| from diffusers import AutoencoderKL |
| from PIL import Image |
| import io |
| from transformers import CLIPProcessor, CLIPModel |
| from model import Model |
| from pathlib import Path |
| from noise_scheduler import NoiseSchedule |
| import torch |
| import base64 |
| from typing import Any, Dict |
|
|
| LDM = True |
| image_size = 512 |
| latent_size = 64 |
| filters = [64, 128, 256, 512] |
| latent_dim = 4 |
| t_dim = 512 |
| T = 1000 |
| depth = 2 |
|
|
| class CLIP: |
| def __init__(self): |
| self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") |
| self.model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") |
| self.model.eval() |
| for name, param in self.model.named_parameters(): |
| param.requires_grad = False |
|
|
| @torch.inference_mode() |
| def embed_images(self, images): |
| image = self.processor(images=images, return_tensors="pt").to(self.model.device) |
| return self.model.get_image_features(**image) |
| |
| @torch.inference_mode() |
| def embed_text(self, text): |
| text = self.processor(text, padding=True, return_tensors="pt").to(self.model.device) |
| return self.model.get_text_features(**text) |
|
|
| class Inference: |
| def __init__(self): |
| here = Path(__file__).resolve().parent |
| ckpt_path = here / "unet.pt" |
|
|
| self.clip = CLIP() |
| self.ae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae").to('cuda' if torch.cuda.is_available() else "cpu") |
| self.ae.eval() |
| for name, param in self.ae.named_parameters(): |
| param.requires_grad = False |
| self.unet = Model(T=T, filters=[64,128,256,512], t_dim=t_dim, depth=depth, LDM=LDM) |
| self.unet.load_state_dict(torch.load(ckpt_path, weights_only=False, map_location=torch.device('cpu'))) |
| self.unet.eval() |
| for name, param in self.unet.named_parameters(): |
| param.requires_grad = False |
| self.noise_scheduler = NoiseSchedule(T=1000, shape=(4,64,64), ddim_mod=50, trainer_mode=True) |
| self.target_vector = self.clip.embed_text("A photo of a cat")[0] |
| self.target_vector = self.target_vector / self.target_vector.norm(p=2, dim=-1, keepdim=True) |
| @torch.inference_mode() |
| def __call__(self, num_images=8): |
| imgs = self.noise_scheduler.generate(self.unet, num_images=num_images, device='cpu') |
| max_img = None |
| max_score = -1 |
| images = [] |
| for img in imgs: |
| image = self.ae.decode(img.unsqueeze(0) / self.ae.config.scaling_factor)[0][0].cpu().permute(1,2,0)/2 + 0.5 |
| image = torch.clamp(image, 0.0, 1.0) |
| images.append(image) |
| embeddings = self.clip.embed_images(images) |
| scores = (embeddings / embeddings.norm(p=2, dim=-1, keepdim=True)) @ self.target_vector.T |
| i = torch.argmax(scores).item() |
| return images[i], scores[i], scores |
|
|
| class EndpointHandler: |
| def __init__(self, path: str = ""): |
| self.engine = Inference() |
|
|
| def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]: |
| img_tensor, score, _ = self.engine(num_images=1) |
| img_uint8 = (img_tensor.clamp(0,1).numpy() * 255).astype("uint8") |
| pil_img = Image.fromarray(img_uint8) |
| |
| buf = io.BytesIO() |
| pil_img.save(buf, format="PNG") |
| png_bytes = buf.getvalue() |
|
|
| b64 = base64.b64encode(png_bytes).decode("utf-8") |
| return {"image": b64, "score": float(score)} |
|
|
|
|
