DMD2

Podemos usar la tubería de difusión estándar:

4-step UNet generation

import torch
from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
repo_name = "tianweiy/DMD2"
ckpt_name = "dmd2_sdxl_4step_unet_fp16.bin"
# Load model.
unet = UNet2DConditionModel.from_config(base_model_id, subfolder="unet").to("cuda", torch.float16)
unet.load_state_dict(torch.load(hf_hub_download(repo_name, ckpt_name), map_location="cuda"))
pipe = DiffusionPipeline.from_pretrained(base_model_id, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
prompt="una foto de un gato"
# Los pasos de tiempo por defecto de LCMScheduler son diferentes de los que usamos para el entrenamiento
image=pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0, timesteps=[999, 749, 499, 249]).images[0]

4-step LoRA generation

import torch
from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
repo_name = "tianweiy/DMD2"
ckpt_name = "dmd2_sdxl_4step_lora_fp16.safetensors"
# Load model.
pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to("cuda")
pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name))
pipe.fuse_lora(lora_scale=1.0) # podríamos querer reducir la escala para modelos comunitarios
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
prompt="una foto de un gato"
# Los pasos de tiempo por defecto de LCMScheduler son diferentes de los que usamos para el entrenamiento
image=pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0, timesteps=[999, 749, 499, 249]).images[0]

1-step UNet generation

import torch
from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
repo_name = "tianweiy/DMD2"
ckpt_name = "dmd2_sdxl_1step_unet_fp16.bin"
# Load model.
unet = UNet2DConditionModel.from_config(base_model_id, subfolder="unet").to("cuda", torch.float16)
unet.load_state_dict(torch.load(hf_hub_download(repo_name, ckpt_name), map_location="cuda"))
pipe = DiffusionPipeline.from_pretrained(base_model_id, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
prompt="una foto de un gato"
image=pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0, timesteps=[399]).images[0]

4-step T2I Adapter

from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, AutoencoderKL, UNet2DConditionModel, LCMScheduler
from diffusers.utils import load_image, make_image_grid
from controlnet_aux.canny import CannyDetector
from huggingface_hub import hf_hub_download
import torch
# load adapter
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-canny-sdxl-1.0", torch_dtype=torch.float16, varient="fp16").to("cuda")
vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
repo_name = "tianweiy/DMD2"
ckpt_name = "dmd2_sdxl_4step_unet_fp16.bin"
# Load model.
unet = UNet2DConditionModel.from_config(base_model_id, subfolder="unet").to"cuda", torch.float16)
unet.load_state_dict(torch.load(hf_hub_download(repo_name, ckpt_name), map_location="cuda"))
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
base_model_id, unet=unet, vae=vae, adapter=adapter, torch_dtype=torch.float16, variant="fp16", 
).to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
canny_detector = CannyDetector()
url = "https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_canny.jpg"
image = load_image(url)
# Detect the canny map in low resolution to avoid high-frequency details
image = canny_detector(image, detect_resolution=384, image_resolution=1024)#.resize((1024, 1024))
prompt = "Hada mística en la vida real, mágica, imagen 4k, alta calidad"
gen_images = pipe(
prompt=prompt,
image=image,
num_inference_steps=4,
guidance_scale=0, 
adapter_conditioning_scale=0.8, 
adapter_conditioning_factor=0.5,
timesteps=[999, 749, 499, 249]
).images[0]
gen_images.save('out_canny.png')

Para obtener más información, consulte el repositorio de código.