Create app.py

app.py

@@ -0,0 +1,184 @@
+import gradio as gr
+import io
+import random
+import os
+import time
+import numpy as np
+import subprocess
+import torch
+import json
+from transformers import AutoProcessor, AutoModelForCausalLM
+from PIL import Image
+from deep_translator import GoogleTranslator
+from datetime import datetime
+from model import models
+from theme import theme
+from fastapi import FastAPI
+
+app = FastAPI()
+
+
+API_TOKEN = os.getenv("HF_READ_TOKEN")
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+timeout = 100
+max_images = 6
+
+def flip_image(x):
+    return np.fliplr(x)
+
+def clear():
+  return None    
+
+def query(lora_id, prompt, is_negative=False, steps=28, cfg_scale=3.5, sampler="DPM++ 2M Karras", seed=-1, strength=100, width=896, height=1152):
+    if prompt == "" or prompt == None:
+        return None
+
+    if lora_id.strip() == "" or lora_id == None:
+        lora_id = "black-forest-labs/FLUX.1-dev"
+
+    key = random.randint(0, 999)
+
+    API_URL = "https://api-inference.huggingface.co/models/"+ lora_id.strip()
+
+    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    
+    # prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
+    # print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
+    prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
+    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
+
+    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
+    print(f'\033[1mGeneration {key}:\033[0m {prompt}')
+
+    # If seed is -1, generate a random seed and use it
+    if seed == -1:
+        seed = random.randint(1, 1000000000)
+
+    # Prepare the payload for the API call, including width and height
+    payload = {
+        "inputs": prompt,
+        "is_negative": is_negative,
+        "steps": steps,
+        "cfg_scale": cfg_scale,
+        "seed": seed if seed != -1 else random.randint(1, 1000000000),
+        "strength": strength,
+        "parameters": {
+            "width": width,  # Pass the width to the API
+            "height": height  # Pass the height to the API
+        }
+    }
+
+    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
+    if response.status_code != 200:
+        print(f"Error: Failed to get image. Response status: {response.status_code}")
+        print(f"Response content: {response.text}")
+        if response.status_code == 503:
+            raise gr.Error(f"{response.status_code} : The model is being loaded")
+        raise gr.Error(f"{response.status_code}")
+
+    try:
+        image_bytes = response.content
+        image = Image.open(io.BytesIO(image_bytes))
+        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
+        return image, seed
+    except Exception as e:
+        print(f"Error when trying to open the image: {e}")
+        return None
+        
+with gr.Group():
+    examples = [
+        "a beautiful woman with blonde hair and blue eyes",
+        "a beautiful woman with brown hair and grey eyes",
+        "a beautiful woman with black hair and brown eyes",
+    ]
+
+
+
+css = """
+.title { font-size: 3em; align-items: center; text-align: center; }
+.info { align-items: center; text-align: center; }
+.model_info { text-align: center; }
+.output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
+.gallery { min_width=512px; min_height=512px; max_height=1024px; !important; }
+"""
+
+with gr.Blocks(theme=theme, fill_width=True, css=css) as app:
+    with gr.Tab("Image Generator"):
+        with gr.Row():
+            with gr.Column(scale=10, elem_id="prompt-container"): 
+                with gr.Group():
+                    with gr.Row(equal_height=True):
+                        text_prompt = gr.Textbox(label="Image Prompt ✍️", placeholder="Enter a prompt here", lines=2, show_copy_button = True, elem_id="prompt-text-input")
+                    with gr.Row():
+                        with gr.Accordion("🎨 Lora trigger words", open=False):
+                            gr.Markdown("""
+                                        - **Canopus-Pencil-Art-LoRA**: Pencil Art
+                                        - **Flux-Realism-FineDetailed**: Fine Detailed
+                                        - **Fashion-Hut-Modeling-LoRA**: Modeling
+                                        - **SD3.5-Large-Turbo-HyperRealistic-LoRA**: hyper realistic
+                                        - **Flux-Fine-Detail-LoRA**: Super Detail
+                                        - **SD3.5-Turbo-Realism-2.0-LoRA**: Turbo Realism
+                                        - **Canopus-LoRA-Flux-UltraRealism-2.0**: Ultra realistic 
+                                        - **Canopus-Pencil-Art-LoRA**: Pencil Art
+                                        - **SD3.5-Large-Photorealistic-LoRA**: photorealistic
+                                        - **Flux.1-Dev-LoRA-HDR-Realism**: HDR
