Instructions to use hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt

SGLang

How to use hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt with Docker Model Runner:
```
docker model run hf.co/hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt
```

模型使用在生成稳定扩散（Stable Diffusion）的提示语（prompt）。它主要通过中文生成相应的英文提示语，充分利用Qwen的能力，支持包括古诗词在内的多种形式进行提示语的生成。此模型基于35k 条数据进行特定于任务的微调(SFT)

模型的对应代码已发布于GitHub，您可以通过以下链接访问并使用SDXL-Turbo进行快速测试生成的提示语： https://github.com/zhongpei/Qwen-SDXL-Turbo.git

This model is designed to generate prompts for Stable Diffusion. It primarily translates Chinese into corresponding English prompts, leveraging the capabilities of Qwen, and supports a variety of formats including classical poetry for prompt generation. The model has been fine-tuned specifically for this task (SFT) using a dataset of 35k entries.

The corresponding code for the model is available on GitHub. You can access and use SDXL-Turbo for rapid test generation of prompts through the following link: https://github.com/zhongpei/Qwen-SDXL-Turbo.git

参数表格（Parameters Table）:

参数	值
数据量（Data）	35k
训练周期（Epochs）	1
学习率（Learning Rate）	1e-05
损失值（Loss）	1.03

Downloads last month: 25

Safetensors

Model size

2B params

Tensor type

BF16

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