import gradio as gr import requests 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 fastapi import FastAPI app = FastAPI() #----------Start of theme---------- theme = gr.themes.Ocean( primary_hue="zinc", secondary_hue="slate", neutral_hue="neutral", font=[gr.themes.GoogleFont('Kavivanar'), gr.themes.GoogleFont('Kavivanar'), 'system-ui', 'sans-serif'], font_mono=[gr.themes.GoogleFont('Source Code Pro'), gr.themes.GoogleFont('Inconsolata'), gr.themes.GoogleFont('Inconsolata'), 'monospace'], ).set( #Body Settings body_background_fill='linear-gradient(10deg, *primary_200, *secondary_50)', body_text_color='secondary_600', body_text_color_subdued='*primary_500', body_text_weight='500', #Background Settings background_fill_primary='*primary_100', background_fill_secondary='*secondary_200', color_accent='*primary_300', #Border Settings border_color_accent_subdued='*primary_400', border_color_primary='*primary_400', #Block Settings block_radius='*radius_md', block_background_fill='*primary_200', block_border_color='*primary_500', block_border_width='*panel_border_width', block_info_text_color='*primary_700', block_info_text_size='*text_md', container_radius='*radius_xl', panel_background_fill='*primary_200', accordion_text_color='*primary_600', checkbox_border_radius='*radius_xl', slider_color='*primary_500', table_text_color='*primary_600', input_background_fill='*primary_50', input_background_fill_focus='*primary_100', #Button Settings button_border_width='1px', button_transform_hover='scale(1.01)', button_transition='all 0.1s ease-in-out', button_transform_active='Scale(0.9)', button_large_radius='*radius_xl', button_small_radius='*radius_xl', button_primary_border_color='*primary_500', button_secondary_border_color='*primary_400', button_primary_background_fill_hover='linear-gradient(90deg, *primary_400, *secondary_200, *primary_400)', button_primary_background_fill='linear-gradient(90deg,*secondary_300 , *primary_500, *secondary_300)', button_primary_text_color='*primary_100', button_primary_text_color_hover='*primary_700', button_cancel_background_fill='*primary_500', button_cancel_background_fill_hover='*primary_400' ) #----------End of theme---------- API_TOKEN = os.getenv("HF_READ_TOKEN") headers = {"Authorization": f"Bearer {API_TOKEN}"} timeout = 100 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 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 = """ #app-container { max-width: 930px; margin-left: auto; margin-right: auto; } ".gradio-container {background: url('file=abstract.jpg')} """ with gr.Blocks(theme=theme, css=css, elem_id="app-container") as app: gr.HTML("
🎨 FLUX.1-Dev with LoRA 🇬🇧
") with gr.Tab("Text to Image"): with gr.Column(elem_id="app-container"): with gr.Row(): with gr.Column(elem_id="prompt-container"): with gr.Row(): 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(""" - **sdxl-realistic**: szn style - **stylesdxl-cyberpunk**: szn style - **SD3.5-Turbo-Realism-2.0-LoRA**: Turbo Realism - **surreal-harmony**: Surreal Harmony - **extremely-detailed**: extremely detailed - **dark-fantasy**: Dark Fantasy - **Flux.1-Dev-LoRA-HDR-Realism**: HDR - **jules-bastien-lepage-style**: Jules Bastien Lepage Style - **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([" ", "jwu114/lora-sdxl-realistic", "issaccyj/lora-sdxl-cyberpunk", "prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA", "hugovntr/flux-schnell-realism", "fofr/sdxl-deep-down", "KappaNeuro/surreal-harmony", "ntc-ai/SDXL-LoRA-slider.extremely-detailed", "prithivMLmods/Canopus-LoRA-Flux-FaceRealism", "KappaNeuro/dark-fantasy", "prithivMLmods/Flux.1-Dev-LoRA-HDR-Realism", "KappaNeuro/jules-bastien-lepage-style", "KappaNeuro/john-singer-sargent-style", "KappaNeuro/alphonse-mucha-style", "ntc-ai/SDXL-LoRA-slider.ultra-realistic-illustration", "ntc-ai/SDXL-LoRA-slider.eye-catching", "KappaNeuro/john-constable-style", "dvyio/flux-lora-film-noir", "dvyio/flux-lora-pro-headshot"], label="Custom LoRA",) with gr.Row(): with gr.Accordion("⚙️ Advanced Settings", open=False, elem_id="settings-container"): negative_prompt = gr.Textbox(label="Negative Prompt", lines=5, placeholder="What should not be in the image", value=" Bad anatomy, Bad proportions, Deformed, Disconnected limbs, Disfigured, Extra arms, Extra limbs, Extra hands, Fused fingers, Gross proportions, Long neck, Malformed limbs, Mutated, Mutated hands, Mutated limbs, Missing arms, Missing fingers, Poorly drawn hands, Poorly drawn face ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, disfigured, deformed, body out of frame, bad anatomy, watermark, signature, cut off, low contrast, underexposed, overexposed, bad art, beginner, amateur, distorted face, blurry, draft, grainy, text, logo, watermark, banner, extra digits, signature, unprompted-nsfw") with gr.Row(): 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) 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) method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "DEIS", "LMS", "DPM Adaptive", "DPM++ 2M", "DPM2 Ancestral", "DPM++ S", "DPM++ SDE", "DDPM", "DPM Fast", "dpmpp_2s_ancestral", "Euler", "Euler CFG PP", "Euler a", "Euler Ancestral", "Euler+beta", "Heun", "Heun PP2", "DDIM", "PLMS", "UniPC", "UniPC BH2"]) strength = gr.Slider(label="Prompt Strength", value=100, minimum=0, maximum=100, step=1) seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1) with gr.Row(): with gr.Accordion("🫘Seed", open=False): seed_output = gr.Textbox(label="Seed Used", show_copy_button = True, elem_id="seed-output") with gr.Row(): text_button = gr.Button("Run", variant='primary', elem_id="gen-button") clr_button =gr.Button("Clear Prompt",variant="primary", elem_id="clear_button") clr_button.click(lambda: gr.Textbox(value=""), None, text_prompt) with gr.Row(): image_output = gr.Image(type="pil", label="Image Output", format="png", elem_id="gallery") with gr.Row(): clear_btn = gr.Button(value="Clear Image", variant="primary", elem_id="clear_button") clear_btn.click(clear, inputs=[], outputs=[image_output]) gr.Examples( examples = examples, inputs = [text_prompt], ) text_button.click(query, inputs=[custom_lora, text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=[image_output, seed_output]) with gr.Tab("Flip Image"): with gr.Row(): image_input = gr.Image() image_output = gr.Image(format="png") with gr.Row(): image_button = gr.Button("Run", variant='primary') image_button.click(flip_image, inputs=image_input, outputs=image_output, concurrency_limit=2) app.queue() # <-- Sets up a queue with default parameters if __name__ == "__main__": app.launch(show_api=False, share=True)