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)