Datasets:

sayakpaul

/

diffusers-qa-chatbot-artifacts

like 1

# 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,

)

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()

prompt = "elden ring style astronaut in a jungle" # include the token "elden ring style" in the prompt

negative_prompt = "ugly, deformed, disfigured, poor details, bad anatomy"

image_elden_ring = pipeline(prompt, negative_prompt=negative_prompt, image=image_control_net, strength=0.45, guidance_scale=10.5).images[0]

make_image_grid([init_image, depth_image, image_control_net, image_elden_ring], rows=2, cols=2) Optimize Running diffusion models is computationally expensive and intensive, but with a few optimization tricks, it is entirely possible to run them on consumer and free-tier GPUs. For example, you can use a more memory-efficient form of attention such as PyTorch 2.0’s scaled-dot product attention or xFormers (you can use one or the other, but there’s no need to use both). You can also offload the model to the GPU while the other pipeline components wait on the CPU. Copied + pipeline.enable_model_cpu_offload()

+ pipeline.enable_xformers_memory_efficient_attention() With torch.compile, you can boost your inference speed even more by wrapping your UNet with it: Copied pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True) To learn more, take a look at the Reduce memory usage and Torch 2.0 guides.