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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... |
import torch |
from diffusers.utils import make_image_grid |
pipeline = AutoPipelineForText2Image.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() |
text2image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k").images[0] |
text2image Now you can pass this generated image to the image-to-image pipeline: Copied 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() |
image2image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", image=text2image).images[0] |
make_image_grid([text2image, image2image], rows=1, cols=2) Image-to-image-to-image You can also chain multiple image-to-image pipelines together to create more interesting images. This can be useful for iteratively performing style transfer on an image, generating short GIFs, restoring color to an image, or restoring ... |
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, output_type="latent").images[0] It is important to specify output_type="latent" in the pipeline to keep all the outputs in latent space to avoid an unnecessary decode-encode step. This only works if the chained pipelines are using the same VAE. Pass the latent output from this... |
"ogkalu/Comic-Diffusion", torch_dtype=torch.float16 |
) |
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() |
# need to include the token "charliebo artstyle" in the prompt to use this checkpoint |
image = pipeline("Astronaut in a jungle, charliebo artstyle", image=image, output_type="latent").images[0] Repeat one more time to generate the final image in a pixel art style: Copied pipeline = AutoPipelineForImage2Image.from_pretrained( |
"kohbanye/pixel-art-style", torch_dtype=torch.float16 |
) |
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() |
# need to include the token "pixelartstyle" in the prompt to use this checkpoint |
image = pipeline("Astronaut in a jungle, pixelartstyle", image=image).images[0] |
make_image_grid([init_image, image], rows=1, cols=2) Image-to-upscaler-to-super-resolution Another way you can chain your image-to-image pipeline is with an upscaler and super-resolution pipeline to really increase the level of details in an image. Start with an image-to-image pipeline: 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_1 = pipeline(prompt, image=init_image, output_type="latent").images[0] It is important to specify output_type="latent" in the pipeline to keep all the outputs in latent space to avoid an unnecessary decode-encode step. This only works if the chained pipelines are using the same VAE. Chain it to an upscaler pipeli... |
upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained( |
"stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, variant="fp16", use_safetensors=True |
) |
upscaler.enable_model_cpu_offload() |
upscaler.enable_xformers_memory_efficient_attention() |
image_2 = upscaler(prompt, image=image_1, output_type="latent").images[0] Finally, chain it to a super-resolution pipeline to further enhance the resolution: Copied from diffusers import StableDiffusionUpscalePipeline |
super_res = StableDiffusionUpscalePipeline.from_pretrained( |
"stabilityai/stable-diffusion-x4-upscaler", torch_dtype=torch.float16, variant="fp16", use_safetensors=True |
) |
super_res.enable_model_cpu_offload() |
super_res.enable_xformers_memory_efficient_attention() |
image_3 = super_res(prompt, image=image_2).images[0] |
make_image_grid([init_image, image_3.resize((512, 512))], rows=1, cols=2) Control image generation Trying to generate an image that looks exactly the way you want can be difficult, which is why controlled generation techniques and models are so useful. While you can use the negative_prompt to partially control image g... |
import torch |
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() |
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