423 lines
14 KiB
Python
423 lines
14 KiB
Python
from __future__ import annotations
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import io
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import os
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from urllib.request import Request, urlopen
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import diffusers
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import yaml
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from modal import Image, Mount, Secret, Stub, method
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from modal.cls import ClsMixin
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BASE_CACHE_PATH = "/vol/cache"
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BASE_CACHE_PATH_LORA = "/vol/cache/lora"
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BASE_CACHE_PATH_TEXTUAL_INVERSION = "/vol/cache/textual_inversion"
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BASE_CACHE_PATH_CONTROLNET = "/vol/cache/controlnet"
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def download_file(url, file_name, file_path):
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"""
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Download files.
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"""
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req = Request(url, headers={"User-Agent": "Mozilla/5.0"})
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downloaded = urlopen(req).read()
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dir_names = os.path.join(file_path, file_name)
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os.makedirs(os.path.dirname(dir_names), exist_ok=True)
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with open(dir_names, mode="wb") as f:
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f.write(downloaded)
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def download_controlnet(name: str, repo_id: str, token: str):
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"""
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Download a controlnet.
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"""
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cache_path = os.path.join(BASE_CACHE_PATH_CONTROLNET, name)
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controlnet = diffusers.ControlNetModel.from_pretrained(
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repo_id,
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use_auth_token=token,
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cache_dir=cache_path,
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)
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controlnet.save_pretrained(cache_path, safe_serialization=True)
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def download_vae(name: str, repo_id: str, token: str):
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"""
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Download a vae.
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"""
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cache_path = os.path.join(BASE_CACHE_PATH, name)
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vae = diffusers.AutoencoderKL.from_pretrained(
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repo_id,
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use_auth_token=token,
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cache_dir=cache_path,
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)
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vae.save_pretrained(cache_path, safe_serialization=True)
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def download_model(name: str, repo_id: str, token: str):
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"""
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Download a model.
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"""
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cache_path = os.path.join(BASE_CACHE_PATH, name)
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pipe = diffusers.StableDiffusionPipeline.from_pretrained(
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repo_id,
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use_auth_token=token,
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cache_dir=cache_path,
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)
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pipe.save_pretrained(cache_path, safe_serialization=True)
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def build_image():
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"""
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Build the Docker image.
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"""
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token = os.environ["HUGGING_FACE_TOKEN"]
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config = {}
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with open("/config.yml", "r") as file:
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config = yaml.safe_load(file)
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model = config.get("model")
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if model is not None:
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download_model(name=model["name"], repo_id=model["repo_id"], token=token)
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vae = config.get("vae")
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if vae is not None:
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download_vae(name=model["name"], repo_id=vae["repo_id"], token=token)
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controlnets = config.get("controlnets")
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if controlnets is not None:
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for controlnet in controlnets:
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download_controlnet(name=controlnet["name"], repo_id=controlnet["repo_id"], token=token)
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loras = config.get("loras")
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if loras is not None:
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for lora in loras:
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download_file(
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url=lora["download_url"],
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file_name=lora["name"],
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file_path=BASE_CACHE_PATH_LORA,
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)
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textual_inversions = config.get("textual_inversions")
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if textual_inversions is not None:
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for textual_inversion in textual_inversions:
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download_file(
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url=textual_inversion["download_url"],
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file_name=textual_inversion["name"],
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file_path=BASE_CACHE_PATH_TEXTUAL_INVERSION,
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)
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stub = Stub("stable-diffusion-cli")
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base_stub = Image.from_dockerfile(
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path="./setup_files/Dockerfile",
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context_mount=Mount.from_local_file("./setup_files/requirements.txt"),
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)
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stub.image = base_stub.extend(
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dockerfile_commands=[
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"FROM base",
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"COPY ./config.yml /",
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],
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context_mount=Mount.from_local_file("./setup_files/config.yml"),
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).run_function(
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build_image,
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secrets=[Secret.from_dotenv(__file__)],
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)
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@stub.cls(
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gpu="A10G",
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secrets=[Secret.from_dotenv(__file__)],
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)
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class StableDiffusion(ClsMixin):
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"""
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A class that wraps the Stable Diffusion pipeline and scheduler.
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"""
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def __enter__(self):
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import torch
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config = {}
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with open("/config.yml", "r") as file:
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config = yaml.safe_load(file)
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self.cache_path = os.path.join(BASE_CACHE_PATH, config["model"]["name"])
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if os.path.exists(self.cache_path):
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print(f"The directory '{self.cache_path}' exists.")
