LCM_Dreamshaper_v7:可在昇腾处理器（Ascend310、Ascend910系列）上实现文本到图像生成，适配昇腾硬件，提供开发环境配置指南及推理测试脚本，支持快速部署与使用。【此简介由AI生成】

1、适配昇腾处理器：Ascend310、Ascend910系列 2、开发环境：Ascend-cann-toolkit_xxx、Ascend-cann-kernels-xxx(可选)、python3.8 3、下载代码：git clone https://modelers.cn/ShanXi/LCM_Dreamshaper_v7.git 4、安装依赖：pip install -r examples/requirements.txt 5、推理测试：python examples/inference.py 6、推理脚本：

import argparse import torch from openmind import pipeline, is_torch_npu_available from diffusers import DiffusionPipeline

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_name_or_path",type=str,help="模型路径",default="./",)
    args = parser.parse_args()
    return args


if is_torch_npu_available():
    device = "npu:0"
else:
    device = "cpu"


args = parse_args()
model_path = args.model_name_or_path


pipe = DiffusionPipeline.from_pretrained(model_path, device_map="auto",torch_dtype=torch.float32)
prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"

# 可设置为1~50步。LCM即使在<=4步时也支持快速推理。推荐：1~8步。
num_inference_steps = 4 

images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=8.0, lcm_origin_steps=50, output_type="pil").images

import argparse import torch from openmind import pipeline, is_torch_npu_available from diffusers import DiffusionPipeline

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_name_or_path",type=str,help="模型路径",default="./",)
    args = parser.parse_args()
    return args


if is_torch_npu_available():
    device = "npu:0"
else:
    device = "cpu"


args = parse_args()
model_path = args.model_name_or_path


pipe = DiffusionPipeline.from_pretrained(model_path, device_map="auto",torch_dtype=torch.float32)
prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"

# 可设置为1~50步。LCM即使在<=4步时也支持快速推理。推荐：1~8步。
num_inference_steps = 4 

images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=8.0, lcm_origin_steps=50, output_type="pil").images