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RegalRinse/
roomviz-wall-editor

Name: roomviz-wall-editor
Availability: InStock

diffusers

safetensors

QwenImageEditPlusPipeline

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🏠 RoomViz Wall Editor

RoomViz Wall Editor is a domain-adapted image editing model built on top of Qwen/Qwen-Image-Edit-2511, configured and optimized for transforming under-construction Indian room images into fully finished wall visualizations with user-specified colors.

🎯 Use Case

This model is designed for a specific real-world application:

Input: A photograph of an under-construction or rough Indian room (exposed brick, unplastered walls, raw concrete)
Instruction: A natural language color instruction from the user
(e.g. "paint the walls light beige")
Output: The same room rendered with smooth, fully plastered, and painted walls in the desired color — with floors, ceilings, doors and windows preserved exactly

🔧 Intended Prompt Format

Make the walls smooth, fully plastered and painted [COLOR].
Keep the floor, ceiling, doors, windows and furniture exactly as they are.

Example prompts:

Make the walls smooth, fully plastered and painted off-white. Keep the floor, ceiling, doors, windows and furniture exactly as they are.
Make the walls smooth, fully plastered and painted sage green. Keep the floor, ceiling, doors, windows and furniture exactly as they are.
Make the walls smooth, fully plastered and painted warm beige. Keep the floor, ceiling, doors, windows and furniture exactly as they are.

🚀 How to Use

python

from diffusers import FlowMatchEulerDiscreteScheduler
from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
import torch
from PIL import Image

dtype = torch.bfloat16
device = "cuda"

pipe = QwenImageEditPlusPipeline.from_pretrained(
    "RegalRinse/roomviz-wall-editor",
    transformer=QwenImageTransformer2DModel.from_pretrained(
        "RegalRinse/roomviz-transformer",
        torch_dtype=dtype,
        device_map="cuda"
    ),
    torch_dtype=dtype
).to(device)

image = Image.open("your_room.jpg").convert("RGB")

result = pipe(
    image=[image],
    prompt="Make the walls smooth, fully plastered and painted light beige. Keep the floor, ceiling, doors, windows and furniture exactly as they are.",
    negative_prompt="worst quality, low quality, blurry, unrealistic",
    height=1024,
    width=1024,
    num_inference_steps=4,
    true_cfg_scale=3.5,
).images

result.save("output.png")

⚙️ Technical Details

Property	Value
Base Model	Qwen/Qwen-Image-Edit-2511
Transformer	RegalRinse/roomviz-transformer (Lightning-distilled, 4-step)
Inference Steps	4 (Lightning)
Recommended CFG Scale	3.5
Input Resolution	Up to 1024×1024
Dtype	bfloat16
Task	Text-guided image-to-image editing
Domain	Indian under-construction rooms

🏗️ Architecture

This model uses the QwenImageEditPlus pipeline with a Lightning-distilled transformer that enables high-quality image editing in just 4 inference steps instead of the standard 50, making it suitable for real-time deployment on shared GPU infrastructure.

📦 Deployment

This model is deployed as part of the RoomViz application stack:

Model Hosting: Hugging Face Model Hub
Inference: Hugging Face ZeroGPU Space (free H200)
API Layer: FastAPI on Render
Frontend: React on Vercel

⚠️ Limitations

Works best on images where walls are the dominant surface
May not perfectly isolate walls if room has complex geometry
Output quality depends on input image clarity and lighting
Optimized for Indian construction room aesthetics