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else:
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print(f"The directory '{self.cache_path}' does not exist.")
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torch.cuda.memory._set_allocator_settings("max_split_size_mb:256")
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self.pipe = diffusers.StableDiffusionPipeline.from_pretrained(
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self.cache_path,
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custom_pipeline="lpw_stable_diffusion",
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torch_dtype=torch.float16,
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)
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# TODO: Add support for other schedulers.
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self.pipe.scheduler = diffusers.EulerAncestralDiscreteScheduler.from_pretrained(
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# self.pipe.scheduler = diffusers.DPMSolverMultistepScheduler.from_pretrained(
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self.cache_path,
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subfolder="scheduler",
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)
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vae = config.get("vae")
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if vae is not None:
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self.pipe.vae = diffusers.AutoencoderKL.from_pretrained(
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self.cache_path,
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subfolder="vae",
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)
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self.pipe.to("cuda")
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loras = config.get("loras")
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if loras is not None:
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for lora in loras:
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path = os.path.join(BASE_CACHE_PATH_LORA, lora["name"])
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if os.path.exists(path):
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print(f"The directory '{path}' exists.")
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else:
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print(f"The directory '{path}' does not exist. Download it...")
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download_file(lora["download_url"], lora["name"], BASE_CACHE_PATH_LORA)
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self.pipe.load_lora_weights(".", weight_name=path)
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textual_inversions = config.get("textual_inversions")
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if textual_inversions is not None:
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for textual_inversion in textual_inversions:
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path = os.path.join(BASE_CACHE_PATH_TEXTUAL_INVERSION, textual_inversion["name"])
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if os.path.exists(path):
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print(f"The directory '{path}' exists.")
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else:
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print(f"The directory '{path}' does not exist. Download it...")
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download_file(
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textual_inversion["download_url"],
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textual_inversion["name"],
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BASE_CACHE_PATH_TEXTUAL_INVERSION,
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)
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self.pipe.load_textual_inversion(path)
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self.pipe.enable_xformers_memory_efficient_attention()
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# TODO: Add support for controlnets.
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# controlnet = diffusers.ControlNetModel.from_pretrained(
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# "lllyasviel/control_v11f1e_sd15_tile",
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# # "lllyasviel/sd-controlnet-canny",
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# # self.cache_path,
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# # subfolder="controlnet",
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# torch_dtype=torch.float16,
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# )
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# self.controlnet_pipe = diffusers.StableDiffusionControlNetPipeline.from_pretrained(
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# self.cache_path,
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# controlnet=controlnet,
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# custom_pipeline="lpw_stable_diffusion",
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# # custom_pipeline="stable_diffusion_controlnet_img2img",
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# scheduler=self.pipe.scheduler,
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# vae=self.pipe.vae,
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# torch_dtype=torch.float16,
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# )
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# self.controlnet_pipe.to("cuda")
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# self.controlnet_pipe.enable_xformers_memory_efficient_attention()
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@method()
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def count_token(self, p: str, n: str) -> int:
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"""
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Count the number of tokens in the prompt and negative prompt.
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"""
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from transformers import CLIPTokenizer
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tokenizer = CLIPTokenizer.from_pretrained(
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self.cache_path,
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subfolder="tokenizer",
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)
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token_size_p = len(tokenizer.tokenize(p))
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token_size_n = len(tokenizer.tokenize(n))
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token_size = token_size_p
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if token_size_p <= token_size_n:
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token_size = token_size_n
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max_embeddings_multiples = 1
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max_length = tokenizer.model_max_length - 2
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if token_size > max_length:
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max_embeddings_multiples = token_size // max_length + 1
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print(f"token_size: {token_size}, max_embeddings_multiples: {max_embeddings_multiples}")
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return max_embeddings_multiples
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@method()
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def run_inference(
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self,
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prompt: str,
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n_prompt: str,
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height: int = 512,
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width: int = 512,
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samples: int = 1,
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batch_size: int = 1,
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steps: int = 30,
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seed: int = 1,
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upscaler: str = "",
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use_face_enhancer: bool = False,
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use_hires_fix: bool = False,
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) -> list[bytes]:
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"""
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Runs the Stable Diffusion pipeline on the given prompt and outputs images.
