262 lines
9.1 KiB
Python
262 lines
9.1 KiB
Python
from __future__ import annotations
|
|
|
|
import io
|
|
import os
|
|
|
|
import PIL.Image
|
|
from modal import Secret, enter, method
|
|
from setup import BASE_CACHE_PATH, app
|
|
|
|
|
|
@app.cls(
|
|
gpu="A10G",
|
|
secrets=[Secret.from_dotenv(__file__)],
|
|
)
|
|
class SDXLTxt2Img:
|
|
"""
|
|
A class that wraps the Stable Diffusion pipeline and scheduler.
|
|
"""
|
|
|
|
@enter()
|
|
def _setup(self):
|
|
import diffusers
|
|
import torch
|
|
import yaml
|
|
|
|
config = {}
|
|
with open("/config.yml", "r") as file:
|
|
config = yaml.safe_load(file)
|
|
self.cache_path = os.path.join(BASE_CACHE_PATH, config["model"]["name"])
|
|
if os.path.exists(self.cache_path):
|
|
print(f"The directory '{self.cache_path}' exists.")
|
|
else:
|
|
print(f"The directory '{self.cache_path}' does not exist.")
|
|
|
|
self.pipe = diffusers.AutoPipelineForText2Image.from_pretrained(
|
|
self.cache_path,
|
|
torch_dtype=torch.float16,
|
|
use_safetensors=True,
|
|
variant="fp16",
|
|
)
|
|
|
|
self.refiner_cache_path = self.cache_path + "-refiner"
|
|
self.refiner = diffusers.StableDiffusionXLImg2ImgPipeline.from_pretrained(
|
|
self.refiner_cache_path,
|
|
torch_dtype=torch.float16,
|
|
use_safetensors=True,
|
|
variant="fp16",
|
|
)
|
|
|
|
# controlnets = config.get("controlnets")
|
|
# if controlnets is not None:
|
|
# for controlnet in controlnets:
|
|
# path = os.path.join(BASE_CACHE_PATH_CONTROLNET, controlnet["name"])
|
|
# controlnet = diffusers.ControlNetModel.from_pretrained(path, torch_dtype=torch.float16)
|
|
# self.controlnet_pipe = diffusers.StableDiffusionControlNetPipeline.from_pretrained(
|
|
# self.cache_path,
|
|
# controlnet=controlnet,
|
|
# custom_pipeline="lpw_stable_diffusion",
|
|
# scheduler=self.pipe.scheduler,
|
|
# vae=self.pipe.vae,
|
|
# torch_dtype=torch.float16,
|
|
# use_safetensors=True,
|
|
# )
|
|
|
|
def _count_token(self, p: str, n: str) -> int:
|
|
"""
|
|
Count the number of tokens in the prompt and negative prompt.
|
|
"""
|
|
from transformers import CLIPTokenizer
|
|
|
|
tokenizer = CLIPTokenizer.from_pretrained(
|
|
self.cache_path,
|
|
subfolder="tokenizer",
|
|
)
|
|
token_size_p = len(tokenizer.tokenize(p))
|
|
token_size_n = len(tokenizer.tokenize(n))
|
|
token_size = token_size_p
|
|
if token_size_p <= token_size_n:
|
|
token_size = token_size_n
|
|
|
|
max_embeddings_multiples = 1
|
|
max_length = tokenizer.model_max_length - 2
|
|
if token_size > max_length:
|
|
max_embeddings_multiples = token_size // max_length + 1
|
|
|
|
print(f"token_size: {token_size}, max_embeddings_multiples: {max_embeddings_multiples}")
|
|
|
|
return max_embeddings_multiples
|
|
|
|
@method()
|
|
def run_inference(
|
|
self,
|
|
prompt: str,
|
|
n_prompt: str,
|
|
height: int = 1024,
|
|
width: int = 1024,
|
|
steps: int = 30,
|
|
seed: int = 1,
|
|
upscaler: str = "",
|
|
use_face_enhancer: bool = False,
|
|
output_format: str = "png",
|
|
) -> list[bytes]:
|
|
"""
|
|
Runs the Stable Diffusion pipeline on the given prompt and outputs images.
|
|
"""
|
|
import pillow_avif # noqa
|
|
import torch
|
|
|
|
generator = torch.Generator("cuda").manual_seed(seed)
|
|
self.pipe.to("cuda")
|
|
generated_images = self.pipe(
|
|
prompt=prompt,
|
|
negative_prompt=n_prompt,
|
|
height=height,
|
|
width=width,
|
|
generator=generator,
|
|
).images
|
|
base_images = generated_images
|
|
|
|
for image in base_images:
|
|
image = self._resize_image(image=image, scale_factor=2)
|
|
self.refiner.to("cuda")
|
|
refined_images = self.refiner(
|
|
prompt=prompt,
|
|
negative_prompt=n_prompt,
|
|
num_inference_steps=steps,
|
|
strength=0.1,
|
|
# guidance_scale=7.5,
|
|
generator=generator,
|
|
image=image,
|
|
).images
|
|
generated_images.extend(refined_images)
|
|
base_images = refined_images
|
|
|
|
"""
|
|
Fix the generated images by the control_v11f1e_sd15_tile when `fix_by_controlnet_tile` is `True`.
