Util Fixes , and Match Qwen Conditioning processor that can accept an image batch instead of individual images - works with up to 5 images

__init__.py

@@ -51,6 +51,8 @@ from .material_advanced import NODE_CLASS_MAPPINGS as MATERIAL_ADV_CLASS_MAPPING
 from .material_advanced import NODE_DISPLAY_NAME_MAPPINGS as MATERIAL_ADV_DISPLAY_MAPPINGS
 from .filter_utils import NODE_CLASS_MAPPINGS as FILTER_CLASS_MAPPINGS
 from .filter_utils import NODE_DISPLAY_NAME_MAPPINGS as FILTER_DISPLAY_MAPPINGS
+from .qwen_extras import NODE_CLASS_MAPPINGS as QWEN_EXTRAS_MAPPINGS
+from .qwen_extras import NODE_DISPLAY_NAME_MAPPINGS as QWEN_EXTRAS_DISPLAY_MAPPINGS
 
 # Lighting and relighting (NEW!)
 from .relight_utils import NODE_CLASS_MAPPINGS as RELIGHT_CLASS_MAPPINGS
@@ -83,6 +85,7 @@ NODE_CLASS_MAPPINGS = {
     **ANALYSIS_CLASS_MAPPINGS,
     **MATERIAL_ADV_CLASS_MAPPINGS,
     **FILTER_CLASS_MAPPINGS,
+    **QWEN_EXTRAS_MAPPINGS,
     # Lighting
     **RELIGHT_CLASS_MAPPINGS,
     **ENVIRONMENT_CLASS_MAPPINGS,
@@ -112,6 +115,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     **ANALYSIS_DISPLAY_MAPPINGS,
     **MATERIAL_ADV_DISPLAY_MAPPINGS,
     **FILTER_DISPLAY_MAPPINGS,
+    **QWEN_EXTRAS_DISPLAY_MAPPINGS,
     # Lighting
     **RELIGHT_DISPLAY_MAPPINGS,
     **ENVIRONMENT_DISPLAY_MAPPINGS,

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "TextureAlchemy"
-version = "1.3.0"
+version = "1.4.0"
 description = "Complete PBR & texture processing toolkit for ComfyUI"
 
 [project.urls]

qwen_extras.py

@@ -0,0 +1,80 @@
+"""
+Texture Alchemy- Qwen Image Edit Plus (Batch) – same behaviour as the built-in
+Text Encode Qwen Image Edit Plus but accepts a single IMAGE batch and chains
+each image in the batch.
+"""
+import math
+
+import node_helpers
+import comfy.utils
+
+
+class TextEncodeQwenImageEditPlusBatchNode:
+    """
+    Same as Text Encode Qwen Image Edit Plus but takes one IMAGE batch and
+    chains every image in the batch (Picture 1, Picture 2, ...) into one
+    conditioning.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "clip": ("CLIP",),
+                "prompt": ("STRING", {"default": "", "multiline": True}),
+            },
+            "optional": {
+                "vae": ("VAE", {"default": None}),
+                "images": ("IMAGE", {"default": None}),
+            },
+        }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    RETURN_NAMES = ("conditioning",)
+    FUNCTION = "encode_batch"
+    CATEGORY = "Conditioning"
+
+    def encode_batch(self, clip, prompt, vae=None, images=None):
+        ref_latents = []
+        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 = ""
+
+        if images is not None:
+            batch_size = images.shape[0]
+            for i in range(batch_size):
+                image = images[i : i + 1]
+                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,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "TextEncodeQwenImageEditPlusBatchNode": TextEncodeQwenImageEditPlusBatchNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "TextEncodeQwenImageEditPlusBatchNode": "Text Encode Qwen Image Edit Plus (Batch)",
+}