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"""
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import torch
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max_embeddings_multiples = self.count_token(p=prompt, n=n_prompt)
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generator = torch.Generator("cuda").manual_seed(seed)
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with torch.inference_mode():
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with torch.autocast("cuda"):
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base_images = self.pipe.text2img(
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prompt * batch_size,
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negative_prompt=n_prompt * batch_size,
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height=height,
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width=width,
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num_inference_steps=steps,
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guidance_scale=7.5,
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max_embeddings_multiples=max_embeddings_multiples,
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generator=generator,
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).images
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# for image in base_images:
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# image = self.resize_image(image=image, scale_factor=2)
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# with torch.inference_mode():
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# with torch.autocast("cuda"):
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# generatedWithControlnet = self.controlnet_pipe(
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# prompt=prompt * batch_size,
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# negative_prompt=n_prompt * batch_size,
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# num_inference_steps=steps,
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# strength=0.3,
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# guidance_scale=7.5,
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# max_embeddings_multiples=max_embeddings_multiples,
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# generator=generator,
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# image=image,
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# ).images
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# base_images.extend(generatedWithControlnet)
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if upscaler != "":
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upscaled = self.upscale(
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base_images=base_images,
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half_precision=False,
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tile=700,
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upscaler=upscaler,
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use_face_enhancer=use_face_enhancer,
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use_hires_fix=use_hires_fix,
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)
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base_images.extend(upscaled)
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if use_hires_fix:
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for img in upscaled:
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with torch.inference_mode():
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with torch.autocast("cuda"):
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hires_fixed = self.pipe.img2img(
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prompt=prompt * batch_size,
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negative_prompt=n_prompt * batch_size,
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num_inference_steps=steps,
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strength=0.3,
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guidance_scale=7.5,
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max_embeddings_multiples=max_embeddings_multiples,
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generator=generator,
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image=img,
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).images
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base_images.extend(hires_fixed)
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image_output = []
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for image in base_images:
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with io.BytesIO() as buf:
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image.save(buf, format="PNG")
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image_output.append(buf.getvalue())
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return image_output
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@method()
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def resize_image(self, image: Image.Image, scale_factor: int) -> Image.Image:
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from PIL import Image
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image = image.convert("RGB")
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width, height = image.size
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img = image.resize((width * scale_factor, height * scale_factor), resample=Image.LANCZOS)
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return img
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@method()
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def upscale(
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self,
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base_images: list[Image.Image],
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half_precision: bool = False,
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tile: int = 0,
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tile_pad: int = 10,
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pre_pad: int = 0,
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upscaler: str = "",
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use_face_enhancer: bool = False,
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use_hires_fix: bool = False,
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) -> list[Image.Image]:
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"""
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Upscales the given images using a upscaler.
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https://github.com/xinntao/Real-ESRGAN
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"""
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import numpy
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import torch
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from PIL import Image
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from realesrgan import RealESRGANer
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from tqdm import tqdm
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model_name = upscaler
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if model_name == "RealESRGAN_x4plus":
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upscale_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
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netscale = 4
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elif model_name == "RealESRNet_x4plus":
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upscale_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
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netscale = 4
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elif model_name == "RealESRGAN_x4plus_anime_6B":
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upscale_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
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netscale = 4
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elif model_name == "RealESRGAN_x2plus":
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upscale_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
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netscale = 2
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else:
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raise NotImplementedError("Model name not supported")
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upsampler = RealESRGANer(
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scale=netscale,
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model_path=os.path.join(BASE_CACHE_PATH, "esrgan", f"{model_name}.pth"),
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dni_weight=None,
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model=upscale_model,
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tile=tile,
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tile_pad=tile_pad,
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pre_pad=pre_pad,
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half=half_precision,
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gpu_id=None,
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)
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from gfpgan import GFPGANer
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if use_face_enhancer:
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face_enhancer = GFPGANer(
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model_path=os.path.join(BASE_CACHE_PATH, "esrgan", "GFPGANv1.3.pth"),
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upscale=netscale,
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arch="clean",
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channel_multiplier=2,
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bg_upsampler=upsampler,
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)
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upscaled_imgs = []
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with tqdm(total=len(base_images)) as progress_bar:
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for img in base_images:
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img = numpy.array(img)
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if use_face_enhancer:
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_, _, enhance_result = face_enhancer.enhance(
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img,
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has_aligned=False,
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only_center_face=False,
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paste_back=True,
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)
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else:
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enhance_result, _ = upsampler.enhance(img)
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upscaled_imgs.append(Image.fromarray(enhance_result))
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progress_bar.update(1)
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return upscaled_imgs
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