Spaces:
Runtime error
Runtime error
File size: 2,734 Bytes
74eeb71 |
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 |
import gradio as gr
import numpy as np
from PIL import Image
from io import BytesIO
import requests
import json
# List of available models
models = [
"HHM29/finetuning_dream_fin",
"KappaNeuro/needlepoint",
"Norod78/ClaymationX_LoRA",
"KappaNeuro/movie-poster",
"digiplay/MixTape_RocknRoll_v3punk_bake_fp16",
"digiplay/BeautifulFantasyRealMix_diffusers",
"Yntec/pineappleAnimeMix",
"Yntec/DucHaiten-Retro-Diffusers",
"joachimsallstrom/aether-pixel-lora-for-sdxl",
"runwayml/stable-diffusion-v1-5",
"stabilityai/stable-diffusion-xl-base-1.0",
"CompVis/stable-diffusion-v1-4",
]
def generate_image(model_name, image, prompt, length, temperature, n_samples, use_image2image=False):
data = {
"image_prompt": image,
"prompt": prompt,
"length": length,
"temperature": temperature,
"n_samples": n_samples,
"model": model_name,
}
if use_image2image:
data["use_image2image"] = True
data["image2image_prompt"] = image # Provide the target image for image2image
response = requests.post("https://api.stable-diffusion.ml/generate", json=data)
response_json = response.json()
if response.status_code == 200:
results = response_json["generated_images"]
generated_image = np.frombuffer(BytesIO(results[0]["image"]).read(), dtype=np.uint8)
generated_image = generated_image.reshape(results[0]["metadata"]["height"], results[0]["metadata"]["width"], 3)
return Image.fromarray(generated_image)
else:
return None
def app(model=gr.inputs.Selector(options=models),
image=gr.inputs.Image(shape=(None, None)),
prompt=gr.inputs.Textbox(default="an image generated with"),
length=gr.inputs.Slider(1, 20, step=1, default=8),
temperature=gr.inputs.Slider(0.5, 1.5, step=0.1, default=1),
n_samples=gr.inputs.Slider(1, 5, step=1, default=1),
use_image2image=gr.inputs.Boolean(default=False)):
generated_image = generate_image(model,
image=image.data if image else None,
prompt=prompt,
length=int(length),
temperature=float(temperature),
n_samples=int(n_samples),
use_image2image=use_image2image)
return gr.outputs.Image(as_pil=True)(generated_image) if generated_image else None
if __name__ == "__main__":
title = "Image Generation App"
description = "Select a model and customize your image generation or image2image settings!"
gradio.launch(app, port=8000, title=title, description=description)
|