X-ART
/

LeX-Enhancer

@@ -3,197 +3,69 @@ library_name: transformers
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 tags: []
 ---
 ## Uses
+Use this code for inference:
+```python
+import torch, os
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
+SYSTEM_TEMPLATE = (
+    "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. "
+    "The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. "
+    "The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., "
+    "<think> reasoning process here </think> <answer> answer here </answer>."
+)
+model_path = 'deepseek-ai/DeepSeek-R1-Distill-Qwen-14B'
+lora_path = "X-ART/LeX-Enhancer_LoRA"
+simple_caption = "A thank you card with the words very much, with the text on it: \"VERY\" in black, \"MUCH\" in yellow."
+def create_chat_template(user_prompt):
+    return [
+        {"role": "system", "content": SYSTEM_TEMPLATE},
+        {"role": "user", "content": user_prompt},
+        {"role": "assistant", "content": "<think>"}
+    ]
+def create_direct_template(user_prompt):
+    return user_prompt + "<think>" # better
+def create_user_prompt(simple_caption):
+    return (
+        "Below is the simple caption of an image with text. Please deduce the detailed description of the image based on this simple caption. "
+        "Note: 1. The description should only include visual elements and should not contain any extended meanings. "
+        "2. The visual elements should be as rich as possible, such as the main objects in the image, their respective attributes, "
+        "the spatial relationships between the objects, lighting and shadows, color style, any text in the image and its style, etc. "
+        "3. The output description should be a single paragraph and should not be structured. "
+        "4. The description should avoid certain situations, such as pure white or black backgrounds, blurry text, excessive rendering of text, "
+        "or harsh visual styles. "
+        "5. The detailed caption should be human readable and fluent. "
+        "6. Avoid using vague expressions such as \"may be\" or \"might be\"; the generated caption must be in a definitive, narrative tone. "
+        "7. Do not use negative sentence structures, such as \"there is nothing in the image,\" etc. The entire caption should directly describe the content of the image. "
+        "8. The entire output should be limited to 200 words.\n\n"
+        "SIMPLE CAPTION: {0}"
+    ).format(simple_caption)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", torch_dtype=torch.bfloat16)
+# Tokenize the input prompt
+messages = create_direct_template(create_user_prompt(simple_caption))  # 3.for direct template
+input_ids = tokenizer.encode(messages, return_tensors="pt")
+# Generate text using the model
+streamer = TextStreamer(tokenizer, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+output = model.generate(
+    input_ids.to(model.device),
+    max_length=2048,
+    num_return_sequences=1,
+    do_sample=True,
+    temperature=0.6,
+    repetition_penalty=1.1,
+    streamer=streamer
+)
+# Print the generated text
+print("*" * 80)
+# print(generated_text)
+```