Create app.py

app.py

@@ -0,0 +1,91 @@
+import os
+import gradio as gr
+import numpy as np
+import random
+from huggingface_hub import AsyncInferenceClient
+from translatepy import Translator
+import requests
+import re
+import asyncio
+from PIL import Image
+from gradio_client import Client, handle_file
+from huggingface_hub import login
+from gradio_imageslider import ImageSlider
+
+MAX_SEED = np.iinfo(np.int32).max
+HF_TOKEN = os.environ.get("HF_TOKEN")
+HF_TOKEN_UPSCALER = os.environ.get("HF_TOKEN_UPSCALER")
+
+def enable_lora(lora_add, basemodel):
+    return basemodel if not lora_add else lora_add
+
+async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
+    try:
+        if seed == -1:
+            seed = random.randint(0, MAX_SEED)
+        seed = int(seed)
+        text = str(Translator().translate(prompt, 'English')) + "," + lora_word
+        client = AsyncInferenceClient()
+        image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
+        return image, seed
+    except Exception as e:
+        print(f"Error generando imagen: {e}")
+        return None, None
+
+def get_upscale_finegrain(prompt, img_path, upscale_factor):
+    try:
+        client = Client("finegrain/finegrain-image-enhancer", hf_token=HF_TOKEN_UPSCALER)
+        result = client.predict(input_image=handle_file(img_path), prompt=prompt, negative_prompt="", seed=42, upscale_factor=upscale_factor, controlnet_scale=0.6, controlnet_decay=1, condition_scale=6, tile_width=112, tile_height=144, denoise_strength=0.35, num_inference_steps=18, solver="DDIM", api_name="/process")
+        return result[1]
+    except Exception as e:
+        print(f"Error escalando imagen: {e}")
+        return None
+
+async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
+    model = enable_lora(lora_model, basemodel) if process_lora else basemodel
+    image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
+    if image is None:
+        return [None, None]
+    
+    image_path = "temp_image.jpg"
+    image.save(image_path, format="JPEG")
+    
+    if process_upscale:
+        upscale_image_path = get_upscale_finegrain(prompt, image_path, upscale_factor)
+        if upscale_image_path is not None:
+            upscale_image = Image.open(upscale_image_path)
+            upscale_image.save("upscale_image.jpg", format="JPEG")
+            return [image_path, "upscale_image.jpg"]
+        else:
+            print("Error: The scaled image path is None")
+            return [image_path, image_path]
+    else:
+        return [image_path, image_path]
+
+css = """
+#col-container{ margin: 0 auto; max-width: 1024px;}
+"""
+
+with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
+    with gr.Column(elem_id="col-container"):
+        with gr.Row():
+            with gr.Column(scale=3):
+                output_res = ImageSlider(label="Flux / Upscaled")
+            with gr.Column(scale=2):
+                prompt = gr.Textbox(label="Image Description")
+                basemodel_choice = gr.Dropdown(label="Model", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV", "enhanceaiteam/Flux-uncensored", "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "Shakker-Labs/FLUX.1-dev-LoRA-add-details", "city96/FLUX.1-dev-gguf"], value="black-forest-labs/FLUX.1-schnell")
+                lora_model_choice = gr.Dropdown(label="LoRA", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora", "enhanceaiteam/Flux-uncensored"], value="XLabs-AI/flux-RealismLora")
+                process_lora = gr.Checkbox(label="LoRA Process")
+                process_upscale = gr.Checkbox(label="Scale Process")
+                upscale_factor = gr.Radio(label="Scaling Factor", choices=[2, 4, 8], value=2)
+                
+                with gr.Accordion(label="Advanced Options", open=False):
+                    width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=1280)
+                    height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=768)
+                    scales = gr.Slider(label="Scale", minimum=1, maximum=20, step=1, value=8)
+                    steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=8)
+                    seed = gr.Number(label="Seed", value=-1)
+    
+                btn = gr.Button("Generate")
+                btn.click(fn=gen, inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora], outputs=output_res,)
+    demo.launch()