app.py

 
import gradio as gr 
#import peft 
import transformers 
import os 
import re 
 
device = "cpu" 
is_peft = False 
model_id = "treadon/promt-fungineer-355M" 
# if is_peft: 
# config = peft.PeftConfig.from_pretrained(model_id) 
# model = transformers.AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, low_cpu_mem_usage=True) 
# tokenizer = transformers.AutoTokenizer.from_pretrained(config.base_model_name_or_path) 
# model = peft.PeftModel.from_pretrained(model, model_id) 
# else: 
auth_token = os.environ.get("hub_token") or True 
 
model = transformers.AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True,use_auth_token=auth_token) 
tokenizer = transformers.AutoTokenizer.from_pretrained("gpt2") 
 
 
def format_prompt(prompt, enhancers=True, inspiration=False, negative_prompt=False): 
 try: 
 pattern = r"(BRF:|POS:|ENH:|INS:|NEG:) (.*?)(?= (BRF:|POS:|ENH:|INS:|NEG:)|$)" 
 matches = re.findall(pattern, prompt) 
 vals = {key: value.strip() for key, value,ex in matches} 
 result = vals["POS:"] 
 if enhancers: 
 result += " " + vals["ENH:"] 
 if inspiration: 
 result += " " + vals["INS:"] 
 if negative_prompt: 
 result += "\n\n--no " + vals["NEG:"] 
 
 return result 
 except Exception as e: 
 return "Failed to generate prompt." 
 
 
def generate_text(prompt, extra=False, top_k=100, top_p=0.95, temperature=0.85, enhancers = True, inpspiration = False , negative_prompt = False): 
 
 if not prompt.startswith("BRF:"): 
 prompt = "BRF: " + prompt 
 
 if not extra: 
 prompt = prompt + " POS:" 
 
 model.eval() 
 # SOFT SAMPLE 
 inputs = tokenizer(prompt, return_tensors="pt").to(device) 
 samples = [] 
 try: 
 for i in range(1): 
 outputs = model.generate(**inputs, max_length=256, do_sample=True, top_k=top_k, top_p=top_p, temperature=temperature, num_return_sequences=4, pad_token_id=tokenizer.eos_token_id) 
 for output in outputs: 
 sample = tokenizer.decode(output, skip_special_tokens=True) 
 sample = format_prompt(sample, enhancers, inpspiration, negative_prompt) 
 samples.append(sample) 
 except Exception as e: 
 print(e) 
 
 return samples 
 
 
# inputs = [ 
# gr.Textbox(lines=5, label="Base Prompt", placeholder="An astronaut in space", info="Enter a very simple prompt that will be fungineered into something exciting!"), 
# gr.Checkbox(value=True, label="Extra Fungineer Imagination", info="If checked, the model will be allowed to go wild with its imagination."), 
# gr.Slider( minimum=10, maximum=1000, value=100, label="Top K", info="Top K sampling"), 
# gr.Slider( minimum=0.1, maximum=1, value=0.95, step=0.01, label="Top P", info="Top P sampling"), 
# gr.Slider( minimum=0.1, maximum=1.2, value=0.85, step=0.01, label="Temperature", info="Temperature sampling. Higher values will make the model more creative"), 
# ] 
 
# iface = gr.Interface(fn=generate_text, inputs=inputs, outputs=["text","text","text","text"] ) 
 
with gr.Blocks() as fungineer: 
 with gr.Row(): 
 gr.Markdown("""# Midjourney / Dalle 2 / Stable Diffusion Prompt Generator 
This is the 355M parameter model. There is also a 7B parameter model that is much better but far slower (access coming soon). 
Just enter a basic prompt and the fungineering model will use its wildest imagination to expand the prompt in detail.""") 
 with gr.Row(): 
 with gr.Column(): 
 base_prompt = gr.Textbox(lines=5, label="Base Prompt", placeholder="An astronaut in space", info="Enter a very simple prompt that will be fungineered into something exciting!") 
 extra = gr.Checkbox(value=True, label="Extra Fungineer Imagination", info="If checked, the model will be allowed to go wild with its imagination.") 
 with gr.Accordion("Advanced Generation Settings", open=False): 
 top_k = gr.Slider( minimum=10, maximum=1000, value=100, label="Top K", info="Top K sampling") 
 top_p = gr.Slider( minimum=0.1, maximum=1, value=0.95, step=0.01, label="Top P", info="Top P sampling") 
 temperature = gr.Slider( minimum=0.1, maximum=1.2, value=0.85, step=0.01, label="Temperature", info="Temperature sampling. Higher values will make the model more creative") 
 
 with gr.Accordion("Advanced Output Settings", open=False): 
 gr.Checkbox(value=True, label="Enhancers", info="Add image meta information such as lens type, shuffter speed, camera model, etc.") 
 gr.Checkbox(value=False, label="Inpsiration", info="Include inspirational photographers that are known for this type of photography. Sometimes random people will appear here, needs more training.") 
 gr.Checkbox(value=False, label="Negative Prompt", info="Include a negative prompt, more often used in Stable Diffusion. If you're a Stable Diffusion user, chances are you already have a better negative prompt you like to use.") 
 
 with gr.Column(): 
 outputs = [ 
 gr.Textbox(lines=5, label="Fungineered Text 1"), 
 gr.Textbox(lines=5, label="Fungineered Text 2"), 
 gr.Textbox(lines=5, label="Fungineered Text 3"), 
 gr.Textbox(lines=5, label="Fungineered Text 4"), 
 ] 
 
 inputs = [base_prompt, extra, top_k, top_p, temperature] 
 
 
 submit = gr.Button(label="Fungineer",variant="primary") 
 submit.click(generate_text, inputs=inputs, outputs=outputs) 
 
fungineer.launch(enable_queue=True)