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import gradio as gr |
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import random |
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import json |
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import os |
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import re |
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from datetime import datetime |
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from huggingface_hub import InferenceClient |
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import subprocess |
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import torch |
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from PIL import Image |
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from transformers import AutoProcessor, AutoModelForCausalLM |
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subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) |
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huggingface_token = os.getenv("HUGGINGFACE_TOKEN") |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval() |
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florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True) |
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def florence_caption(image): |
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if not isinstance(image, Image.Image): |
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image = Image.fromarray(image) |
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inputs = florence_processor(text="<MORE_DETAILED_CAPTION>", images=image, return_tensors="pt").to(device) |
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generated_ids = florence_model.generate( |
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input_ids=inputs["input_ids"], |
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pixel_values=inputs["pixel_values"], |
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max_new_tokens=1024, |
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early_stopping=False, |
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do_sample=False, |
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num_beams=3, |
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) |
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generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0] |
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parsed_answer = florence_processor.post_process_generation( |
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generated_text, |
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task="<MORE_DETAILED_CAPTION>", |
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image_size=(image.width, image.height) |
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) |
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return parsed_answer["<MORE_DETAILED_CAPTION>"] |
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def load_json_file(file_name): |
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file_path = os.path.join("data", file_name) |
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with open(file_path, "r") as file: |
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return json.load(file) |
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ARTFORM = load_json_file("artform.json") |
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PHOTO_TYPE = load_json_file("photo_type.json") |
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BODY_TYPES = load_json_file("body_types.json") |
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DEFAULT_TAGS = load_json_file("default_tags.json") |
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ROLES = load_json_file("roles.json") |
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HAIRSTYLES = load_json_file("hairstyles.json") |
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ADDITIONAL_DETAILS = load_json_file("additional_details.json") |
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PHOTOGRAPHY_STYLES = load_json_file("photography_styles.json") |
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DEVICE = load_json_file("device.json") |
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PHOTOGRAPHER = load_json_file("photographer.json") |
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ARTIST = load_json_file("artist.json") |
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DIGITAL_ARTFORM = load_json_file("digital_artform.json") |
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PLACE = load_json_file("place.json") |
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LIGHTING = load_json_file("lighting.json") |
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CLOTHING = load_json_file("clothing.json") |
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COMPOSITION = load_json_file("composition.json") |
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POSE = load_json_file("pose.json") |
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BACKGROUND = load_json_file("background.json") |
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class PromptGenerator: |
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def __init__(self, seed=None): |
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self.rng = random.Random(seed) |
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def split_and_choose(self, input_str): |
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choices = [choice.strip() for choice in input_str.split(",")] |
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return self.rng.choices(choices, k=1)[0] |
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def get_choice(self, input_str, default_choices): |
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if input_str.lower() == "disabled": |
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return "" |
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elif "," in input_str: |
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return self.split_and_choose(input_str) |
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elif input_str.lower() == "random": |
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return self.rng.choices(default_choices, k=1)[0] |
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else: |
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return input_str |
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def clean_consecutive_commas(self, input_string): |
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cleaned_string = re.sub(r',\s*,', ',', input_string) |
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return cleaned_string |
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def process_string(self, replaced, seed): |
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replaced = re.sub(r'\s*,\s*', ',', replaced) |
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replaced = re.sub(r',+', ',', replaced) |
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original = replaced |
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first_break_clipl_index = replaced.find("BREAK_CLIPL") |
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second_break_clipl_index = replaced.find("BREAK_CLIPL", first_break_clipl_index + len("BREAK_CLIPL")) |
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if first_break_clipl_index != -1 and second_break_clipl_index != -1: |
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clip_content_l = replaced[first_break_clipl_index + len("BREAK_CLIPL"):second_break_clipl_index] |
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replaced = replaced[:first_break_clipl_index].strip(", ") + replaced[second_break_clipl_index + len("BREAK_CLIPL"):].strip(", ") |
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clip_l = clip_content_l |
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else: |
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clip_l = "" |
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first_break_clipg_index = replaced.find("BREAK_CLIPG") |
