| import asyncio |
| import glob |
| import os |
| import pathlib |
| import random |
| import threading |
|
|
| import gradio as gr |
| import discord |
| from gradio_client import Client |
| from PIL import Image |
| from discord.ext import commands |
| from discord.ui import Button, View |
|
|
| HF_TOKEN = os.getenv("HF_TOKEN") |
| deepfloydif_client = Client("huggingface-projects/IF", HF_TOKEN) |
| DISCORD_TOKEN = os.getenv("DISCORD_TOKEN") |
| intents = discord.Intents.all() |
| bot = commands.Bot(command_prefix="/", intents=intents) |
|
|
|
|
| @bot.event |
| async def on_ready(): |
| print(f"Logged in as {bot.user} (ID: {bot.user.id})") |
| synced = await bot.tree.sync() |
| print(f"Synced commands: {', '.join([s.name for s in synced])}.") |
| print("------") |
|
|
|
|
| @bot.hybrid_command( |
| name="deepfloydif", |
| description="Enter a prompt to generate an image! Can generate realistic text, too!", |
| ) |
| async def deepfloydif(ctx, prompt: str): |
| """DeepfloydIF stage 1 generation""" |
| try: |
| await deepfloydif_generate64(ctx, prompt) |
| except Exception as e: |
| print(f"Error: {e}") |
|
|
|
|
| def deepfloydif_generate64_inference(prompt): |
| """Generates four images based on a prompt""" |
| negative_prompt = "" |
| seed = random.randint(0, 1000) |
| number_of_images = 4 |
| guidance_scale = 7 |
| custom_timesteps_1 = "smart50" |
| number_of_inference_steps = 50 |
| ( |
| stage_1_images, |
| stage_1_param_path, |
| path_for_upscale256_upscaling, |
| ) = deepfloydif_client.predict( |
| prompt, |
| negative_prompt, |
| seed, |
| number_of_images, |
| guidance_scale, |
| custom_timesteps_1, |
| number_of_inference_steps, |
| api_name="/generate64", |
| ) |
| return [stage_1_images, stage_1_param_path, path_for_upscale256_upscaling] |
|
|
|
|
| def deepfloydif_upscale256_inference(index, path_for_upscale256_upscaling): |
| """Upscales one of the images from deepfloydif_generate64_inference based on the chosen index""" |
| selected_index_for_upscale256 = index |
| seed_2 = 0 |
| guidance_scale_2 = 4 |
| custom_timesteps_2 = "smart50" |
| number_of_inference_steps_2 = 50 |
| result_path = deepfloydif_client.predict( |
| path_for_upscale256_upscaling, |
| selected_index_for_upscale256, |
| seed_2, |
| guidance_scale_2, |
| custom_timesteps_2, |
| number_of_inference_steps_2, |
| api_name="/upscale256", |
| ) |
| return result_path |
|
|
|
|
| def deepfloydif_upscale1024_inference(index, path_for_upscale256_upscaling, prompt): |
| """Upscales to stage 2, then stage 3""" |
| selected_index_for_upscale256 = index |
| seed_2 = 0 |
| guidance_scale_2 = 4 |
| custom_timesteps_2 = "smart50" |
| number_of_inference_steps_2 = 50 |
| negative_prompt = "" |
|
|
| seed_3 = 0 |
| guidance_scale_3 = 9 |
| number_of_inference_steps_3 = 40 |
|
|
| result_path = deepfloydif_client.predict( |
| path_for_upscale256_upscaling, |
| selected_index_for_upscale256, |
| seed_2, |
| guidance_scale_2, |
| custom_timesteps_2, |
| number_of_inference_steps_2, |
| prompt, |
| negative_prompt, |
| seed_3, |
| guidance_scale_3, |
| number_of_inference_steps_3, |
| api_name="/upscale1024", |
| ) |
| return result_path |
|
|
|
|
| def load_image(png_files, stage_1_images): |
| """Opens images as variables so we can combine them later""" |
| results = [] |
| for file in png_files: |
| png_path = os.path.join(stage_1_images, file) |
| results.append(Image.open(png_path)) |
| return results |
|
|
|
|
| def combine_images(png_files, stage_1_images, partial_path): |
| if os.environ.get("TEST_ENV") == "True": |
| print("Combining images for deepfloydif_generate64") |
| images = load_image(png_files, stage_1_images) |
| combined_image = Image.new("RGB", (images[0].width * 2, images[0].height * 2)) |
| combined_image.paste(images[0], (0, 0)) |
| combined_image.paste(images[1], (images[0].width, 0)) |
| combined_image.paste(images[2], (0, images[0].height)) |
| combined_image.paste(images[3], (images[0].width, images[0].height)) |
| combined_image_path = os.path.join(stage_1_images, f"{partial_path}.png") |
| combined_image.save(combined_image_path) |
| return combined_image_path |
|
|
|
|
| async def deepfloydif_generate64(ctx, prompt): |
| """DeepfloydIF command (generate images with realistic text using slash commands)""" |
| try: |
| if ctx.guild.id == 879548962464493619: |
| if ctx.channel.id != 1119313215675973714: |
| return |
| channel = ctx.channel |
| |
| message = await ctx.send(f"**{prompt}** - {ctx.author.mention} (generating...)") |
|
|
| loop = asyncio.get_running_loop() |
| result = await loop.run_in_executor(None, deepfloydif_generate64_inference, prompt) |
| stage_1_images = result[0] |
| path_for_upscale256_upscaling = result[2] |
|
|
