update backend + revise styles

update backend + revise styles

fooocus_extras/ip_adapter.py

@@ -7,6 +7,7 @@ import fcbh.ldm.modules.attention as attention
 
 from fooocus_extras.resampler import Resampler
 from fcbh.model_patcher import ModelPatcher
+from modules.core import numpy_to_pytorch
 
 
 SD_V12_CHANNELS = [320] * 4 + [640] * 4 + [1280] * 4 + [1280] * 6 + [640] * 6 + [320] * 6 + [1280] * 2
@@ -144,14 +145,27 @@ def load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path):
     return
 
 
+@torch.no_grad()
+@torch.inference_mode()
+def clip_preprocess(image):
+    mean = torch.tensor([0.48145466, 0.4578275, 0.40821073], device=image.device, dtype=image.dtype).view([1, 3, 1, 1])
+    std = torch.tensor([0.26862954, 0.26130258, 0.27577711], device=image.device, dtype=image.dtype).view([1, 3, 1, 1])
+    image = image.movedim(-1, 1)
+
+    # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75
+    B, C, H, W = image.shape
+    assert H == 224 and W == 224
+
+    return (image - mean) / std
+
+
 @torch.no_grad()
 @torch.inference_mode()
 def preprocess(img):
     global ip_unconds
 
-    inputs = clip_vision.processor(images=img, return_tensors="pt")
     fcbh.model_management.load_model_gpu(clip_vision.patcher)
-    pixel_values = inputs['pixel_values'].to(clip_vision.load_device)
+    pixel_values = clip_preprocess(numpy_to_pytorch(img).to(clip_vision.load_device))
 
     if clip_vision.dtype != torch.float32:
         precision_scope = torch.autocast