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from diffusers import AutoPipelineForImage2Image |
from diffusers.utils import make_image_grid, load_image |
pipeline = AutoPipelineForImage2Image.from_pretrained( |
"stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True |
) |
pipeline.enable_model_cpu_offload() |
# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed |
pipeline.enable_xformers_memory_efficient_attention() |
# prepare image |
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-sdxl-init.png" |
init_image = load_image(url) |
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" |
# pass prompt and image to pipeline |
image = pipeline(prompt, image=init_image, strength=0.5).images[0] |
make_image_grid([init_image, image], rows=1, cols=2) initial image generated image Kandinsky 2.2 The Kandinsky model is different from the Stable Diffusion models because it uses an image prior model to create image embeddings. The embeddings help create a better alignment between text and images, allowing the latent diffusion model to generate better images. The simplest way to use Kandinsky 2.2 is: Copied import torch |
from diffusers import AutoPipelineForImage2Image |
from diffusers.utils import make_image_grid, load_image |
pipeline = AutoPipelineForImage2Image.from_pretrained( |
"kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16, use_safetensors=True |
) |
pipeline.enable_model_cpu_offload() |
# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed |
pipeline.enable_xformers_memory_efficient_attention() |
# prepare image |
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png" |
init_image = load_image(url) |
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" |
# pass prompt and image to pipeline |
image = pipeline(prompt, image=init_image).images[0] |
make_image_grid([init_image, image], rows=1, cols=2) initial image generated image Configure pipeline parameters There are several important parameters you can configure in the pipeline that’ll affect the image generation process and image quality. Let’s take a closer look at what these parameters do and how changing them affects the output. Strength strength is one of the most important parameters to consider and it’ll have a huge impact on your generated image. It determines how much the generated image resembles the initial image. In other words: 📈 a higher strength value gives the model more “creativity” to generate an image that’s different from the initial image; a strength value of 1.0 means the initial image is more or less ignored 📉 a lower strength value means the generated image is more similar to the initial image The strength and num_inference_steps parameters are related because strength determines the number of noise steps to add. For example, if the num_inference_steps is 50 and strength is 0.8, then this means adding 40 (50 * 0.8) steps of noise to the initial image and then denoising for 40 steps to get the newly generated image. Copied import torch |
from diffusers import AutoPipelineForImage2Image |
from diffusers.utils import make_image_grid, load_image |
pipeline = AutoPipelineForImage2Image.from_pretrained( |
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True |
) |
pipeline.enable_model_cpu_offload() |
# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed |
pipeline.enable_xformers_memory_efficient_attention() |
# prepare image |
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png" |
init_image = load_image(url) |
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" |
# pass prompt and image to pipeline |
image = pipeline(prompt, image=init_image, strength=0.8).images[0] |
make_image_grid([init_image, image], rows=1, cols=2) strength = 0.4 strength = 0.6 strength = 1.0 Guidance scale The guidance_scale parameter is used to control how closely aligned the generated image and text prompt are. A higher guidance_scale value means your generated image is more aligned with the prompt, while a lower guidance_scale value means your generated image has more space to deviate from the prompt. You can combine guidance_scale with strength for even more precise control over how expressive the model is. For example, combine a high strength + guidance_scale for maximum creativity or use a combination of low strength and low guidance_scale to generate an image that resembles the initial image but is not as strictly bound to the prompt. Copied import torch |
from diffusers import AutoPipelineForImage2Image |
from diffusers.utils import make_image_grid, load_image |
pipeline = AutoPipelineForImage2Image.from_pretrained( |
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True |
) |
pipeline.enable_model_cpu_offload() |
# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed |
pipeline.enable_xformers_memory_efficient_attention() |
# prepare image |
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png" |
init_image = load_image(url) |
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" |
# pass prompt and image to pipeline |
image = pipeline(prompt, image=init_image, guidance_scale=8.0).images[0] |
make_image_grid([init_image, image], rows=1, cols=2) guidance_scale = 0.1 guidance_scale = 5.0 guidance_scale = 10.0 Negative prompt A negative prompt conditions the model to not include things in an image, and it can be used to improve image quality or modify an image. For example, you can improve image quality by including negative prompts like “poor details” or “blurry” to encourage the model to generate a higher quality image. Or you can modify an image by specifying things to exclude from an image. Copied import torch |
from diffusers import AutoPipelineForImage2Image |
from diffusers.utils import make_image_grid, load_image |
pipeline = AutoPipelineForImage2Image.from_pretrained( |
"stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True |
) |
pipeline.enable_model_cpu_offload() |
# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed |
pipeline.enable_xformers_memory_efficient_attention() |
# prepare image |
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png" |
init_image = load_image(url) |
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" |
negative_prompt = "ugly, deformed, disfigured, poor details, bad anatomy" |
# pass prompt and image to pipeline |
image = pipeline(prompt, negative_prompt=negative_prompt, image=init_image).images[0] |
make_image_grid([init_image, image], rows=1, cols=2) negative_prompt = "ugly, deformed, disfigured, poor details, bad anatomy" negative_prompt = "jungle" Chained image-to-image pipelines There are some other interesting ways you can use an image-to-image pipeline aside from just generating an image (although that is pretty cool too). You can take it a step further and chain it with other pipelines. Text-to-image-to-image Chaining a text-to-image and image-to-image pipeline allows you to generate an image from text and use the generated image as the initial image for the image-to-image pipeline. This is useful if you want to generate an image entirely from scratch. For example, let’s chain a Stable Diffusion and a Kandinsky model. Start by generating an image with the text-to-image pipeline: Copied from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image |
import torch |
from diffusers.utils import make_image_grid |
pipeline = AutoPipelineForText2Image.from_pretrained( |