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second_break_clipg_index = replaced.find("BREAK_CLIPG", first_break_clipg_index + len("BREAK_CLIPG")) |
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if first_break_clipg_index != -1 and second_break_clipg_index != -1: |
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clip_content_g = replaced[first_break_clipg_index + len("BREAK_CLIPG"):second_break_clipg_index] |
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replaced = replaced[:first_break_clipg_index].strip(", ") + replaced[second_break_clipg_index + len("BREAK_CLIPG"):].strip(", ") |
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clip_g = clip_content_g |
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else: |
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clip_g = "" |
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t5xxl = replaced |
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original = original.replace("BREAK_CLIPL", "").replace("BREAK_CLIPG", "") |
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original = re.sub(r'\s*,\s*', ',', original) |
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original = re.sub(r',+', ',', original) |
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clip_l = re.sub(r'\s*,\s*', ',', clip_l) |
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clip_l = re.sub(r',+', ',', clip_l) |
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clip_g = re.sub(r'\s*,\s*', ',', clip_g) |
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clip_g = re.sub(r',+', ',', clip_g) |
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if clip_l.startswith(","): |
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clip_l = clip_l[1:] |
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if clip_g.startswith(","): |
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clip_g = clip_g[1:] |
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if original.startswith(","): |
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original = original[1:] |
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if t5xxl.startswith(","): |
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t5xxl = t5xxl[1:] |
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return original, seed, t5xxl, clip_l, clip_g |
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def generate_prompt(self, seed, custom, subject, artform, photo_type, body_types, default_tags, roles, hairstyles, |
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additional_details, photography_styles, device, photographer, artist, digital_artform, |
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place, lighting, clothing, composition, pose, background, input_image): |
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kwargs = locals() |
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del kwargs['self'] |
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seed = kwargs.get("seed", 0) |
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if seed is not None: |
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self.rng = random.Random(seed) |
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components = [] |
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custom = kwargs.get("custom", "") |
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if custom: |
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components.append(custom) |
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is_photographer = kwargs.get("artform", "").lower() == "photography" or ( |
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kwargs.get("artform", "").lower() == "random" |
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and self.rng.choice([True, False]) |
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) |
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subject = kwargs.get("subject", "") |
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if is_photographer: |
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selected_photo_style = self.get_choice(kwargs.get("photography_styles", ""), PHOTOGRAPHY_STYLES) |
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if not selected_photo_style: |
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selected_photo_style = "photography" |
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components.append(selected_photo_style) |
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if kwargs.get("photography_style", "") != "disabled" and kwargs.get("default_tags", "") != "disabled" or subject != "": |
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components.append(" of") |
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default_tags = kwargs.get("default_tags", "random") |
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body_type = kwargs.get("body_types", "") |
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if not subject: |
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if default_tags == "random": |
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if body_type != "disabled" and body_type != "random": |
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selected_subject = self.get_choice(kwargs.get("default_tags", ""), DEFAULT_TAGS).replace("a ", "").replace("an ", "") |
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components.append("a ") |
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components.append(body_type) |
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components.append(selected_subject) |
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elif body_type == "disabled": |
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selected_subject = self.get_choice(kwargs.get("default_tags", ""), DEFAULT_TAGS) |
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components.append(selected_subject) |
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else: |
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body_type = self.get_choice(body_type, BODY_TYPES) |
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components.append("a ") |
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components.append(body_type) |
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selected_subject = self.get_choice(kwargs.get("default_tags", ""), DEFAULT_TAGS).replace("a ", "").replace("an ", "") |
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components.append(selected_subject) |
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elif default_tags == "disabled": |
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pass |
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else: |
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components.append(default_tags) |
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else: |
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if body_type != "disabled" and body_type != "random": |
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components.append("a ") |
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components.append(body_type) |
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elif body_type == "disabled": |
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pass |
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else: |
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body_type = self.get_choice(body_type, BODY_TYPES) |
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components.append("a ") |
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components.append(body_type) |
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components.append(subject) |
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params = [ |
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("roles", ROLES), |
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("hairstyles", HAIRSTYLES), |
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("additional_details", ADDITIONAL_DETAILS), |
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] |
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for param in params: |
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components.append(self.get_choice(kwargs.get(param[0], ""), param[1])) |
