Solutions for generating high-quality graphic content
To achieve high quality graphic creation, you can go through the following steps:
- Environmental Preparation:Ensure availability of Python 3.9+, NVIDIA GPU and CUDA environments (11.x or 12.x versions recommended)
- Model loading:Use Hugging Face to download pre-trained model code:
model = AutoModel.from_pretrained('internlm/internlm-xcomposer2d5-7b', torch_dtype=torch.bfloat16, trust_ remote_code=True).cuda().eval() - Instruction design:Enter a clear prompt, such as "Write an article about the cityscape, including two pictures of landmarks", the more specific the instruction, the better.
- Parameter Adjustment:Adjust generation parameters such as num_beams (3-5 recommended) and avoid setting do_sample=True to ensure content consistency.
- Post-processing:The generated Markdown result can be converted directly to a web page, and the image description will be tagged with the placeholder.
Note: 24GB or more video memory for best results, 4-bit quantized version available for lower configured devices (requires modification of loading parameters)
This answer comes from the articleInternLM-XComposer: a multimodal macromodel for outputting very long text and image-video comprehensionThe
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