/stable-diffusion

name: stable-diffusion-image-generation
description: State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.
version: 1.0.0
author: Orchestra Research
license: MIT
dependencies: [diffusers>=0.30.0, transformers>=4.41.0, accelerate>=0.31.0, torch>=2.0.0]
metadata:
  zorro:
    tags: [Image Generation, Stable Diffusion, Diffusers, Text-to-Image, Multimodal, Computer Vision]

Stable Diffusion Image Generation

Comprehensive guide to generating images with Stable Diffusion using the HuggingFace Diffusers library.

When to use Stable Diffusion

**Use Stable Diffusion when:**

• Generating images from text descriptions
• Performing image-to-image translation (style transfer, enhancement)
• Inpainting (filling in masked regions)
• Outpainting (extending images beyond boundaries)
• Creating variations of existing images
• Building custom image generation workflows

**Key features:**

• **Text-to-Image**: Generate images from natural language prompts
• **Image-to-Image**: Transform existing images with text guidance
• **Inpainting**: Fill masked regions with context-aware content
• **ControlNet**: Add spatial conditioning (edges, poses, depth)
• **LoRA Support**: Efficient fine-tuning and style adaptation
• **Multiple Models**: SD 1.5, SDXL, SD 3.0, Flux support

**Use alternatives instead:**

• **DALL-E 3**: For API-based generation without GPU
• **Midjourney**: For artistic, stylized outputs
• **Imagen**: For Google Cloud integration
• **Leonardo.ai**: For web-based creative workflows

Quick start

Installation

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

Basic text-to-image

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")

Using SDXL (higher quality)

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]

Architecture overview

Three-pillar design

Diffusers is built around three core components:

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

Pipeline inference flow

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

Core concepts

Pipelines

Pipelines orchestrate complete workflows:

| Pipeline | Purpose | |----------|---------| | `StableDiffusionPipeline` | Text-to-image (SD 1.x/2.x) | | `StableDiffusionXLPipeline` | Text-to-image (SDXL) | | `StableDiffusion3Pipeline` | Text-to-image (SD 3.0) | | `FluxPipeline` | Text-to-image (Flux models) | | `StableDiffusionImg2ImgPipeline` | Image-to-image | | `StableDiffusionInpaintPipeline` | Inpainting |

Schedulers

Schedulers control the denoising process:

| Scheduler | Steps | Quality | Use Case | |-----------|-------|---------|----------| | `EulerDiscreteScheduler` | 20-50 | Good | Default choice | | `EulerAncestralDiscreteScheduler` | 20-50 | Good | More variation | | `DPMSolverMultistepScheduler` | 15-25 | Excellent | Fast, high quality | | `DDIMScheduler` | 50-100 | Good | Deterministic | | `LCMScheduler` | 4-8 | Good | Very fast | | `UniPCMultistepScheduler` | 15-25 | Excellent | Fast convergence |

Swapping schedulers

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]

Generation parameters

Key parameters

| Parameter | Default | Description | |-----------|---------|-------------| | `prompt` | Required | Text description of desired image | | `negative_prompt` | None | What to avoid in the image | | `num_inference_steps` | 50 | Denoising steps (more = better quality) | | `guidance_scale` | 7.5 | Prompt adherence (7-12 typical) | | `height`, `width` | 512/1024 | Output dimensions (multiples of 8) | | `generator` | None | Torch generator for reproducibility | | `num_images_per_prompt` | 1 | Batch size |

Reproducible generation

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 prompts

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]

Image-to-image

Transform existing images with text guidance:

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(