app.py

import gradio as gr
import numpy as np
import random
import json
import spaces
import torch
from diffusers import DiffusionPipeline
from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler
from videox_fun.pipeline import ZImageControlPipeline
from videox_fun.models import ZImageControlTransformer2DModel
from transformers import AutoTokenizer, Qwen3ForCausalLM
from diffusers import AutoencoderKL
from utils.image_utils import get_image_latent, scale_image
from utils.prompt_utils import polish_prompt
# from videox_fun.utils.utils import get_image_latent


# MODEL_REPO = "Tongyi-MAI/Z-Image-Turbo"
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1280

# git clone https://huggingface.co/Tongyi-MAI/Z-Image-Turbo
MODEL_LOCAL = "models/Z-Image-Turbo/"
# curl -L -o Z-Image-Turbo-Fun-Controlnet-Union.safetensors https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union/resolve/main/Z-Image-Turbo-Fun-Controlnet-Union.safetensors
TRANSFORMER_LOCAL = "models/Z-Image-Turbo-Fun-Controlnet-Union.safetensors"

weight_dtype = torch.bfloat16

# load transformer
transformer = ZImageControlTransformer2DModel.from_pretrained(
    MODEL_LOCAL,
    subfolder="transformer",
    low_cpu_mem_usage=True,
    torch_dtype=torch.bfloat16,
    transformer_additional_kwargs={
        "control_layers_places": [0, 5, 10, 15, 20, 25],
        "control_in_dim": 16
    },
).to(torch.bfloat16)

if TRANSFORMER_LOCAL is not None:
    print(f"From checkpoint: {TRANSFORMER_LOCAL}")
    if TRANSFORMER_LOCAL.endswith("safetensors"):
        from safetensors.torch import load_file, safe_open
        state_dict = load_file(TRANSFORMER_LOCAL)
    else:
        state_dict = torch.load(TRANSFORMER_LOCAL, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict

    m, u = transformer.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")

# load ZImageControlPipeline
vae = AutoencoderKL.from_pretrained(
    MODEL_LOCAL,
    subfolder="vae"
).to(weight_dtype)

tokenizer = AutoTokenizer.from_pretrained(
    MODEL_LOCAL, subfolder="tokenizer"
)
text_encoder = Qwen3ForCausalLM.from_pretrained(
    MODEL_LOCAL, subfolder="text_encoder", torch_dtype=weight_dtype,
    low_cpu_mem_usage=True,
)
scheduler = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=3)
pipe = ZImageControlPipeline(
    vae=vae,
    tokenizer=tokenizer,
    text_encoder=text_encoder,
    transformer=transformer,
    scheduler=scheduler,
)
pipe.transformer = transformer
pipe.to("cuda")

# ======== AoTI compilation + FA3 ========
pipe.transformer.layers._repeated_blocks = ["ZImageTransformerBlock"]
spaces.aoti_blocks_load(pipe.transformer.layers,
                        "zerogpu-aoti/Z-Image", variant="fa3")

def prepare(prompt, input_image):
    polished_prompt = polish_prompt(prompt)
    return polished_prompt

@spaces.GPU
def inference(
    prompt,
    input_image,
    image_scale=1.0,
    control_context_scale = 0.75,
    seed=42,
    randomize_seed=True,
    guidance_scale=1.5,
    num_inference_steps=8,
    progress=gr.Progress(track_tqdm=True),
):
    # process image
    if input_image is None:
        print("Error: input_image is empty.")
        return None
    
    input_image, width, height = scale_image(input_image, image_scale)

    control_image = get_image_latent(input_image, sample_size=[height, width])[:, :, 0]

    # generation
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    image = pipe(
        prompt=prompt,
        height=height,
        width=width,
        generator=generator,
        guidance_scale=guidance_scale,
        control_image=control_image,
        num_inference_steps=num_inference_steps,
        control_context_scale=control_context_scale,
    ).images[0]

    return image, seed


def read_file(path: str) -> str:
    with open(path, 'r', encoding='utf-8') as f:
        content = f.read()
    return content


css = """
#col-container {
    margin: 0 auto;
    max-width: 960px;
}
"""

with open('static/data.json', 'r') as file:
    data = json.load(file)
examples = data['examples']

with gr.Blocks() as demo:
    with gr.Column(elem_id="col-container"):
        with gr.Column():
            gr.HTML(read_file("static/header.html"))
        with gr.Row(equal_height=True):
            with gr.Column():
                input_image = gr.Image(
                    height=290, sources=['upload', 'clipboard'], 
                    image_mode='RGB', 
                    # elem_id="image_upload", 
                    type="pil", label="Upload")
                    
                prompt = gr.Textbox(
                    label="Prompt",
                    show_label=False,
                    lines=2,
                    placeholder="Enter your prompt",
                    container=False,
                )

                run_button = gr.Button("Run", variant="primary")
            with gr.Column():
                output_image = gr.Image(label="Generated image", show_label=False)
                
                with gr.Accordion("Preprocessor output", open=False):
                    control_image = gr.Image(label="Control image", show_label=False)
                    polished_prompt = gr.Textbox(label="Polished prompt", interactive=False)

        with gr.Accordion("Advanced Settings", open=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():
                    image_scale = gr.Slider(
                        label="Image scale",
                        minimum=0.5,
                        maximum=2.0,
                        step=0.1,
                        value=1.0,
                    )
                    control_context_scale = gr.Slider(
                        label="Control context scale",
                        minimum=0.0,
                        maximum=1.0,
                        step=0.1,
                        value=0.75,
                    )

                with gr.Row():
                    guidance_scale = gr.Slider(
                        label="Guidance scale",
                        minimum=0.0,
                        maximum=10.0,
                        step=0.1,
                        value=2.5,
                    )

                    num_inference_steps = gr.Slider(
                        label="Number of inference steps",
                        minimum=1,
                        maximum=30,
                        step=1,
                        value=8,
                    )
        gr.Examples(examples=examples, inputs=[input_image, prompt])
        
        gr.HTML(read_file("static/footer.html"))
    run_button.click(
        fn=prepare,
        inputs=prompt,
        outputs=[polished_prompt]
        # outputs=gr.State(),  # Pass to the next function, not to UI at this step
    ).then(
        # fn=generate_image,
        # inputs=None,   # This will automatically use the previous result
        # outputs=output
    )
    # gr.on(
    #     triggers=[run_button.click, prompt.submit],
    #     fn=inference,
    #     inputs=[
    #         prompt,
    #         input_image,
    #         image_scale,
    #         control_context_scale,
    #         seed,
    #         randomize_seed,
    #         guidance_scale,
    #         num_inference_steps,
    #     ],
    #     outputs=[output_image, seed],
    # ).then(
        
    # )

if __name__ == "__main__":
    demo.launch(mcp_server=True, css=css)