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import logging
from typing import Any, Optional
import torch
from diffusers import (
AutoencoderKL,
DDPMScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
LCMScheduler,
Transformer2DModel,
UNet2DConditionModel,
)
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from models.RewardPixart import RewardPixartPipeline, freeze_params
from models.RewardStableDiffusion import RewardStableDiffusion
from models.RewardStableDiffusionXL import RewardStableDiffusionXL
from models.RewardFlux import RewardFluxPipeline
def get_model(
model_name: str,
dtype: torch.dtype,
device: torch.device,
cache_dir: str,
memsave: bool = False,
enable_sequential_cpu_offload: bool = False,
):
logging.info(f"Loading model: {model_name}")
if model_name == "sd-turbo":
pipe = RewardStableDiffusion.from_pretrained(
"stabilityai/sd-turbo",
torch_dtype=dtype,
variant="fp16",
cache_dir=cache_dir,
memsave=memsave,
)
#pipe = pipe.to(device, dtype)
elif model_name == "sdxl-turbo":
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix",
torch_dtype=torch.float16,
cache_dir=cache_dir,
)
pipe = RewardStableDiffusionXL.from_pretrained(
"stabilityai/sdxl-turbo",
vae=vae,
torch_dtype=dtype,
variant="fp16",
use_safetensors=True,
cache_dir=cache_dir,
memsave=memsave,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(
pipe.scheduler.config, timestep_spacing="trailing"
)
#pipe = pipe.to(device, dtype)
elif model_name == "pixart":
pipe = RewardPixartPipeline.from_pretrained(
"PixArt-alpha/PixArt-XL-2-1024-MS",
torch_dtype=dtype,
cache_dir=cache_dir,
memsave=memsave,
)
pipe.transformer = Transformer2DModel.from_pretrained(
"PixArt-alpha/PixArt-Alpha-DMD-XL-2-512x512",
subfolder="transformer",
torch_dtype=dtype,
cache_dir=cache_dir,
)
pipe.scheduler = DDPMScheduler.from_pretrained(
"PixArt-alpha/PixArt-Alpha-DMD-XL-2-512x512",
subfolder="scheduler",
cache_dir=cache_dir,
)
# speed-up T5
pipe.text_encoder.to_bettertransformer()
pipe.transformer.eval()
freeze_params(pipe.transformer.parameters())
pipe.transformer.enable_gradient_checkpointing()
#pipe = pipe.to(device)
elif model_name == "hyper-sd":
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
repo_name = "ByteDance/Hyper-SD"
ckpt_name = "Hyper-SDXL-1step-Unet.safetensors"
# Load model but don't specify device or dtype (defaults to CPU and float32)
unet = UNet2DConditionModel.from_config(
base_model_id, subfolder="unet", cache_dir=cache_dir
)
# Load state dict into unet (stays on CPU by default)
unet.load_state_dict(
load_file(
hf_hub_download(repo_name, ckpt_name, cache_dir=cache_dir),
device="cuda",
)
)
# Initialize the pipeline (it will stay on CPU initially, using default dtype)
pipe = RewardStableDiffusionXL.from_pretrained(
base_model_id,
unet=unet,
torch_dtype=torch.float16,
variant="fp16", # Still set fp16 for later use on GPU
cache_dir=cache_dir,
is_hyper=True,
memsave=memsave,
)
# Use LCM scheduler instead of ddim scheduler to support specific timestep number inputs
pipe.scheduler = LCMScheduler.from_config(
pipe.scheduler.config, cache_dir=cache_dir
)
elif model_name == "flux":
pipe = RewardFluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-schnell",
torch_dtype=torch.float16,
cache_dir=cache_dir,
)
#pipe.to(device, dtype)
else:
raise ValueError(f"Unknown model name: {model_name}")
#if enable_sequential_cpu_offload:
# pipe.enable_sequential_cpu_offload()
return pipe
def get_multi_apply_fn(
model_type: str,
seed: int,
pipe: Optional[Any] = None,
cache_dir: Optional[str] = None,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
):
generator = torch.Generator("cuda").manual_seed(seed)
if model_type == "flux":
return lambda latents, prompt: torch.no_grad(pipe.apply)(
latents=latents,
prompt=prompt,
num_inference_steps=4,
generator=generator,
)
elif model_type == "sdxl":
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix",
torch_dtype=torch.float16,
cache_dir=cache_dir,
)
pipe = RewardStableDiffusionXL.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
vae=vae,
use_safetensors=True,
cache_dir=cache_dir,
)
pipe = pipe.to(device, dtype)
pipe.enable_sequential_cpu_offload()
return lambda latents, prompt: torch.no_grad(pipe.apply)(
latents=latents,
prompt=prompt,
guidance_scale=5.0,
num_inference_steps=50,
generator=generator,
)
elif model_type == "sd2":
sd2_base = "stabilityai/stable-diffusion-2-1-base"
scheduler = EulerDiscreteScheduler.from_pretrained(
sd2_base,
subfolder="scheduler",
cache_dir=cache_dir,
)
pipe = RewardStableDiffusion.from_pretrained(
sd2_base,
torch_dtype=dtype,
cache_dir=cache_dir,
scheduler=scheduler,
)
pipe = pipe.to(device, dtype)
pipe.enable_sequential_cpu_offload()
return lambda latents, prompt: torch.no_grad(pipe.apply)(
latents=latents,
prompt=prompt,
guidance_scale=7.5,
num_inference_steps=50,
generator=generator,
)
else:
raise ValueError(f"Unknown model type: {model_type}")
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