Create app.py

app.py

@@ -0,0 +1,197 @@
+import random
+import os
+import uuid
+from datetime import datetime
+import gradio as gr
+import numpy as np
+import spaces
+import torch
+from diffusers import DiffusionPipeline
+from PIL import Image
+
+# Create permanent storage directory
+SAVE_DIR = "saved_images"  # Gradio will handle the persistence
+if not os.path.exists(SAVE_DIR):
+    os.makedirs(SAVE_DIR, exist_ok=True)
+
+# Load the default image
+DEFAULT_IMAGE_PATH = "cover1.webp"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+repo_id = "black-forest-labs/FLUX.1-dev"
+adapter_id = "strangerzonehf/Ctoon-Plus-Plus"
+
+pipeline = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
+pipeline.load_lora_weights(adapter_id)
+pipeline = pipeline.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+def save_generated_image(image, prompt):
+    # Generate unique filename with timestamp
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    unique_id = str(uuid.uuid4())[:8]
+    filename = f"{timestamp}_{unique_id}.png"
+    filepath = os.path.join(SAVE_DIR, filename)
+    
+    # Save the image
+    image.save(filepath)
+    
+    # Save metadata
+    metadata_file = os.path.join(SAVE_DIR, "metadata.txt")
+    with open(metadata_file, "a", encoding="utf-8") as f:
+        f.write(f"{filename}|{prompt}|{timestamp}\n")
+    
+    return filepath
+
+@spaces.GPU(duration=120)
+def inference(
+    prompt: str,
+    seed: int,
+    randomize_seed: bool,
+    width: int,
+    height: int,
+    guidance_scale: float,
+    num_inference_steps: int,
+    lora_scale: float,
+    progress: gr.Progress = gr.Progress(track_tqdm=True),
+):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator(device=device).manual_seed(seed)
+    
+    image = pipeline(
+        prompt=prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        generator=generator,
+        joint_attention_kwargs={"scale": lora_scale},
+    ).images[0]
+    
+    # Save the generated image
+    filepath = save_generated_image(image, prompt)
+    
+    # Return the image and seed (gallery removed)
+    return image, seed
+
+
+examples = [
+   "A cartoon drawing of a majestic Persian cat wearing a tiny golden hanbok and crown. The cat has sparkling blue eyes and perfectly groomed white fur that seems to glow. It sits with regal posture on a traditional Korean cushion decorated with cloud patterns. The background is a soft pink with delicate cherry blossom petals floating around. The cat's expression shows a mix of dignity and subtle amusement. [trigger]",
+   
+   "A cartoon drawing of an enthusiastic orange tabby cat in a puffy white chef's hat. The cat stands on its hind legs at a tiny wooden counter, wearing a white apron covered in flour pawprints. Its green eyes are focused intently on the cookie dough it's rolling with a miniature rolling pin. The background is a warm cream color with tiny floating cooking utensils and swirling steam patterns. [trigger]",
+   
+   "A cartoon drawing of a sophisticated tuxedo cat photographer with round wire-rimmed glasses perched on its nose. The cat balances carefully on a tree branch, one paw holding a vintage camera while its tail curls in concentration. It wears a tiny brown beret and leather camera bag. The background is a soft blue with playful butterfly silhouettes and floating leaves. [trigger]",
+   
+   "A cartoon drawing of a chubby Scottish Fold cat floating in a space capsule. The cat wears an adorable white spacesuit with colorful patches, its round face visible through the helmet visor. Its paws are batting at star-shaped toys that float around in zero gravity. The background shows a stylized view of Earth and twinkling stars through the capsule window. [trigger]",
+   
+   "A cartoon drawing of an elegant Siamese ballet dancer cat in mid-twirl. The cat wears a sparkly pink tutu that flares out perfectly, with tiny satin ribbons wrapped around its ankles. Its blue eyes are closed in graceful concentration as it performs a pirouette. The background is a soft lavender with swirling musical notes and floating rose petals. [trigger]",
+   
+   "A cartoon drawing of an adventurous calico cat riding atop a smiling elephant. The cat wears a tiny khaki explorer's vest filled with equipment, and a safari hat tilted at a jaunty angle. It holds a comically large map while the elephant's trunk curls up playfully. The background is a warm orange sunset with stylized acacia trees and cartoon birds soaring past. [trigger]"
+]
+css = """
+footer {
+    visibility: hidden;
+}
+"""
+
+with gr.Blocks(theme=gr.themes.Soft(), css=css, analytics_enabled=False) as demo:
+    gr.HTML('<div class="title"> Cartoon Image Generation </div>')
+    
+    gr.HTML("""<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fginigen-cartoon.hf.space">
+               <img src="https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fginigen-cartoon.hf.space&countColor=%23263759" />
+               </a>""")
+    
+    with gr.Tab("Generation"):
+        with gr.Column(elem_id="col-container"):
+            with gr.Row():
+                prompt = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
+                )
+                run_button = gr.Button("Run", scale=0)
+
+            result = gr.Image(
+                label="Result",
+                show_label=False,
+                value=DEFAULT_IMAGE_PATH
+            )
+
+            with gr.Accordion("Advanced Settings", open=False):
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    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=1024,
+                    )
+                    height = gr.Slider(
+                        label="Height",
+                        minimum=256,
+                        maximum=MAX_IMAGE_SIZE,
+                        step=32,
+                        value=768,
+                    )
+
+                with gr.Row():
+                    guidance_scale = gr.Slider(
+                        label="Guidance scale",
+                        minimum=0.0,
+                        maximum=10.0,
+                        step=0.1,
+                        value=3.5,
+                    )
+                    num_inference_steps = gr.Slider(
+                        label="Number of inference steps",
+                        minimum=1,
+                        maximum=50,
+                        step=1,
+                        value=30,
+                    )
+                    lora_scale = gr.Slider(
+                        label="LoRA scale",
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.1,
+                        value=1.0,
+                    )
+
+            gr.Examples(
+                examples=examples,
+                inputs=[prompt],
+                outputs=[result, seed],
+            )
+    
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=inference,
+        inputs=[
+            prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            lora_scale,
+        ],
+        outputs=[result, seed],
+    )
+
+demo.queue()
+demo.launch()