Spaces:
Running
Running
| #!/usr/bin/env python | |
| import gradio as gr | |
| import requests | |
| import io | |
| import random | |
| import os | |
| import time | |
| import numpy as np | |
| import subprocess | |
| import torch | |
| import json | |
| import uuid | |
| import spaces | |
| from typing import Tuple | |
| from transformers import AutoProcessor, AutoModelForCausalLM | |
| from PIL import Image | |
| from deep_translator import GoogleTranslator | |
| from datetime import datetime | |
| from theme import theme | |
| from typing import Tuple | |
| from fastapi import FastAPI | |
| app = FastAPI() | |
| API_TOKEN = os.getenv("HF_READ_TOKEN") | |
| headers = {"Authorization": f"Bearer {API_TOKEN}"} | |
| timeout = 100 | |
| NUM_IMAGES_PER_PROMPT = 1 | |
| def flip_image(x): | |
| return np.fliplr(x) | |
| def clear(): | |
| return None | |
| def result(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, | |
| "num_images_per_prompt": NUM_IMAGES_PER_PROMPT, | |
| "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("<center><h6>🎨 FLUX.1-Dev with LoRA 🇬🇧</h6></center>") | |
| 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 | |
| - **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 | |
| - **extremely-detailed**: extremely detailed | |
| - **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([" ", "jwu114/lora-sdxl-realistic", "prithivMLmods/Flux-Realism-FineDetailed", "prithivMLmods/Fashion-Hut-Modeling-LoRA", "prithivMLmods/SD3.5-Large-Turbo-HyperRealistic-LoRA", "prithivMLmods/Flux-Fine-Detail-LoRA", "prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA", "hugovntr/flux-schnell-realism", "fofr/sdxl-deep-down", "prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0", "ntc-ai/SDXL-LoRA-slider.extremely-detailed", "prithivMLmods/Canopus-LoRA-Flux-FaceRealism", "prithivMLmods/SD3.5-Large-Photorealistic-LoRA", "prithivMLmods/Flux.1-Dev-LoRA-HDR-Realism", "prithivMLmods/Ton618-Epic-Realism-Flux-LoRA", "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=" (visible hand:1.3), (ugly:1.3), (duplicate:1.2), (morbid:1.1), (mutilated:1.1), out of frame, bad face, extra fingers, mutated hands, (poorly drawn hands:1.1), (poorly drawn face:1.3), (mutation:1.3), (deformed:1.3), blurry, (bad anatomy:1.1), (bad proportions:1.2), (extra limbs:1.1), cloned face, (disfigured:1.2), gross proportions, malformed limbs, (missing arms:1.1), (missing legs:1.1), (extra arms:1.2), (extra legs:1.2), fused fingers, too many fingers, (long neck:1.2), sketched by bad-artist, (bad-image-v2-39000:1.3) ") | |
| 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) | |
| num_images_per_prompt = gr.Slider(label="Images", minimum=1, maximum=5, step=1, value=2, interactive=True) | |
| 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") | |
| result = gr.Gallery(label="Result", columns=1, preview=True) | |
| 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", show_share_button=False, 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], | |
| outputs=[result, seed], | |
| ) | |
| text_button.click(result, 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(default_concurrency_limit=200, max_size=200) # <-- Sets up a queue with default parameters | |
| if __name__ == "__main__": | |
| app.launch(show_api=False, share=True) | |