Create app.py

app.py

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+# Sagar_Flux Image Generator
+# To use this notebook:
+# 1. Make sure you're using a GPU runtime: Runtime > Change runtime type > GPU
+# 2. Run each cell in order
+
+# Install required libraries
+!pip install -q diffusers transformers torch gradio
+
+import gradio as gr
+import torch
+from PIL import Image
+from diffusers import DiffusionPipeline
+import random
+
+# Initialize the base model and specific LoRA
+base_model = "black-forest-labs/FLUX.1-dev"
+pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.float16)
+pipe.to("cuda")
+
+lora_repo = "sagar007/sagar_flux"
+trigger_word = "sagar"  # Use "sagar" as the trigger word
+pipe.load_lora_weights(lora_repo)
+
+MAX_SEED = 2**32-1
+
+def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
+    # Set random seed for reproducibility
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+
+    # Update progress bar
+    progress(0, "Starting image generation...")
+
+    # Generate image with progress updates
+    for i in range(1, steps + 1):
+        if i % (steps // 10) == 0:
+            progress(i / steps * 100, f"Processing step {i} of {steps}...")
+
+    # Generate image using the pipeline
+    image = pipe(
+        prompt=f"{prompt} {trigger_word}",
+        num_inference_steps=steps,
+        guidance_scale=cfg_scale,
+        width=width,
+        height=height,
+        generator=generator,
+        cross_attention_kwargs={"scale": lora_scale},
+    ).images[0]
+
+    # Final update
+    progress(100, "Completed!")
+
+    return image, seed
+
+# Gradio interface
+with gr.Blocks() as app:
+    gr.Markdown("# Sagar_Flux Image Generator")
+    with gr.Row():
+        with gr.Column(scale=3):
+            prompt = gr.TextArea(label="Prompt", placeholder="Type a prompt (include 'sagar' for best results)", lines=5)
+            generate_button = gr.Button("Generate")
+            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=7)
+            steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=30)
+            width = gr.Slider(label="Width", minimum=256, maximum=1024, step=64, value=512)
+            height = gr.Slider(label="Height", minimum=256, maximum=1024, step=64, value=512)
+            randomize_seed = gr.Checkbox(False, label="Randomize seed")
+            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42)
+            lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=0.75)
+        with gr.Column(scale=1):
+            result = gr.Image(label="Generated Image")
+            gr.Markdown("Generate images using Sagar_Flux and a text prompt.")
+
+    generate_button.click(
+        run_lora,
+        inputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale],
+        outputs=[result, seed]
+    )
+
+app.launch()