291 lines
10 KiB
Python
291 lines
10 KiB
Python
from __future__ import annotations
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import io
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import os
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import PIL.Image
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from modal import Secret, method
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from setup import (
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BASE_CACHE_PATH,
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BASE_CACHE_PATH_CONTROLNET,
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BASE_CACHE_PATH_LORA,
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BASE_CACHE_PATH_TEXTUAL_INVERSION,
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stub,
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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:
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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 diffusers
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import torch
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import yaml
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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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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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use_safetensors=True,
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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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use_safetensors=True,
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)
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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. Need to execute 'modal deploy' first.")
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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. Need to execute 'modal deploy' first.")
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self.pipe.load_textual_inversion(path)
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# TODO: Repair the controlnet loading.
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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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path = os.path.join(BASE_CACHE_PATH_CONTROLNET, controlnet["name"])
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controlnet = diffusers.ControlNetModel.from_pretrained(path, torch_dtype=torch.float16)
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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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scheduler=self.pipe.scheduler,
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vae=self.pipe.vae,
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torch_dtype=torch.float16,
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use_safetensors=True,
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)
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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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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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fix_by_controlnet_tile: bool = False,
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output_format: str = "png",
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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 pillow_avif
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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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self.pipe.to("cuda")
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self.pipe.enable_vae_tiling()
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self.pipe.enable_xformers_memory_efficient_attention()
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with torch.autocast("cuda"):
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generated_images = self.pipe(
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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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base_images = generated_images
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"""
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Fix the generated images by the control_v11f1e_sd15_tile when `fix_by_controlnet_tile` is `True`.
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https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile
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"""
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if fix_by_controlnet_tile:
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self.controlnet_pipe.to("cuda")
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self.controlnet_pipe.enable_vae_tiling()
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self.controlnet_pipe.enable_xformers_memory_efficient_attention()
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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.autocast("cuda"):
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fixed_by_controlnet = 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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generated_images.extend(fixed_by_controlnet)
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base_images = fixed_by_controlnet
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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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)
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generated_images.extend(upscaled)
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image_output = []
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for image in generated_images:
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with io.BytesIO() as buf:
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image.save(buf, format=output_format)
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image_output.append(buf.getvalue())
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return image_output
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def _resize_image(self, image: PIL.Image.Image, scale_factor: int) -> PIL.Image.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=PIL.Image.LANCZOS)
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return img
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def _upscale(
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self,
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base_images: list[PIL.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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) -> list[PIL.Image]:
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"""
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Upscale the generated images by the upscaler when `upscaler` is selected.
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The upscaler can be selected from the following list:
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- `RealESRGAN_x4plus`
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- `RealESRNet_x4plus`
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- `RealESRGAN_x4plus_anime_6B`
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- `RealESRGAN_x2plus`
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https://github.com/xinntao/Real-ESRGAN
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"""
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import numpy
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from gfpgan import GFPGANer
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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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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(PIL.Image.fromarray(enhance_result))
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progress_bar.update(1)
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return upscaled_imgs
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