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Stable Diffusion 图像生成

通过 HuggingFace Diffusers 使用 Stable Diffusion 模型实现最先进的文本到图像生成。适用于从文本 prompt（提示词）生成图像、执行图像到图像转换、图像修复（inpainting），或构建自定义扩散 pipeline。

Skill 元数据​

来源可选 — 通过 aigenlabs skills install official/mlops/stable-diffusion 安装
路径optional-skills/mlops/stable-diffusion
版本1.0.0
作者Orchestra Research
许可证MIT
依赖项diffusers>=0.30.0, transformers>=4.41.0, accelerate>=0.31.0, torch>=2.0.0
平台linux, macos, windows
标签Image Generation, Stable Diffusion, Diffusers, Text-to-Image, Multimodal, Computer Vision

参考：完整 SKILL.md​

信息

以下是 AigenLabs 在触发此 skill 时加载的完整 skill 定义。这是 agent 在 skill 激活时所看到的指令内容。

Stable Diffusion 图像生成

使用 HuggingFace Diffusers 库通过 Stable Diffusion 生成图像的综合指南。

何时使用 Stable Diffusion​

在以下情况下使用 Stable Diffusion：

  • 从文本描述生成图像
  • 执行图像到图像转换（风格迁移、增强）
  • Inpainting（填充遮罩区域）
  • Outpainting（将图像扩展至边界之外）
  • 创建现有图像的变体
  • 构建自定义图像生成工作流

核心功能：

  • 文本到图像：从自然语言 prompt 生成图像
  • 图像到图像：在文本引导下转换现有图像
  • Inpainting：用上下文感知内容填充遮罩区域
  • ControlNet：添加空间条件控制（边缘、姿态、深度）
  • LoRA 支持：高效微调与风格适配
  • 多模型支持：支持 SD 1.5、SDXL、SD 3.0、Flux

改用以下替代方案：

  • DALL-E 3：无需 GPU 的 API 生成
  • Midjourney：艺术化、风格化输出
  • Imagen：Google Cloud 集成
  • Leonardo.ai：基于 Web 的创意工作流

快速开始​

安装​

pip install diffusers transformers accelerate torch
pip install xformers  # Optional: memory-efficient attention

基础文本到图像​

from diffusers import DiffusionPipeline
import torch

# Load pipeline (auto-detects model type)
pipe = DiffusionPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5",
    torch_dtype=torch.float16
)
pipe.to("cuda")

# Generate image
image = pipe(
    "A serene mountain landscape at sunset, highly detailed",
    num_inference_steps=50,
    guidance_scale=7.5
).images[0]

image.save("output.png")

使用 SDXL（更高质量）​

from diffusers import AutoPipelineForText2Image
import torch

pipe = AutoPipelineForText2Image.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16"
)
pipe.to("cuda")

# Enable memory optimization
pipe.enable_model_cpu_offload()

image = pipe(
    prompt="A futuristic city with flying cars, cinematic lighting",
    height=1024,
    width=1024,
    num_inference_steps=30
).images[0]

架构概览​

三支柱设计​

Diffusers 围绕三个核心组件构建：

Pipeline (orchestration)
├── Model (neural networks)
│   ├── UNet / Transformer (noise prediction)
│   ├── VAE (latent encoding/decoding)
│   └── Text Encoder (CLIP/T5)
└── Scheduler (denoising algorithm)

Pipeline 推理流程​

Text Prompt → Text Encoder → Text Embeddings
                                    ↓
Random Noise → [Denoising Loop] ← Scheduler
                      ↓
               Predicted Noise
                      ↓
              VAE Decoder → Final Image

核心概念​

Pipeline​

Pipeline 编排完整工作流：

Pipeline用途
StableDiffusionPipeline文本到图像（SD 1.x/2.x）
StableDiffusionXLPipeline文本到图像（SDXL）
StableDiffusion3Pipeline文本到图像（SD 3.0）
FluxPipeline文本到图像（Flux 模型）
StableDiffusionImg2ImgPipeline图像到图像
StableDiffusionInpaintPipelineInpainting

Scheduler​

Scheduler 控制去噪过程：

Scheduler步数质量适用场景
EulerDiscreteScheduler20-50良好默认选择
EulerAncestralDiscreteScheduler20-50良好更多变化
DPMSolverMultistepScheduler15-25优秀快速、高质量
DDIMScheduler50-100良好确定性生成
LCMScheduler4-8良好极速生成
UniPCMultistepScheduler15-25优秀快速收敛

切换 Scheduler​

from diffusers import DPMSolverMultistepScheduler

# Swap for faster generation
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
    pipe.scheduler.config
)

# Now generate with fewer steps
image = pipe(prompt, num_inference_steps=20).images[0]

生成参数​

关键参数​

参数默认值说明
prompt必填目标图像的文本描述
negative_promptNone图像中需要避免的内容
num_inference_steps50去噪步数（越多质量越好）
guidance_scale7.5Prompt 遵循程度（通常为 7-12）
height, width512/1024输出尺寸（8 的倍数）
generatorNone用于可复现性的 Torch generator
num_images_per_prompt1批量大小

可复现生成​

import torch

generator = torch.Generator(device="cuda").manual_seed(42)

image = pipe(
    prompt="A cat wearing a top hat",
    generator=generator,
    num_inference_steps=50
).images[0]

Negative prompt​

image = pipe(
    prompt="Professional photo of a dog in a garden",
    negative_prompt="blurry, low quality, distorted, ugly, bad anatomy",
    guidance_scale=7.5
).images[0]

