import gradio as gr import numpy as np import random import spaces import torch import time from diffusers import DiffusionPipeline, AutoencoderTiny from custom_pipeline import FLUXPipelineWithIntermediateOutputs # 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 = FLUXPipelineWithIntermediateOutputs.from_pretrained( "black-forest-labs/FLUX.1-schnell", torch_dtype=dtype ).to("cuda") pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.float16).to("cuda") 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() # 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 ) 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 a wiener schnitzel", "Create mage of Modern house in minecraft style", "Imagine steve jobs as Star Wars movie character", "Lion", "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("Note: Sometimes it stucks or stops generating images (I don't know why). In that situation just refresh the site.") 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" ) def enhance_image(prompt, current_seed, width, height): gr.Info("Enhancing Image") # currently just runs optimized pipeline for 2 steps. Further implementations later. return next(generate_image(prompt, current_seed, width, height)) enhanceBtn.click( fn=enhance_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 return next(generate_image(*args[1:])) 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=False, concurrency_limit=None ) # Launch the app demo.launch()