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import torch
from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL
from PIL import Image
from ip_adapter.ip_adapter_faceid import IPAdapterFaceIDPlus
import cv2
from insightface.app import FaceAnalysis
from insightface.utils import face_align
import gradio as gr
from huggingface_hub import hf_hub_download
def download_models():
hf_hub_download(
repo_id='h94/IP-Adapter-FaceID',
filename='ip-adapter-faceid-plus_sd15.bin',
local_dir='IP-Adapter-FaceID')
hf_hub_download(
repo_id='h94/IP-Adapter',
filename='models/image_encoder/config.json',
local_dir='IP-Adapter')
hf_hub_download(
repo_id='h94/IP-Adapter',
filename='models/image_encoder/pytorch_model.bin',
local_dir='IP-Adapter')
def get_ip_model():
download_models()
base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE"
vae_model_path = "stabilityai/sd-vae-ft-mse"
image_encoder_path = "IP-Adapter/models/image_encoder"
ip_ckpt = "IP-Adapter-FaceID/ip-adapter-faceid-plus_sd15.bin"
if torch.cuda.is_available():
device = 'cuda'
torch_dtype = torch.float16
else:
device = 'cpu'
torch_dtype = torch.float32
print(f'Using device: {device}')
noise_scheduler = DDIMScheduler(
num_train_timesteps=1000,
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
steps_offset=1,
)
vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch_dtype)
pipe = StableDiffusionPipeline.from_pretrained(
base_model_path,
torch_dtype=torch_dtype,
scheduler=noise_scheduler,
vae=vae,
feature_extractor=None,
safety_checker=None
)
ip_model = IPAdapterFaceIDPlus(pipe, image_encoder_path, ip_ckpt, device, num_tokens=4, torch_dtype=torch_dtype)
return ip_model
ip_model = get_ip_model()
app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640), det_thresh=0.2)
def generate_images(img_filepath, prompt, n_images=3,
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality, blurry",
img_prompt_scale=0.5,
num_inference_steps=30,
seed=None):
print(prompt)
image = cv2.imread(img_filepath)
faces = app.get(image)
faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
face_image = face_align.norm_crop(image, landmark=faces[0].kps, image_size=224) # you can also segment the face
images = ip_model.generate(
prompt=prompt, negative_prompt=negative_prompt, face_image=face_image, faceid_embeds=faceid_embeds,
num_samples=n_images, width=512, height=512, num_inference_steps=num_inference_steps, seed=seed,
scale=img_prompt_scale, # with scale=1 I get weird images
)
return [images, Image.fromarray(face_image[..., [2, 1, 0]])]
with gr.Blocks() as demo:
gr.Markdown(
"""
# IP-Adapter-FaceID-plus
Generate images conditioned on a image prompt and a text prompt. Learn more here: https://huggingface.co/h94/IP-Adapter-FaceID
""")
with gr.Row():
with gr.Column():
demo_inputs = []
demo_inputs.append(gr.Image(type='filepath', label='image prompt'))
demo_inputs.append(gr.Textbox(label='text prompt', value='headshot of a man, green moss wall in the background'))
demo_inputs.append(gr.Slider(maximum=3, minimum=1, value=3, step=1, label='number of images'))
with gr.Accordion(label='Advanced options', open=False):
demo_inputs.append(gr.Textbox(label='negative text prompt', value="monochrome, lowres, bad anatomy, worst quality, low quality, blurry"))
demo_inputs.append(gr.Slider(maximum=1, minimum=0, value=0.5, step=0.05, label='image prompt scale'))
btn = gr.Button("Generate")
with gr.Column():
demo_outputs = []
demo_outputs.append(gr.Gallery(label='generated images'))
demo_outputs.append(gr.Image(label='detected face', height=224, width=224))
btn.click(generate_images, inputs=demo_inputs, outputs=demo_outputs)
sample_prompts = [
'headshot of a man, green moss wall in the background',
'linkedin profile picture of a macdonalds worker',
'LinkedIn profile picture of a beautiful man dressed in a suit, huge explosion in the background',
]
gr.Examples(sample_prompts, inputs=demo_inputs[1], label='Sample prompts')
demo.launch(share=True, debug=True) |