File size: 3,913 Bytes
07b8c5e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
import gradio as gr
import requests
import io
from PIL import Image
import json
import os
import logging
import time
from tqdm import tqdm

# Import additional modules
from pixelart import Script as PixelArtScript
from postprocessing_pixelart import ScriptPostprocessingUpscale
from utils import downscale_image, limit_colors, resize_image, convert_to_grayscale, convert_to_black_and_white

# Placeholder class for processed images
class SomeClass:
    def __init__(self):
        self.images = []

with open('loras.json', 'r') as f:
    loras = json.load(f)

def update_selection(selected_state: gr.SelectData):
    selected_lora_index = selected_state.index
    selected_lora = loras[selected_lora_index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"
    lora_repo = selected_lora["repo"]
    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
    return (
        gr.update(placeholder=new_placeholder),
        updated_text,
        selected_state
    )

def run_lora(prompt, selected_state, pixel_art_options, postprocess_options, progress=gr.Progress(track_tqdm=True)):
    selected_lora_index = selected_state.index
    selected_lora = loras[selected_lora_index]
    api_url = f"https://api-inference.huggingface.co/models/{selected_lora['repo']}"
    payload = {
        "inputs": f"{prompt} {selected_lora['trigger_word']}",
        "parameters": {"negative_prompt": "bad art, ugly, watermark, deformed"},
    }
    response = requests.post(api_url, json=payload)
    if response.status_code == 200:
        original_image = Image.open(io.BytesIO(response.content))
        
        processed = SomeClass()
        processed.images = [original_image]
        
        pixel_art_script = PixelArtScript()
        postprocess_script = ScriptPostprocessingUpscale()
        
        pixel_art_script.postprocess(
            processed,
            **pixel_art_options
        )
        
        postprocess_script.process(
            processed,
            **postprocess_options
        )
        
        refined_image = processed.images[-1]
        
        return original_image, refined_image

with gr.Blocks() as app:
    title = gr.Markdown("# artificialguybr LoRA portfolio")
    description = gr.Markdown("### This is a Pixel Art Generator using SD Loras.")
    selected_state = gr.State()
    
    with gr.Row():
        gallery = gr.Gallery(
            [(item["image"], item["title"]) for item in loras],
            label="LoRA Gallery",
            allow_preview=False,
            columns=3
        )
        
        with gr.Column():
            prompt_title = gr.Markdown("### Click on a LoRA in the gallery to create with it")
            selected_info = gr.Markdown("")
            
            with gr.Row():
                prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, placeholder="Type a prompt after selecting a LoRA")
                button = gr.Button("Run")
            
            result = gr.Image(interactive=False, label="Generated Image")
            refined_result = gr.Image(interactive=False, label="Refined Generated Image")  # New Output
            
            # New UI elements for pixel art options
            with gr.Row():
                pixel_art_options = PixelArtScript().ui(True)
                postprocess_options = ScriptPostprocessingUpscale().ui()
    
    gallery.select(
        update_selection,
        outputs=[prompt, selected_info, selected_state]
    )
    
    prompt.submit(
        fn=run_lora,
        inputs=[prompt, selected_state, pixel_art_options, postprocess_options],
        outputs=[result, refined_result]
    )
    
    button.click(
        fn=run_lora,
        inputs=[prompt, selected_state, pixel_art_options, postprocess_options],
        outputs=[result, refined_result]
    )

app.queue(max_size=20, concurrency_count=5)
app.launch()