| from typing import Dict, List, Any |
| from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler |
| import torch |
| import base64 |
| from PIL import Image |
| import io |
|
|
| class EndpointHandler(): |
| def __init__(self, path=""): |
| scheduler = EulerAncestralDiscreteScheduler.from_pretrained(path, subfolder="scheduler") |
| self.pipeline = DiffusionPipeline.from_pretrained(path, scheduler=scheduler, torch_dtype=torch.float32) |
| if torch.cuda.is_available(): |
| self.pipeline.to("cuda") |
|
|
| def __call__(self, data: Dict[str, any]) -> List[Dict[str, Any]]: |
| mask_bytes = base64.b64decode(data["inputs"]["mask"]) |
| mask = Image.open(io.BytesIO(mask_bytes)).convert('RGB') |
|
|
| image_bytes = base64.b64decode(data["inputs"]["image"]) |
| image = Image.open(io.BytesIO(image_bytes)).convert('RGB') |
|
|
| prompt = "a picture of a person with a nice haircut" |
|
|
| new_image = self.pipeline(prompt=prompt, image=image, mask_image=mask, height=512, width=512, num_inference_steps=20).images[0] |
| return [{"image": new_image}] |
|
|