| partial_path = pathlib.Path(path_for_upscale256_upscaling).name |
| png_files = list(glob.glob(f"{stage_1_images}/**/*.png")) |
|
|
| if png_files: |
| await message.delete() |
| combined_image_path = combine_images(png_files, stage_1_images, partial_path) |
| if os.environ.get("TEST_ENV") == "True": |
| print("Images combined for deepfloydif_generate64") |
|
|
| with Image.open(combined_image_path) as img: |
| width, height = img.size |
| new_width = width * 3 |
| new_height = height * 3 |
| resized_img = img.resize((new_width, new_height)) |
| x2_combined_image_path = combined_image_path |
| resized_img.save(x2_combined_image_path) |
|
|
| |
| with open(x2_combined_image_path, "rb") as f: |
| button1 = Button(custom_id="0", emoji="↖") |
| button2 = Button(custom_id="1", emoji="↗") |
| button3 = Button(custom_id="2", emoji="↙") |
| button4 = Button(custom_id="3", emoji="↘") |
|
|
| async def button_callback(interaction): |
| index = int(interaction.data["custom_id"]) |
|
|
| await interaction.response.send_message( |
| f"{interaction.user.mention} (upscaling...)", ephemeral=True |
| ) |
| result_path = await deepfloydif_upscale256(index, path_for_upscale256_upscaling) |
|
|
| |
| with open(result_path, "rb") as f: |
| upscale1024 = Button(label="High-quality upscale (x4)", custom_id=str(index)) |
| upscale1024.callback = upscale1024_callback |
| view = View(timeout=None) |
| view.add_item(upscale1024) |
|
|
| await interaction.delete_original_response() |
| await channel.send( |
| content=( |
| f"{interaction.user.mention} Here is the upscaled image! Click the button" |
| " to upscale even more!" |
| ), |
| file=discord.File(f, f"{prompt}.png"), |
| view=view, |
| ) |
|
|
| async def upscale1024_callback(interaction): |
| index = int(interaction.data["custom_id"]) |
|
|
| await interaction.response.send_message( |
| f"{interaction.user.mention} (upscaling...)", ephemeral=True |
| ) |
| result_path = await deepfloydif_upscale1024(index, path_for_upscale256_upscaling, prompt) |
|
|
| with open(result_path, "rb") as f: |
| await interaction.delete_original_response() |
| await channel.send( |
| content=f"{interaction.user.mention} Here's your high-quality x16 image!", |
| file=discord.File(f, f"{prompt}.png"), |
| ) |
|
|
| button1.callback = button_callback |
| button2.callback = button_callback |
| button3.callback = button_callback |
| button4.callback = button_callback |
|
|
| view = View(timeout=None) |
| view.add_item(button1) |
| view.add_item(button2) |
| view.add_item(button3) |
| view.add_item(button4) |
|
|
| |
| await channel.send( |
| f"**{prompt}** - {ctx.author.mention} Click a button to upscale! (make larger + enhance quality)", |
| file=discord.File(f, f"{partial_path}.png"), |
| view=view, |
| ) |
| else: |
| await ctx.send(f"{ctx.author.mention} No PNG files were found, cannot post them!") |
|
|
| except Exception as e: |
| print(f"Error: {e}") |
|
|
|
|
| async def deepfloydif_upscale256(index: int, path_for_upscale256_upscaling): |
| """upscaling function for images generated using /deepfloydif""" |
| try: |
| loop = asyncio.get_running_loop() |
| result_path = await loop.run_in_executor( |
| None, deepfloydif_upscale256_inference, index, path_for_upscale256_upscaling |
| ) |
| return result_path |
|
|
| except Exception as e: |
| print(f"Error: {e}") |
|
|
|
|
| async def deepfloydif_upscale1024(index: int, path_for_upscale256_upscaling, prompt): |
| """upscaling function for images generated using /deepfloydif""" |
| try: |
| loop = asyncio.get_running_loop() |
| result_path = await loop.run_in_executor( |
| None, deepfloydif_upscale1024_inference, index, path_for_upscale256_upscaling, prompt |
| ) |
| return result_path |
|
|
| except Exception as e: |
| print(f"Error: {e}") |
|
|
|
|
| def run_bot(): |
| bot.run(DISCORD_TOKEN) |
|
|
|
|
| threading.Thread(target=run_bot).start() |
|
|
|
|
| welcome_message = """ |
| ## Add this bot to your server by clicking this link: |
| |
| https://discord.com/api/oauth2/authorize?client_id=1154395078735953930&permissions=51200&scope=bot |
| |
| ## How to use it? |
| |
| The bot can be triggered via `/deepfloydif` followed by your text prompt. |
| |
| This will generate images based on the text prompt. You can upscale the images using the buttons up to 16x! |
| |
| ⚠️ Note ⚠️: Please make sure this bot's command does have the same name as another command in your server. |
| |
| ⚠️ Note ⚠️: Bot commands do not work in DMs with the bot as of now. |
| """ |
|
|
|
|
| with gr.Blocks() as demo: |
| gr.Markdown(f""" |
| # Discord bot of https://huggingface.co/spaces/DeepFloyd/IF |
| {welcome_message} |
| """) |
|
|
|
|
| demo.queue(concurrency_count=100) |
| demo.queue(max_size=100) |
| demo.launch() |
|
|