app.py

import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
from huggingface_hub import hf_hub_download
import os

dtype = torch.bfloat16

if torch.cuda.is_available():
    device = "cuda"
elif torch.backends.mps.is_available():
    if not torch.backends.mps.is_built():
        print("MPS not available because the current PyTorch install was not "
              "built with MPS enabled.")
    device = "mps"
else:
    device = "cpu"

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

print(f"Device is: {device}")
# Initialize the pipeline globally
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)


@spaces.GPU(duration=300)
def infer(prompt, lora_models, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0,
          num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
    global pipe
    
    # Load LoRAs if specified
    if lora_models:
        print(f"Loading Loras: {lora_models}")
        try:
            for lora_model in lora_models:
                pipe.load_lora_weights(lora_model)
        except Exception as e:
            return None, seed, f"Failed to load LoRA model: {str(e)}"
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    
    try:
        image = pipe(
            prompt=prompt,
            width=width,
            height=height,
            num_inference_steps=num_inference_steps,
            generator=generator,
            guidance_scale=guidance_scale
        ).images[0]
        
        # Unload LoRA weights after generation
        if lora_models:
            pipe.unload_lora_weights()
        
        return image, seed, "Image generated successfully."
    except Exception as e:
        return None, seed, f"Error during image generation: {str(e)}"


css = """
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            run_button = gr.Button("Run", scale=0)
        
        # lora_model = gr.Text(
        #     label="LoRA Model ID (optional)",
        #     placeholder="Enter Hugging Face LoRA model ID",
        # )
        lora_models = gr.Dropdown([
            ("jerry", "bryanbrunetti/jerryfluxlora"),
            ("sebastian", "bryanbrunetti/sebastianfluxlora"),
            ("sadie", "bryanbrunetti/sadiefluxlora")
        ], multiselect=True, info="load lora (optional) use the name in the prompt", label="Choose People")
        
        result = gr.Image(label="Result", show_label=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():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    info="How close to follow prompt",
                    minimum=1,
                    maximum=15,
                    step=0.1,
                    value=3.5,
                )
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    info="higher = more details",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28,
                )
        
        output_message = gr.Textbox(label="Output Message")
        
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[prompt, lora_models, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs=[result, seed, output_message]
    )

demo.launch()