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for i in reversed(range(len(components))): |
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if components[i] in PLACE: |
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components[i] += "," |
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break |
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if kwargs.get("clothing", "") != "disabled" and kwargs.get("clothing", "") != "random": |
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components.append(", dressed in ") |
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clothing = kwargs.get("clothing", "") |
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components.append(clothing) |
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elif kwargs.get("clothing", "") == "random": |
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components.append(", dressed in ") |
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clothing = self.get_choice(kwargs.get("clothing", ""), CLOTHING) |
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components.append(clothing) |
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if kwargs.get("composition", "") != "disabled" and kwargs.get("composition", "") != "random": |
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components.append(",") |
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composition = kwargs.get("composition", "") |
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components.append(composition) |
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elif kwargs.get("composition", "") == "random": |
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components.append(",") |
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composition = self.get_choice(kwargs.get("composition", ""), COMPOSITION) |
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components.append(composition) |
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if kwargs.get("pose", "") != "disabled" and kwargs.get("pose", "") != "random": |
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components.append(",") |
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pose = kwargs.get("pose", "") |
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components.append(pose) |
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elif kwargs.get("pose", "") == "random": |
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components.append(",") |
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pose = self.get_choice(kwargs.get("pose", ""), POSE) |
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components.append(pose) |
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components.append("BREAK_CLIPG") |
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if kwargs.get("background", "") != "disabled" and kwargs.get("background", "") != "random": |
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components.append(",") |
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background = kwargs.get("background", "") |
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components.append(background) |
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elif kwargs.get("background", "") == "random": |
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components.append(",") |
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background = self.get_choice(kwargs.get("background", ""), BACKGROUND) |
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components.append(background) |
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if kwargs.get("place", "") != "disabled" and kwargs.get("place", "") != "random": |
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components.append(",") |
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place = kwargs.get("place", "") |
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components.append(place) |
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elif kwargs.get("place", "") == "random": |
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components.append(",") |
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place = self.get_choice(kwargs.get("place", ""), PLACE) |
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components.append(place + ",") |
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lighting = kwargs.get("lighting", "").lower() |
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if lighting == "random": |
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selected_lighting = ", ".join(self.rng.sample(LIGHTING, self.rng.randint(2, 5))) |
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components.append(",") |
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components.append(selected_lighting) |
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elif lighting == "disabled": |
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pass |
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else: |
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components.append(", ") |
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components.append(lighting) |
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components.append("BREAK_CLIPG") |
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components.append("BREAK_CLIPL") |
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if is_photographer: |
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if kwargs.get("photo_type", "") != "disabled": |
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photo_type_choice = self.get_choice(kwargs.get("photo_type", ""), PHOTO_TYPE) |
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if photo_type_choice and photo_type_choice != "random" and photo_type_choice != "disabled": |
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random_value = round(self.rng.uniform(1.1, 1.5), 1) |
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components.append(f", ({photo_type_choice}:{random_value}), ") |
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params = [ |
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("device", DEVICE), |
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("photographer", PHOTOGRAPHER), |
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] |
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components.extend([self.get_choice(kwargs.get(param[0], ""), param[1]) for param in params]) |
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if kwargs.get("device", "") != "disabled": |
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components[-2] = f", shot on {components[-2]}" |
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if kwargs.get("photographer", "") != "disabled": |
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components[-1] = f", photo by {components[-1]}" |
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else: |
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digital_artform_choice = self.get_choice(kwargs.get("digital_artform", ""), DIGITAL_ARTFORM) |
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if digital_artform_choice: |
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components.append(f"{digital_artform_choice}") |
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if kwargs.get("artist", "") != "disabled": |
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components.append(f"by {self.get_choice(kwargs.get('artist', ''), ARTIST)}") |
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components.append("BREAK_CLIPL") |
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prompt = " ".join(components) |
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prompt = re.sub(" +", " ", prompt) |
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replaced = prompt.replace("of as", "of") |
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replaced = self.clean_consecutive_commas(replaced) |
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return self.process_string(replaced, seed) |
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def add_caption_to_prompt(self, prompt, caption): |
