alimama-creative/EcomXL_controlnet_inpaint

EcomXL Inpaint ControlNet

EcomXL contains a series of text-to-image diffusion models optimized for e-commerce scenarios, developed based on Stable Diffusion XL.
For e-commerce scenarios, we trained Inpaint ControlNet to control diffusion models. Unlike the inpaint controlnets used for general scenarios, this model is fine-tuned with instance masks to prevent foreground outpainting. 

Examples

These cases are generated using AUTOMATIC1111/stable-diffusion-webui.

Foreground	Mask	w/o instance mask	w/ instance mask

Usage with Diffusers

from diffusers import (
    ControlNetModel,
    StableDiffusionXLControlNetPipeline,
    DDPMScheduler
)
from diffusers.utils import load_image
import torch
from PIL import Image
import numpy as np

def make_inpaint_condition(init_image, mask_image):
    init_image = np.array(init_image.convert("RGB")).astype(np.float32) / 255.0
    mask_image = np.array(mask_image.convert("L")).astype(np.float32) / 255.0
    assert init_image.shape[0:1] == mask_image.shape[0:1], "image and image_mask must have the same image size"
    init_image[mask_image > 0.5] = -1.0  # set as masked pixel
    init_image = np.expand_dims(init_image, 0).transpose(0, 3, 1, 2)
    init_image = torch.from_numpy(init_image)
    return init_image

def add_fg(full_img, fg_img, mask_img):
    full_img = np.array(full_img).astype(np.float32)
    fg_img = np.array(fg_img).astype(np.float32)
    mask_img = np.array(mask_img).astype(np.float32) / 255.
    full_img = full_img * mask_img + fg_img * (1-mask_img)
    return Image.fromarray(np.clip(full_img, 0, 255).astype(np.uint8))

controlnet = ControlNetModel.from_pretrained(
    "alimama-creative/EcomXL_controlnet_inpaint",
    use_safetensors=True,
)

pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", 
    controlnet=controlnet, 
)
pipe.to("cuda")
pipe.scheduler = DDPMScheduler.from_config(pipe.scheduler.config)

image = load_image(
    "https://huggingface.co./alimama-creative/EcomXL_controlnet_inpaint/resolve/main/images/inp_0.png"
)
mask = load_image(
    "https://huggingface.co./alimama-creative/EcomXL_controlnet_inpaint/resolve/main/images/inp_1.png"
)
mask = Image.fromarray(255 - np.array(mask))

control_image = make_inpaint_condition(image, mask)

prompt="a product on the table"

generator = torch.Generator(device="cuda").manual_seed(1234)

res_image = pipe(
    prompt,
    image=control_image,
    num_inference_steps=25,
    guidance_scale=7,
    width=1024,
    height=1024,
    controlnet_conditioning_scale=0.5,
    generator=generator,
).images[0]

res_image = add_fg(res_image, image, mask)
res_image.save(f'res.png')

The model exhibits good performance when the controlnet weight (controlnet_condition_scale) is 0.5.

Training details

In the first phase, the model was trained on 12M laion2B and internal source images with random masks for 20k steps. In the second phase, the model was trained on 3M e-commerce images with the instance mask for 20k steps.
Mixed precision: FP16
Learning rate: 1e-4
batch size: 2048
Noise offset: 0.05