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from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img import (
retrieve_timesteps,
retrieve_latents,
)
import torch
from functools import partial
from diffusers import DDPMScheduler
from model.pipeline_sdxl import StableDiffusionXLImg2ImgPipeline
SAMPLING_DEVICE = "cpu" # "cuda"
VAE_SAMPLE = "argmax" # "argmax" or "sample"
RESIZE_TYPE = None # Image.LANCZOS
device = "cuda" if torch.cuda.is_available() else "cpu"
def encode_image(image, pipe, generator):
pipe_dtype = pipe.dtype
image = pipe.image_processor.preprocess(image)
image = image.to(device=device, dtype=pipe.dtype)
if pipe.vae.config.force_upcast:
image = image.float()
pipe.vae.to(dtype=torch.float32)
init_latents = retrieve_latents(
pipe.vae.encode(image), generator=generator, sample_mode=VAE_SAMPLE
)
if pipe.vae.config.force_upcast:
pipe.vae.to(pipe_dtype)
init_latents = init_latents.to(pipe_dtype)
init_latents = pipe.vae.config.scaling_factor * init_latents
return init_latents
def create_xts(
noise_shift_delta,
noise_timesteps,
generator,
scheduler,
timesteps,
x_0,
):
if noise_timesteps is None:
noising_delta = noise_shift_delta * (timesteps[0] - timesteps[1])
noise_timesteps = [timestep - int(noising_delta) for timestep in timesteps]
# noise_timesteps = [timestep for timestep in timesteps]
# print(noise_timesteps, timesteps)
first_x_0_idx = len(noise_timesteps)
for i in range(len(noise_timesteps)):
if noise_timesteps[i] <= 0:
first_x_0_idx = i
break
noise_timesteps = noise_timesteps[:first_x_0_idx]
x_0_expanded = x_0.expand(len(noise_timesteps), -1, -1, -1)
noise = torch.randn(
x_0_expanded.size(), generator=generator, device=SAMPLING_DEVICE
).to(x_0.device)
x_ts = scheduler.add_noise(
x_0_expanded,
noise,
torch.IntTensor(noise_timesteps),
)
x_ts = [t.unsqueeze(dim=0) for t in list(x_ts)]
x_ts += [x_0] * (len(timesteps) - first_x_0_idx)
x_ts += [x_0]
return x_ts
def load_pipeline(fp16, cache_dir):
kwargs = (
{
"torch_dtype": torch.float16,
"variant": "fp16",
}
if fp16
else {}
)
from model.unet_sdxl import OursUNet2DConditionModel
unet = OursUNet2DConditionModel.from_pretrained(
"stabilityai/sdxl-turbo",
subfolder="unet",
cache_dir=cache_dir,
safety_checker=None,
**kwargs,
)
pipeline = StableDiffusionXLImg2ImgPipeline.from_pretrained(
"stabilityai/sdxl-turbo",
unet=unet,
cache_dir=cache_dir,
safety_checker=None,
**kwargs,
)
pipeline = pipeline.to(device)
pipeline.scheduler = DDPMScheduler.from_pretrained( # type: ignore
"stabilityai/sdxl-turbo",
subfolder="scheduler",
)
return pipeline
def set_pipeline(pipeline: StableDiffusionXLImg2ImgPipeline, num_timesteps, generator, config):
if config.timesteps is None:
denoising_start = config.step_start / config.num_steps_inversion
timesteps, num_inference_steps = retrieve_timesteps(
pipeline.scheduler, config.num_steps_inversion, device, None
)
timesteps, num_inference_steps = pipeline.get_timesteps(
num_inference_steps=num_inference_steps,
device=device,
denoising_start=denoising_start,
strength=0,
)
timesteps = timesteps.type(torch.int64)
pipeline.__call__ = partial(
pipeline.__call__,
num_inference_steps=config.num_steps_inversion,
guidance_scale=config.guidance_scale,
generator=generator,
denoising_start=denoising_start,
strength=0,
)
pipeline.scheduler.set_timesteps(
timesteps=timesteps.cpu(),
)
else:
timesteps = torch.tensor(config.timesteps, dtype=torch.int64)
pipeline.__call__ = partial(
pipeline.__call__,
timesteps=timesteps,
guidance_scale=config.guidance_scale,
denoising_start=0,
strength=1,
)
pipeline.scheduler.set_timesteps(
timesteps=config.timesteps, # device=pipeline.device
)
timesteps = [torch.tensor(t) for t in timesteps.tolist()]
return timesteps, config
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