FantasyPortrait reqs and cleanup

unsure if I want to add onnx in the requirements at this stage...

fantasyportrait/face_utils.py

@@ -3,9 +3,12 @@ import time
 
 import cv2
 import numpy as np
-import onnx
-import onnxruntime
 
+try:
+    import onnx
+    import onnxruntime
+except ImportError:
+    print("ONNX and ONNX Runtime are required for FantasyPortrait. Please install them using 'pip install onnx onnxruntime-gpu'")
 
 def create_onnx_session(onnx_path, gpu_id=None) -> onnxruntime.InferenceSession:
     start = time.perf_counter()

fantasyportrait/model.py

@@ -3,8 +3,6 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from ..wanvideo.modules.attention import attention
-
 def FeedForward(dim, mult=4):
     inner_dim = int(dim * mult)
     return nn.Sequential(
@@ -51,81 +49,6 @@ class MultiProjModel(nn.Module):
 
         return result_final
 
-
-class SingleStreamBlockProcessor(nn.Module):
-    def __init__(self, context_dim, hidden_dim):
-        super().__init__()
-
-        self.context_dim = context_dim
-        self.hidden_dim = hidden_dim
-
-        self.ip_adapter_single_stream_k_proj = nn.Linear(
-            context_dim, hidden_dim, bias=False
-        )
-        self.ip_adapter_single_stream_v_proj = nn.Linear(
-            context_dim, hidden_dim, bias=False
-        )
-
-        nn.init.zeros_(self.ip_adapter_single_stream_k_proj.weight)
-        nn.init.zeros_(self.ip_adapter_single_stream_v_proj.weight)
-
-    def __call__(
-        self,
-        attn: nn.Module,
-        x: torch.Tensor,
-        context: torch.Tensor,
-        context_lens: torch.Tensor,
-        adapter_proj: torch.Tensor,
-        adapter_context_lens: torch.Tensor,
-        latents_num_frames: int = 21,
-        ip_scale: float = 1.0,
-        adapter_attn_mask: torch.Tensor = None,
-    ) -> torch.Tensor:
-        context_img = context[:, :257]
-        context = context[:, 257:]
-        b, n, d = x.size(0), attn.num_heads, attn.head_dim
-
-        # compute query, key, value
-        q = attn.norm_q(attn.q(x)).view(b, -1, n, d)
-        k = attn.norm_k(attn.k(context)).view(b, -1, n, d)
-        v = attn.v(context).view(b, -1, n, d)
-        k_img = attn.norm_k_img(attn.k_img(context_img)).view(b, -1, n, d)
-        v_img = attn.v_img(context_img).view(b, -1, n, d)
-        img_x = attention(q, k_img, v_img)
-        # compute attention
-        x = attention(q, k, v)
-
-        x = x.flatten(2)
-        img_x = img_x.flatten(2)
-
-        if len(adapter_proj.shape) == 4:
-            adapter_q = q.view(b * latents_num_frames, -1, n, d)
-            ip_key = self.ip_adapter_single_stream_k_proj(adapter_proj).view(
-                b * latents_num_frames, -1, n, d
-            )
-            ip_value = self.ip_adapter_single_stream_v_proj(adapter_proj).view(
-                b * latents_num_frames, -1, n, d
-            )
-            adapter_x = attention(
-                adapter_q, ip_key, ip_value, attn_mask=adapter_attn_mask
-            )
-            adapter_x = adapter_x.view(b, q.size(1), n, d)
-            adapter_x = adapter_x.flatten(2)
-        elif len(adapter_proj.shape) == 3:
-            ip_key = self.ip_adapter_single_stream_k_proj(adapter_proj).view(
-                b, -1, n, d
-            )
-            ip_value = self.ip_adapter_single_stream_v_proj(adapter_proj).view(
-                b, -1, n, d
-            )
-            adapter_x = attention(q, ip_key, ip_value, attn_mask=adapter_attn_mask)
-            adapter_x = adapter_x.flatten(2)
-
-        x = x + img_x + adapter_x * ip_scale
-        x = attn.o(x)
-        return x
-
-
 class PerceiverAttention(nn.Module):
     def __init__(self, *, dim, dim_head=64, heads=8):
         super().__init__()