Create README.md

README.md

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+# NeuroPrompts Model Card
+
+NeuroPrompts is an interface to Stable Diffusion which automatically optimizes a user’s prompt for improved image aesthetics while maintaining stylistic control according to the user’s preferences.
+Preprint: [arxiv.org/abs/2311.12229](https://arxiv.org/abs/2311.12229)
+
+NeuroPrompts was accepted to EACL 2024.
+
+## The interface of NeuroPrompts in side-by-side comparison mode
+
+![NeuroPrompts overview](images/comparisons_interface.png)
+
+
+
+## Usage
+
+```shell script
+pip install torch torchvision gradio==3.39.0 transformers diffusers flair==0.12.2 numpy tqdm webdataset pytorch_lightning datasets openai-clip scipy==1.10.1
+```
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import sys
+import os
+# from categories import styles_list, artists_list, formats_list, perspective_list, booster_list, vibe_list
+
+
+# Set environment variables and PyTorch configurations
+os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
+torch.backends.cudnn.benchmark = False
+torch.use_deterministic_algorithms(True)
+
+
+# Set the path for the 'neurologic' module
+neurologic_path = os.path.abspath('neurologic/')
+os.environ['NEUROLOGIC_PATH'] = neurologic_path
+sys.path.insert(0,neurologic_path)
+from neurologic_pe import generate_neurologic
+
+# Load the pre-trained model and tokenizer
+model_name = "/home/srosenma/src/test_45"
+model_type = 'finetuned'
+# model_type = 'ppo'
+rand_seed = 1535471403
+
+
+model = AutoModelForCausalLM.from_pretrained(model_name).to('cuda')
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.pad_token = tokenizer.eos_token
+
+
+# Set the inference parameters
+length_penalty = 1.0
+max_length = 77
+beam_size = 5
+inference_steps = 25
+
+
+# Initialize the input constraints
+curr_input_artist = None
+curr_input_style = None
+curr_input_format = None
+curr_input_perspective = None
+curr_input_booster = None
+curr_input_vibe = None
+curr_input_negative = ""
+
+
+# Set the plain text input
+plain_text = "A boy and his dog"
+
+
+# Construct the positive and negative constraints
+constraints = []
+for clause in [curr_input_artist, curr_input_style, curr_input_format, curr_input_perspective, curr_input_booster, curr_input_vibe]:
+      if clause is not None and len(clause) > 0:
+        constraints.append([clause.lower(), clause.title()])
+
+print(f"Positive constraints:{constraints}")
+
+
+neg_constraints = []
+neg_inputs = [i.strip() for i in curr_input_negative.split(',')]
+for clause in neg_inputs:
+    if clause is not None and len(clause) > 0:
+        neg_constraints += [clause.lower(), clause.title()]
+
+print(f"Negative constraints:{neg_constraints}")
+
+
+
+# Generate the output using the 'generate_neurologic' function
+res = generate_neurologic(plain_text, 
+                              model=model,
+                              tokenizer=tokenizer,
+                              model_type=model_type, 
+                              constraint_method='clusters', 
+                              clusters_file='/home/philliph/mcai/mm-counterfactuals/prompt_engineering/template_keywords.json',
+                              user_constraints = constraints if len(constraints) > 0 else None, 
+                              negative_constraints = neg_constraints if len(neg_constraints) > 0 else None, 
+                              length_penalty=float(length_penalty),
+                              max_tgt_length=int(max_length),
+                              beam_size=int(beam_size),
+                              num_return_sequences=int(beam_size),
+                              ngram_size=2, 
+                              n_per_cluster=1,
+                              seed=None)[0][0]
+
+
+
+# Print the result
+print(f"\nResult:\n{res}")
+
+
+
+
+```
+