import gradio as gr
import numpy as np
import time
import math
import random
import torch
import spaces
from diffusers import AutoPipelineForImage2Image
from PIL import Image, ImageFilter
from pillow_heif import register_heif_opener
register_heif_opener()
max_64_bit_int = np.iinfo(np.int32).max
# Automatic device detection
if torch.cuda.is_available():
device = "cuda"
floatType = torch.float16
variant = "fp16"
else:
device = "cpu"
floatType = torch.float32
variant = None
pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype = floatType, variant = variant)
pipe = pipe.to(device)
def update_seed(is_randomize_seed, seed):
if is_randomize_seed:
return random.randint(0, max_64_bit_int)
return seed
def toggle_debug(is_debug_mode):
return [gr.update(visible = is_debug_mode)]
def check(
source_img,
prompt,
negative_prompt,
num_inference_steps,
guidance_scale,
image_guidance_scale,
strength,
denoising_steps,
seed,
is_randomize_seed,
debug_mode,
progress = gr.Progress()
):
if source_img is None:
raise gr.Error("Please provide an image.")
if prompt is None or prompt == "":
raise gr.Error("Please provide a prompt input.")
@spaces.GPU(duration=420)
def redraw(
source_img,
prompt,
negative_prompt,
num_inference_steps,
guidance_scale,
image_guidance_scale,
strength,
denoising_steps,
is_randomize_seed,
seed,
debug_mode,
progress = gr.Progress()
):
check(
source_img,
prompt,
negative_prompt,
num_inference_steps,
guidance_scale,
image_guidance_scale,
strength,
denoising_steps,
is_randomize_seed,
seed,
debug_mode
)
start = time.time()
progress(0, desc = "Preparing data...")
if negative_prompt is None:
negative_prompt = ""
if num_inference_steps is None:
num_inference_steps = 25
if guidance_scale is None:
guidance_scale = 7
if image_guidance_scale is None:
image_guidance_scale = 1.1
if strength is None:
strength = 0.5
if denoising_steps is None:
denoising_steps = 1000
if seed is None:
seed = random.randint(0, max_64_bit_int)
random.seed(seed)
torch.manual_seed(seed)
input_image = source_img.convert("RGB")
original_height, original_width, original_channel = np.array(input_image).shape
output_width = original_width
output_height = original_height
# Limited to 1 million pixels
if 1024 * 1024 < output_width * output_height:
factor = ((1024 * 1024) / (output_width * output_height))**0.5
process_width = math.floor(output_width * factor)
process_height = math.floor(output_height * factor)
limitation = " Due to technical limitation, the image have been downscaled and then upscaled.";
else:
process_width = output_width
process_height = output_height
limitation = "";
# Width and height must be multiple of 8
if (process_width % 8) != 0 or (process_height % 8) != 0:
if ((process_width - (process_width % 8) + 8) * (process_height - (process_height % 8) + 8)) <= (1024 * 1024):
process_width = process_width - (process_width % 8) + 8
process_height = process_height - (process_height % 8) + 8
elif (process_height % 8) <= (process_width % 8) and ((process_width - (process_width % 8) + 8) * process_height) <= (1024 * 1024):
process_width = process_width - (process_width % 8) + 8
process_height = process_height - (process_height % 8)
elif (process_width % 8) <= (process_height % 8) and (process_width * (process_height - (process_height % 8) + 8)) <= (1024 * 1024):
process_width = process_width - (process_width % 8)
process_height = process_height - (process_height % 8) + 8
else:
process_width = process_width - (process_width % 8)
process_height = process_height - (process_height % 8)
progress(None, desc = "Processing...")
output_image = pipe(
seeds = [seed],
width = process_width,
height = process_height,
prompt = prompt,
negative_prompt = negative_prompt,
image = input_image,
num_inference_steps = num_inference_steps,
guidance_scale = guidance_scale,
image_guidance_scale = image_guidance_scale,
strength = strength,
denoising_steps = denoising_steps,
show_progress_bar = True
).images[0]
if limitation != "":
output_image = output_image.resize((output_width, output_height))
if debug_mode == False:
input_image = None
end = time.time()
secondes = int(end - start)
minutes = math.floor(secondes / 60)
secondes = secondes - (minutes * 60)
hours = math.floor(minutes / 60)
minutes = minutes - (hours * 60)
return [
output_image,
("Start again to get a different result. " if is_randomize_seed else "") + "The image has been generated in " + ((str(hours) + " h, ") if hours != 0 else "") + ((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + str(secondes) + " sec." + limitation,
input_image
]
with gr.Blocks() as interface:
gr.HTML(
"""
Image-to-Image
Modifies the global render of your image, at any resolution, freely, without account, without watermark, without installation, which can be downloaded
✨ Powered by SDXL Turbo artificial intellingence. For illustration purpose, not information purpose. The new content is not based on real information but imagination.
- To change the view angle of your image, I recommend to use Zero123,
- To upscale your image, I recommend to use SUPIR,
- To change one detail on your image, I recommend to use Inpaint SDXL,
- If you need to enlarge the viewpoint of your image, I recommend you to use Uncrop,
- To remove the background of your image, I recommend to use BRIA,
- To make a tile of your image, I recommend to use Make My Image Tile,
- To modify anything else on your image, I recommend to use Instruct Pix2Pix.
