Add Qwen Image Edit Single MXD node

Introduces the QwenImageEditSingleMXD node for single-image editing with prompt and optional image input, supporting reference latents and VAE encoding. Updates node mappings and display names accordingly. Bumps project version to 1.5.1.

maxedoutnodes.py

@@ -3,6 +3,7 @@ import torch, math, comfy, os, folder_paths, node_helpers, comfy.model_managemen
 from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
 import numpy as np
 from PIL import Image, ImageOps, ImageSequence
+from comfy_api.latest import io
 
 ########################################################################################################################
 # Flux Empty Latent Image (SD3-compatible)
@@ -425,6 +426,65 @@ class PromptWithGuidance(ComfyNodeABC):
         conditioning = clip.encode_from_tokens_scheduled(tokens)
         conditioning = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
         return (conditioning,)
+
+########################################################################################################################
+# Qwen Image Edit Single MXD
+class QwenImageEditSingleMXD(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="QwenImageEditSingleMXD",
+            display_name="Qwen Image Edit Prompt Single MXD",
+            category="MXD/conditioning",
+            description="Encode a prompt and optional image for Qwen single-image editing.",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.Vae.Input("vae", optional=True),
+                io.Image.Input("image", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, vae=None, image=None) -> io.NodeOutput:
+        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 image is not None:
+            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 1: <|vision_start|><|image_pad|><|vision_end|>"
+
+        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 io.NodeOutput(conditioning)
     
 ########################################################################################################################    
 class FluxResolutionMatcher:
@@ -1080,6 +1140,7 @@ NODE_CLASS_MAPPINGS = {
     "Image Scale To Total Pixels (SDXL Safe)": SDXLImageScaleToTotalPixelsSafe,
     "Flux Image Scale To Total Pixels (Flux Safe)": FluxImageScaleToTotalPixelsSafe,
     "Prompt With Guidance (Flux)": PromptWithGuidance,
+    "QwenImageEditSingleMXD": QwenImageEditSingleMXD,
     "FluxResolutionMatcher": FluxResolutionMatcher,
     "SDXLResolutionMatcher": SDXLResolutionMatcher,
     "LatentHalfMasks": LatentHalfMasks,
@@ -1099,6 +1160,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "Image Scale To Total Pixels (SDXL Safe)": "Scale SDXL Image MXD",
     "Flux Image Scale To Total Pixels (Flux Safe)": "Scale Flux Image MXD",
     "Prompt With Guidance (Flux)": "Prompt with Flux Guidance MXD",
+    "QwenImageEditSingleMXD": "Qwen Image Edit Prompt Single MXD",
     "FluxResolutionMatcher": "Flux Resolution Matcher MXD",
     "SDXLResolutionMatcher": "SDXL Resolution Matcher MXD",
     "LatentHalfMasks": "Latent to L/R Masks MXD",

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "maxedout"
 description = "Custom ComfyUI nodes used in Maxed Out workflows (SDXL, Flux, Wan 2.2, etc.)"
-version = "1.5.0"
+version = "1.5.1"
 license = {file = "LICENSE"} 
 # classifiers = [
 #     # For OS-independent nodes (works on all operating systems)