Update app.py

app.py

@@ -2,24 +2,30 @@ import os
 import gradio as gr
 import numpy as np
 import random
-from huggingface_hub import AsyncInferenceClient, login
+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
-
 
 translator = Translator()
 HF_TOKEN = os.environ.get("HF_TOKEN", None)
 basemodel = "black-forest-labs/FLUX.1-schnell"
 MAX_SEED = np.iinfo(np.int32).max
 
+CSS = """
+footer {
+    visibility: hidden;
+}
+"""
 
-CSS = "footer {visibility: hidden;}"
-JS = "function () {gradioURL = window.location.href;if (!gradioURL.endsWith('?__theme=dark')) {window.location.replace(gradioURL + '?__theme=dark');}}"
-
+JS = """function () {
+  gradioURL = window.location.href
+  if (!gradioURL.endsWith('?__theme=dark')) {
+    window.location.replace(gradioURL + '?__theme=dark');
+  }
+}"""
 
 def enable_lora(lora_add):
     if not lora_add:
@@ -27,93 +33,131 @@ def enable_lora(lora_add):
     else:
         return lora_add
 
+async def generate_image(
+    prompt:str,
+    model:str,
+    lora_word:str,
+    width:int=768,
+    height:int=1024,
+    scales:float=3.5,
+    steps:int=24,
+    seed:int=-1):
 
-def get_upscale_finegrain(prompt, img_path, upscale_factor):
-    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"
-    )
-    return result[1]
-
-
-async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
     if seed == -1:
         seed = random.randint(0, MAX_SEED)
     seed = int(seed)
+    print(f'prompt:{prompt}')
+    
     text = str(translator.translate(prompt, 'English')) + "," + lora_word
-    async with AsyncInferenceClient() as client:
-        try:
-            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}")
-    return image, seed
 
+    client = AsyncInferenceClient()
+    try:
+        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}")
+    
+    return image, seed
 
-async def gen(prompt, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor):
+async def gen(
+    prompt:str,
+    lora_add:str="",
+    lora_word:str="",
+    width:int=768,
+    height:int=1024,
+    scales:float=3.5,
+    steps:int=24,
+    seed:int=-1,
+    progress=gr.Progress(track_tqdm=True)
+):
     model = enable_lora(lora_add)
-    image, seed = await generate_image(prompt, model, lora_word, width, height, scales, steps, seed)
-    if upscale_factor != 0:
-        upscaled_image = get_upscale_finegrain(prompt, image, upscale_factor)
-        combined_image = Image.new('RGB', (image.width + upscaled_image.width, image.height))
-        combined_image.paste(image, (0, 0))
-        combined_image.paste(upscaled_image, (image.width, 0))
-        return combined_image, seed
-    else:
-        return image, seed
-
-
+    print(model)
+    image, seed = await generate_image(prompt,model,lora_word,width,height,scales,steps,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>")
     with gr.Row():
         with gr.Column(scale=4):
             with gr.Row():
-                img = gr.Image(type="filepath", label='Comparison Image', height=600)
+                img = gr.Image(type="filepath", label='flux Generated Image', height=600)
             with gr.Row():
                 prompt = gr.Textbox(label='Enter Your Prompt (Multi-Languages)', placeholder="Enter prompt...", scale=6)
                 sendBtn = gr.Button(scale=1, variant='primary')
         with gr.Accordion("Advanced Options", open=True):
             with gr.Column(scale=1):
-                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="")
-            upscale_factor = gr.Radio(label="UpScale Factor", choices=[0, 2, 3, 4], value=0, scale=2)
+                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],
+        triggers=[
+            prompt.submit,
+            sendBtn.click,
+        ],
         fn=gen,
         inputs=[
             prompt,
             lora_add,
             lora_word,
-            width,
-            height,
-            scales,
-            steps,
-            seed,
-            upscale_factor
+            width, 
+            height, 
+            scales, 
+            steps, 
+            seed
         ],
         outputs=[img, seed]
-    )
+    )
+    
+if __name__ == "__main__":
+    demo.queue(api_open=False).launch(show_api=False, share=False)