app.py

import gradio as gr
import numpy as np
import random
import os
from huggingface_hub import login


import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
from diffusers import FluxPipeline
import torch


hf_token = os.environ.get("HF_TOKEN")
if hf_token is None:
    raise RuntimeError(
        "HF_TOKEN secret is missing. "
        "Add it in the Space → Settings → Secrets panel."
    )
login(token=hf_token)           

device = "cuda" if torch.cuda.is_available() else "cpu"

model_repo_id  = "black-forest-labs/FLUX.1-dev"
LORA_REPO   = "rmsandu/fourviews-incontext-lora"
LORA_FILE   = "4views.safetensors"

if torch.cuda.is_available():
    torch_dtype = torch.bfloat16
else:
    torch_dtype = torch.float32


pipe = FluxPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype, token=hf_token)
#pipe.enable_model_cpu_offload() 
pipe.load_lora_weights(LORA_REPO, weight_name=LORA_FILE)
pipe.fuse_lora()
#if device == "cuda":
#    pipe.enable_xformers_memory_efficient_attention()
pipe.to(device)
pipe.set_progress_bar_config(disable=True)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024


@spaces.GPU 
def infer(
    prompt,
    negative_prompt,
    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)

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

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

    return image, seed


examples = [
    "[FOUR-VIEWS] a set of four images showing a green boho bedroom from different view angles;[TOP-LEFT] a 45-degree angle of the bedroom;[TOP-RIGHT] a high-angle shot of the bedroom;[BOTTOM-LEFT]a side view shot of bedroom;[BOTTOM-RIGHT] A low angle view of the bedroom.",
    "[FOUR-VIEWS] A set of four images showing a teddy bear toy from multiple photo angles"
]

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(" # Text-to-Image Gradio Template")

        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, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                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=512,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=3,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=25,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

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