import gradio as gr import torch from transformers import AutoConfig, AutoModelForCausalLM from janus.models import MultiModalityCausalLM, VLChatProcessor from janus.utils.io import load_pil_images from PIL import Image import numpy as np import os import time # import spaces # Import spaces for ZeroGPU compatibility # Global variables to store model and processor (initially for 7B) vl_gpt = None vl_chat_processor = None tokenizer = None cuda_device = 'cuda' if torch.cuda.is_available() else 'cpu' current_model_path = "deepseek-ai/Janus-Pro-7B" # Default model def load_model_components(model_path): global vl_gpt, vl_chat_processor, tokenizer, current_model_path # Declare current_model_path as global here if vl_gpt is not None and current_model_path == model_path: print(f"Using cached model: {model_path}") return vl_gpt, vl_chat_processor, tokenizer print(f"Loading model: {model_path}") config = AutoConfig.from_pretrained(model_path) language_config = config.language_config language_config._attn_implementation = 'eager' vl_gpt_local = AutoModelForCausalLM.from_pretrained(model_path, language_config=language_config, trust_remote_code=True) if torch.cuda.is_available(): vl_gpt_local = vl_gpt_local.to(torch.bfloat16).cuda() else: vl_gpt_local = vl_gpt_local.to(torch.float16) vl_chat_processor_local = VLChatProcessor.from_pretrained(model_path) tokenizer_local = vl_chat_processor_local.tokenizer vl_gpt = vl_gpt_local vl_chat_processor = vl_chat_processor_local tokenizer = tokenizer_local current_model_path = model_path print(f"Model loaded: {model_path}") return vl_gpt, vl_chat_processor, tokenizer @torch.inference_mode() # @spaces.GPU(duration=120) # Multimodal Understanding function def multimodal_understanding(model_name, image, question, seed, top_p, temperature): # Load model based on selection load_model_components(model_name) # Clear CUDA cache before generating torch.cuda.empty_cache() # set seed torch.manual_seed(seed) np.random.seed(seed) torch.cuda.manual_seed(seed) conversation = [ { "role": "<|User|>", "content": f"\n{question}", "images": [image], }, {"role": "<|Assistant|>", "content": ""}, ] pil_images = [Image.fromarray(image)] prepare_inputs = vl_chat_processor( conversations=conversation, images=pil_images, force_batchify=True ).to(cuda_device, dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float16) inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs) outputs = vl_gpt.language_model.generate( inputs_embeds=inputs_embeds, attention_mask=prepare_inputs.attention_mask, pad_token_id=tokenizer.eos_token_id, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, max_new_tokens=512, do_sample=False if temperature == 0 else True, use_cache=True, temperature=temperature, top_p=top_p, ) answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True) return answer def generate(input_ids, width, height, temperature: float = 1, parallel_size: int = 5, cfg_weight: float = 5, image_token_num_per_image: int = 576, patch_size: int = 16): # Clear CUDA cache before generating torch.cuda.empty_cache() tokens = torch.zeros((parallel_size * 2, len(input_ids)), dtype=torch.int).to(cuda_device) for i in range(parallel_size * 2): tokens[i, :] = input_ids if i % 2 != 0: tokens[i, 1:-1] = vl_chat_processor.pad_id inputs_embeds = vl_gpt.language_model.get_input_embeddings()(tokens) generated_tokens = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).to(cuda_device) pkv = None for i in range(image_token_num_per_image): with torch.no_grad(): outputs = vl_gpt.language_model.model(inputs_embeds=inputs_embeds, use_cache=True, past_key_values=pkv) pkv = outputs.past_key_values hidden_states = outputs.last_hidden_state logits = vl_gpt.gen_head(hidden_states[:, -1, :]) logit_cond = logits[0::2, :] logit_uncond = logits[1::2, :] logits = logit_uncond + cfg_weight * (logit_cond - logit_uncond) probs = torch.softmax(logits / temperature, dim=-1) next_token = torch.multinomial(probs, num_samples=1) generated_tokens[:, i] = next_token.squeeze(dim=-1) next_token = torch.cat([next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1) img_embeds = vl_gpt.prepare_gen_img_embeds(next_token) inputs_embeds = img_embeds.unsqueeze(dim=1) patches = vl_gpt.gen_vision_model.decode_code(generated_tokens.to(dtype=torch.int), shape=[parallel_size, 8, width // patch_size, height // patch_size]) return generated_tokens.to(dtype=torch.int), patches def unpack(dec, width, height, parallel_size=5): dec = dec.to(torch.float32).cpu().numpy().transpose(0, 2, 3, 1) dec = np.clip((dec + 1) / 2 * 255, 0, 255) visual_img = np.zeros((parallel_size, width, height, 3), dtype=np.uint8) visual_img[:, :, :] = dec return visual_img @torch.inference_mode() # @spaces.GPU(duration=120) # Specify a duration to avoid timeout def generate_image(model_name, prompt, seed, guidance, t2i_temperature, parallel_size_slider): # Load model based on selection load_model_components(model_name) # Clear CUDA cache and avoid tracking gradients torch.cuda.empty_cache() # Set