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Qwen Image Edit Plus

Qwen Image Edit Plus

Official

Advanced AI model for multi-image editing and enhanced single-image consistency.

Nodespell AI
AI / Image / Alibaba

Advanced AI model for multi-image editing and enhanced single-image consistency.

Model Overview

A powerful AI model designed for advanced image editing, capable of manipulating multiple images simultaneously and maintaining high consistency within single-image edits.

Best At

  • Multi-image Editing: Seamlessly combines elements from 1-3 input images to create new compositions (e.g., person + person, person + product, person + scene).
  • Single-image Consistency: Significantly improves editing accuracy for people (preserving identity, facial features, poses), products (maintaining product identity), and text (editing content, fonts, colors, materials).
  • ControlNet Integration: Natively supports ControlNet conditions like depth maps, edge maps, and keypoints for precise control over edits.

Limitations / Not Good At

  • While it supports up to 3 images, performance might be optimal with fewer.
  • Detailed fine-tuning for highly specific artistic styles may require further iteration.

Ideal Use Cases

  • Creative Composition: Merging different visual elements into a cohesive new image.
  • Product Mockups: Editing product images and placing them in new scenes or contexts.
  • Character Design: Modifying character appearances, poses, and clothing.
  • Meme Generation: Editing images with custom text and styles.
  • Poster Design: Creating posters with integrated text and imagery.
  • Photo Restoration: Enhancing and restoring old photographs.
  • Pose Transfer: Applying poses from one image to a subject in another.

Input & Output Format

  • Input: Text prompt, one or more reference images (JPEG, PNG, GIF, WEBP), optional parameters like aspect ratio, seed, output format, etc.
  • Output: Array of URIs pointing to the generated edited images.

Performance Notes

  • Supports a go_fast option for quicker predictions.
  • Offers control over output image format and quality.
Inputs (2)

Prompt

String

Text instruction on how to edit the given image.

Multi InputMin: 0Max: 100

Image

String

Images to use as reference. Must be jpeg, png, gif, or webp.

Multi InputMin: 0Max: 100
Parameters (7)

Seed

Number

Random seed. Set for reproducible generation

Default: -1

Prompt

String

Text instruction on how to edit the given image.

Default:

Go Fast

Boolean

Run faster predictions with additional optimizations.

Default: true

Aspect Ratio

String

Aspect ratio for the generated image

Default: match_input_image

Output Format

String

Format of the output images

Default: webp

Output Quality

Number

Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs

Default: 95

Disable Safety Checker

Boolean

Disable safety checker for generated images.

Default: false
Outputs (1)

Output

Inferred

Output

Used in Snippets (2)

Fashion Print Design
Snippet
# Fashion Print Design ## Overview This Fashion Print Design workflow turns your fabric motifs and reference photos into production-ready dress visuals and motion previews. It combines multiple image models to apply prints to cotton garments with realistic studio lighting and consistent color. ## What You'll Build - High‑resolution **1:1 garment mockups** with your print applied across the full dress. - 2K **fabric texture tiles** suitable for textile sampling or e‑commerce. - Short **5‑second fashion clips** that showcase the dress and print in motion. - Iterative concept boards that stay aligned to your fashion print moodboard. ## How It Works 1. A moodboard image input (e.g., `fashion_print_moodboard`) anchors the overall style, palette, and motif direction. 2. Multiple **Seedream 4** nodes (25 total, key ones like `seedream9`, `seedream11`, `seedream13`) generate 2048×2048, 2K print swatches and fabric renders at a **1:1 aspect ratio**, optimized for color consistency where Nano Banana struggles with flower tones. 3. **googleNanoBanana** nodes (7 total, JPG output, 1:1) support fast ideation passes, while sticky notes guide prompts such as changing the dress material to match the reference and ensuring the print pattern wraps cleanly across the garment. 4. A dedicated instruction note drives photorealism: applying the print texture to **cotton fabric** under clear, realistic studio lighting. 5. **qwenImageEditPlus** and **qwenImage** refine fit, fabric details, and print placement, while **reveCreate** and `hailuo23Fast` assist with stylistic variations and composition. 6. **kling25ImageToVideo** nodes transform key frames into **5‑second videos** (CFG scale 0.5, negative prompt to avoid blur, distortion, and low quality), giving you animated fashion previews. ## Best For - Fashion and textile designers developing new print collections. - Apparel brands needing fast dress and fabric mockups from reference art. - Surface pattern designers pitching prints to clothing labels. - E‑commerce teams creating on‑model visuals and motion previews without a full photoshoot. - Creative studios prototyping AI‑assisted fashion print design workflows. Try this Fashion Print Design snippet in Nodespell to turn flat print references into polished, motion‑ready fashion visuals in a few guided steps.
NTNodespell Team
Recent
AI Sunglasses Product Mockup Generator
Snippet
## Overview This Nodespell snippet is an **AI sunglasses mockup workflow** that turns multiple reference photos and style notes into polished, production-ready eyewear visuals. It generates new sunglasses designs that match frame shape, lens color, and viewing angle instructions. ## What You'll Build - High-resolution (2K) sunglasses product renders based on Etsy-style reference images. - Front and side-view variations, including a right-hand side profile of the same frame. - Customised frame silhouettes with more curved, rounded outer edges and refined corners. - Consistent lens colour and finish driven by a dedicated lens colour reference image. ## How It Works 1. **Reference intake via stickyImage nodes** – Seven `stickyImage` nodes load base inspiration images from Etsy URLs plus a dedicated `lens_colour_ref` node to lock in lens tint and finish. 2. **Design intent with stickyNote prompts** – Fourteen `stickyNote` nodes (e.g. `sticky_note4`, `sticky_note5`, `sticky_note18`) specify shape changes, side-view requirements, and which reference to follow for frames vs. lenses. 3. **Primary 2K generation with seedream4** – Four `seedream4` nodes generate core sunglasses renders at 2048×2048 resolution (`size: 2K`, `aspect_ratio: match_input_image`, `max_images: 1`) based on the combined textual and visual guidance. 4. **Variant and layout handling with googleNanoBanana** – Nine `googleNanoBanana` nodes create 16:9 PNG outputs for marketing-ready images, web product cards, and banner layouts (`aspect_ratio: 16:9`, `output_format: png`). 5. **Detail edits with qwenImageEditPlus** – A single `qwenImageEditPlus` node performs targeted refinements like softening frame edges, rounding corners, and aligning the side view to the front-view design. 6. **Complex graph orchestration** – The 35 nodes and 38 connections coordinate 21 inputs and 12 outputs, ensuring reference images, notes, and model calls stay in sync through the full design cycle. ## Best For - Eyewear brands and independent makers prototyping new sunglasses lines. - Etsy and DTC sellers needing fast, on-brand sunglasses mockups. - Product designers exploring frame variations without manual 3D work. - Marketers creating consistent hero images and ad creatives from a few reference photos. Try this snippet in Nodespell to rapidly turn your reference shots into polished AI-generated sunglasses visuals ready for product pages and campaigns.
NTNodespell Team
Recent
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Type

Node

Status

Official

Package

Nodespell AI

Category

AI / Image / Alibaba

Input

TextImage

Output

Image

Keywords (7)

Image GenerationPrompt ConditioningStyle ControlResolution ControlImage EditMulti Output
Use in Workflow