|
|
https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile
|
|
"""
|
|
# if fix_by_controlnet_tile:
|
|
# max_embeddings_multiples = self._count_token(p=prompt, n=n_prompt)
|
|
# self.controlnet_pipe.to("cuda")
|
|
# self.controlnet_pipe.enable_vae_tiling()
|
|
# self.controlnet_pipe.enable_xformers_memory_efficient_attention()
|
|
# for image in base_images:
|
|
# image = self._resize_image(image=image, scale_factor=2)
|
|
# with torch.autocast("cuda"):
|
|
# fixed_by_controlnet = self.controlnet_pipe(
|
|
# prompt=prompt * batch_size,
|
|
# negative_prompt=n_prompt * batch_size,
|
|
# num_inference_steps=steps,
|
|
# strength=0.3,
|
|
# guidance_scale=7.5,
|
|
# max_embeddings_multiples=max_embeddings_multiples,
|
|
# generator=generator,
|
|
# image=image,
|
|
# ).images
|
|
# generated_images.extend(fixed_by_controlnet)
|
|
# base_images = fixed_by_controlnet
|
|
|
|
if upscaler != "":
|
|
upscaled = self._upscale(
|
|
base_images=base_images,
|
|
half_precision=False,
|
|
tile=700,
|
|
upscaler=upscaler,
|
|
use_face_enhancer=use_face_enhancer,
|
|
)
|
|
generated_images.extend(upscaled)
|
|
|
|
image_output = []
|
|
for image in generated_images:
|
|
with io.BytesIO() as buf:
|
|
image.save(buf, format=output_format)
|
|
image_output.append(buf.getvalue())
|
|
|
|
return image_output
|
|
|
|
def _resize_image(self, image: PIL.Image.Image, scale_factor: int) -> PIL.Image.Image:
|
|
image = image.convert("RGB")
|
|
width, height = image.size
|
|
img = image.resize((width * scale_factor, height * scale_factor), resample=PIL.Image.LANCZOS)
|
|
return img
|
|
|
|
def _upscale(
|
|
self,
|
|
base_images: list[PIL.Image],
|
|
half_precision: bool = False,
|
|
tile: int = 0,
|
|
tile_pad: int = 10,
|
|
pre_pad: int = 0,
|
|
upscaler: str = "",
|
|
use_face_enhancer: bool = False,
|
|
) -> list[PIL.Image]:
|
|
"""
|
|
Upscale the generated images by the upscaler when `upscaler` is selected.
|
|
The upscaler can be selected from the following list:
|
|
- `RealESRGAN_x4plus`
|
|
- `RealESRNet_x4plus`
|
|
- `RealESRGAN_x4plus_anime_6B`
|
|
- `RealESRGAN_x2plus`
|
|
https://github.com/xinntao/Real-ESRGAN
|
|
"""
|
|
import numpy
|
|
from basicsr.archs.rrdbnet_arch import RRDBNet
|
|
from gfpgan import GFPGANer
|
|
from realesrgan import RealESRGANer
|
|
from tqdm import tqdm
|
|
|
|
model_name = upscaler
|
|
if model_name == "RealESRGAN_x4plus":
|
|
upscale_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
|
|
netscale = 4
|
|
elif model_name == "RealESRNet_x4plus":
|
|
upscale_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
|
|
netscale = 4
|
|
elif model_name == "RealESRGAN_x4plus_anime_6B":
|
|
upscale_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
|
|
netscale = 4
|
|
elif model_name == "RealESRGAN_x2plus":
|
|
upscale_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
|
|
netscale = 2
|
|
else:
|
|
raise NotImplementedError("Model name not supported")
|
|
|
|
upsampler = RealESRGANer(
|
|
scale=netscale,
|
|
model_path=os.path.join(BASE_CACHE_PATH, "esrgan", f"{model_name}.pth"),
|
|
dni_weight=None,
|
|
model=upscale_model,
|
|
tile=tile,
|
|
tile_pad=tile_pad,
|
|
pre_pad=pre_pad,
|
|
half=half_precision,
|
|
gpu_id=None,
|
|
)
|
|
|
|
if use_face_enhancer:
|
|
face_enhancer = GFPGANer(
|
|
model_path=os.path.join(BASE_CACHE_PATH, "esrgan", "GFPGANv1.3.pth"),
|
|
upscale=netscale,
|
|
arch="clean",
|
|
channel_multiplier=2,
|
|
bg_upsampler=upsampler,
|
|
)
|
|
|
|
upscaled_imgs = []
|
|
for img in base_images:
|
|
img = numpy.array(img)
|
|
if use_face_enhancer:
|
|
_, _, enhance_result = face_enhancer.enhance(
|
|
img,
|
|
has_aligned=False,
|
|
only_center_face=False,
|
|
paste_back=True,
|
|
)
|
|
else:
|
|
enhance_result, _ = upsampler.enhance(img)
|
|
|
|
upscaled_imgs.append(PIL.Image.fromarray(enhance_result))
|
|
|
|
return upscaled_imgs
|