This repository contains a IP-Adapter for SD3.5-Large model released by researchers from
InstantX Team
, where image work just like text, so it may not be responsive or interfere with other text, but we do hope you enjoy this model, have fun and share your creative works with us
on Twitter
.
Model Card
This is a regular IP-Adapter, where the new layers are added into all 38 blocks. We use
google/siglip-so400m-patch14-384
to encode image for its superior performance, and adopt a TimeResampler to project. The image token number is set to 64.
Showcases
Inference
The code has not been integrated into diffusers yet, please use our local files at this moment.
import torch
from PIL import Image
from models.transformer_sd3 import SD3Transformer2DModel
from pipeline_stable_diffusion_3_ipa import StableDiffusion3Pipeline
model_path = 'stabilityai/stable-diffusion-3.5-large'
ip_adapter_path = './ip-adapter.bin'
image_encoder_path = "google/siglip-so400m-patch14-384"
transformer = SD3Transformer2DModel.from_pretrained(
model_path, subfolder="transformer", torch_dtype=torch.bfloat16
)
pipe = StableDiffusion3Pipeline.from_pretrained(
model_path, transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
pipe.init_ipadapter(
ip_adapter_path=ip_adapter_path,
image_encoder_path=image_encoder_path,
nb_token=64,
)
ref_img = Image.open('./assets/1.jpg').convert('RGB')
# please note that SD3.5 Large is sensitive to highres generation like 1536x1536
image = pipe(
width=1024,
height=1024,
prompt='a cat',
negative_prompt="lowres, low quality, worst quality",
num_inference_steps=24,
guidance_scale=5.0,
generator=torch.Generator("cuda").manual_seed(42),
clip_image=ref_img,
ipadapter_scale=0.5,
).images[0]
image.save('./result.jpg')
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