import streamlit as st from gradio_client import Client import time import concurrent.futures import os from PIL import Image import io import requests from huggingface_hub import HfApi, login # Initialize session state - must be first if 'hf_token' not in st.session_state: st.session_state['hf_token'] = None if 'is_authenticated' not in st.session_state: st.session_state['is_authenticated'] = False class ModelGenerator: @staticmethod def generate_midjourney(prompt, token): try: client = Client("mukaist/Midjourney", hf_token=token) result = client.predict( prompt=prompt, negative_prompt="(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck", use_negative_prompt=True, style="2560 x 1440", seed=0, width=1024, height=1024, guidance_scale=6, randomize_seed=True, api_name="/run" ) if isinstance(result, list) and len(result) > 0: image_data = result[0] if isinstance(image_data, str): if image_data.startswith('http'): response = requests.get(image_data) image = Image.open(io.BytesIO(response.content)) else: image = Image.open(image_data) else: image = Image.open(io.BytesIO(image_data)) return ("Midjourney", image) else: return ("Midjourney", f"Error: Unexpected result format: {type(result)}") except Exception as e: return ("Midjourney", f"Error: {str(e)}") @staticmethod def generate_stable_cascade(prompt, token): try: client = Client("multimodalart/stable-cascade", hf_token=token) result = client.predict( prompt=prompt, negative_prompt=prompt, seed=0, width=1024, height=1024, prior_num_inference_steps=20, prior_guidance_scale=4, decoder_num_inference_steps=10, decoder_guidance_scale=0, num_images_per_prompt=1, api_name="/run" ) return ("Stable Cascade", result) except Exception as e: return ("Stable Cascade", f"Error: {str(e)}") @staticmethod def generate_stable_diffusion_3(prompt, token): try: client = Client("stabilityai/stable-diffusion-3-medium", hf_token=token) result = client.predict( prompt=prompt, negative_prompt=prompt, seed=0, randomize_seed=True, width=1024, height=1024, guidance_scale=5, num_inference_steps=28, api_name="/infer" ) return ("SD 3 Medium", result) except Exception as e: return ("SD 3 Medium", f"Error: {str(e)}") @staticmethod def generate_stable_diffusion_35(prompt, token): try: client = Client("stabilityai/stable-diffusion-3.5-large", hf_token=token) result = client.predict( prompt=prompt, negative_prompt=prompt, seed=0, randomize_seed=True, width=1024, height=1024, guidance_scale=4.5, num_inference_steps=40, api_name="/infer" ) return ("SD 3.5 Large", result) except Exception as e: return ("SD 3.5 Large", f"Error: {str(e)}") @staticmethod def generate_playground_v2_5(prompt, token): try: client = Client("https://playgroundai-playground-v2-5.hf.space/--replicas/ji5gy/", hf_token=token) result = client.predict( prompt, prompt, # negative prompt True, # use negative prompt 0, # seed 1024, # width 1024, # height 7.5, # guidance scale True, # randomize seed api_name="/run" ) if result and isinstance(result, tuple) and result[0]: return ("Playground v2.5", result[0][0]['image']) return ("Playground v2.5", "Error: No image generated") except Exception as e: return ("Playground v2.5", f"Error: {str(e)}") def generate_images(prompt, selected_models): token = st.session_state.get('hf_token') if not token: return [("Error", "No authentication token found")] results = [] with concurrent.futures.ThreadPoolExecutor() as executor: futures = [] model_map = { "Midjourney": lambda p: ModelGenerator.generate_midjourney(p, token), "Stable Cascade": lambda p: ModelGenerator.generate_stable_cascade(p, token), "SD 3 Medium": lambda p: ModelGenerator.generate_stable_diffusion_3(p, token), "SD 3.5 Large": lambda p: ModelGenerator.generate_stable_diffusion_35(p, token), "Playground v2.5": lambda p: ModelGenerator.generate_playground_v2_5(p, token) } for model in selected_models: if model in model_map: futures.append(executor.submit(model_map[model], prompt)) for future in concurrent.futures.as_completed(futures): results.append(future.result()) return results def