app.py

from diffusers import AutoPipelineForImage2Image, AutoPipelineForText2Image
import torch
import os

try:
    import intel_extension_for_pytorch as ipex
except:
    pass

from PIL import Image
import numpy as np
import gradio as gr
import psutil
import time
import math

SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)
HF_TOKEN = os.environ.get("HF_TOKEN", None)
# check if MPS is available OSX only M1/M2/M3 chips
mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
device = torch.device(
    "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu"
)
torch_device = device
torch_dtype = torch.float16

print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
print(f"TORCH_COMPILE: {TORCH_COMPILE}")
print(f"device: {device}")

if mps_available:
    device = torch.device("mps")
    torch_device = "cpu"
    torch_dtype = torch.float32

if SAFETY_CHECKER == "True":
    i2i_pipe = AutoPipelineForImage2Image.from_pretrained(
        "stabilityai/sdxl-turbo",
        torch_dtype=torch_dtype,
        variant="fp16" if torch_dtype == torch.float16 else "fp32",
    )
    t2i_pipe = AutoPipelineForText2Image.from_pretrained(
        "stabilityai/sdxl-turbo",
        torch_dtype=torch_dtype,
        variant="fp16" if torch_dtype == torch.float16 else "fp32",
    )
else:
    i2i_pipe = AutoPipelineForImage2Image.from_pretrained(
        "stabilityai/sdxl-turbo",
        safety_checker=None,
        torch_dtype=torch_dtype,
        variant="fp16" if torch_dtype == torch.float16 else "fp32",
    )
    t2i_pipe = AutoPipelineForText2Image.from_pretrained(
        "stabilityai/sdxl-turbo",
        safety_checker=None,
        torch_dtype=torch_dtype,
        variant="fp16" if torch_dtype == torch.float16 else "fp32",
    )

t2i_pipe.to(device=torch_device, dtype=torch_dtype).to(device)
t2i_pipe.set_progress_bar_config(disable=True)
i2i_pipe.to(device=torch_device, dtype=torch_dtype).to(device)
i2i_pipe.set_progress_bar_config(disable=True)

def resize_crop(image, size=512):
    image = image.convert("RGB")
    w, h = image.size
    image = image.resize((size, int(size * (h / w))), Image.BICUBIC)
    return image

# Global variable to store the selected image index
selected_image_index = None

# Load images from the 'images' folder
image_folder = 'images'
images = [Image.open(os.path.join(image_folder, img)) for img in sorted(os.listdir(image_folder)) if img.endswith(('.png', '.jpg', '.jpeg'))]

# Ensure that there are 34 images
assert len(images) == 34, "There should be exactly 34 images in the 'images' folder."

# Function to handle image selection
async def select_fn(data: gr.SelectData, prompt: str):
    global selected_image_index
    selected_image_index = data.index
    if prompt == "":
        print("Prompt is empty, returning original image")
        return images[selected_image_index]
    return await predict(prompt) 
    
async def predict(prompt):
    global selected_image_index
    strength = 0.49999999999999999
    steps = 2
    if selected_image_index is not None:
        init_image = images[selected_image_index]
        init_image = resize_crop(init_image)
        generator = torch.manual_seed(123123)
        last_time = time.time()
    
        if int(steps * strength) < 1:
            steps = math.ceil(1 / max(0.10, strength))
            
        results = i2i_pipe(
            prompt=prompt,
            image=init_image,
            generator=generator,
            num_inference_steps=steps,
            guidance_scale=0.0,
            strength=strength,
            width=512,
            height=512,
            output_type="pil",
        )

        print(f"Pipe took {time.time() - last_time} seconds")
        nsfw_content_detected = (
            results.nsfw_content_detected[0]
            if "nsfw_content_detected" in results
            else False
        )
        if nsfw_content_detected:
            gr.Warning("NSFW content detected.")
            return Image.new("RGB", (512, 512))
        return results.images[0]

# Create the Gradio interface
with gr.Blocks() as app:
    gr.Markdown('''# Rorschach 🎭
### 1. Select a CRASH REPORT image
### 2. Describe what you see
<small>CRASH REPORT was a self-published, 72-page book by NoPattern Studio released in November, 2019. Limited to an edition of 300, the book contained a year's worth of experimental, exploratory 3D imagery generated entirely in Photoshop. [CRASH REPORT site](https://nopattern.com/CRASH-REPORT) [see this space's lineage graph](https://huggingface.co/spaces/EQTYLab/lineage-explorer?repo=https://huggingface.co/NoPattern/Rorschach)</small>''', elem_id="main_title")
    #gr.LoginButton(elem_classes=["login_logout"])
    with gr.Row():
        with gr.Column():
            image_gallery = gr.Gallery(value=images, columns=4)  
        with gr.Column():
            prompt = gr.Textbox(label="I see...")
            output = gr.Image(label="Generation")

    # button = gr.Button("Rorschachify!")

    image_gallery.select(select_fn, inputs=[prompt], outputs=output, show_progress=False)
    # button.click(fn=predict, inputs=[prompt], outputs=output, show_progress=False)
    prompt.change(fn=predict, inputs=[prompt], outputs=output, show_progress=False)

# Run the app
app.queue()
app.launch()