File size: 2,121 Bytes
5cf6b7e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import gradio as gr
import requests
from PIL import Image
from io import BytesIO
import base64

api_url = "https://5cb20b40-572c-426f-9466-995256f9b6eb.id.repl.co/generate_image"

def generate_image(model="Dreamlike Diffusion", prompt="", seed=0, negative_prompt="", sampler="k_dpmpp_2s_a", steps=50):
    data = "?model=" + model + "&prompt=" + prompt + "&seed=" + str(seed) + "&negative_prompt=" + negative_prompt + "&sampler=" + sampler + "&steps=" + str(steps)
    response = requests.post(api_url + data, timeout=400)
    if response.status_code == 200:
        img_base64 = response.json()["url"]
        img_bytes = base64.b64decode(img_base64)
        img = Image.open(BytesIO(img_bytes))
        return img
    else:
        return None

inputs = [
     gr.inputs.Dropdown(['Analog Diffusion', 'Anything Diffusion', 'Anything v3', 'ChilloutMix', 'Counterfeit', 'CyriousMix', 'Deliberate', 'Dreamshaper', 'Dreamlike Diffusion', 'Dreamlike Photoreal',  'Experience', 'FaeTastic', 'Hassanblend', 'Mega Merge Diffusion',  'Midjourney Diffusion', 'ModernArt Diffusion', 'Movie Diffusion', 'NeverEnding Dream', 'Perfect World', 'PortraitPlus', 'ProtoGen',  'Protogen Anime', 'Protogen Infinity', 'RealBiter', 'Realism Engine', 'Realistic Vision', 'Rev Animated',  'RPG', 'Seek.art MEGA', 'stable_diffusion', 'stable_diffusion_2.1' , 'Unstable Ink Dream'], label="Model", default="Dreamlike Diffusion"),
    gr.inputs.Textbox(label="Prompt"),
    gr.inputs.Number(label="Seed", default=0),
    gr.inputs.Textbox(label="Negative Prompt", default=""),
    gr.inputs.Dropdown(["k_lms", "k_heun", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "DDIM", "k_dpm_fast", "k_dpm_adaptive", "k_dpmpp_2m", "k_dpmpp_2s_a", "k_dpmpp_sde"], label="Sampler", default="k_dpmpp_2s_a"),
    gr.inputs.Number(label="Steps", default=50)
]

outputs = gr.outputs.Image(label="Generated Image", type="pil")

interface = gr.Interface(generate_image, inputs, outputs, title="Diffusion 50", 
                         description="<center>Top 50 Diffusion models in one place</center>", 
                         examples=[])

interface.launch()