From d07d6e25f538f07c027ac35312a50eb4065a2a4a Mon Sep 17 00:00:00 2001
From: Ethanfel <ethan.fel@ts-pc.fr>
Date: Sat, 10 Jan 2026 16:25:18 +0100
Subject: [PATCH] Add qwen_plus_node.py

---
 qwen_plus_node.py | 58 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 58 insertions(+)
 create mode 100644 qwen_plus_node.py

diff --git a/qwen_plus_node.py b/qwen_plus_node.py
new file mode 100644
index 0000000..a99e7a9
--- /dev/null
+++ b/qwen_plus_node.py
@@ -0,0 +1,58 @@
+import torch
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+
+class Mamad8_QwenEditPlus_Standalone:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "clip": ("CLIP",),
+                "image1": ("IMAGE",),
+                "text": ("STRING", {"multiline": True, "default": "Describe the change..."}),
+            },
+            "optional": {
+                "image2": ("IMAGE",),
+                "image3": ("IMAGE",),
+                "negative_prompt": ("STRING", {"multiline": True, "default": "low quality, blurry"}),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
+    RETURN_NAMES = ("conditioning", "negative_conditioning")
+    FUNCTION = "encode"
+    CATEGORY = "Qwen/Edit_Standalone"
+
+    def common_preprocessing(self, image):
+        # Conversion du tensor ComfyUI (BHWC) en format PIL pour Qwen
+        if len(image.shape) == 4:
+            image = image[0]
+        img = 255. * image.cpu().numpy()
+        img = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
+        return img
+
+    def encode(self, clip, image1, text, image2=None, image3=None, negative_prompt="", strength=1.0):
+        # 1. Préparation des images pour le conditionnement visuel
+        images_input = [self.common_preprocessing(image1)]
+        
+        if image2 is not None:
+            images_input.append(self.common_preprocessing(image2))
+        if image3 is not None:
+            images_input.append(self.common_preprocessing(image3))
+
+        # 2. Encodage du texte positif avec les images injectées
+        # Note: Cette méthode utilise l'implémentation spécifique de Qwen2-VL CLIP
+        tokens = clip.tokenize(text)
+        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
+        
+        # Le dictionnaire 'images' est essentiel pour que le modèle Qwen sache quoi modifier
+        conditioning = [[cond, {"pooled_output": pooled, "images": images_input, "strength": strength}]]
+
+        # 3. Encodage du texte négatif
+        n_tokens = clip.tokenize(negative_prompt)
+        n_cond, n_pooled = clip.encode_from_tokens(n_tokens, return_pooled=True)
+        negative_conditioning = [[n_cond, {"pooled_output": n_pooled}]]
+
+        return (conditioning, negative_conditioning)
\ No newline at end of file