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if caption: |
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return f"{prompt}, {caption}" |
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return prompt |
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class HuggingFaceInferenceNode: |
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def __init__(self): |
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self.clients = { |
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"Mixtral": InferenceClient("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO"), |
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"Mistral": InferenceClient("mistralai/Mistral-7B-Instruct-v0.3"), |
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"Llama 3": InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct"), |
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"Mistral-Nemo": InferenceClient("mistralai/Mistral-Nemo-Instruct-2407") |
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} |
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self.prompts_dir = "./prompts" |
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os.makedirs(self.prompts_dir, exist_ok=True) |
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def save_prompt(self, prompt): |
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filename_text = "hf_" + prompt.split(',')[0].strip() |
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filename_text = re.sub(r'[^\w\-_\. ]', '_', filename_text) |
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filename_text = filename_text[:30] |
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") |
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base_filename = f"{filename_text}_{timestamp}.txt" |
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filename = os.path.join(self.prompts_dir, base_filename) |
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with open(filename, "w") as file: |
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file.write(prompt) |
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print(f"Prompt saved to {filename}") |
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def generate(self, model, input_text, happy_talk, compress, compression_level, poster, custom_base_prompt=""): |
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try: |
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client = self.clients[model] |
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default_happy_prompt = """Create a detailed visually descriptive caption of this description, which will be used as a prompt for a text to image AI system (caption only, no instructions like "create an image").Remove any mention of digital artwork or artwork style. Give detailed visual descriptions of the character(s), including ethnicity, skin tone, expression etc. Imagine using keywords for a still for someone who has aphantasia. Describe the image style, e.g. any photographic or art styles / techniques utilized. Make sure to fully describe all aspects of the cinematography, with abundant technical details and visual descriptions. If there is more than one image, combine the elements and characters from all of the images creatively into a single cohesive composition with a single background, inventing an interaction between the characters. Be creative in combining the characters into a single cohesive scene. Focus on two primary characters (or one) and describe an interesting interaction between them, such as a hug, a kiss, a fight, giving an object, an emotional reaction / interaction. If there is more than one background in the images, pick the most appropriate one. Your output is only the caption itself, no comments or extra formatting. The caption is in a single long paragraph. If you feel the images are inappropriate, invent a new scene / characters inspired by these. Additionally, incorporate a specific movie director's visual style (e.g. Wes Anderson, Christopher Nolan, Quentin Tarantino) and describe the lighting setup in detail, including the type, color, and placement of light sources to create the desired mood and atmosphere. Always frame the scene as a screen grab from a 35mm film still, including details about the film grain, color grading, and any artifacts or characteristics specific to 35mm film photography.""" |
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default_simple_prompt = """Create a brief, straightforward caption for this description, suitable for a text-to-image AI system. Focus on the main elements, key characters, and overall scene without elaborate details. Provide a clear and concise description in one or two sentences.""" |
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poster_prompt = """Analyze the provided description and extract key information to create a movie poster style description. Format the output as follows: |
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Title: A catchy, intriguing title that captures the essence of the scene, place the title in "". |
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Main character: Give a description of the main character. |
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Background: Describe the background in detail. |
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Supporting characters: Describe the supporting characters |
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Branding type: Describe the branding type |
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Tagline: Include a tagline that captures the essence of the movie. |
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Visual style: Ensure that the visual style fits the branding type and tagline. |
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You are allowed to make up film and branding names, and do them like 80's, 90's or modern movie posters.""" |
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if poster: |
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base_prompt = poster_prompt |
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elif custom_base_prompt.strip(): |
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base_prompt = custom_base_prompt |
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else: |
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base_prompt = default_happy_prompt if happy_talk else default_simple_prompt |
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if compress and not poster: |
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compression_chars = { |
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"soft": 600 if happy_talk else 300, |
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"medium": 400 if happy_talk else 200, |
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"hard": 200 if happy_talk else 100 |
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} |
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char_limit = compression_chars[compression_level] |
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base_prompt += f" Compress the output to be concise while retaining key visual details. MAX OUTPUT SIZE no more than {char_limit} characters." |
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messages = f"<|im_start|>system\nYou are a helpful assistant. Try your best to give best response possible to user.<|im_end|>" |
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messages += f"\n<|im_start|>user\n{base_prompt}\nDescription: {input_text}<|im_end|>\n<|im_start|>assistant\n" |
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stream = client.text_generation(messages, max_new_tokens=4000, do_sample=True, stream=True, details=True, return_full_text=False) |
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output = "" |
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for response in stream: |
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if not response.token.text == "<|im_end|>": |
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output += response.token.text |
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self.save_prompt(output) |
