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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" | |
| prompt = name | |
| 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() |