!pip install safetensors

from diffusers import StableDiffusionXLPipeline
import torch

# base model
pipeline = StableDiffusionXLPipeline.from_pretrained(
    "Lykon/dreamshaper-xl-1-0", torch_dtype=torch.float16, variant="fp16"
).to("cuda")

# download LoRA weights
!wget https://civitai.com/api/download/models/168776 -O blueprintify.safetensors

# load LoRA weights
pipeline.load_lora_weights(".", weight_name="blueprintify.safetensors")
prompt = "bl3uprint, a highly detailed blueprint of the empire state building, explaining how to build all parts, many txt, blueprint grid backdrop"
negative_prompt = "lowres, cropped, worst quality, low quality, normal quality, artifacts, signature, watermark, username, blurry, more than one bridge, bad architecture"

image = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    generator=torch.manual_seed(0),
).images[0]
image

from diffusers import StableDiffusionPipeline

pipeline = StableDiffusionPipeline.from_single_file(
    "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned.ckpt"
)

from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True,
).to("cuda")

import torch
from diffusers import StableDiffusionXLPipeline

pipeline = StableDiffusionXLPipeline.from_single_file(
    "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors",
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True,
).to("cuda")

from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained(
    "DDUF/FLUX.1-dev-DDUF", dduf_file="FLUX.1-dev.dduf", torch_dtype=torch.bfloat16
).to("cuda")
image = pipe(
    "photo a cat holding a sign that says Diffusers", num_inference_steps=50, guidance_scale=3.5
).images[0]
image.save("cat.png")

from huggingface_hub import export_folder_as_dduf
from diffusers import DiffusionPipeline
import torch 

pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)

save_folder = "flux-dev"
pipe.save_pretrained("flux-dev")
export_folder_as_dduf("flux-dev.dduf", folder_path=save_folder)

> [!TIP]
> Packaging and loading quantized checkpoints in the DDUF format is supported as long as they respect the multi-folder structure.

## Convert layout and files

Diffusers provides many scripts and methods to convert storage layouts and file formats to enable broader support across the diffusion ecosystem.

Take a look at the [diffusers/scripts](https://github.com/huggingface/diffusers/tree/main/scripts) collection to find a script that fits your conversion needs.

> [!TIP]
> Scripts that have "`to_diffusers`" appended at the end mean they convert a model to the Diffusers-multifolder layout. Each script has their own specific set of arguments for configuring the conversion, so make sure you check what arguments are available!

For example, to convert a Stable Diffusion XL model stored in Diffusers-multifolder layout to a single-file layout, run the [convert_diffusers_to_original_sdxl.py](https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_sdxl.py) script. Provide the path to the model to convert, and the path to save the converted model to. You can optionally specify whether you want to save the model as a safetensors file and whether to save the model in half-precision.

```bash
python convert_diffusers_to_original_sdxl.py --model_path path/to/model/to/convert --checkpoint_path path/to/save/model/to --use_safetensors

from diffusers import StableDiffusionXLPipeline

pipeline = StableDiffusionXLPipeline.from_single_file(
    "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors",
)
pipeline.save_pretrained()

from diffusers import StableDiffusionXLPipeline, DDIMScheduler

ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
scheduler = DDIMScheduler()
pipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path, scheduler=scheduler)

from diffusers import StableDiffusionControlNetPipeline, ControlNetModel

ckpt_path = "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors"
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
pipeline = StableDiffusionControlNetPipeline.from_single_file(ckpt_path, controlnet=controlnet)

from huggingface_hub import hf_hub_download, snapshot_download

my_local_checkpoint_path = hf_hub_download(
    repo_id="segmind/SSD-1B",
    filename="SSD-1B.safetensors"
    local_dir="my_local_checkpoints",
    local_dir_use_symlinks=False
)
print("My local checkpoint: ", my_local_checkpoint_path)

my_local_config_path = snapshot_download(
    repo_id="segmind/SSD-1B",
    allow_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
    local_dir_use_symlinks=False,
)
print("My local config: ", my_local_config_path)

pipeline = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)