app.py

import gradio as gr
import torch
from transformers import AutoConfig, AutoModelForCausalLM
from janus.models import MultiModalityCausalLM, VLChatProcessor
from PIL import Image
import numpy as np
import spaces  # Import spaces for ZeroGPU compatibility

# Load the model and processor
model_path = "deepseek-ai/Janus-Pro-7B"
config = AutoConfig.from_pretrained(model_path)
language_config = config.language_config
language_config._attn_implementation = 'eager'

vl_gpt = AutoModelForCausalLM.from_pretrained(
    model_path,
    language_config=language_config,
    trust_remote_code=True
)
vl_gpt = vl_gpt.to(torch.bfloat16).cuda() if torch.cuda.is_available() else vl_gpt.to(torch.float16)

vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer
cuda_device = 'cuda' if torch.cuda.is_available() else 'cpu'

# Helper functions
def generate(input_ids, width, height, cfg_weight=5, temperature=1.0, parallel_size=5, patch_size=16):
    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, 576), dtype=torch.int).to(cuda_device)

    pkv = None
    for i in range(576):
        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)] * 2, 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 patches

def unpack(patches, width, height, parallel_size=5):
    patches = patches.to(torch.float32).cpu().numpy().transpose(0, 2, 3, 1)
    patches = np.clip((patches + 1) / 2 * 255, 0, 255)

    images = [Image.fromarray(patches[i].astype(np.uint8)) for i in range(parallel_size)]
    return images

@torch.inference_mode()
@spaces.GPU(duration=120)
def generate_image(prompt, seed=None, guidance=5, t2i_temperature=1.0):
    torch.cuda.empty_cache()
    
    if seed is not None:
        torch.manual_seed(seed)
        torch.cuda.manual_seed(seed)
        np.random.seed(seed)

    width, height, parallel_size = 384, 384, 5

    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 += vl_chat_processor.image_start_tag

    input_ids = torch.LongTensor(tokenizer.encode(text))
    patches = generate(input_ids, width, height, cfg_weight=guidance, temperature=t2i_temperature, parallel_size=parallel_size)

    return unpack(patches, width, height, parallel_size)

# Gradio interface
def create_interface():
    with gr.Blocks() as demo:
        gr.Markdown("""
        # Text-to-Image Generation with Janus-Pro-7B

        Welcome to the Janus-Pro-7B Text-to-Image Generator! This advanced AI model by DeepSeek offers state-of-the-art capabilities in generating images from textual descriptions. Leveraging a unified multimodal framework, Janus-Pro-7B excels in both understanding and generating content, providing detailed and accurate visual representations based on your prompts.

        **Key Features:**
        - **Unified Multimodal Framework:** Integrates visual encoding and language processing for seamless understanding and generation.
        - **High-Quality Image Generation:** Produces stable and detailed images that often surpass those from other leading models.
        - **Flexible and Efficient:** Designed for a wide range of applications, from creative image generation to detailed visualizations.

        For more information about Janus-Pro-7B, visit the [official Hugging Face model page](https://huggingface.co/deepseek-ai/Janus-Pro-7B).
        """)
        
        prompt_input = gr.Textbox(label="Prompt (describe the image)")
        seed_input = gr.Number(label="Seed (Optional)", value=12345, precision=0)
        guidance_slider = gr.Slider(label="CFG Guidance Weight", minimum=1, maximum=10, value=5, step=0.5)
        temperature_slider = gr.Slider(label="Temperature", minimum=0, maximum=1, value=1.0, step=0.05)

        generate_button = gr.Button("Generate Images")
        output_gallery = gr.Gallery(label="Generated Images", columns=2, height=300)

        generate_button.click(
            generate_image,
            inputs=[prompt_input, seed_input, guidance_slider, temperature_slider],
            outputs=output_gallery
        )

    return demo

demo = create_interface()
demo.launch(share=True)