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Image-to-image Image-to-image is similar to text-to-image, but in addition to a prompt, you can also pass an initial image as a starting point for the diffusion process. The initial image is encoded to latent space and noise is added to it. Then the latent diffusion model takes a prompt and the noisy latent image, predicts the added noise, and removes the predicted noise from the initial latent image to get the new latent image. Lastly, a decoder decodes the new latent image back into an image. With 🤗 Diffusers, this is as easy as 1-2-3: Load a checkpoint into the AutoPipelineForImage2Image class; this pipeline automatically handles loading the correct pipeline class based on the checkpoint: Copied import torch |
from diffusers import AutoPipelineForImage2Image |
from diffusers.utils import load_image, make_image_grid |
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() You’ll notice throughout the guide, we use enable_model_cpu_offload() and enable_xformers_memory_efficient_attention(), to save memory and increase inference speed. If you’re using PyTorch 2.0, then you don’t need to call enable_xformers_memory_efficient_attention() on your pipeline because it’ll already be using PyTorch 2.0’s native scaled-dot product attention. Load an image to pass to the pipeline: Copied init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") Pass a prompt and image to the pipeline to generate an image: Copied prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k" |
image = pipeline(prompt, image=init_image).images[0] |
make_image_grid([init_image, image], rows=1, cols=2) initial image generated image Popular models The most popular image-to-image models are Stable Diffusion v1.5, Stable Diffusion XL (SDXL), and Kandinsky 2.2. The results from the Stable Diffusion and Kandinsky models vary due to their architecture differences and training process; you can generally expect SDXL to produce higher quality images than Stable Diffusion v1.5. Let’s take a quick look at how to use each of these models and compare their results. Stable Diffusion v1.5 Stable Diffusion v1.5 is a latent diffusion model initialized from an earlier checkpoint, and further finetuned for 595K steps on 512x512 images. To use this pipeline for image-to-image, you’ll need to prepare an initial image to pass to the pipeline. Then you can pass a prompt and the image to the pipeline to generate a new 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).images[0] |
make_image_grid([init_image, image], rows=1, cols=2) initial image generated image Stable Diffusion XL (SDXL) SDXL is a more powerful version of the Stable Diffusion model. It uses a larger base model, and an additional refiner model to increase the quality of the base model’s output. Read the SDXL guide for a more detailed walkthrough of how to use this model, and other techniques it uses to produce high quality images. 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-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" |
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