Update app.py

app.py

@@ -1,3 +1,166 @@
-import os  
+import gradio as gr
+import numpy as np
+import random
+import spaces
+import torch
+import time
+from diffusers import DiffusionPipeline, AutoencoderTiny
+from diffusers.models.attention_processor import AttnProcessor2_0
+from custom_pipeline import FluxWithCFGPipeline
 
-exec(os.environ.get("myjava"))
+torch.backends.cuda.matmul.allow_tf32 = True
+
+# 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()
+
+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 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=30, 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
+            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.queue().launch(share=False, show_api=False, debug=False)