app.py

import gradio as gr
import spaces
import torch
from diffusers import HiDreamImagePipeline
from transformers import PreTrainedTokenizerFast, LlamaForCausalLM
import random
import numpy as np

# Set data type
dtype = torch.bfloat16
device = "cpu"  # Use CPU for model loading to avoid CUDA initialization

# Load tokenizer and text encoder for Llama
try:
    tokenizer_4 = PreTrainedTokenizerFast.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
    text_encoder_4 = LlamaForCausalLM.from_pretrained(
        "meta-llama/Meta-Llama-3.1-8B-Instruct",
        output_hidden_states=True,
        output_attentions=True,
        attn_implementation="eager",
        torch_dtype=dtype,
    ).to(device)
except Exception as e:
    raise Exception(f"Failed to load Llama model: {e}. Ensure you have access to 'meta-llama/Meta-Llama-3.1-8B-Instruct' and are logged in via `huggingface-cli login`.")

# Load the HiDreamImagePipeline
try:
    pipe = HiDreamImagePipeline.from_pretrained(
        "HiDream-ai/HiDream-I1-Fast",
        tokenizer_4=tokenizer_4,
        text_encoder_4=text_encoder_4,
        torch_dtype=dtype,
    ).to(device)
    pipe.enable_model_cpu_offload()  # Offload to CPU, automatically manages GPU placement
except Exception as e:
    raise Exception(f"Failed to load HiDreamImagePipeline: {e}. Ensure you have access to 'HiDream-ai/HiDream-I1-Full'.")

# Define maximum values
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

# Inference function with GPU access
@spaces.GPU()
def infer(prompt, negative_prompt="", seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=16, guidance_scale=3.5, progress=gr.Progress(track_tqdm=True)):
    pipe.to("cuda")
    try:
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)
        generator = torch.Generator("cuda").manual_seed(seed)
        
        # Generate the image; offloading handles device placement
        image = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            generator=generator,
            output_type="pil",
        ).images[0]
        
        return image, seed
    finally:
        # Clear GPU memory
        torch.cuda.empty_cache()

# Define examples
examples = [
    ["A cat holding a sign that says \"Hi-Dreams.ai\".", ""],
    ["A futuristic cityscape with flying cars.", "blurry, low quality"],
    ["A serene landscape with mountains and a lake.", ""],
]

# CSS styling
css = """
#col-container {
   margin: 0 auto;
   max-width: 960px;
}
.generate-btn {
   background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important;
   border: none !important;
   color: white !important;
}
.generate-btn:hover {
   transform: translateY2px);
   box-shadow: 0 5px 15px rgba(0,0,0,0.2);
}
"""

# Create Gradio interface
with gr.Blocks(css=css) as app:
    gr.HTML("<center><h1>HiDreamImage Generator</h1></center>")
    with gr.Column(elem_id="col-container"):
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    text_prompt = gr.Textbox(
                        label="Prompt",
                        placeholder="Enter a prompt here",
                        lines=3,
                        elem_id="prompt-text-input"
                    )
                with gr.Row():
                    negative_prompt = gr.Textbox(
                        label="Negative Prompt",
                        placeholder="Enter what to avoid (optional)",
                        lines=2
                    )
                with gr.Row():
                    with gr.Accordion("Advanced Settings", open=False):
                        with gr.Row():
                            width = gr.Slider(
                                label="Width",
                                value=1024,
                                minimum=64,
                                maximum=MAX_IMAGE_SIZE,
                                step=8
                            )
                            height = gr.Slider(
                                label="Height",
                                value=1024,
                                minimum=64,
                                maximum=MAX_IMAGE_SIZE,
                                step=8
                            )
                        with gr.Row():
                            steps = gr.Slider(
                                label="Inference Steps",
                                value=16,
                                minimum=1,
                                maximum=100,
                                step=1
                            )
                            cfg = gr.Slider(
                                label="Guidance Scale",
                                value=3.5,
                                minimum=1,
                                maximum=20,
                                step=0.5
                            )
                        with gr.Row():
                            seed = gr.Slider(
                                label="Seed",
                                value=42,
                                minimum=0,
                                maximum=MAX_SEED,
                                step=1
                            )
                            randomize_seed = gr.Checkbox(
                                label="Randomize Seed",
                                value=True
                            )
                with gr.Row():
                    text_button = gr.Button(
                        "✨ Generate Image",
                        variant='primary',
                        elem_classes=["generate-btn"]
                    )
            with gr.Column():
                with gr.Row():
                    image_output = gr.Image(
                        type="pil",
                        label="Generated Image",
                        elem_id="gallery"
                    )
                seed_output = gr.Textbox(
                    label="Seed Used",
                    interactive=False
                )
        
        with gr.Column():
            gr.Examples(
                examples=examples,
                inputs=[text_prompt, negative_prompt],
            )
    
    # Connect the button and textbox submit to the inference function
    gr.on(
        triggers=[text_button.click, text_prompt.submit],
        fn=infer,
        inputs=[text_prompt, negative_prompt, seed, randomize_seed, width, height, steps, cfg],
        outputs=[image_output, seed_output]
    )

app.launch(share=True)