interactivechat.py

# Adapted from https://github.com/deepseek-ai/Janus to work with MindSpore.

import os
import re
import time

import numpy as np
import PIL.Image
from janus.models import MultiModalityCausalLM, VLChatProcessor
from transformers import AutoModelForCausalLM

import mindspore as ms
from mindspore import mint

# Specify the path to the model
model_path = "deepseek-ai/Janus-1.3B"
vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer

vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
vl_gpt = vl_gpt.to(ms.bfloat16)


def create_prompt(user_input: str) -> str:
    conversation = [
        {
            "role": "User",
            "content": user_input,
        },
        {"role": "Assistant", "content": ""},
    ]

    sft_format = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(
        conversations=conversation,
        sft_format=vl_chat_processor.sft_format,
        system_prompt="",
    )
    prompt = sft_format + vl_chat_processor.image_start_tag
    return prompt


def generate(
    mmgpt: MultiModalityCausalLM,
    vl_chat_processor: VLChatProcessor,
    prompt: str,
    short_prompt: str,
    parallel_size: int = 16,
    temperature: float = 1,
    cfg_weight: float = 5,
    image_token_num_per_image: int = 576,
    img_size: int = 384,
    patch_size: int = 16,
):
    input_ids = vl_chat_processor.tokenizer.encode(prompt)
    input_ids = ms.int64(input_ids)

    tokens = mint.zeros((parallel_size * 2, len(input_ids)), dtype=ms.int32)
    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 = mmgpt.language_model.get_input_embeddings()(tokens)

    generated_tokens = mint.zeros((parallel_size, image_token_num_per_image), dtype=ms.int32)
    outputs = None  # Initialize outputs for use in the loop

    for i in range(image_token_num_per_image):
        outputs = mmgpt.language_model.model(
            inputs_embeds=inputs_embeds,
            use_cache=True,
            past_key_values=outputs.past_key_values if i != 0 else None,
        )
        hidden_states = outputs.last_hidden_state

        logits = mmgpt.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 = mint.nn.functional.softmax(logits / temperature, dim=-1)

        next_token = mint.multinomial(probs, num_samples=1)
        generated_tokens[:, i] = next_token.squeeze(dim=-1)

        next_token = mint.cat([next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1)
        img_embeds = mmgpt.prepare_gen_img_embeds(next_token)
        inputs_embeds = img_embeds.unsqueeze(dim=1)

    dec = mmgpt.gen_vision_model.decode_code(
        generated_tokens.to(dtype=ms.int32),
        shape=[parallel_size, 8, img_size // patch_size, img_size // patch_size],
    )
    dec = dec.to(ms.float32).asnumpy().transpose(0, 2, 3, 1)

    dec = np.clip((dec + 1) / 2 * 255, 0, 255)

    visual_img = np.zeros((parallel_size, img_size, img_size, 3), dtype=np.uint8)
    visual_img[:, :, :] = dec

    os.makedirs("generated_samples", exist_ok=True)

    # Create a timestamp
    timestamp = time.strftime("%Y%m%d-%H%M%S")

    # Sanitize the short_prompt to ensure it's safe for filenames
    short_prompt = re.sub(r"\W+", "_", short_prompt)[:50]

    # Save images with timestamp and part of the user prompt in the filename
    for i in range(parallel_size):
        save_path = os.path.join("generated_samples", f"img_{timestamp}_{short_prompt}_{i}.jpg")
        PIL.Image.fromarray(visual_img[i]).save(save_path)


def interactive_image_generator():
    print("Welcome to the interactive image generator!")

    # Ask for the number of images at the start of the session
    while True:
        num_images_input = input("How many images would you like to generate per prompt? (Enter a positive integer): ")
        if num_images_input.isdigit() and int(num_images_input) > 0:
            parallel_size = int(num_images_input)
            break
        else:
            print("Invalid input. Please enter a positive integer.")

    while True:
        user_input = input("Please describe the image you'd like to generate (or type 'exit' to quit): ")

        if user_input.lower() == "exit":
            print("Exiting the image generator. Goodbye!")
            break

        prompt = create_prompt(user_input)

        # Create a sanitized version of user_input for the filename
        short_prompt = re.sub(r"\W+", "_", user_input)[:50]

        print(f"Generating {parallel_size} image(s) for: '{user_input}'")
        generate(
            mmgpt=vl_gpt,
            vl_chat_processor=vl_chat_processor,
            prompt=prompt,
            short_prompt=short_prompt,
            parallel_size=parallel_size,  # Pass the user-specified number of images
        )

        print("Image generation complete! Check the 'generated_samples' folder for the output.\n")


if __name__ == "__main__":
    interactive_image_generator()