app.py

import torch
import spaces
from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL
from transformers import AutoFeatureExtractor
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from ip_adapter.ip_adapter_faceid import IPAdapterFaceID, IPAdapterFaceIDPlus
from huggingface_hub import hf_hub_download
from insightface.app import FaceAnalysis
from insightface.utils import face_align
import gradio as gr
import cv2

base_model_paths = {
    "RealisticVisionV4": "SG161222/Realistic_Vision_V4.0_noVAE",
    "RealisticVisionV6": "SG161222/Realistic_Vision_V6.0_B1_noVAE",
    "Deliberate": "Yntec/Deliberate",
    "DeliberateV2": "Yntec/Deliberate2",
    "Dreamshaper8": "Lykon/dreamshaper-8",
    "EpicRealism": "emilianJR/epiCRealism"
}


vae_model_path = "stabilityai/sd-vae-ft-mse"
image_encoder_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
ip_ckpt = hf_hub_download(repo_id="h94/IP-Adapter-FaceID", filename="ip-adapter-faceid_sd15.bin", repo_type="model")
ip_plus_ckpt = hf_hub_download(repo_id="h94/IP-Adapter-FaceID", filename="ip-adapter-faceid-plusv2_sd15.bin", repo_type="model")

safety_model_id = "CompVis/stable-diffusion-safety-checker"
safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)

device = "cuda"

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.float16)

def load_model(base_model_path):
    pipe = StableDiffusionPipeline.from_pretrained(
        base_model_path,
        torch_dtype=torch.float16,
        scheduler=noise_scheduler,
        vae=vae,
        feature_extractor=safety_feature_extractor,
        safety_checker=None  # <--- Disable safety checker
    ).to(device)
    return pipe

ip_model = None
ip_model_plus = None

app = FaceAnalysis(name="buffalo_l", providers=['CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))

cv2.setNumThreads(1)

@spaces.GPU(enable_queue=True)
def generate_image(images, prompt, negative_prompt, preserve_face_structure, face_strength, likeness_strength, nfaa_negative_prompt, base_model, num_inference_steps, guidance_scale, width, height, progress=gr.Progress(track_tqdm=True)):
    global ip_model, ip_model_plus
    base_model_path = base_model_paths[base_model]
    pipe = load_model(base_model_path)
    ip_model = IPAdapterFaceID(pipe, ip_ckpt, device)
    ip_model_plus = IPAdapterFaceIDPlus(pipe, image_encoder_path, ip_plus_ckpt, device)

    faceid_all_embeds = []
    first_iteration = True
    for image in images:
        face = cv2.imread(image)
        faces = app.get(face)
        faceid_embed = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
        faceid_all_embeds.append(faceid_embed)
        if(first_iteration and preserve_face_structure):
            face_image = face_align.norm_crop(face, landmark=faces[0].kps, image_size=224) # you can also segment the face
            first_iteration = False
            
    average_embedding = torch.mean(torch.stack(faceid_all_embeds, dim=0), dim=0)
    
    total_negative_prompt = f"{negative_prompt} {nfaa_negative_prompt}"
    
    if(not preserve_face_structure):
        print("Generating normal")
        image = ip_model.generate(
            prompt=prompt, negative_prompt=total_negative_prompt, faceid_embeds=average_embedding,
            scale=likeness_strength, width=width, height=height, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale
        )
    else:
        print("Generating plus")
        image = ip_model_plus.generate(
            prompt=prompt, negative_prompt=total_negative_prompt, faceid_embeds=average_embedding,
            scale=likeness_strength, face_image=face_image, shortcut=True, s_scale=face_strength, width=width, height=height, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale
        )
    print(image)
    return image

def change_style(style):
    if style == "Photorealistic":
        return(gr.update(value=True), gr.update(value=1.3), gr.update(value=1.0))
    else:
        return(gr.update(value=True), gr.update(value=0.1), gr.update(value=0.8))

def swap_to_gallery(images):
    return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False)

def remove_back_to_files():
    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)

css = '''
h1{margin-bottom: 0 !important}
footer{display:none !important}
'''

with gr.Blocks(css=css) as demo:
    gr.Markdown("")
    gr.Markdown("")
    with gr.Row():
        with gr.Column():
            files = gr.Files(
                        label="Drag 1 or more photos of your face",
                        file_types=["image"]
                    )
            uploaded_files = gr.Gallery(label="Your images", visible=False, columns=5, rows=1, height=125)
            with gr.Column(visible=False) as clear_button:
                remove_and_reupload = gr.ClearButton(value="Remove and upload new ones", components=files, size="sm")
            prompt = gr.Textbox(label="Prompt",
                       info="Try something like 'a photo of a man/woman/person'",
                       placeholder="A photo of a [man/woman/person]...")
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="low quality")
            style = gr.Radio(label="Generation type", info="For stylized try prompts like 'a watercolor painting of a woman'", choices=["Photorealistic", "Stylized"], value="Photorealistic")
            base_model = gr.Dropdown(label="Base Model", choices=list(base_model_paths.keys()), value="Realistic_Vision_V4.0_noVAE")
            submit = gr.Button("Submit")
            with gr.Accordion(open=False, label="Advanced Options"):
                preserve = gr.Checkbox(label="Preserve Face Structure", info="Higher quality, less versatility (the face structure of your first photo will be preserved). Unchecking this will use the v1 model.", value=True)
                face_strength = gr.Slider(label="Face Structure strength", info="Only applied if preserve face structure is checked", value=1.3, step=0.1, minimum=0, maximum=3)
                likeness_strength = gr.Slider(label="Face Embed strength", value=1.0, step=0.1, minimum=0, maximum=5)
                nfaa_negative_prompts = gr.Textbox(label="Appended Negative Prompts", info="Negative prompts to steer generations towards safe for all audiences outputs", value="naked, bikini, skimpy, scanty, bare skin, lingerie, swimsuit, exposed, see-through")
                num_inference_steps = gr.Slider(label="Number of Inference Steps", value=30, step=1, minimum=10, maximum=100)
                guidance_scale = gr.Slider(label="Guidance Scale", value=7.5, step=0.1, minimum=1, maximum=20)
                width = gr.Slider(label="Width", value=512, step=64, minimum=256, maximum=1024)
                height = gr.Slider(label="Height", value=512, step=64, minimum=256, maximum=1024)
        with gr.Column():
            gallery = gr.Gallery(label="Generated Images")
        style.change(fn=change_style,
                    inputs=style,
                    outputs=[preserve, face_strength, likeness_strength])
        files.upload(fn=swap_to_gallery, inputs=files, outputs=[uploaded_files, clear_button, files])
        remove_and_reupload.click(fn=remove_back_to_files, outputs=[uploaded_files, clear_button, files])
        submit.click(fn=generate_image,
                    inputs=[files,prompt,negative_prompt,preserve, face_strength, likeness_strength, nfaa_negative_prompts, base_model, num_inference_steps, guidance_scale, width, height],
                    outputs=gallery)
    
    gr.Markdown("")
    
demo.launch()