app.py

import torch

torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
import gradio as gr
import numpy as np
import random
import spaces
import time
from diffusers import DiffusionPipeline, AutoencoderTiny
from diffusers.models.attention_processor import AttnProcessor2_0
from custom_pipeline import FluxWithCFGPipeline

# Constants
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
DEFAULT_WIDTH = 1024
DEFAULT_HEIGHT = 1024
DEFAULT_INFERENCE_STEPS = 1

# Device and model setup
dtype = torch.float16
pipe = FluxWithCFGPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=dtype
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype)
pipe.to("cuda")
pipe.load_lora_weights(
    "hugovntr/flux-schnell-realism",
    weight_name="schnell-realism_v2.3.safetensors",
    adapter_name="better",
)
pipe.set_adapters(["better"], adapter_weights=[1.0])
pipe.fuse_lora(adapter_name=["better"], lora_scale=1.0)
pipe.unload_lora_weights()

# Correctly set memory format
pipe.transformer.to(memory_format=torch.channels_last)
pipe.vae.to(memory_format=torch.channels_last)

# Conditionally enable xformers only for the transformer
if hasattr(pipe, "transformer") and torch.cuda.is_available():
    try:
        pipe.transformer.enable_xformers_memory_efficient_attention()
    except Exception as e:
        print(
            "Warning: Could not enable xformers for the transformer due to the following error:"
        )
        print(e)

torch.cuda.empty_cache()

# Inference function
@spaces.GPU(duration=25)
def generate_image(
    prompt,
    seed=24,
    width=DEFAULT_WIDTH,
    height=DEFAULT_HEIGHT,
    randomize_seed=False,
    num_inference_steps=2,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(int(float(seed)))

    start_time = time.time()

    # Dynamically determine shapes based on input width/height
    latents_shape = (1, 4, height // 8, width // 8)
    prompt_embeds_shape = (
        1,
        pipe.transformer.text_encoder.config.max_position_embeddings,
        pipe.transformer.text_encoder.config.hidden_size,
    )
    pooled_prompt_embeds_shape = (
        1,
        pipe.transformer.text_encoder.config.hidden_size,
    )

    # Only generate the last image in the sequence
    img = pipe.generate_images(
        prompt=prompt,
        width=width,
        height=height,
        num_inference_steps=num_inference_steps,
        generator=generator,
        latents_shape=latents_shape,
        prompt_embeds_shape=prompt_embeds_shape,
        pooled_prompt_embeds_shape=pooled_prompt_embeds_shape
    )
    latency = f"Latency: {(time.time()-start_time):.2f} seconds"
    return img, seed, latency

# Example prompts
examples = [
    "a tiny astronaut hatching from an egg on the moon",
    "a cute white cat holding a sign that says hello world",
    "an anime illustration of Steve Jobs",
    "Create image of Modern house in minecraft style",
    "photo of a woman on the beach, shot from above. She is facing the sea, while wearing a white dress. She has long blonde hair",
    "Selfie photo of a wizard with long beard and purple robes, he is apparently in the middle of Tokyo. Probably taken from a phone.",
    "Photo of a young woman with long, wavy brown hair tied in a bun and glasses. She has a fair complexion and is wearing subtle makeup, emphasizing her eyes and lips. She is dressed in a black top. The background appears to be an urban setting with a building facade, and the sunlight casts a warm glow on her face.",
]

# --- Gradio UI ---
with gr.Blocks() as demo:
    with gr.Column(elem_id="app-container"):
        gr.Markdown("# 🎨 Realtime FLUX Image Generator")
        gr.Markdown(
            "Generate stunning images in real-time with Modified Flux.Schnell pipeline."
        )
        gr.Markdown(
            "<span style='color: red;'>Note: Sometimes it stucks or stops generating images (I don't know why). In that situation just refresh the site.</span>"
        )

        with gr.Row():
            with gr.Column(scale=2.5):
                result = gr.Image(
                    label="Generated Image", show_label=False, interactive=False
                )
            with gr.Column(scale=1):
                prompt = gr.Text(
                    label="Prompt",
                    placeholder="Describe the image you want to generate...",
                    lines=3,
                    show_label=False,
                    container=False,
                )
                generateBtn = gr.Button("🖼️ Generate Image")
                enhanceBtn = gr.Button("🚀 Enhance Image")

                with gr.Column("Advanced Options"):
                    with gr.Row():
                        realtime = gr.Checkbox(
                            label="Realtime Toggler",
                            info="If TRUE then uses more GPU but create image in realtime.",
                            value=False,
                        )
                        latency = gr.Text(label="Latency")
                    with gr.Row():
                        seed = gr.Number(label="Seed", value=42)
                        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=DEFAULT_WIDTH,
                        )
                        height = gr.Slider(
                            label="Height",
                            minimum=256,
                            maximum=MAX_IMAGE_SIZE,
                            step=32,
                            value=DEFAULT_HEIGHT,
                        )
                        num_inference_steps = gr.Slider(
                            label="Inference Steps",
                            minimum=1,
                            maximum=4,
                            step=1,
                            value=DEFAULT_INFERENCE_STEPS,
                        )

        with gr.Row():
            gr.Markdown("### 🌟 Inspiration Gallery")
        with gr.Row():
            gr.Examples(
                examples=examples,
                fn=generate_image,
                inputs=[prompt],
                outputs=[result, seed, latency],
                cache_examples="lazy",
            )

    enhanceBtn.click(
        fn=generate_image,
        inputs=[prompt, seed, width, height],
        outputs=[result, seed, latency],
        show_progress="full",
        queue=False,
        concurrency_limit=None,
    )

    generateBtn.click(
        fn=generate_image,
        inputs=[prompt, seed, width, height, randomize_seed, num_inference_steps],
        outputs=[result, seed, latency],
        show_progress="full",
        api_name="RealtimeFlux",
        queue=False
    )

    def update_ui(realtime_enabled):
        return {
            prompt: gr.update(interactive=True),
            generateBtn: gr.update(visible=not realtime_enabled),
        }

    realtime.change(
        fn=update_ui,
        inputs=[realtime],
        outputs=[prompt, generateBtn],
        queue=False,
        concurrency_limit=None,
    )

    def realtime_generation(*args):
        if args[0]:  # If realtime is enabled
            img, seed, latency = generate_image(*args[1:])
            return img, seed, latency

    prompt.submit(
        fn=generate_image,
        inputs=[prompt, seed, width, height, randomize_seed, num_inference_steps],
        outputs=[result, seed, latency],
        show_progress="full",
        queue=False,
        concurrency_limit=None,
    )

    for component in [prompt, width, height, num_inference_steps]:
        component.input(
            fn=realtime_generation,
            inputs=[
                realtime,
                prompt,
                seed,
                width,
                height,
                randomize_seed,
                num_inference_steps,
            ],
            outputs=[result, seed, latency],
            show_progress="hidden",
            trigger_mode="always_last",
            queue=True,
            concurrency_limit=None,
        )

# Launch the app
demo.launch()