134 lines
4.0 KiB
Python
134 lines
4.0 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, enter, method
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from setup import BASE_CACHE_PATH, app
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@app.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 SDXLTxt2Img:
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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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@enter()
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def _setup(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.StableDiffusionXLPipeline.from_pretrained(
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self.cache_path,
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torch_dtype=torch.float16,
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use_safetensors=True,
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)
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self.refiner = diffusers.StableDiffusionXLImg2ImgPipeline.from_pretrained(
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self.cache_path,
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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 = 1024,
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width: int = 1024,
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steps: int = 30,
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seed: int = 1,
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use_upscaler: 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 # noqa
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import torch
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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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generated_image = self.pipe(
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prompt=prompt,
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negative_prompt=n_prompt,
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guidance_scale=7,
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height=height,
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width=width,
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generator=generator,
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num_inference_steps=steps,
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).images[0]
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generated_images = [generated_image]
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if use_upscaler:
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self.refiner.to("cuda")
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self.refiner.enable_vae_tiling()
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self.refiner.enable_xformers_memory_efficient_attention()
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base_image = self._double_image_size(generated_image)
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image = self.refiner(
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prompt=prompt,
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negative_prompt=n_prompt,
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num_inference_steps=50,
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strength=0.3,
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guidance_scale=7.5,
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generator=generator,
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image=base_image,
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).images[0]
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generated_images.append(image)
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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 _double_image_size(self, image: PIL.Image.Image) -> PIL.Image.Image:
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image = image.convert("RGB")
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width, height = image.size
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return image.resize((width * 2, height * 2), resample=PIL.Image.LANCZOS)
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