Stable Diffusion Image Generation Agent Skill

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.

11k tokens

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Install

one command, takes just this skill from the repository

npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill stable-diffusion-image-generation

The instruction itself

38 sections, as written by the author

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:

|-----------|-------|---------|----------|

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((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

Fill masked regions:

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

Add spatial conditioning for precise control:

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]

Available ControlNets

| ControlNet | Input Type | Use Case |

|------------|------------|----------|

| canny | Edge maps | Preserve structure |

| openpose | Pose skeletons | Human poses |

| depth | Depth maps | 3D-aware generation |

| normal | Normal maps | Surface details |

| mlsd | Line segments | Architectural lines |

| scribble | Rough sketches | Sketch-to-image |

LoRA adapters

Load fine-tuned style adapters:

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()

Multiple LoRAs

# 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]

Memory optimization

Enable CPU offloading

# 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 slicing

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

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

xFormers memory-efficient attention

# Requires xformers package
pipe.enable_xformers_memory_efficient_attention()

VAE slicing for large images

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

Model variants

Loading different precisions

# 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
)

Loading specific components

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
)

Batch generation

Generate multiple images efficiently:

# 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

Common workflows

Workflow 1: High-quality generation

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]

Workflow 2: Fast prototyping

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]

Common issues

CUDA out of memory:

# 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)

Black/noise images:

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

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

Slow generation:

# 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]

References

Advanced Usage - Custom pipelines, fine-tuning, deployment
Troubleshooting - Common issues and solutions

Resources

Documentation: https://huggingface.co/docs/diffusers
Repository: https://github.com/huggingface/diffusers
Model Hub: https://huggingface.co/models?library=diffusers
Discord: https://discord.gg/diffusers

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