stable diffusion finetuned
简介
核心亮点
- 基于 SD 微调，画质细节与光影表现更出色
- 降低 Prompt 调试难度，出图风格更稳定
- 兼容主流 SD 生态工具，上手门槛极低
- 采用 Apache-2.0 协议，商业化应用灵活
使用方法
# 安装 Hugging Face transformers
pip install transformers torch# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("ImageInception/stable-diffusion-finetuned")
tokenizer = AutoTokenizer.from_pretrained("ImageInception/stable-diffusion-finetuned")Hugging Face 下载
我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。
操作指引：在下载前，请先通过如下命令安装 huggingface_hub：
pip install -U huggingface_hub命令行下载
下载完整模型库
huggingface-cli download ImageInception/stable-diffusion-finetuned下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）
huggingface-cli download ImageInception/stable-diffusion-finetuned config.json --local-dir ./dirSDK 下载
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('ImageInception/stable-diffusion-finetuned')Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://huggingface.co/ImageInception/stable-diffusion-finetuned如果您希望跳过 lfs 大文件下载，可以使用如下命令
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/ImageInception/stable-diffusion-finetuned模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。
PyTorch / Transformers 使用
安装 Transformers
pip install -U transformers torch模型加载和推理
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('ImageInception/stable-diffusion-finetuned')
tokenizer = AutoTokenizer.from_pretrained('ImageInception/stable-diffusion-finetuned')完整文档
Certainly! Below are two model card templates for your models: Stable Diffusion Finetuned and PRNet 3D Face Reconstruction. These model cards can be published on Hugging Face or similar platforms to provide useful information about each model, including usage, limitations, and training details.
---
Model Card: Stable Diffusion Finetuned
Model Name: stable-diffusion-finetuned
#### Model Description:
This is a fine-tuned version of the Stable Diffusion model, a state-of-the-art generative model capable of producing high-quality images from textual descriptions. The model has been fine-tuned on a custom dataset for improved performance in a specific domain.
- Architecture: Stable Diffusion
- Base Model: Stable Diffusion 1.x (before fine-tuning)
- Training Data: Custom dataset of images and corresponding textual descriptions.
- Purpose: This model is intended for generating images based on specific domain-related text descriptions (e.g., architecture, landscapes, characters).
#### Model Details:
- Training: Fine-tuned using Google Colab with the Stable Diffusion base model. The training used the free quota on Colab and was optimized for generating images based on domain-specific prompts.
- Optimizations: The model was fine-tuned for a reduced number of epochs to prevent overfitting and to ensure generalizability across different prompts.
#### Usage:
This model is intended for generating images from text inputs. The quality of generated images may vary based on the input prompt and the specificity of the fine-tuning dataset.
##### Example:
from transformers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("your-hf-username/stable-diffusion-finetuned")
prompt = "A scenic view of mountains during sunset"
image = pipe(prompt).images[0]
image.show()
#### Intended Use:
- Domain-Specific Image Generation: Designed to generate images for specific scenarios (e.g., concept art, landscape images, etc.).
- Text-to-Image: Works by taking text prompts and producing visually coherent images.
#### Limitations and Risks:
- Bias in Generation: Since the model was fine-tuned on a specific dataset, it may produce biased outputs, and its applicability outside the fine-tuned domain may be limited.
- Sensitive Content: The model may inadvertently generate inappropriate or unintended imagery depending on the prompt.
- Performance: Since the model was trained on limited resources (free Colab), generation may not be as fast or optimized for large-scale use cases.
#### How to Cite:
If you use this model, please cite the original Stable Diffusion authors and mention that this version is fine-tuned for specific tasks:
@misc{stable-diffusion-finetuned,
title={Stable Diffusion Finetuned Model},
author={Mostafa Aly},
year={2024},
howpublished={\url{https://huggingface.co/your-hf-username/stable-diffusion-finetuned}},
}