Update run_Transformers_imgedit.py

run_Transformers_imgedit.py

+import torch
+from modelscope import AutoModelForCausalLM, AutoTokenizer
+from encoder.image_tokenizer import ImageTokenizer
+from decoder.utils import generate_crop_size_list, var_center_crop
+from decoder import decode_vq_tokens
+from PIL import Image
+
+model_path = "inclusionAI/LLaDA2.0-Uni"
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_path, device_map="cuda", torch_dtype="bfloat16", trust_remote_code=True
+).eval()
+model.tokenizer = tokenizer
+
+# Encode source image
+image_tokenizer = ImageTokenizer(model_path=model_path, device="cuda")
+crop_size_list = generate_crop_size_list((512 // 32) ** 2, 32)
+pil_image = var_center_crop(Image.open("./assets/edit_example.png").convert("RGB"), crop_size_list=crop_size_list)
+info = image_tokenizer.encode_with_info(pil_image)
+image_tokens = [x + model.config.image_token_offset for x in info["token_ids"]]
+_, h, w = info["grid_thw"]
+
+# Edit the image
+result = model.edit_image(
+    image_tokens, h, w,
+    instruction="Change the background to a beach.",
+    steps=8, cfg_text_scale=4.0,
+)
+
+# Decode to PIL image
+edited_image = decode_vq_tokens(result["token_ids"], result["h"], result["w"], model_path, "cuda", num_steps=8, decode_mode="decoder-turbo",)
+edited_image.save("edited.png")