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
import asyncio

# 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()

    # Initialize static inputs for CUDA graph
    static_latents = torch.randn(
        (1, 4, height // 8, width // 8), dtype=dtype, device="cuda"
    )
    static_prompt_embeds = torch.randn(
        (2, 77, 768), dtype=dtype, device="cuda"
    )  # Adjust dimensions as needed
    static_pooled_prompt_embeds = torch.randn(
        (2, 768), dtype=dtype, device="cuda"
    )  # Adjust dimensions as needed
    static_text_ids = torch.tensor([[[1, 2, 3]]], dtype=torch.int32, device="cuda")
    static_latent_image_ids = torch.tensor([1], dtype=torch.int64, device="cuda")
    static_timestep = torch.tensor([999], dtype=dtype, device="cuda")

    # Warmup
    s = torch.cuda.Stream()
    s.wait_stream(torch.cuda.current_stream())
    with torch.cuda.stream(s):
        for _ in range(3):
            _ = pipe.transformer(
                hidden_states=static_latents,
                timestep=static_timestep / 1000,
                guidance=None,
                pooled_projections=static_pooled_prompt_embeds,
                encoder_hidden_states=static_prompt_embeds,
                txt_ids=static_text_ids,
                img_ids=static_latent_image_ids,
                joint_attention_kwargs=pipe.joint_attention_kwargs,
                return_dict=False,
            )
    torch.cuda.current_stream().wait_stream(s)

    # Capture CUDA Graph
    g = torch.cuda.CUDAGraph()
    with torch.cuda.graph(g):
        static_noise_pred = pipe.transformer(
            hidden_states=static_latents,
            timestep=static_timestep / 1000,
            guidance=None,
            pooled_projections=static_pooled_prompt_embeds,
            encoder_hidden_states=static_prompt_embeds,
            txt_ids=static_text_ids,
            img_ids=static_latent_image_ids,
            joint_attention_kwargs=pipe.joint_attention_kwargs,
            return_dict=False,
        )[0]
        static_latents_out = pipe.scheduler.step(
            static_noise_pred, static_timestep, static_latents, return_dict=False
        )[0]
        static_output = pipe._decode_latents_to_image(
            static_latents_out, height, width, "pil"
        )

    # Graph-based generation function
    def generate_with_graph(
        latents,
        prompt_embeds,
        pooled_prompt_embeds,
        text_ids,
        latent_image_ids,
        timestep,
    ):
        static_latents.copy_(latents)
        static_prompt_embeds.copy_(prompt_embeds)
        static_pooled_prompt_embeds.copy_(pooled_prompt_embeds)
        static_text_ids.copy_(text_ids)
        static_latent_image_ids.copy_(latent_image_ids)
        static_timestep.copy_(timestep)
        g.replay()
        return static_output

    # 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,
        generate_with_graph=generate_with_graph,
    )
    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,
    )

    async def realtime_generation(*args):
        if args[0]:  # If realtime is enabled
            loop = asyncio.get_event_loop()
            result = await loop.run_in_executor(None, next, generate_image(*args[1:]))
            return result

    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()