Update app.py

app.py

@@ -1,20 +1,16 @@
-import gradio as gr
 import os
-from gradio_client import Client, handle_file
-from huggingface_hub import login
-from PIL import Image
+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 gradio_imageslider import ImageSlider
-
-hf_tkn = os.environ.get("HF_TKN")
-login(hf_tkn)
+from PIL import Image
 
 translator = Translator()
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
 basemodel = "black-forest-labs/FLUX.1-dev"
 MAX_SEED = np.iinfo(np.int32).max
 
@@ -37,44 +33,6 @@ def enable_lora(lora_add):
     else:
         return lora_add
 
-def handle_file(img_path):
-    return Image.open(img_path)
-
-def get_upscale_finegrain(prompt, img_path, upscale_factor):
-    if upscale_factor == 0:
-        return handle_file(img_path)
-    client = Client("finegrain/finegrain-image-enhancer")
-    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"
-    )
-    print(result)
-    return result[1]
-
-async def upscale_image(image, upscale_factor):
-    try:
-        result = get_upscale_finegrain(
-            prompt="",
-            img_path=image,
-            upscale_factor=upscale_factor
-        )
-    except Exception as e:
-        raise gr.Error(f"Error in {e}")
-    
-    return result
-
 async def generate_image(
     prompt:str,
     model:str,
@@ -83,8 +41,8 @@ async def generate_image(
     height:int=1024,
     scales:float=3.5,
     steps:int=24,
-    seed:int=-1
-):
+    seed:int=-1):
+
     if seed == -1:
         seed = random.randint(0, MAX_SEED)
     seed = int(seed)
@@ -92,8 +50,16 @@ async def generate_image(
     
     text = str(translator.translate(prompt, 'English')) + "," + lora_word
 
+    client = AsyncInferenceClient()
     try:
-        image = gr.Image(type="pil", image=gr.processing_utils.encode_pil_image(text_to_image(text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)))
+        image = await client.text_to_image(
+            prompt=text,
+            height=height,
+            width=width,
+            guidance_scale=scales,
+            num_inference_steps=steps,
+            model=model,
+        )
     except Exception as e:
         raise gr.Error(f"Error in {e}")
     
@@ -101,37 +67,98 @@ async def generate_image(
 
 async def gen(
     prompt:str,
-    lora_add:str="XLabs-AI/flux-RealismLora",
+    lora_add:str="",
     lora_word:str="",
     width:int=768,
     height:int=1024,
     scales:float=3.5,
     steps:int=24,
     seed:int=-1,
-    upscale_factor:int=0
+    progress=gr.Progress(track_tqdm=True)
 ):
     model = enable_lora(lora_add)
+    print(model)
     image, seed = await generate_image(prompt,model,lora_word,width,height,scales,steps,seed)
-    
-    upscaled_image = await upscale_image(image, upscale_factor)
-    return upscaled_image, seed
+    return image, seed
      
 with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
     gr.HTML("<h1><center>Flux Lab Light</center></h1>")
+    gr.HTML("<p><center>Powered By HF Inference API</center></p>")
     with gr.Row():
         with gr.Column(scale=4):
             with gr.Row():
-                img = gr.Image(type="filepath", label='Imagen generada por Flux', height=600)
+                img = gr.Image(type="filepath", label='flux Generated Image', height=600)
             with gr.Row():
-                prompt = gr.Textbox(label='Ingresa tu prompt (Multi-Idiomas)', placeholder="Ingresa prompt...", scale=6)
+                prompt = gr.Textbox(label='Enter Your Prompt (Multi-Languages)', placeholder="Enter prompt...", scale=6)
                 sendBtn = gr.Button(scale=1, variant='primary')
-        with gr.Accordion("Opciones avanzadas", open=True):
+        with gr.Accordion("Advanced Options", open=True):
             with gr.Column(scale=1):
-                width = gr.Slider(label="Ancho", minimum=512, maximum=1280, step=8, value=768)
-                height = gr.Slider(label="Alto", minimum=512, maximum=1280, step=8, value=1024)
-                scales = gr.Slider(label="Guía", minimum=3.5, maximum=7, step=0.1, value=3.5)
-                steps = gr.Slider(label="Pasos", minimum=1, maximum=50, step=1)
-                upscale_factor = gr.Slider(label="Factor de escala", minimum=0, maximum=4, step=1, value=0)
-                seed = gr.Number(label="Semilla", value=-1)
-                sendBtn.click(gen, inputs=[prompt, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor], outputs=[img])
-demo.launch()
+                width = gr.Slider(
+                    label="Width",
+                    minimum=512,
+                    maximum=1280,
+                    step=8,
+                    value=768,
+                )
+                height = gr.Slider(
+                    label="Height",
+                    minimum=512,
+                    maximum=1280,
+                    step=8,
+                    value=1024,
+                )
+                scales = gr.Slider(
+                    label="Guidance",
+                    minimum=3.5,
+                    maximum=7,
+                    step=0.1,
+                    value=3.5,
+                )
+                steps = gr.Slider(
+                    label="Steps",
+                    minimum=1,
+                    maximum=100,
+                    step=1,
+                    value=24,
+                )
+                seed = gr.Slider(
+                    label="Seeds",
+                    minimum=-1,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=-1,
+                )
+                lora_add = gr.Textbox(
+                    label="Add Flux LoRA",
+                    info="Copy the HF LoRA model name here",
+                    lines=1,
+                    placeholder="Please use Warm status model",
+                )
+                lora_word = gr.Textbox(
+                    label="Add Flux LoRA Trigger Word",
+                    info="Add the Trigger Word",
+                    lines=1,
+                    value="",
+                )
+
+    gr.on(
+        triggers=[
+            prompt.submit,
+            sendBtn.click,
+        ],
+        fn=gen,
+        inputs=[
+            prompt,
+            lora_add,
+            lora_word,
+            width, 
+            height, 
+            scales, 
+            steps, 
+            seed
+        ],
+        outputs=[img, seed]
+    )
+    
+if __name__ == "__main__":
+    demo.queue(api_open=False).launch(show_api=False, share=False)