Upload infer.py

infer.py

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+from transformers import GPT2LMHeadModel, AutoTokenizer
+import demo_util
+import numpy as np
+import torch
+from PIL import Image
+import os
+
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.manual_seed(0)
+
+device = "cuda:1"
+dtype = torch.float16
+
+config = demo_util.get_config("configs/titok_l32.yaml")
+titok_tokenizer = demo_util.get_titok_tokenizer(config)
+titok_tokenizer = titok_tokenizer.to(device)
+tokenizer = AutoTokenizer.from_pretrained("./image_tokenizer")
+model = GPT2LMHeadModel.from_pretrained("./checkpoint-20000").to(device).to(dtype).eval()
+
+def detokenize(tokens):
+    encoded_tokens = torch.from_numpy(np.array(tokens).astype(np.int64)).view(1, 1, -1).to(device)
+    reconstructed_image = titok_tokenizer.decode_tokens(encoded_tokens)
+    reconstructed_image = torch.clamp(reconstructed_image, 0.0, 1.0)
+    reconstructed_image = (reconstructed_image * 255.0).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()[0]
+    return Image.fromarray(reconstructed_image)
+
+prompt = ""
+
+inputs = tokenizer(f"{text}<|startofimage|>", return_tensors="pt").to(device)
+input_ids = inputs["input_ids"]
+init = input_ids.shape[-1]
+soi_token = tokenizer.encode("<|image:0|>")[0]
+
+for _ in range(33):
+    logits = model(input_ids).logits[:, -1, :]
+    probas = torch.nn.functional.softmax(logits, dim=-1)
+    pred_idx = torch.argmax(probas, dim=-1, keepdim=True)
+    input_ids = torch.cat((input_ids, pred_idx), dim=-1)
+tokenizer.decode(input_ids[0])
+tokens = input_ids[:, init:-1].detach().cpu().squeeze().numpy() - soi_token
+
+if np.any(tokens < 0) or np.any(tokens >= 4096):
+    print("Illegal Image Tokens")
+
+else:
+    img = detokenize(tokens)
+    img.save(f"./out.png")