""" + ("🏃♀️ Estimated time: few minutes." if torch.cuda.is_available() else "🐌 Slow process... ~2 hours.") + """
Your computer must not enter into standby mode.
You can duplicate this space on a free account, it's designed to work on CPU, GPU and ZeroGPU.
⚖️ You can use, modify and share the generated images but not for commercial uses.
"""
)
with gr.Column():
source_img = gr.Image(label = "Your image", sources = ["upload", "webcam", "clipboard"], type = "pil")
prompt = gr.Textbox(label = "Prompt", info = "Describe the subject, the background and the style of image; 77 token limit", placeholder = "Describe what you want to see", lines = 2)
strength = gr.Slider(value = 0.5, minimum = 0.01, maximum = 1.0, step = 0.01, label = "Strength", info = "lower=follow the original image, higher=follow the prompt")
with gr.Accordion("Advanced options", open = False):
negative_prompt = gr.Textbox(label = "Negative prompt", placeholder = "Describe what you do NOT want to see", value = "Ugly, malformed, noise, blur, watermark")
num_inference_steps = gr.Slider(minimum = 10, maximum = 100, value = 25, step = 1, label = "Number of inference steps", info = "lower=faster, higher=image quality")
guidance_scale = gr.Slider(minimum = 1, maximum = 13, value = 7, step = 0.1, label = "Guidance Scale", info = "lower=image quality, higher=follow the prompt")
image_guidance_scale = gr.Slider(minimum = 1, value = 1.1, step = 0.1, label = "Image Guidance Scale", info = "lower=image quality, higher=follow the image")
denoising_steps = gr.Slider(minimum = 0, maximum = 1000, value = 1000, step = 1, label = "Denoising", info = "lower=irrelevant result, higher=relevant result")
randomize_seed = gr.Checkbox(label = "\U0001F3B2 Randomize seed", value = True, info = "If checked, result is always different")
seed = gr.Slider(minimum = 0, maximum = max_64_bit_int, step = 1, randomize = True, label = "Seed")
debug_mode = gr.Checkbox(label = "Debug mode", value = False, info = "Show intermediate results")
submit = gr.Button("🚀 Redraw", variant = "primary")
redrawn_image = gr.Image(label = "Redrawn image")
information = gr.HTML()
original_image = gr.Image(label = "Original image", visible = False)
submit.click(update_seed, inputs = [
randomize_seed,
seed
], outputs = [
seed
], queue = False, show_progress = False).then(toggle_debug, debug_mode, [
original_image
], queue = False, show_progress = False).then(check, inputs = [
source_img,
prompt,
negative_prompt,
num_inference_steps,
guidance_scale,
image_guidance_scale,
strength,
denoising_steps,
randomize_seed,
seed,
debug_mode
], outputs = [], queue = False, show_progress = False).success(redraw, inputs = [
source_img,
prompt,
negative_prompt,
num_inference_steps,
guidance_scale,
image_guidance_scale,
strength,
denoising_steps,
randomize_seed,
seed,
debug_mode
], outputs = [
redrawn_image,
information,
original_image
], scroll_to_output = True)
gr.Examples(
run_on_click = True,
fn = redraw,
inputs = [
source_img,
prompt,
negative_prompt,
num_inference_steps,
guidance_scale,
image_guidance_scale,
strength,
denoising_steps,
randomize_seed,
seed,
debug_mode
],
outputs = [
redrawn_image,
information,
original_image
],
examples = [
[
"./Examples/Example1.png",
"Drawn image, line art, illustration, picture",
"3d, photo, realistic, noise, blur, watermark",
25,
7,
1.1,
0.6,
1000,
False,
42,
False
],
],
cache_examples = False,
)
gr.Markdown(
"""
## How to prompt your image
To easily read your prompt, start with the subject, then describ the pose or action, then secondary elements, then the background, then the graphical style, then the image quality:
```
A Vietnamese woman, red clothes, walking, smilling, in the street, a car on the left, in a modern city, photorealistic, 8k
```
You can use round brackets to increase the importance of a part:
```
A Vietnamese woman, (red clothes), walking, smilling, in the street, a car on the left, in a modern city, photorealistic, 8k
```
You can use several levels of round brackets to even more increase the importance of a part:
```
A Vietnamese woman, ((red clothes)), (walking), smilling, in the street, a car on the left, in a modern city, photorealistic, 8k
```
You can use number instead of several round brackets:
```
A Vietnamese woman, (red clothes:1.5), (walking), smilling, in the street, a car on the left, in a modern city, photorealistic, 8k
```
You can do the same thing with square brackets to decrease the importance of a part:
```
A [Vietnamese] woman, (red clothes:1.5), (walking), smilling, in the street, a car on the left, in a modern city, photorealistic, 8k
```
To easily read your negative prompt, organize it the same way as your prompt (not important for the AI):
```
man, boy, hat, running, tree, bicycle, forest, drawing, painting, cartoon, 3d, monochrome, blurry, noisy, bokeh
```
"""
)
interface.queue().launch()