app.py

import torch
import spaces
from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL, StableDiffusionXLPipeline
from transformers import AutoFeatureExtractor
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
import ipown
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_path = "SG161222/RealVisXL_V3.0"
ip_ckpt = hf_hub_download(repo_id="h94/IP-Adapter-FaceID", filename="ip-adapter-faceid_sdxl.bin", repo_type="model")
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)
pipe = StableDiffusionXLPipeline.from_pretrained(
    base_model_path,
    torch_dtype=torch.float16,
    scheduler=noise_scheduler,
    add_watermarker=False
    # vae=vae,
    #feature_extractor=safety_feature_extractor,
    #safety_checker=safety_checker
)

#pipe.load_lora_weights("h94/IP-Adapter-FaceID", weight_name="ip-adapter-faceid-plusv2_sd15_lora.safetensors")
#pipe.fuse_lora()

ip_model = ipown.IPAdapterFaceIDXL(pipe, ip_ckpt, device)

@spaces.GPU(enable_queue=True)
def generate_image(images, prompt, negative_prompt, face_strength, likeness_strength, progress=gr.Progress(track_tqdm=True)):
    
    # Clear GPU memory
    torch.cuda.empty_cache()
    
    # Start the process
    pipe.to(device)
    app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
    app.prepare(ctx_id=0, det_size=(640, 640))
    
    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)
    
    total_negative_prompt = negative_prompt
    
    print("Generating SDXL")
    image = ip_model.generate(
        prompt=prompt, negative_prompt=total_negative_prompt, faceid_embeds=average_embedding,
        scale=likeness_strength, width=1024, height=1024, guidance_scale=face_strength, num_inference_steps=30
    )

    print(image)
    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("My own Demo for the [h94/IP-Adapter-FaceID SDXL model](https://huggingface.co/h94/IP-Adapter-FaceID).")
    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]...",
                        value="A photo of a man, professional photoshoot, plain black shirt, on plain black background, shaved head, trimmed beard, wrinkles on forehead, intense, stoic, dramatic lighting")
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="low quality", value="low quality, worst quality")
            style = "Photorealistic"
            submit = gr.Button("Submit")
            with gr.Column(open=True):
                face_strength = gr.Slider(label="Guidance Scale", info="Dunno what this actually is", value=7.5, step=0.1, minimum=1, maximum=10)
                likeness_strength = gr.Slider(label="Scale", info="Dunno what this actually is, either", value=1.0, step=0.1, minimum=0, maximum=5)
        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,prompt,negative_prompt, face_strength, likeness_strength],
                    outputs=gallery)
    
    # gr.Markdown("This demo includes extra features to mitigate the implicit bias of the model and prevent explicit usage of it to generate content with faces of people, including third parties, that is not safe for all audiences, including naked or semi-naked people.")
    
demo.launch()