Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| from PIL import Image | |
| from pathlib import Path | |
| from transformers import CLIPTokenizer | |
| import torch | |
| import subprocess | |
| import os | |
| import random | |
| import model_loader, pipeline | |
| # Configure devices | |
| DEVICE = "cpu" | |
| ALLOW_CUDA = False | |
| ALLOW_MPS = True | |
| if torch.cuda.is_available() and ALLOW_CUDA: | |
| DEVICE = "cuda" | |
| elif torch.backends.mps.is_available() and ALLOW_MPS: | |
| DEVICE = "mps" | |
| print(f"Using device: {DEVICE}") | |
| # Load Stable Diffusion model | |
| tokenizer_vocab_path = Path("C:\\Users\\Esmail\\Desktop\\nanograd\\nanograd\\models\\stable_diffusion\\sd_data\\tokenizer_vocab.json") | |
| tokenizer_merges_path = Path("C:\\Users\\Esmail\\Desktop\\nanograd\\nanograd\\models\\stable_diffusion\\sd_data\\tokenizer_merges.txt") | |
| model_file = Path("C:\\Users\\Esmail\\Desktop\\nanograd\\nanograd\\models\\stable_diffusion\\sd_data\\v1-5-pruned-emaonly.ckpt") | |
| tokenizer = CLIPTokenizer(str(tokenizer_vocab_path), merges_file=str(tokenizer_merges_path)) | |
| models = model_loader.preload_models_from_standard_weights(str(model_file), DEVICE) | |
| # Blueprints for image generation and text generation | |
| blueprints = { | |
| "Visual Story": { | |
| "sd_prompts": [ | |
| "A futuristic city skyline at dusk, flying cars, neon lights, cyberpunk style", | |
| "A bustling marketplace in a futuristic city, holograms, diverse crowd", | |
| "A serene park in a futuristic city with advanced technology blending with nature" | |
| ], | |
| "sd_cfg_scales": [9, 8, 7], | |
| "sd_num_inference_steps": [60, 50, 45], | |
| "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"], | |
| "ollama_prompts": [ | |
| "Describe a futuristic city that blends natural elements with advanced technology.", | |
| "Write about an advanced cityscape with unique technological elements.", | |
| "Imagine a futuristic metropolis where nature and technology harmoniously coexist." | |
| ], | |
| "ollama_models": ["llama3", "aya", "codellama"] | |
| }, | |
| # Other blueprints with similar structure... | |
| "Nature & Poetry": { | |
| "sd_prompts": [ | |
| "A peaceful mountain landscape at sunrise, photorealistic, serene", | |
| "A tranquil lake surrounded by autumn trees, soft light, misty atmosphere", | |
| "A hidden waterfall in a dense jungle, lush greenery, crystal clear water" | |
| ], | |
| "sd_cfg_scales": [9, 8, 7], | |
| "sd_num_inference_steps": [60, 50, 45], | |
| "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"], | |
| "ollama_prompts": [ | |
| "Write a short poem about a tranquil sunrise over the mountains.", | |
| "Describe the beauty of a hidden waterfall in a jungle.", | |
| "Compose a poetic reflection on the serenity of a lake at dawn." | |
| ], | |
| "ollama_models": ["llama3", "aya", "codellama"] | |
| }, | |
| # Additional blueprints with multiple prompts... | |
| "Dreamscape": { | |
| "sd_prompts": [ | |
| "A surreal dreamscape with floating islands and bioluminescent creatures", | |
| "An endless horizon of strange landscapes, blending day and night", | |
| "A fantastical world with floating rocks and neon-lit skies" | |
| ], | |
| "sd_cfg_scales": [9, 8, 7], | |
| "sd_num_inference_steps": [60, 50, 45], | |
| "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"], | |
| "ollama_prompts": [ | |
| "Describe a dreamlike world filled with wonder and mystery.", | |
| "Write about a place where time doesn't exist, only dreams.", | |
| "Create a story where reality and fantasy blur together." | |
| ], | |
| "ollama_models": ["llama3", "aya", "codellama"] | |
