick_gelu",
+}
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=comfy.ops):
+        super().__init__()
+        if use_residual:
+            assert in_dim == out_dim
+        self.layernorm = operations.LayerNorm(in_dim)
+        self.fc1 = operations.Linear(in_dim, hidden_dim)
+        self.fc2 = operations.Linear(hidden_dim, out_dim)
+        self.use_residual = use_residual
+        self.act_fn = nn.GELU()
+
+    def forward(self, x):
+        residual = x
+        x = self.layernorm(x)
+        x = self.fc1(x)
+        x = self.act_fn(x)
+        x = self.fc2(x)
+        if self.use_residual:
+            x = x + residual
+        return x
+
+
+class FuseModule(nn.Module):
+    def __init__(self, embed_dim, operations):
+        super().__init__()
+        self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations)
+        self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations)
+        self.layer_norm = operations.LayerNorm(embed_dim)
+
+    def fuse_fn(self, prompt_embeds, id_embeds):
+        stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1)
+        stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds
+        stacked_id_embeds = self.mlp2(stacked_id_embeds)
+        stacked_id_embeds = self.layer_norm(stacked_id_embeds)
+        return stacked_id_embeds
+
+    def forward(
+        self,
+        prompt_embeds,
+        id_embeds,
+        class_tokens_mask,
+    ) -> torch.Tensor:
+        # id_embeds shape: [b, max_num_inputs, 1, 2048]
+        id_embeds = id_embeds.to(prompt_embeds.dtype)
+        num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case
+        batch_size, max_num_inputs = id_embeds.shape[:2]
+        # seq_length: 77
+        seq_length = prompt_embeds.shape[1]
+        # flat_id_embeds shape: [b*max_num_inputs, 1, 2048]
+        flat_id_embeds = id_embeds.view(
+            -1, id_embeds.shape[-2], id_embeds.shape[-1]
+        )
+        # valid_id_mask [b*max_num_inputs]
+        valid_id_mask = (
+            torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :]
+            < num_inputs[:, None]
+        )
+        valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()]
+
+        prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1])
+        class_tokens_mask = class_tokens_mask.view(-1)
+        valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1])
+        # slice out the image token embeddings
+        image_token_embeds = prompt_embeds[class_tokens_mask]
+        stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds)
+        assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}"
+        prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype))
+        updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1)
+        return updated_prompt_embeds
+
+class PhotoMakerIDEncoder(comfy.clip_model.CLIPVisionModelProjection):
+    def __init__(self):
+        self.load_device = comfy.model_management.text_encoder_device()
+        offload_device = comfy.model_management.text_encoder_offload_device()
+        dtype = comfy.model_management.text_encoder_dtype(self.load_device)
+
+        super().__init__(VISION_CONFIG_DICT, dtype, offload_device, comfy.ops.manual_cast)
+        self.visual_projection_2 = comfy.ops.manual_cast.Linear(1024, 1280, bias=False)
+        self.fuse_module = FuseModule(2048, comfy.ops.manual_cast)
+
+    def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask):
+        b, num_inputs, c, h, w = id_pixel_values.shape
+        id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w)
+
+        shared_id_embeds = self.vision_model(id_pixel_values)[2]
+        id_embeds = self.visual_projection(shared_id_embeds)
+        id_embeds_2 = self.visual_projection_2(shared_id_embeds)
+
+        id_embeds = id_embeds.view(b, num_inputs, 1, -1)
+        id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1)
+
+        id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1)
+        updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask)
+
+        return updated_prompt_embeds
+
+
+class PhotoMakerLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "photomaker_model_name": (folder_paths.get_filename_list("photomaker"), )}}
+
+    RETURN_TYPES = ("PHOTOMAKER",)
+    FUNCTION = "load_photomaker_model"
+
+    CATEGORY = "_for_testing/photomaker"
+
+    def load_photomaker_model(self, photomaker_model_name):
+        photomaker_model_path = folder_paths.get_full_path("photomaker", photomaker_model_name)
+        photomaker_model = PhotoMakerIDEncoder()
+        data = comfy.utils.load_torch_file(photomaker_model_path, safe_load=True)
+        if "id_encoder" in data:
+            data = data["id_encoder"]
+        photomaker_model.load_state_dict(data)
+        return (photomaker_model,)
+
+
+class PhotoMakerEncode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "photomaker": ("PHOTOMAKER",),
+                              "image": ("IMAGE",),
+                              "clip": ("CLIP", ),
+                              "text": ("STRING", {"multiline": True, "default": "photograph of photomaker"}),
+                             }}
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "apply_photomaker"
+
+    CATEGORY = "_for_testing/photomaker"
+
+    def apply_photomaker(self, photomaker, image, clip, text):
+        special_token = "photomaker"
+        pixel_values = comfy.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float()
+        try:
+            index = text.split(" ").index(special_token) + 1
+        except ValueError:
+            index = -1
+        tokens = clip.tokenize(text, return_word_ids=True)
+        out_tokens = {}
+        for k in tokens:
+            out_tokens[k] = []
+            for t in tokens[k]:
+                f = list(filter(lambda x: x[2] != index, t))
+                while len(f) < len(t):
+                    f.append(t[-1])
+                out_tokens[k].append(f)
+
+        cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True)
+
+        if index > 0:
+            token_index = index - 1
+            num_id_images = 1
+            class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)]
+            out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device),
+                            class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0))
+        else:
+            out = cond
+
+        return ([[out, {"pooled_output": pooled}]], )
+
+
+NODE_CLASS_MAPPINGS = {
+    "PhotoMakerLoader": PhotoMakerLoader,
+    "PhotoMakerEncode": PhotoMakerEncode,
+}
+

folder_paths.py

@@ -29,6 +29,8 @@ folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes
 
 folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions)
 
+folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions)
+
 folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""})
 
 output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")

nodes.py

@@ -1943,6 +1943,7 @@ def init_custom_nodes():
         "nodes_perpneg.py",
         "nodes_stable3d.py",
         "nodes_sdupscale.py",
+        "nodes_photomaker.py",
     ]
 
     for node_file in extras_files: