Patch release 0.18.1: Stable Diffusion XL 0.9 Research Release

Stable Diffusion XL 0.9 is now fully supported under the SDXL 0.9 Research License license here.

Having received access to stabilityai/stable-diffusion-xl-base-0.9, you can easily use it with diffusers:

Text-to-Image

from diffusers import StableDiffusionXLPipeline
import torch

pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
)
pipe.to("cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt=prompt).images[0]

Refining the image output

from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
import torch

pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
)
pipe.to("cuda")

refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
)
refiner.to("cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"

image = pipe(prompt=prompt, output_type="latent" if use_refiner else "pil").images[0]
image = refiner(prompt=prompt, image=image[None, :]).images[0]

Loading single file checkpoitns / original file format

from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
import torch

pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
)
pipe.to("cuda")

refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
)
refiner.to("cuda")

Memory optimization via model offloading

- pipe.to("cuda")
+ pipe.enable_model_cpu_offload()

and

- refiner.to("cuda")
+ refiner.enable_model_cpu_offload()

Speed-up inference with torch.compile

+ pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+ refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)

Note: If you're running the model with < torch 2.0, please make sure to run:

+pipe.enable_xformers_memory_efficient_attention()
+refiner.enable_xformers_memory_efficient_attention()

For more details have a look at the official docs.

All commits

• typo in safetensors (safetenstors) by @YoraiLevi in #3976
• Fix code snippet for Audio Diffusion by @osanseviero in #3987
• feat: add Dropout to Flax UNet by @SauravMaheshkar in #3894
• Add 'rank' parameter to Dreambooth LoRA training script by @isidentical in #3945
• Don't use bare prints in a library by @cmd410 in #3991
• [Tests] Fix some slow tests by @patrickvonplaten in #3989
• Add sdxl prompt embeddings by @patrickvonplaten in #3995