Delete app_backup.py

app_backup.py

@@ -1,165 +0,0 @@
-import gradio as gr
-import numpy as np
-import random
-import spaces
-import torch
-import time
-from diffusers import DiffusionPipeline
-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")
-torch.cuda.empty_cache()
-
-# Inference function
-@spaces.GPU(duration=25)
-def generate_image(prompt, seed=42, 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
-    for img in pipe.generate_images(  
-            prompt=prompt,
-            guidance_scale=0, # as Flux schnell is guidance free
-            num_inference_steps=num_inference_steps,
-            width=width,
-            height=height,
-            generator=generator
-        ): 
-        latency = f"Latency: {(time.time()-start_time):.2f} seconds"    
-        yield 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("<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=False)
-                    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(*args):
-        gr.Info("Enhancing Image") # currently just runs optimized pipeline for 2 steps. Further implementations later.
-        return next(generate_image(*args))
-
-    enhanceBtn.click(
-        fn=enhance_image,
-        inputs=[prompt, seed, width, height],
-        outputs=[result, seed, latency],
-        show_progress="hidden",
-        api_name="Enhance",
-        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,
-        concurrency_limit=None
-    )
-
-    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",
-        api_name=False,
-        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",
-            api_name=False,
-            trigger_mode="always_last",
-            queue=False,
-            concurrency_limit=None
-        )
-
-# Launch the app
-demo.launch()