剑锋1.3B
Janus 是一个新的自回归框架，集成了多模态理解和生成。与之前的模型使用单个视觉编码器来执行理解和生成任务不同，Janus 为这些功能引入了两个独立的视觉编码路径。
理解和生成编码的差异
- 在多模态理解任务中，视觉编码器提取高级语义信息，例如对象类别和视觉属性。该编码器专注于推断复杂的含义，强调高维语义元素。
- 另一方面，在视觉生成任务中，重点放在生成精细细节并保持整体一致性。因此，需要能够捕获空间结构和纹理的低维编码。
设置环境
以下是在 Google Colab 中运行 Janus 的步骤：
git clone https://github.com/deepseek-ai/Janus
cd Janus
pip install -e .
# If needed, install the following as well
# pip install wheel
# pip install flash-attn --no-build-isolation
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愿景任务
加载模型
使用以下代码加载视觉任务所需的模型：
import torch
from transformers import AutoModelForCausalLM
from janus.models import MultiModalityCausalLM, VLChatProcessor
from janus.utils.io import load_pil_images
# Specify the model path
model_path = "deepseek-ai/Janus-1.3B"
vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer
vl_gpt = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
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加载和准备图像以进行编码
接下来，加载图像并将其转换为模型可以理解的格式：
conversation = [
{
"role": "User",
"content": "<image_placeholder>\nDescribe this chart.",
"images": ["images/pie_chart.png"],
},
{"role": "Assistant", "content": ""},
]
# Load the image and prepare input
pil_images = load_pil_images(conversation)
prepare_inputs = vl_chat_processor(
conversations=conversation, images=pil_images, force_batchify=True
).to(vl_gpt.device)
# Run the image encoder and obtain image embeddings
inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
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生成响应
最后，运行模型以生成响应：
# Run the model and generate a response
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,
use_cache=True,
)
answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
print(f"{prepare_inputs['sft_format'][0]}", answer)
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示例输出
The image depicts a pie chart that illustrates the distribution of four different categories among four distinct groups. The chart is divided into four segments, each representing a category with a specific percentage. The categories and their corresponding percentages are as follows:
1. **Hogs**: This segment is colored in orange and represents 30.0% of the total.
2. **Frog**: This segment is colored in blue and represents 15.0% of the total.
3. **Logs**: This segment is colored in red and represents 10.0% of the total.
4. **Dogs**: This segment is colored in green and represents 45.0% of the total.
The pie chart is visually divided into four segments, each with a different color and corresponding percentage. The segments are arranged in a clockwise manner starting from the top-left, moving clockwise. The percentages are clearly labeled next to each segment.
The chart is a simple visual representation of data, where the size of each segment corresponds to the percentage of the total category it represents. This type of chart is commonly used to compare the proportions of different categories in a dataset.
To summarize, the pie chart shows the following:
- Hogs: 30.0%
- Frog: 15.0%
- Logs: 10.0%
- Dogs: 45.0%
This chart can be used to understand the relative proportions of each category in the given dataset.
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输出展示了对图像的适当理解，包括其颜色和文本。
图像生成任务
加载模型
使用以下代码加载图像生成任务所需的模型：
import os
import PIL.Image
import torch
import numpy as np
from transformers import AutoModelForCausalLM
from janus.models import MultiModalityCausalLM, VLChatProcessor
# Specify the model path
model_path = "deepseek-ai/Janus-1.3B"
vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer
vl_gpt = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
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准备提示
接下来，根据用户的要求准备提示：
# Set up the prompt
conversation = [
{
"role": "User",
"content": "cute japanese girl, wearing a bikini, in a beach",
},
{"role": "Assistant", "content": ""},
]
# Convert the prompt into the appropriate format
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
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生成图像
以下函数用于生成图像。默认情况下，生成 16 张图像：
@torch.inference_mode()
def generate(
mmgpt: MultiModalityCausalLM,
vl_chat_processor: VLChatProcessor,
prompt: str,
temperature: float = 1,
parallel_size: int = 16,
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 = torch.LongTensor(input_ids)
tokens = torch.zeros((parallel_size*2, len(input_ids)), dtype=torch.int).cuda()
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 = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).cuda()
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 = 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 = 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=torch.int),
shape=[parallel_size, 8, img_size // patch_size, img_size // patch_size],
)
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, img_size, img_size, 3), dtype=np.uint8)
visual_img[:, :, :] = dec
os.makedirs('generated_samples', exist_ok=True)
for i in range(parallel_size):
save_path = os.path.join('generated_samples', f"img_{i}.jpg")
PIL.Image.fromarray(visual_img[i]).save(save_path)
# Run the image generation
generate(vl_gpt, vl_chat_processor, prompt)
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生成的图像将保存在 generated_samples 文件夹中。
生成结果示例
下面是生成图像的示例：
-
狗的描绘相对较好。
-
建筑物保持整体形状，但某些细节（例如窗户）可能显得不切实际。
-
人类，然而，要很好地生成是很有挑战性的，在真实感和类似动漫的风格中都存在明显的扭曲。
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