CLIPVisionEncode can now encode multiple images.

comfy/clip_vision.py

@@ -24,8 +24,8 @@ class ClipVisionModel():
         return self.model.load_state_dict(sd, strict=False)
 
     def encode_image(self, image):
-        img = torch.clip((255. * image[0]), 0, 255).round().int()
-        inputs = self.processor(images=[img], return_tensors="pt")
+        img = torch.clip((255. * image), 0, 255).round().int()
+        inputs = self.processor(images=img, return_tensors="pt")
         outputs = self.model(**inputs)
         return outputs
 

comfy/model_base.py

@@ -120,7 +120,7 @@ class SD21UNCLIP(BaseModel):
             weights = []
             noise_aug = []
             for unclip_cond in unclip_conditioning:
-                adm_cond = unclip_cond["clip_vision_output"].image_embeds
+                for adm_cond in unclip_cond["clip_vision_output"].image_embeds:
                     weight = unclip_cond["strength"]
                     noise_augment = unclip_cond["noise_augmentation"]
                     noise_level = round((self.noise_augmentor.max_noise_level - 1) * noise_augment)

nodes.py

@@ -771,7 +771,7 @@ class StyleModelApply:
     CATEGORY = "conditioning/style_model"
 
     def apply_stylemodel(self, clip_vision_output, style_model, conditioning):
-        cond = style_model.get_cond(clip_vision_output)
+        cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
         c = []
         for t in conditioning:
             n = [torch.cat((t[0], cond), dim=1), t[1].copy()]