openclaw-home-pc/skills/image-edit/style-transfer.md

Style Transfer

Transform image style while preserving content.

Techniques

img2img (Stable Diffusion)

from diffusers import StableDiffusionImg2ImgPipeline
import torch

pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    torch_dtype=torch.float16
)
pipe.to("cuda")

result = pipe(
    prompt="oil painting style, impressionist",
    image=init_image,
    strength=0.6,  # 0=no change, 1=full generation
    guidance_scale=7.5
).images[0]

Strength parameter:

• 0.3-0.4 — Light style, preserves most detail
• 0.5-0.6 — Balanced transformation
• 0.7-0.8 — Heavy restyle, may lose detail

ControlNet

from diffusers import StableDiffusionControlNetPipeline, ControlNetModel

controlnet = ControlNetModel.from_pretrained(
    "lllyasviel/sd-controlnet-canny",
    torch_dtype=torch.float16
)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    controlnet=controlnet,
    torch_dtype=torch.float16
)

# Extract edges for structure guidance
import cv2
canny = cv2.Canny(image, 100, 200)

result = pipe(
    prompt="anime style illustration",
    image=canny,
    num_inference_steps=30
).images[0]

ControlNet modes:

• canny — Edge detection
• depth — Depth map
• pose — Human pose
• lineart — Line drawing

IP-Adapter (Style Reference)

from diffusers import StableDiffusionPipeline
from transformers import CLIPVisionModelWithProjection

# Use reference image as style guide
pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models")
pipe.set_ip_adapter_scale(0.6)

result = pipe(
    prompt="a portrait",
    ip_adapter_image=style_reference,  # Your style image
    image=content_image
).images[0]

Style Types

Style	Prompt Keywords
Oil painting	oil painting, brushstrokes, impasto
Watercolor	watercolor, soft edges, wet medium
Anime	anime style, cel shaded, studio ghibli
Pencil sketch	pencil drawing, graphite, sketch
3D render	3D render, octane, blender
Pixel art	pixel art, 8-bit, retro
Photorealistic	hyperrealistic, photography, DSLR

Workflow

1. Choose technique based on control needed
2. Start with low strength (0.3-0.4)
3. Iterate — adjust strength and prompt
4. ControlNet for precise structure preservation
5. Post-process — color match to original if needed

Best Practices

• Lower strength = more original — start low
• ControlNet for precision — when structure matters
• Style reference images — IP-Adapter for specific styles
• Consistent results — lock seed, batch variations
• Resolution — match input resolution

Common Issues

• Lost detail — reduce strength
• Wrong style — add more specific keywords
• Artifacts — increase steps, reduce guidance
• Color shift — color correct after