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import base64
import gradio as gr
import torch
import torchvision
from diffusers import DiffusionPipeline
import PIL.Image
import numpy as np
from io import BytesIO
ldm = DiffusionPipeline.from_pretrained("fusing/latent-diffusion-text2im-large")
generator = torch.manual_seed(42)
def greet(name):
prompt = "A squirrel eating a burger"
image = ldm([prompt], generator=generator, eta=0.3, guidance_scale=6.0, num_inference_steps=50)
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = image_processed * 255.
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])
# save image as buffer
buffered = BytesIO()
image_pil.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue())
print(img_str.decode('utf-8'))
return img_str.decode('utf-8')
#return "Gello " + prompt + "!!"
image = gr.Image(type="pil", label="Your result")
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch() |