| }, | |
| "Abstract Art": { | |
| "sd_prompts": [ | |
| "Abstract painting with vibrant colors and dynamic shapes", | |
| "A digital artwork with chaotic patterns and bold contrasts", | |
| "Geometric abstraction with a focus on form and color" | |
| ], | |
| "sd_cfg_scales": [9, 8, 7], | |
| "sd_num_inference_steps": [60, 50, 45], | |
| "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"], | |
| "ollama_prompts": [ | |
| "Write a short description of an abstract painting.", | |
| "Describe a piece of modern art that defies traditional norms.", | |
| "Imagine a world where art is created by emotions, not hands." | |
| ], | |
| "ollama_models": ["llama3", "aya", "codellama"] | |
| }, | |
| "Fashion Design": { | |
| "sd_prompts": [ | |
| "A high-fashion model wearing a futuristic outfit, neon colors, catwalk pose", | |
| "A chic ensemble blending classic elegance with modern flair", | |
| "Avant-garde fashion with bold textures and unconventional shapes" | |
| ], | |
| "sd_cfg_scales": [9, 8, 7], | |
| "sd_num_inference_steps": [60, 50, 45], | |
| "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"], | |
| "ollama_prompts": [ | |
| "Describe a unique and innovative fashion design.", | |
| "Write about a new fashion trend inspired by nature.", | |
| "Imagine a clothing line that combines style with sustainability." | |
| ], | |
| "ollama_models": ["llama3", "aya", "codellama"] | |
| }, | |
| "Food & Recipe": { | |
| "sd_prompts": [ | |
| "Abstract painting with vibrant colors and dynamic shapes", | |
| "A digital artwork with chaotic patterns and bold contrasts", | |
| "Geometric abstraction with a focus on form and color" | |
| ], | |
| "sd_cfg_scales": [9, 8, 7], | |
| "sd_num_inference_steps": [60, 50, 45], | |
| "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"], | |
| "ollama_prompts": [ | |
| "Write a short description of an abstract painting.", | |
| "Describe a piece of modern art that defies traditional norms.", | |
| "Imagine a world where art is created by emotions, not hands." | |
| ], | |
| "ollama_models": ["llama3", "aya", "codellama"] | |
| }, | |
| "Interior Design": { | |
| "sd_prompts": [ | |
| "A modern living room with sleek furniture, minimalist design, and natural light", | |
| "A cozy study room with rich textures, warm colors, and elegant decor", | |
| "An open-plan kitchen with contemporary appliances and stylish finishes" | |
| ], | |
| "sd_cfg_scales": [9, 8, 7], | |
| "sd_num_inference_steps": [60, 50, 45], | |
| "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"], | |
| "ollama_prompts": [ | |
| "Describe an interior design that combines modern and classic elements.", | |
| "Write about a space that enhances productivity and relaxation through design.", | |
| "Imagine a luxurious interior design for a high-end apartment." | |
| ], | |
| "ollama_models": ["llama3", "aya", "codellama"] | |
| }, | |
| "Historical Fiction": { | |
| "sd_prompts": [ | |
| "A bustling Victorian-era street with horse-drawn carriages and period architecture", | |
| "A grand historical ballroom with opulent decor and elegantly dressed guests", | |
| "An ancient battlefield with detailed historical accuracy and dramatic scenery" | |
| ], | |
| "sd_cfg_scales": [9, 8, 7], | |
| "sd_num_inference_steps": [60, 50, 45], | |
| "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"], | |
| "ollama_prompts": [ | |
| "Describe a significant historical event as if it were a scene in a novel.", | |
| "Write about a character navigating the challenges of a historical setting.", | |
| "Imagine a historical figure interacting with modern technology." | |