图像到图像​

在文本引导下转换现有图像：

from diffusers import AutoPipelineForImage2Image
from PIL import Image

pipe = AutoPipelineForImage2Image.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5",
    torch_dtype=torch.float16
).to("cuda")

init_image = Image.open("input.jpg").resize((512, 512))

image = pipe(
    prompt="A watercolor painting of the scene",
    image=init_image,
    strength=0.75,  # How much to transform (0-1)
    num_inference_steps=50
).images[0]

Inpainting​

填充遮罩区域：

from diffusers import AutoPipelineForInpainting
from PIL import Image

pipe = AutoPipelineForInpainting.from_pretrained(
    "runwayml/stable-diffusion-inpainting",
    torch_dtype=torch.float16
).to("cuda")

image = Image.open("photo.jpg")
mask = Image.open("mask.png")  # White = inpaint region

result = pipe(
    prompt="A red car parked on the street",
    image=image,
    mask_image=mask,
    num_inference_steps=50
).images[0]

ControlNet​

添加空间条件控制以实现精确控制：

from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch

# Load ControlNet for edge conditioning
controlnet = ControlNetModel.from_pretrained(
    "lllyasviel/control_v11p_sd15_canny",
    torch_dtype=torch.float16
)

pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5",
    controlnet=controlnet,
    torch_dtype=torch.float16
).to("cuda")

# Use Canny edge image as control
control_image = get_canny_image(input_image)

image = pipe(
    prompt="A beautiful house in the style of Van Gogh",
    image=control_image,
    num_inference_steps=30
).images[0]

可用的 ControlNet​

ControlNet输入类型适用场景
canny边缘图保留结构
openpose姿态骨架人体姿态
depth深度图3D 感知生成
normal法线图表面细节
mlsd线段建筑线条
scribble粗略草图草图到图像

LoRA 适配器​

加载微调风格适配器：

from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5",
    torch_dtype=torch.float16
).to("cuda")

# Load LoRA weights
pipe.load_lora_weights("path/to/lora", weight_name="style.safetensors")

# Generate with LoRA style
image = pipe("A portrait in the trained style").images[0]

# Adjust LoRA strength
pipe.fuse_lora(lora_scale=0.8)

# Unload LoRA
pipe.unload_lora_weights()

多个 LoRA​

# Load multiple LoRAs
pipe.load_lora_weights("lora1", adapter_name="style")
pipe.load_lora_weights("lora2", adapter_name="character")

# Set weights for each
pipe.set_adapters(["style", "character"], adapter_weights=[0.7, 0.5])

image = pipe("A portrait").images[0]

内存优化​

启用 CPU 卸载​

# Model CPU offload - moves models to CPU when not in use
pipe.enable_model_cpu_offload()

# Sequential CPU offload - more aggressive, slower
pipe.enable_sequential_cpu_offload()

Attention 切片​

# Reduce memory by computing attention in chunks
pipe.enable_attention_slicing()

# Or specific chunk size
pipe.enable_attention_slicing("max")

xFormers 内存高效 Attention​

# Requires xformers package
pipe.enable_xformers_memory_efficient_attention()

大图像的 VAE 切片​

# Decode latents in tiles for large images
pipe.enable_vae_slicing()
pipe.enable_vae_tiling()

模型变体​

加载不同精度​

# FP16 (recommended for GPU)
pipe = DiffusionPipeline.from_pretrained(
    "model-id",
    torch_dtype=torch.float16,
    variant="fp16"
)

# BF16 (better precision, requires Ampere+ GPU)
pipe = DiffusionPipeline.from_pretrained(
    "model-id",
    torch_dtype=torch.bfloat16
)

加载特定组件​

from diffusers import UNet2DConditionModel, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")

# Use with pipeline
pipe = DiffusionPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5",
    vae=vae,
    torch_dtype=torch.float16
)

批量生成​

高效生成多张图像：

# Multiple prompts
prompts = [
    "A cat playing piano",
    "A dog reading a book",
    "A bird painting a picture"
]

images = pipe(prompts, num_inference_steps=30).images

# Multiple images per prompt
images = pipe(
    "A beautiful sunset",
    num_images_per_prompt=4,
    num_inference_steps=30
).images

常见工作流​

工作流 1：高质量生成​

from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler
import torch

# 1. Load SDXL with optimizations
pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16"
)
pipe.to("cuda")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()

# 2. Generate with quality settings
image = pipe(
    prompt="A majestic lion in the savanna, golden hour lighting, 8k, detailed fur",
    negative_prompt="blurry, low quality, cartoon, anime, sketch",
    num_inference_steps=30,
    guidance_scale=7.5,
    height=1024,
    width=1024
).images[0]

工作流 2：快速原型验证​

from diffusers import AutoPipelineForText2Image, LCMScheduler
import torch

# Use LCM for 4-8 step generation
pipe = AutoPipelineForText2Image.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16
).to("cuda")

# Load LCM LoRA for fast generation
pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.fuse_lora()

# Generate in ~1 second
image = pipe(
    "A beautiful landscape",
    num_inference_steps=4,
    guidance_scale=1.0
).images[0]

常见问题​

CUDA 内存不足：

# Enable memory optimizations
pipe.enable_model_cpu_offload()
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()

# Or use lower precision
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)

黑色/噪声图像：

# Check VAE configuration
# Use safety checker bypass if needed
pipe.safety_checker = None

# Ensure proper dtype consistency
pipe = pipe.to(dtype=torch.float16)

生成速度慢：

# Use faster scheduler
from diffusers import DPMSolverMultistepScheduler
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)

# Reduce steps
image = pipe(prompt, num_inference_steps=20).images[0]

参考资料​

资源​