app.py

import torch
import spaces
from diffusers import DDIMScheduler, StableDiffusionXLPipeline
import ipown
from huggingface_hub import hf_hub_download
from insightface.app import FaceAnalysis
import gradio as gr
import cv2

# List of models for switching
model_options = {
    "CyberRealistic": "John6666/cyberrealistic-pony-v61-sdxl",
    "StallionDreams": "John6666/stallion-dreams-pony-realistic-v1-sdxl",
    "PonyRealism": "John6666/pony-realism-v21main-sdxl"
}

# Full style list for applying styles to the prompt
style_list = [
    {
        "name": "(No style)",
        "prompt": "{prompt}",
        "negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
    },
    {
        "name": "Cinematic",
        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
    },
    {
        "name": "3D Model",
        "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
    },
    {
        "name": "Anime",
        "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime,  highly detailed",
        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
    },
    {
        "name": "Digital Art",
        "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
        "negative_prompt": "photo, photorealistic, realism, ugly",
    },
    {
        "name": "Photographic",
        "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
    },
    {
        "name": "Pixel art",
        "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
        "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
    },
    {
        "name": "Fantasy art",
        "prompt": "ethereal fantasy concept art of  {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
    },
    {
        "name": "Neonpunk",
        "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
    },
    {
        "name": "Manga",
        "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style",
        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
    },
]

# Styles dictionary to map style names to prompts and negative prompts
styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
STYLE_NAMES = list(styles.keys())
DEFAULT_STYLE_NAME = "(No style)"

# Function to apply the selected style
def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
    return p.replace("{prompt}", positive), n + negative

# Download the necessary model component
ip_ckpt = hf_hub_download(repo_id="h94/IP-Adapter-FaceID", filename="ip-adapter-faceid_sdxl.bin", repo_type="model")
device = "cuda"

# Configure the noise scheduler
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,
)

# Function to initialize the pipeline with a selected model
def get_pipeline(model_path):
    return StableDiffusionXLPipeline.from_pretrained(
        model_path,
        torch_dtype=torch.float16,
        scheduler=noise_scheduler,
        use_safetensors=True,
    )

# Initialize with a default model
current_model = model_options["PonyRealism"]
pipe = get_pipeline(current_model)
ip_model = ipown.IPAdapterFaceIDXL(pipe, ip_ckpt, device)

@spaces.GPU()
def generate_image(images, model_choice, style_name, prompt, negative_prompt, face_strength, likeness_strength, num_inference_steps, guidance_scale, width, height):
    global current_model, pipe, ip_model

    # Update the model if the choice has changed
    if model_options[model_choice] != current_model:
        current_model = model_options[model_choice]
        pipe = get_pipeline(current_model)
        ip_model = ipown.IPAdapterFaceIDXL(pipe, ip_ckpt, device)

    torch.cuda.empty_cache()

    app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
    app.prepare(ctx_id=0, det_size=(512, 512))

    faceid_all_embeds = []
    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)

    average_embedding = torch.mean(torch.stack(faceid_all_embeds, dim=0), dim=0)
    
    # Apply the selected style
    styled_prompt, styled_negative_prompt = apply_style(style_name, prompt, negative_prompt)
    
    image = ip_model.generate(
        prompt=styled_prompt, negative_prompt=styled_negative_prompt, faceid_embeds=average_embedding,
        scale=likeness_strength, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps
    )

    return image

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}
'''

with gr.Blocks(css=css) as demo:
    gr.Markdown("# IP-Adapter-FaceID SDXL demo")
    gr.Markdown("A simple Demo for the [h94/IP-Adapter-FaceID SDXL model](https://huggingface.co/h94/IP-Adapter-FaceID).")
    with gr.Row():
        with gr.Column():
            model_dropdown = gr.Dropdown(label="Select Model", choices=list(model_options.keys()), value="PonyRealism")
            style_dropdown = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
            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=250)
            with gr.Column(visible=False) as clear_button:
                remove_and_reupload = gr.ClearButton(value="Remove files and upload new ones", components=files, size="sm")
            prompt = gr.Textbox(label="Prompt", placeholder="A photo of a man/woman/person ...")
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="low quality")
            face_strength = gr.Slider(label="Face Strength", value=7.5, step=0.1, minimum=0, maximum=15)
            likeness_strength = gr.Slider(label="Likeness Strength", value=1.0, step=0.1, minimum=0, maximum=5)
            with gr.Accordion("Advanced Options", open=False):
                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)
            submit = gr.Button("Submit", variant="primary")
        with gr.Column():
            gallery = gr.Gallery(label="Generated Images")
        
        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, model_dropdown, style_dropdown, prompt, negative_prompt, face_strength, likeness_strength, num_inference_steps, guidance_scale, width, height], outputs=gallery)

demo.launch()