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return output |
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except Exception as e: |
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print(f"An error occurred: {e}") |
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return f"Error occurred while processing the request: {str(e)}" |
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|
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def create_interface(): |
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prompt_generator = PromptGenerator() |
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huggingface_node = HuggingFaceInferenceNode() |
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|
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with gr.Blocks(theme='bethecloud/storj_theme') as demo: |
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gr.Markdown("# AI Prompt Generator and Text Generator") |
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|
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with gr.Row(): |
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with gr.Column(scale=2): |
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with gr.Accordion("Basic Settings"): |
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seed = gr.Number(label="Seed", value=0) |
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custom = gr.Textbox(label="Custom") |
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subject = gr.Textbox(label="Subject") |
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artform = gr.Dropdown(["disabled"] + ARTFORM, label="Artform", value="disabled") |
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photo_type = gr.Dropdown(["disabled"] + PHOTO_TYPE, label="Photo Type", value="disabled") |
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|
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with gr.Accordion("Character Details", value=False): |
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body_types = gr.Dropdown(["disabled"] + BODY_TYPES, label="Body Types", value="disabled") |
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default_tags = gr.Dropdown(["disabled"] + DEFAULT_TAGS, label="Default Tags", value="disabled") |
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roles = gr.Dropdown(["disabled"] + ROLES, label="Roles", value="disabled") |
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hairstyles = gr.Dropdown(["disabled"] + HAIRSTYLES, label="Hairstyles", value="disabled") |
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clothing = gr.Dropdown(["disabled"] + CLOTHING, label="Clothing", value="disabled") |
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|
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with gr.Accordion("Scene Details", value=False): |
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place = gr.Dropdown(["disabled"] + PLACE, label="Place", value="disabled") |
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lighting = gr.Dropdown(["disabled"] + LIGHTING, label="Lighting", value="disabled") |
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composition = gr.Dropdown(["disabled"] + COMPOSITION, label="Composition", value="disabled") |
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pose = gr.Dropdown(["disabled"] + POSE, label="Pose", value="disabled") |
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background = gr.Dropdown(["disabled"] + BACKGROUND, label="Background", value="disabled") |
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|
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with gr.Accordion("Style and Artist", value=False): |
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additional_details = gr.Dropdown(["disabled"] + ADDITIONAL_DETAILS, label="Additional Details", value="disabled") |
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photography_styles = gr.Dropdown(["disabled"] + PHOTOGRAPHY_STYLES, label="Photography Styles", value="disabled") |
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device = gr.Dropdown(["disabled"] + DEVICE, label="Device", value="disabled") |
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photographer = gr.Dropdown(["disabled"] + PHOTOGRAPHER, label="Photographer", value="disabled") |
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artist = gr.Dropdown(["disabled"] + ARTIST, label="Artist", value="disabled") |
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digital_artform = gr.Dropdown(["disabled"] + DIGITAL_ARTFORM, label="Digital Artform", value="disabled") |
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|
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with gr.Column(scale=2): |
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with gr.Accordion("Image and Caption", open=True): |
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input_image = gr.Image(label="Input Image (optional)") |
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caption_output = gr.Textbox(label="Generated Caption", lines=3) |
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create_caption_button = gr.Button("Create Caption") |
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|
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with gr.Accordion("Prompt Generation", open=True): |
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generate_button = gr.Button("Generate Prompt") |
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output = gr.Textbox(label="Generated Prompt / Input Text", lines=5) |
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add_caption_button = gr.Button("Add Caption to Prompt") |
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t5xxl_output = gr.Textbox(label="T5XXL Output", visible=True) |
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clip_l_output = gr.Textbox(label="CLIP L Output", visible=True) |
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clip_g_output = gr.Textbox(label="CLIP G Output", visible=True) |
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|
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with gr.Column(scale=2): |
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with gr.Accordion("Text Generation", open=True): |
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model = gr.Dropdown(["Mixtral", "Mistral", "Llama 3", "Mistral-Nemo"], label="Model", value="Mixtral") |
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happy_talk = gr.Checkbox(label="Happy Talk", value=True) |
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compress = gr.Checkbox(label="Compress", value=False) |
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compression_level = gr.Radio(["soft", "medium", "hard"], label="Compression Level", value="medium") |
|
poster = gr.Checkbox(label="Poster", value=False) |
|
custom_base_prompt = gr.Textbox(label="Custom Base Prompt", lines=5) |
|
generate_text_button = gr.Button("Generate Text") |
|
text_output = gr.Textbox(label="Generated Text", lines=10) |
|
|
|
def create_caption(image): |
|
if image is not None: |
|
return florence_caption(image) |
|
return "" |
|
|
|
create_caption_button.click( |
|
create_caption, |
|
inputs=[input_image], |
|
outputs=[caption_output] |
|
) |
|
|
|
generate_button.click( |
|
prompt_generator.generate_prompt, |
|
inputs=[seed, custom, subject, artform, photo_type, body_types, default_tags, roles, hairstyles, |
|
additional_details, photography_styles, device, photographer, artist, digital_artform, |
|
place, lighting, clothing, composition, pose, background], |
|
outputs=[output, gr.Number(visible=False), t5xxl_output, clip_l_output, clip_g_output] |
|
) |
|
|
|
add_caption_button.click( |
|
prompt_generator.add_caption_to_prompt, |
|
inputs=[output, caption_output], |
|
outputs=[output] |
|
) |
|
|
|
generate_text_button.click( |
|
huggingface_node.generate, |
|
inputs=[model, output, happy_talk, compress, compression_level, poster, custom_base_prompt], |
|
outputs=text_output |
|
) |
|
|
|
return demo |
|
|
|
if __name__ == "__main__": |
|
demo = create_interface() |
|
demo.launch() |
