Update app.py

app.py

@@ -8,156 +8,55 @@ 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
 
 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; }"
+JS = "function () { gradioURL = window.location.href; if (!gradioURL.endsWith('?__theme=dark')) { window.location.replace(gradioURL + '?__theme=dark'); } }"
 
-CSS = """
-footer {
-    visibility: hidden;
-}
-"""
-
-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:
-        return basemodel
-    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):
-
-    if seed == -1:
-        seed = random.randint(0, MAX_SEED)
+def enable_lora(lora_add): return basemodel if not lora_add else lora_add
+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
-
     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}")
-    
+    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: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)
-):
+async def gen(prompt, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor, progress):
     model = enable_lora(lora_add)
-    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='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="",
-                )
+    image, seed = await generate_image(prompt, model, lora_word, width, height, scales, steps, seed)
+    image_path = "temp_image.png"
+    image.save(image_path)
+    upscale_image = get_upscale_finegrain(prompt, image_path, upscale_factor)
+    return upscale_image, seed
 
-    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)
+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]
+
+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='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=[2, 3, 4], value=2, scale=2)
+    gr.on([prompt.submit, sendBtn.click], gen, [prompt, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor], [img, seed])
+demo.queue(api_open=False).launch(show_api=False, share=False)