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import gradio as gr
import os
import spaces
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("segmind/tiny-sd")
pipe.load_lora_weights(
"philipp-zettl/jon_juarez-lora",
hf_token=os.environ.get('HF_TOKEN')
)
pipe.to('cuda')
@spaces.GPU
def generate(prompt, negative_prompt, num_inference_steps, width, height, num_samples):
return pipe(
prompt,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_steps,
width=width,
height=height,
num_images_per_prompt=num_samples
).images
app = gr.Interface(
fn=generate,
inputs=[
gr.Text(label="Prompt"),
gr.Text("", label="Negative Prompt"),
gr.Slider(minimum=1, maximum=100, value=45, label="Number inference steps"),
gr.Number(512, label='Image width'),
gr.Number(512, label='Image height'),
gr.Slider(minimum=1, maximum=10, value=1, label='Number samples'),
],
outputs=gr.Gallery(),
examples=[
[
"Colorful line shading by JON_JUAREZ a dark cave with toxic mushrooms",
"",
45,
512,
512,
1
]
]
)
app.launch() |