handle_prompt_click(prompt_text, key): if not st.session_state.get('is_authenticated') or not st.session_state.get('hf_token'): st.error("Please login with your HuggingFace account first!") return st.session_state[f'selected_prompt_{key}'] = prompt_text selected_models = st.session_state.get('selected_models', []) if not selected_models: st.warning("Please select at least one model from the sidebar!") return with st.spinner('Generating artwork...'): results = generate_images(prompt_text, selected_models) st.session_state[f'generated_images_{key}'] = results st.success("Artwork generated successfully!") def main(): st.title("🎨 Multi-Model Art Generator") # Handle authentication in sidebar with st.sidebar: st.header("🔐 Authentication") if st.session_state.get('is_authenticated') and st.session_state.get('hf_token'): st.success("✓ Logged in to HuggingFace") if st.button("Logout"): st.session_state['hf_token'] = None st.session_state['is_authenticated'] = False st.rerun() else: token = st.text_input("Enter HuggingFace Token", type="password", help="Get your token from https://huggingface.co/settings/tokens") if st.button("Login"): if token: try: # Verify token is valid api = HfApi(token=token) api.whoami() st.session_state['hf_token'] = token st.session_state['is_authenticated'] = True st.success("Successfully logged in!") st.rerun() except Exception as e: st.error(f"Authentication failed: {str(e)}") else: st.error("Please enter your HuggingFace token") if st.session_state.get('is_authenticated') and st.session_state.get('hf_token'): st.markdown("---") st.header("Model Selection") st.session_state['selected_models'] = st.multiselect( "Choose AI Models", ["Midjourney", "Stable Cascade", "SD 3 Medium", "SD 3.5 Large", "Playground v2.5"], default=["Midjourney"] ) st.markdown("---") st.markdown("### Selected Models:") for model in st.session_state['selected_models']: st.write(f"✓ {model}") st.markdown("---") st.markdown("### Model Information:") st.markdown(""" - **Midjourney**: Best for artistic and creative imagery - **Stable Cascade**: New architecture with high detail - **SD 3 Medium**: Fast and efficient generation - **SD 3.5 Large**: Highest quality, slower generation - **Playground v2.5**: Advanced model with high customization """) # Only show the main interface if authenticated if st.session_state.get('is_authenticated') and st.session_state.get('hf_token'): st.markdown("### Select a prompt style to generate artwork:") prompt_emojis = { "AIart/AIArtistCommunity": "🤖", "Black & White": "⚫⚪", "Black & Yellow": "⚫💛", "Blindfold": "🙈", "Break": "💔", "Broken": "🔨", "Christmas Celebrations art": "🎄", "Colorful Art": "🎨", "Crimson art": "🔴", "Eyes Art": "👁️", "Going out with Style": "💃", "Hooded Girl": "🧥", "Lips": "👄", "MAEKHLONG": "🏮", "Mermaid": "🧜‍♀️", "Morning Sunshine": "🌅", "Music Art": "🎵", "Owl": "🦉", "Pink": "💗", "Purple": "💜", "Rain": "🌧️", "Red Moon": "🌑", "Rose": "🌹", "Snow": "❄️", "Spacesuit Girl": "👩‍🚀", "Steampunk": "⚙️", "Succubus": "😈", "Sunlight": "☀️", "Weird art": "🎭", "White Hair": "👱‍♀️", "Wings art": "👼", "Woman with Sword": "⚔️" } col1, col2, col3 = st.columns(3) for idx, (prompt, emoji) in enumerate(prompt_emojis.items()): full_prompt = f"QT {prompt}" col = [col1, col2, col3][idx % 3] with col: if st.button(f"{emoji} {prompt}", key=f"btn_{idx}"): handle_prompt_click(full_prompt, idx) st.markdown("---") st.markdown("### Generated Artwork:") for key in st.session_state: if key.startswith('selected_prompt_'): idx = key.split('_')[-1] images_key = f'generated_images_{idx}' if images_key in st.session_state: st.write("Prompt:", st.session_state[key]) cols = st.columns(len(st.session_state[images_key])) for col, (model_name, result) in zip(cols, st.session_state[images_key]): with col: st.markdown(f"**{model_name}**") if isinstance(result, str) and result.startswith("Error"): st.error(result) else: st.image(result, use_container_width=True) else: st.info("Please login with your HuggingFace account to use the app") if __name__ == "__main__": main()