app.py

import gradio as gr
import numpy as np
import random
import spaces
from diffusers import StableDiffusionXLPipeline, AutoencoderKL, ControlNetModel
from diffusers.utils import load_image
import torch
from typing import Tuple
from PIL import Image
from controlnet_aux import OpenposeDetector
import insightface
import onnxruntime

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "RunDiffusion/Juggernaut-XL-v9"  # Replace to the model you would like to use
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

pipe = StableDiffusionXLPipeline.from_pretrained(
        "RunDiffusion/Juggernaut-XL-v9",
        vae=vae,
        torch_dtype=torch.float16,
        custom_pipeline="lpw_stable_diffusion_xl",
        use_safetensors=True,
        add_watermarker=False,
        variant="fp16",
    )
pipe.to(device)

controlnet_openpose = ControlNetModel.from_pretrained(
    "lllyasviel/control_v11p_sdxl_openpose", torch_dtype=torch.float16
).to(device)

openpose_detector = OpenposeDetector.from_pretrained("lllyasviel/ControlNet/annotator/ckpts/body_pose_model.pth").to(device)

try:
    pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-faceid_sdxl.bin")
except Exception as e:
    print(f"Could not load IP-Adapter FaceID. Make sure the model exists and paths are correct: {e}")
    print("Trying a common alternative: ip-adapter-plus-face_sdxl_vit-h.safetensors")
    try:
        pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-plus-face_sdxl_vit-h.safetensors")
    except Exception as e2:
        print(f"Could not load second IP-Adapter variant: {e2}")
        print("IP-Adapter will not be available. Please check your IP-Adapter setup.")
        pipe.unload_ip_adapter()

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 4096

style_list = [
    {
        "name": "(No style)",
        "prompt": "{prompt}",
        "negative_prompt": "",
    },
    {
        "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": "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": "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": "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",
    },
    {
        "name": "Digital Art",
        "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
        "negative_prompt": "photo, photorealistic, realism, 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": "3D Model",
        "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
    },
]   

styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
STYLE_NAMES = list(styles.keys())
DEFAULT_STYLE_NAME = "(No style)"

def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
    if not negative:
        negative = ""
    return p.replace("{prompt}", positive), n + negative

@spaces.GPU
def infer(
    prompt,
    negative_prompt,
    style,
    # Removed general img2img reference as we are specializing
    input_image_pose, # New: for ControlNet OpenPose
    pose_strength,    # New: strength for ControlNet
    input_image_face, # New: for IP-Adapter Face
    face_fidelity,    # New: fidelity/strength for IP-Adapter
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
    generator = torch.Generator().manual_seed(seed)

    # --- NEW: Prepare ControlNet and IP-Adapter inputs ---
    controlnet_images = []
    controlnet_conditioning_scales = []
    controlnet_models_to_use = []
    ip_adapter_image_embeddings = None # Will store the face embeddings

    # Process Pose Reference
    if input_image_pose:
        # Preprocess the image to get the OpenPose skeleton
        processed_pose_image = openpose_detector(input_image_pose)
        controlnet_images.append(processed_pose_image)
        controlnet_conditioning_scales.append(pose_strength)
        controlnet_models_to_use.append(controlnet_openpose)

    # Process Face Reference (IP-Adapter)
    if input_image_face and pipe.has_lora_weights("ip_adapter"): # Check if IP-Adapter was loaded successfully
        # For IP-Adapter FaceID, the pipeline itself usually handles embedding extraction
        # You just pass the image directly.
        # The scale is set before the call.
        pipe.set_ip_adapter_scale(face_fidelity)
        # ip_adapter_image_embeddings = pipe.encode_ip_adapter_image(input_image_face) # If you need to manually encode
        # Often, you just pass the image to the main call directly if IP-Adapter is loaded.

    # --- END NEW INPUT PREPARATION ---

    # Adjusting the pipe call to use ControlNet and IP-Adapter
    # Note: If no reference images are provided, it will fall back to text-to-image.
    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        image=controlnet_images if controlnet_images else None, # Pass processed pose image(s) if available
        controlnet_conditioning_scale=controlnet_conditioning_scales if controlnet_conditioning_scales else None,
        controlnet=controlnet_models_to_use if controlnet_models_to_use else None,
        ip_adapter_image=input_image_face if input_image_face else None, # Pass the raw face image for IP-Adapter
        # ip_adapter_image_embeds=ip_adapter_image_embeddings, # Use this if you pre-encode
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]

    return image, seed

examples = [
    "A stunning woman standing on a beach at sunset, dramatic lighting, highly detailed",
    "A man in a futuristic city, cyberpunk style, neon lights",
    "An AI model posing with a friendly robot in a studio, professional photoshoot",
]
css = """#col-container {
    margin: 0 auto;
    max-width: 640px;
}"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # AI Instagram Model Creator")
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Describe your AI model and scene (e.g., 'A confident woman in a red dress, city background')",
                container=False,
            )
            run_button = gr.Button("Generate", scale=0, variant="primary")
        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Reference Images", open=True):
            gr.Markdown("Upload images to control pose and face consistency.")
            input_image_pose = gr.Image(label="Human Pose Reference (for body posture)", type="pil", show_label=True)
            pose_strength = gr.Slider(
                label="Pose Control Strength (0.0 = ignore, 1.0 = strict adherence)",
                minimum=0.0,
                maximum=1.0,
                step=0.01,
                value=0.8, # Good starting point for strong pose control
            )
            gr.Markdown("---") # Separator

            input_image_face = gr.Image(label="Face Reference (for facial consistency)", type="pil", show_label=True)
            face_fidelity = gr.Slider(
                label="Face Fidelity (0.0 = ignore, 1.0 = highly similar)",
                minimum=0.0,
                maximum=1.0,
                step=0.01,
                value=0.7, # Good starting point for face transfer
            )

        with gr.Row(visible=True):
            style_selection = gr.Radio(
                show_label=True,
                container=True,
                interactive=True,
                choices=STYLE_NAMES,
                value=DEFAULT_STYLE_NAME,
                label="Image Style",
            )
        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="What you DON'T want in the image (e.g., 'deformed, blurry, text')",
                visible=False,
            )
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=20.0, # Increased max for more control
                    step=0.1,
                    value=7.0,
                )
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=100, # More typical steps for SDXL (20-50 usually sufficient)
                    step=1,
                    value=30,
                )
        gr.Examples(examples=examples, inputs=[prompt])

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            style_selection,
            input_image_pose,
            pose_strength,
            input_image_face,
            face_fidelity,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch(share=True)