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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() |
image = pipeline(prompt_embeds=prompt_embeds, # generated from Compel |
negative_prompt_embeds=negative_prompt_embeds, # generated from Compel |
image=init_image, |
).images[0] ControlNet ControlNets provide a more flexible and accurate way to control image generation because you can use an additional conditioning image. The conditioning image can be a canny image, depth map, image segmentation, and even scribbles! Whatever type of conditioning image you choose, the ControlNet ge... |
# prepare image |
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png" |
init_image = load_image(url) |
init_image = init_image.resize((958, 960)) # resize to depth image dimensions |
depth_image = load_image("https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png") |
make_image_grid([init_image, depth_image], rows=1, cols=2) Load a ControlNet model conditioned on depth maps and the AutoPipelineForImage2Image: Copied from diffusers import ControlNetModel, AutoPipelineForImage2Image |
import torch |
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11f1p_sd15_depth", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) |
pipeline = AutoPipelineForImage2Image.from_pretrained( |
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, 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() Now generate a new image conditioned on the depth map, initial image, and prompt: Copied prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" |
image_control_net = pipeline(prompt, image=init_image, control_image=depth_image).images[0] |
make_image_grid([init_image, depth_image, image_control_net], rows=1, cols=3) initial image depth image ControlNet image Let’s apply a new style to the image generated from the ControlNet by chaining it with an image-to-image pipeline: Copied pipeline = AutoPipelineForImage2Image.from_pretrained( |
"nitrosocke/elden-ring-diffusion", torch_dtype=torch.float16, |
) |
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