Updates
✨🎉 This model has been merged into Diffusers and can now be used conveniently. 💡 🎉✨
Examples
SD3 Controlnet Inpainting
Finetuned controlnet inpainting model based on sd3-medium, the inpainting model offers several advantages:
-
Leveraging the SD3 16-channel VAE and high-resolution generation capability at 1024, the model effectively preserves the integrity of non-inpainting regions, including text.
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It is capable of generating text through inpainting.
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It demonstrates superior aesthetic performance in portrait generation.
Compared with SDXL-Inpainting
From left to right: Input image, Masked image, SDXL inpainting, Ours.
Using with Diffusers
Install from source and Run
pip uninstall diffusers
pip install git+https://github.com/huggingface/diffusers
import torch
from diffusers.utils import load_image, check_min_version
from diffusers.pipelines import StableDiffusion3ControlNetInpaintingPipeline
from diffusers.models.controlnet_sd3 import SD3ControlNetModel
controlnet = SD3ControlNetModel.from_pretrained(
"alimama-creative/SD3-Controlnet-Inpainting", use_safetensors=True, extra_conditioning_channels=1
)
pipe = StableDiffusion3ControlNetInpaintingPipeline.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers",
controlnet=controlnet,
torch_dtype=torch.float16,
)
pipe.text_encoder.to(torch.float16)
pipe.controlnet.to(torch.float16)
pipe.to("cuda")
image = load_image(
"https://huggingface.co/alimama-creative/SD3-Controlnet-Inpainting/resolve/main/images/dog.png"
)
mask = load_image(
"https://huggingface.co/alimama-creative/SD3-Controlnet-Inpainting/resolve/main/images/dog_mask.png"
)
width = 1024
height = 1024
prompt = "A cat is sitting next to a puppy."
generator = torch.Generator(device="cuda").manual_seed(24)
res_image = pipe(
negative_prompt="deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
prompt=prompt,
height=height,
width=width,
control_image=image,
control_mask=mask,
num_inference_steps=28,
generator=generator,
controlnet_conditioning_scale=0.95,
guidance_scale=7,
).images[0]
res_image.save(f"sd3.png")
Training Detail
The model was trained on 12M laion2B and internal source images for 20k steps at resolution 1024x1024.
- Mixed precision : FP16
- Learning rate : 1e-4
- Batch size : 192
- Timestep sampling mode : 'logit_normal'
- Loss : Flow Matching
Limitation
Due to the fact that only 1024*1024 pixel resolution was used during the training phase, the inference performs best at this size, with other sizes yielding suboptimal results. We will initiate multi-resolution training in the future, and at that time, we will open-source the new weights.
LICENSE
The model is based on SD3 finetuning; therefore, the license follows the original SD3 license.