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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, |
) |
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-e... |
+ 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... |
Using Diffusers for reinforcement learning |
Support for one RL model and related pipelines is included in the experimental source of diffusers. |
More models and examples coming soon! |
Diffuser Value-guided Planning |
You can run the model from Planning with Diffusion for Flexible Behavior Synthesis with Diffusers. |
The script is located in the RL Examples folder. |
Or, run this example in Colab |
class diffusers.experimental.ValueGuidedRLPipeline |
< |
source |
> |
( |
value_function: UNet1DModel |
unet: UNet1DModel |
scheduler: DDPMScheduler |
env |
) |
Parameters |
value_function (UNet1DModel) — A specialized UNet for fine-tuning trajectories base on reward. |
unet (UNet1DModel) — U-Net architecture to denoise the encoded trajectories. |
scheduler (SchedulerMixin) — |
A scheduler to be used in combination with unet to denoise the encoded trajectories. Default for this |
application is DDPMScheduler. |
env — An environment following the OpenAI gym API to act in. For now only Hopper has pretrained models. |
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