+                                        - **prithivMLmods/Ton618-Epic-Realism-Flux-LoRA**: Epic Realism
+                                        - **john-singer-sargent-style**: John Singer Sargent Style
+                                        - **alphonse-mucha-style**: Alphonse Mucha Style
+                                        - **ultra-realistic-illustration**: ultra realistic illustration
+                                        - **eye-catching**: eye-catching
+                                        - **john-constable-style**: John Constable Style
+                                        - **film-noir**: in the style of FLMNR
+                                        - **flux-lora-pro-headshot**: PROHEADSHOT
+                            		""")
+                        with gr.Row():
+                            custom_lora = gr.Dropdown(label="Select Model", choices=list(loaded_models.keys()), value=list(loaded_models.keys())[0], allow_custom_value=True)
+                    with gr.Accordion("Advanced options", open=False):
+                        negative_prompt = gr.Textbox(label="Negative Prompt", lines=5, placeholder="What should not be in the image", value="(((hands:-1.25))), physical-defects:2, unhealthy-deformed-joints:2, unhealthy-hands:2, out of frame, (((bad face))), (bad-image-v2-39000:1.3), (((out of frame))), deformed body features,  (((poor facial details))), (poorly drawn face:1.3), jpeg artifacts, (missing arms:1.1), (missing legs:1.1), (extra arms:1.2), (extra legs:1.2), [asymmetrical features], warped expressions, distorted eyes")
+                        with gr.Row(equal_height=True):
+                                width = gr.Slider(label="Image Width", value=896, minimum=64, maximum=1216, step=32)
+                                height = gr.Slider(label="Image Height", value=1152, minimum=64, maximum=1216, step=32)
+                            strength = gr.Slider(label="Prompt Strength", value=100, minimum=0, maximum=100, step=1)    
+                            steps = gr.Slider(label="Sampling steps", value=50, minimum=1, maximum=100, step=1)
+                            cfg = gr.Slider(label="CFG Scale", value=3.5, minimum=1, maximum=20, step=0.5)
+                            seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)                           
+                            method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ 2S a Karras", "DPM2 Karras", "DPM2 a Karras", "DPM++ SDE Karras", "DPM Adaptive", "DPM++ 2M", "DPM2 Ancestral", "DPM++ S", "DPM++ SDE", "DDPM", "DPM Fast", "dpmpp_2s_ancestral",  "DEIS", "DDIM", "Euler CFG PP", "Euler", "Euler a", "Euler Ancestral", "Euler+beta", "Heun", "Heun PP2", "LMS", "LMS Karras", "PLMS", "UniPC", "UniPC BH2"])
+                        with gr.Row(equal_height=True):
+                            with gr.Accordion("🫘Seed", open=False):
+                                seed_output = gr.Textbox(label="Seed Used", elem_id="seed-output")
+                    with gr.Row(equal_height=True):
+                        image_num = gr.Slider(label="Number of images", minimum=1, maximum=max_images, value=1, step=1, interactive=True, scale=2)
+                    # Add a button to trigger the image generation    
+                with gr.Row(equal_height=True):
+                    text_button = gr.Button("Generate Image 🎨", variant='primary', elem_id="gen-button")
+                    clear_prompt =gr.Button("Clear Prompt 🗑️",variant="primary", elem_id="clear_button")
+                    clear_prompt.click(lambda: (None), None, [text_prompt], queue=False, show_api=False)
+
+                with gr.Column(scale=10): 
+                with gr.Group():
+                    with gr.Row():
+                        image_output = gr.Image(type="pil", label="Image Output", format="png", show_share_button=False, elem_id="gallery")
+
+                with gr.Group():
+                    with gr.Row():
+                        gr.Examples(
+                            examples = examples,
+                            inputs = [text_prompt],
+                        )
+
+                 with gr.Group(): 
+                with gr.Row():
+                    clear_results = gr.Button(value="Clear Image 🗑️", variant="primary", elem_id="clear_button")
+                    clear_results.click(lambda: (None), None, [image_output], queue=False, show_api=False)
+                    
+                text_button.click(query, inputs=[custom_lora, text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=[image_output, seed_output])
+
+   app.queue(default_concurrency_limit=200, max_size=200)  # <-- Sets up a queue with default parameters
+    if __name__ == "__main__":
+        timeout = 100
+        app.launch(show_api=False, share=False)
+        
+                    
+
+