| | 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() |
| |
|