| ], | |
| "ollama_models": ["llama3", "aya", "codellama"] | |
| }, | |
| "Science Fiction": { | |
| "sd_prompts": [ | |
| "A futuristic cityscape with flying cars, neon lights, and towering skyscrapers", | |
| "An alien planet with unique landscapes, strange flora, and advanced technology", | |
| "A space station with cutting-edge design and high-tech equipment" | |
| ], | |
| "sd_cfg_scales": [9, 8, 7], | |
| "sd_num_inference_steps": [60, 50, 45], | |
| "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"], | |
| "ollama_prompts": [ | |
| "Describe a futuristic world where technology has reshaped society.", | |
| "Write about an encounter with an alien civilization.", | |
| "Imagine a story set in a distant future with advanced technology and space exploration." | |
| ], | |
| "ollama_models": ["llama3", "aya", "codellama"] | |
| }, | |
| "Character Design": { | |
| "sd_prompts": [ | |
| "A detailed fantasy character with elaborate costumes and accessories", | |
| "A sci-fi hero with futuristic armor and high-tech gadgets", | |
| "A historical figure portrayed with accurate attire and realistic features" | |
| ], | |
| "sd_cfg_scales": [9, 8, 7], | |
| "sd_num_inference_steps": [60, 50, 45], | |
| "sd_samplers": ["ddpm", "k_euler_ancestral", "euler"], | |
| "ollama_prompts": [ | |
| "Describe a unique character from a fantasy novel, focusing on their appearance and personality.", | |
| "Write about a futuristic character with advanced technology and a compelling backstory.", | |
| "Imagine a historical figure as a character in a modern setting." | |
| ], | |
| "ollama_models": ["llama3", "aya", "codellama"] | |
| } | |
| } | |
| # Define functions for each feature | |
| def generate_image(prompt, cfg_scale, num_inference_steps, sampler): | |
| uncond_prompt = "" | |
| do_cfg = True | |
| input_image = None | |
| strength = 0.9 | |
| seed = 42 | |
| output_image = pipeline.generate( | |
| prompt=prompt, | |
| uncond_prompt=uncond_prompt, | |
| input_image=input_image, | |
| strength=strength, | |
| do_cfg=do_cfg, | |
| cfg_scale=cfg_scale, | |
| sampler_name=sampler, | |
| n_inference_steps=num_inference_steps, | |
| seed=seed, | |
| models=models, | |
| device=DEVICE, | |
| idle_device="cpu", | |
| tokenizer=tokenizer, | |
| ) | |
| output_image = Image.fromarray(output_image) | |
| return output_image | |
| def apply_blueprint(blueprint_name): | |
| if blueprint_name in blueprints: | |
| bp = blueprints[blueprint_name] | |
| sd_prompts = random.choice(bp["sd_prompts"]) | |
| sd_cfg_scale = random.choice(bp["sd_cfg_scales"]) | |
| sd_num_inference_steps = random.choice(bp["sd_num_inference_steps"]) | |
| sd_sampler = random.choice(bp["sd_samplers"]) | |
| ollama_prompts = random.choice(bp["ollama_prompts"]) | |
| ollama_model = random.choice(bp["ollama_models"]) | |
| return ( | |
| sd_prompts, sd_cfg_scale, sd_num_inference_steps, sd_sampler, | |
| ollama_model, ollama_prompts | |
| ) | |
| return "", 7, 20, "ddpm", "aya", "" | |
| def download_checkpoint(checkpoint): | |
| try: | |
| # Run the litgpt download command | |
| command = ["litgpt", "download", checkpoint] | |
| process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) | |
| output, error = process.communicate() | |
| if process.returncode == 0: | |
| return f"Checkpoint '{checkpoint}' downloaded successfully.\n{output}" | |
| else: | |
| return f"Error downloading checkpoint '{checkpoint}':\n{error}" | |
| except Exception as e: | |
| return f"Unexpected error: {str(e)}" | |
| def chat_with_ollama(model_name, prompt): | |
| command = ['ollama', 'run', model_name, prompt] | |