the seed for reproducible results if seed is not None: torch.manual_seed(seed) torch.cuda.manual_seed(seed) np.random.seed(seed) width = 384 height = 384 parallel_size = int(parallel_size_slider) # Use slider value for parallel_size with torch.no_grad(): messages = [{'role': '<|User|>', 'content': prompt}, {'role': '<|Assistant|>', 'content': ''}] text = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(conversations=messages, sft_format=vl_chat_processor.sft_format, system_prompt='') text = text + vl_chat_processor.image_start_tag input_ids = torch.LongTensor(tokenizer.encode(text)) output, patches = generate(input_ids, width // 16 * 16, height // 16 * 16, cfg_weight=guidance, parallel_size=parallel_size, temperature=t2i_temperature) images = unpack(patches, width // 16 * 16, height // 16 * 16, parallel_size=parallel_size) return [Image.fromarray(images[i]).resize((768, 768), Image.LANCZOS) for i in range(parallel_size)] # Gradio interface with gr.Blocks() as demo: gr.Markdown(value="# Multimodal Model Demo: Janus-Pro-7B & 1B") model_selector = gr.Dropdown( ["deepseek-ai/Janus-Pro-7B", "deepseek-ai/Janus-Pro-1B"], value="deepseek-ai/Janus-Pro-7B", label="Select Model" ) with gr.Tab("Multimodal Understanding"): with gr.Row(): image_input = gr.Image() with gr.Column(): question_input = gr.Textbox(label="Question") und_seed_input = gr.Number(label="Seed", precision=0, value=42) top_p = gr.Slider(minimum=0, maximum=1, value=0.95, step=0.05, label="top_p") temperature = gr.Slider(minimum=0, maximum=1, value=0.1, step=0.05, label="temperature") understanding_button = gr.Button("Chat") understanding_output = gr.Textbox(label="Response") examples_inpainting = gr.Examples( label="Multimodal Understanding examples", examples=[ [ "explain this meme", "images/doge.png", ], [ "Convert the formula into latex code.", "images/equation.png", ], ], inputs=[question_input, image_input], ) with gr.Tab("Text-to-Image Generation"): with gr.Row(): cfg_weight_input = gr.Slider(minimum=1, maximum=10, value=5, step=0.5, label="CFG Weight") t2i_temperature = gr.Slider(minimum=0, maximum=1, value=1.0, step=0.05, label="temperature") parallel_size_slider = gr.Slider(minimum=1, maximum=5, value=5, step=1, label="Parallel Size") # New slider prompt_input = gr.Textbox(label="Prompt. (Prompt in more detail can help produce better images!)") seed_input = gr.Number(label="Seed (Optional)", precision=0, value=12345) generation_button = gr.Button("Generate Images") image_output = gr.Gallery(label="Generated Images", columns=2, rows=2, height=300) examples_t2i = gr.Examples( label="Text to image generation examples.", examples=[ "Master shifu racoon wearing drip attire as a street gangster.", "The face of a beautiful girl", "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", "A glass of red wine on a reflective surface.", "A cute and adorable baby fox with big brown eyes, autumn leaves in the background enchanting,immortal,fluffy, shiny mane,Petals,fairyism,unreal engine 5 and Octane Render,highly detailed, photorealistic, cinematic, natural colors.", "The image features an intricately designed eye set against a circular backdrop adorned with ornate swirl patterns that evoke both realism and surrealism. At the center of attention is a strikingly vivid blue iris surrounded by delicate veins radiating outward from the pupil to create depth and intensity. The eyelashes are long and dark, casting subtle shadows on the skin around them which appears smooth yet slightly textured as if aged or weathered over time.\n\nAbove the eye, there's a stone-like structure resembling part of classical architecture, adding layers of mystery and timeless elegance to the composition. This architectural element contrasts sharply but harmoniously with the organic curves surrounding it. Below the eye lies another decorative motif reminiscent of baroque artistry, further enhancing the overall sense of eternity encapsulated within each meticulously crafted detail. \n\nOverall, the atmosphere exudes a mysterious aura intertwined seamlessly with elements suggesting timelessness, achieved through the juxtaposition of realistic textures and surreal artistic flourishes. Each component\u2014from the intricate designs framing the eye to the ancient-looking stone piece above\u2014contributes uniquely towards creating a visually captivating tableau imbued with enigmatic allure.", ], inputs=prompt_input, ) understanding_button.click( multimodal_understanding, inputs=[model_selector, image_input, question_input, und_seed_input, top_p, temperature], # Added model_selector outputs=understanding_output ) generation_button.click( fn=generate_image, inputs=[model_selector, prompt_input, seed_input, cfg_weight_input, t2i_temperature, parallel_size_slider], # Added model_selector and parallel_size_slider outputs=image_output ) demo.launch(share=True) # demo.queue(concurrency_count=1, max_size=10).launch(server_name="0.0.0.0", server_port=37906, root_path="/path")