diff --git a/__init__.py b/__init__.py
index ee43d88..4975ac5 100755
--- a/__init__.py
+++ b/__init__.py
@@ -11,6 +11,8 @@ from .nodes.blend.AQ_BlendImages import AQ_BlendImages
 from .nodes.image.AQ_Image import AQ_LoadImageBase64, AQ_Image_Pad, AQ_Image_DetailTransfer, AQ_ImageMaskSwitch
 from .nodes.save.AQ_SaveImageWebpReturnBase64 import AQ_SaveImageWebpReturnBase64
 from .nodes.filters.AQ_filters import AQ_BatchAverageImage, AQ_ColorMatchImage
+from .nodes.text_encoders.AQ_TextEncodeQwenImage import AQ_TextEncodeQwenImageEdit
+from .nodes.text_encoders.AQ_TextEncodeQwenImage import AQ_TextEncodeQwenImageEditPlus
 
 NODE_CLASS_MAPPINGS = {
     "AQ_MasksAndImagesAsList": AQ_MasksAndImagesAsList,
@@ -29,7 +31,9 @@ NODE_CLASS_MAPPINGS = {
     "AQ_ImageMaskSwitch": AQ_ImageMaskSwitch,
     "AQ_SaveImageWebpReturnBase64": AQ_SaveImageWebpReturnBase64,
     "AQ_BatchAverageImage": AQ_BatchAverageImage,
-    "AQ_ColorMatchImage": AQ_ColorMatchImage
+    "AQ_ColorMatchImage": AQ_ColorMatchImage,
+    "AQ_TextEncodeQwenImageEdit": AQ_TextEncodeQwenImageEdit,
+    "AQ_TextEncodeQwenImageEditPlus": AQ_TextEncodeQwenImageEditPlus
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -49,7 +53,10 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "AQ_ImageMaskSwitch": "AQ_ImageMaskSwitch",
     "AQ_SaveImageWebpReturnBase64": "AQ_SaveImageWebpReturnBase64",
     "AQ_BatchAverageImage": "AQ_BatchAverageImage",
-    "AQ_ColorMatchImage": "AQ_ColorMatchImage"  
+    "AQ_ColorMatchImage": "AQ_ColorMatchImage",
+    "AQ_TextEncodeQwenImageEdit": "AQ_TextEncodeQwenImageEdit",
+    "AQ_TextEncodeQwenImageEditPlus": "AQ_TextEncodeQwenImageEditPlus"
+
 }
 
 
diff --git a/nodes/text_encoders/AQ_TextEncodeQwenImage.py b/nodes/text_encoders/AQ_TextEncodeQwenImage.py
new file mode 100755
index 0000000..17151a3
--- /dev/null
+++ b/nodes/text_encoders/AQ_TextEncodeQwenImage.py
@@ -0,0 +1,123 @@
+import node_helpers
+import comfy.utils
+import math
+
+
+class AQ_TextEncodeQwenImageEdit:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "clip": ("CLIP",),
+                "prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+                "use_image": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "vae": ("VAE",),
+                "image": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "encode"
+
+    CATEGORY = "advanced/conditioning"
+
+    def encode(self, clip, prompt, use_image=True, vae=None, image=None):
+        ref_latent = None
+        if image is None or not use_image:
+            images = []
+        else:
+            samples = image.movedim(-1, 1)
+            total = int(1024 * 1024)
+
+            scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+            width = round(samples.shape[3] * scale_by)
+            height = round(samples.shape[2] * scale_by)
+
+            s = comfy.utils.common_upscale(samples, width, height, "area", "disabled")
+            image = s.movedim(1, -1)
+            images = [image[:, :, :, :3]]
+            if vae is not None:
+                ref_latent = vae.encode(image[:, :, :, :3])
+
+        tokens = clip.tokenize(prompt, images=images)
+        conditioning = clip.encode_from_tokens_scheduled(tokens)
+        if ref_latent is not None:
+            conditioning = node_helpers.conditioning_set_values(
+                conditioning, {"reference_latents": [ref_latent]}, append=True
+            )
+        return (conditioning,)
+
+
+class AQ_TextEncodeQwenImageEditPlus:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "clip": ("CLIP",),
+                "prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+                "use_image1": ("BOOLEAN", {"default": True}),
+                "use_image2": ("BOOLEAN", {"default": True}),
+                "use_image3": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "vae": ("VAE",),
+                "image1": ("IMAGE",),
+                "image2": ("IMAGE",),
+                "image3": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "encode"
+
+    CATEGORY = "advanced/conditioning"
+
+    def encode(self, clip, prompt, use_image1=True, use_image2=True, use_image3=True, vae=None, image1=None, image2=None, image3=None):
+        ref_latents = []
+        images = [image1, image2, image3]
+        use_images = [use_image1, use_image2, use_image3]
+        images_vl = []
+        llama_template = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+        image_prompt = ""
+
+        for i, (image, use_image) in enumerate(zip(images, use_images)):
+            if image is not None and use_image:
+                samples = image.movedim(-1, 1)
+                total = int(384 * 384)
+
+                scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+                width = round(samples.shape[3] * scale_by)
+                height = round(samples.shape[2] * scale_by)
+
+                s = comfy.utils.common_upscale(
+                    samples, width, height, "area", "disabled"
+                )
+                images_vl.append(s.movedim(1, -1))
+                if vae is not None:
+                    total = int(1024 * 1024)
+                    scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+                    width = round(samples.shape[3] * scale_by / 8.0) * 8
+                    height = round(samples.shape[2] * scale_by / 8.0) * 8
+
+                    s = comfy.utils.common_upscale(
+                        samples, width, height, "area", "disabled"
+                    )
+                    ref_latents.append(vae.encode(s.movedim(1, -1)[:, :, :, :3]))
+
+                image_prompt += (
+                    "Picture {}: <|vision_start|><|image_pad|><|vision_end|>".format(
+                        i + 1
+                    )
+                )
+
+        tokens = clip.tokenize(
+            image_prompt + prompt, images=images_vl, llama_template=llama_template
+        )
+        conditioning = clip.encode_from_tokens_scheduled(tokens)
+        if len(ref_latents) > 0:
+            conditioning = node_helpers.conditioning_set_values(
+                conditioning, {"reference_latents": ref_latents}, append=True
+            )
+        return (conditioning,)