| result = subprocess.run(command, capture_output=True, text=True) | |
| return result.stdout | |
| def install_ollama(): | |
| try: | |
| # Command to install Ollama silently | |
| installer_path = "OllamaSetup.exe" | |
| if not os.path.exists(installer_path): | |
| # Download the installer if not already available | |
| subprocess.run(["curl", "-o", installer_path, "https://ollama.com/download/OllamaSetup.exe"], check=True) | |
| # Run the installer silently | |
| subprocess.run([installer_path, "/S"], check=True) | |
| return "Ollama installed successfully." | |
| except Exception as e: | |
| return f"Installation failed: {str(e)}" | |
| def welcome(name): | |
| return f"Welcome to nanograd Engine, {name}!" | |
| js = """ | |
| function createGradioAnimation() { | |
| var container = document.createElement('div'); | |
| container.id = 'gradio-animation'; | |
| container.style.fontSize = '2em'; | |
| container.style.fontWeight = 'bold'; | |
| container.style.textAlign = 'center'; | |
| container.style.marginBottom = '20px'; | |
| var text = 'Welcome to nanograd Engine!'; | |
| for (var i = 0; i < text.length; i++) { | |
| (function(i){ | |
| setTimeout(function(){ | |
| var letter = document.createElement('span'); | |
| letter.style.opacity = '0'; | |
| letter.style.transition = 'opacity 0.5s'; | |
| letter.innerText = text[i]; | |
| container.appendChild(letter); | |
| setTimeout(function() { | |
| letter.style.opacity = '1'; | |
| }, 50); | |
| }, i * 250); | |
| })(i); | |
| } | |
| var gradioContainer = document.querySelector('.gradio-container'); | |
| gradioContainer.insertBefore(container, gradioContainer.firstChild); | |
| return 'Animation created'; | |
| } | |
| """ | |
| # Gradio interface | |
| def gradio_interface(): | |
| with gr.Blocks('ParityError/Interstellar', js=js) as demo: | |
| with gr.Tab("nano-Engine"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| # Text Generation with Ollama | |
| gr.Markdown("### Generate Text with Ollama") | |
| ollama_model_name = gr.Dropdown(label="Select Ollama Model", choices=["aya", "llama3", "codellama"], value="aya") | |
| ollama_prompts = gr.Textbox(label="Prompt", placeholder="Enter your prompt here") | |
| ollama_output = gr.Textbox(label="Output", placeholder="Model output will appear here", interactive=True) | |
| ollama_btn = gr.Button("Generate", variant="primary") | |
| ollama_btn.click(fn=chat_with_ollama, inputs=[ollama_model_name, ollama_prompts], outputs=ollama_output) | |
| gr.Markdown("### GPT Checkpoints Management") | |
| checkpoint_dropdown = gr.Dropdown(label="Select Checkpoint", choices=["EleutherAI/gpt-neo-125M", "EleutherAI/gpt-neo-1.3B", "microsoft/phi-2", "codellama/CodeLlama-13b-hf"], value="EleutherAI/gpt-neo-125M") | |
| download_btn = gr.Button("Download Checkpoint", variant="primary") | |
| checkpoint_status = gr.Textbox(label="Download Status", placeholder="Status will appear here", interactive=True) | |
| download_btn.click(fn=download_checkpoint, inputs=checkpoint_dropdown, outputs=checkpoint_status) | |
| gr.Markdown("### Install Ollama") | |
| install_ollama_btn = gr.Button("Install Ollama", variant="primary") | |
| installation_status = gr.Textbox(label="Installation Status", placeholder="Status will appear here", interactive=True) | |
| install_ollama_btn.click(fn=install_ollama, outputs=installation_status) | |
| with gr.Column(scale=1): | |
| gr.Markdown("### Stable Diffusion Image Generation") | |
| prompt_input = gr.Textbox(label="Prompt", placeholder="A cat stretching on the floor, highly detailed, ultra sharp, cinematic, 100mm lens, 8k resolution") | |
| cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1) | |
