Merge pull request #262 from xiaohu2015/main

add IP-Adapter-FaceID-Portrait

README.md

@@ -16,6 +16,7 @@ we present IP-Adapter, an effective and lightweight adapter to achieve image pro
 ![arch](assets/figs/fig1.png)
 
 ## Release
+- [2024/01/19] 🔥 Add IP-Adapter-FaceID-Portrait, more information can be found [here](https://huggingface.co/h94/IP-Adapter-FaceID).
 - [2024/01/17] 🔥 Add an experimental version of IP-Adapter-FaceID-PlusV2 for SDXL, more information can be found [here](https://huggingface.co/h94/IP-Adapter-FaceID).
 - [2024/01/04] 🔥 Add an experimental version of IP-Adapter-FaceID for SDXL, more information can be found [here](https://huggingface.co/h94/IP-Adapter-FaceID).
 - [2023/12/29] 🔥 Add an experimental version of IP-Adapter-FaceID-PlusV2, more information can be found [here](https://huggingface.co/h94/IP-Adapter-FaceID).

ip_adapter/ip_adapter_faceid_separate.py

@@ -8,7 +8,6 @@ from PIL import Image
 from safetensors import safe_open
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
-from .attention_processor_faceid import LoRAAttnProcessor, LoRAIPAttnProcessor
 from .utils import is_torch2_available
 
 USE_DAFAULT_ATTN = False # should be True for visualization_attnmap
@@ -118,10 +117,11 @@ class ProjPlusModel(torch.nn.Module):
 
 
 class IPAdapterFaceID:
-    def __init__(self, sd_pipe, ip_ckpt, device, num_tokens=4, torch_dtype=torch.float16):
+    def __init__(self, sd_pipe, ip_ckpt, device, num_tokens=4, n_cond=1, torch_dtype=torch.float16):
         self.device = device
         self.ip_ckpt = ip_ckpt
         self.num_tokens = num_tokens
+        self.n_cond = n_cond
         self.torch_dtype = torch_dtype
 
         self.pipe = sd_pipe.to(self.device)
@@ -157,7 +157,7 @@ class IPAdapterFaceID:
                 attn_procs[name] = AttnProcessor()
             else:
                 attn_procs[name] = IPAttnProcessor(
-                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, num_tokens=self.num_tokens,
+                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, num_tokens=self.num_tokens*self.n_cond,
                 ).to(self.device, dtype=self.torch_dtype)
         unet.set_attn_processor(attn_procs)
 
@@ -178,15 +178,26 @@ class IPAdapterFaceID:
 
     @torch.inference_mode()
     def get_image_embeds(self, faceid_embeds):
-        
+
+        multi_face = False
+        if faceid_embeds.dim() == 3:
+            multi_face = True
+            b, n, c = faceid_embeds.shape
+            faceid_embeds = faceid_embeds.reshape(b*n, c)
+
         faceid_embeds = faceid_embeds.to(self.device, dtype=self.torch_dtype)
         image_prompt_embeds = self.image_proj_model(faceid_embeds)
         uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_embeds))
+        if multi_face:
+            c = image_prompt_embeds.size(-1)
+            image_prompt_embeds = image_prompt_embeds.reshape(b, -1, c)
+            uncond_image_prompt_embeds = uncond_image_prompt_embeds.reshape(b, -1, c)
+        
         return image_prompt_embeds, uncond_image_prompt_embeds
 
     def set_scale(self, scale):
         for attn_processor in self.pipe.unet.attn_processors.values():
-            if isinstance(attn_processor, LoRAIPAttnProcessor):
+            if isinstance(attn_processor, IPAttnProcessor):
                 attn_processor.scale = scale
 
     def generate(