| num_inference_steps = gr.Slider(label="Sampling Steps", minimum=10, maximum=100, value=20, step=5) | |
| sampler = gr.Radio(label="Sampling Method", choices=["ddpm", "Euler a", "Euler", "LMS", "Heun", "DPM2 a", "PLMS"], value="ddpm") | |
| generate_img_btn = gr.Button("Generate", variant="primary") | |
| output_image = gr.Image(label="Output", show_label=False, height=700, width=750) | |
| generate_img_btn.click(fn=generate_image, inputs=[prompt_input, cfg_scale, num_inference_steps, sampler], outputs=output_image) | |
| with gr.Tab("Blueprints"): | |
| with gr.Row(): | |
| blueprint_dropdown = gr.Dropdown(label="Select Blueprint", choices=list(blueprints.keys()), value=list(blueprints.keys())[0]) | |
| load_blueprint_btn = gr.Button("Load Blueprint", variant="primary") | |
| # Blueprint Outputs | |
| sd_prompt_output = gr.Textbox(label="SD Prompt", interactive=True) | |
| sd_cfg_output = gr.Slider(label="SD CFG Scale", minimum=1, maximum=20, step=1, interactive=True) | |
| sd_steps_output = gr.Slider(label="SD Sampling Steps", minimum=10, maximum=100, step=5, interactive=True) | |
| sd_sampler_output = gr.Radio(label="SD Sampler", choices=["ddpm", "Euler a", "Euler", "LMS", "Heun", "DPM2 a", "PLMS"], value="ddpm", interactive=True) | |
| ollama_model_output = gr.Dropdown(label="Ollama Model", choices=["aya", "llama3", "codellama"], value="aya", interactive=True) | |
| ollama_prompt_output = gr.Textbox(label="Ollama Prompt", interactive=True) | |
| def load_blueprint(blueprint_name): | |
| if blueprint_name in blueprints: | |
| bp = blueprints[blueprint_name] | |
| sd_prompts = random.choice(bp["sd_prompts"]) | |
| sd_cfg_scale = random.choice(bp["sd_cfg_scales"]) | |
| sd_num_inference_steps = random.choice(bp["sd_num_inference_steps"]) | |
| sd_sampler = random.choice(bp["sd_samplers"]) | |
| ollama_prompts = random.choice(bp["ollama_prompts"]) | |
| ollama_model = random.choice(bp["ollama_models"]) | |
| return ( | |
| sd_prompts, sd_cfg_scale, sd_num_inference_steps, sd_sampler, | |
| ollama_model, ollama_prompts | |
| ) | |
| return "", 7, 20, "ddpm", "aya", "" | |
| def apply_loaded_blueprint(prompt, cfg_scale, num_inference_steps, sampler, model, ollama_prompts): | |
| return ( | |
| gr.update(value=prompt), | |
| gr.update(value=cfg_scale), | |
| gr.update(value=num_inference_steps), | |
| gr.update(value=sampler), | |
| gr.update(value=model), | |
| gr.update(value=ollama_prompts) | |
| ) | |
| load_blueprint_btn.click(fn=load_blueprint, inputs=blueprint_dropdown, outputs=[sd_prompt_output, sd_cfg_output, sd_steps_output, sd_sampler_output, ollama_model_output, ollama_prompt_output]) | |
| load_blueprint_btn.click(fn=apply_loaded_blueprint, inputs=[sd_prompt_output, sd_cfg_output, sd_steps_output, sd_sampler_output, ollama_model_output, ollama_prompt_output], outputs=[prompt_input, cfg_scale, num_inference_steps, sampler, ollama_model_name, ollama_prompts]) | |
| with gr.Tab("Chatbot-Prompts"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| from nanograd.models.GPT.tokenizer import tokenize | |
| gr.Markdown("<h1><center>BPE Tokenizer</h1></center>") | |
| iface = gr.Interface(fn=tokenize, inputs="text", outputs="json") | |
| with gr.Column(scale=1): | |
| from examples import ollama_prompted | |
| gr.Markdown("<h1><center>Chatbot (لغة عربية)</h1></center>") | |
| i = gr.Interface( | |
| fn=ollama_prompted.run, | |
| inputs=gr.Textbox(lines=1, placeholder="Ask a question about travel or airlines"), | |
| outputs=gr.Textbox(label="Aya's response"), | |
| ) | |
| demo.launch() | |
| # Run the Gradio interface | |
| gradio_interface() |