237 lines
7.4 KiB
Python
237 lines
7.4 KiB
Python
from __future__ import annotations
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import io
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import os
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import time
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from modal import Image, Mount, Secret, Stub, method
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import util
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BASE_CACHE_PATH = "/vol/cache"
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def download_models():
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"""
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Downloads the model from Hugging Face and saves it to the cache path using
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diffusers.StableDiffusionPipeline.from_pretrained().
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"""
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import diffusers
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hugging_face_token = os.environ["HUGGING_FACE_TOKEN"]
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model_repo_id = os.environ["MODEL_REPO_ID"]
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cache_path = os.path.join(BASE_CACHE_PATH, os.environ["MODEL_NAME"])
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vae = diffusers.AutoencoderKL.from_pretrained(
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"stabilityai/sd-vae-ft-mse",
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use_auth_token=hugging_face_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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scheduler = diffusers.EulerAncestralDiscreteScheduler.from_pretrained(
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model_repo_id,
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subfolder="scheduler",
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use_auth_token=hugging_face_token,
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cache_dir=cache_path,
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)
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scheduler.save_pretrained(cache_path, safe_serialization=True)
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pipe = diffusers.StableDiffusionPipeline.from_pretrained(
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model_repo_id,
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use_auth_token=hugging_face_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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stub_image = Image.from_dockerfile(
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path="./Dockerfile",
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context_mount=Mount.from_local_file("./requirements.txt"),
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).run_function(
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download_models,
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secrets=[Secret.from_dotenv(__file__)],
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)
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stub = Stub("stable-diffusion-cli")
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stub.image = stub_image
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@stub.cls(gpu="A10G", secrets=[Secret.from_dotenv(__file__)])
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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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cache_path = os.path.join(BASE_CACHE_PATH, os.environ["MODEL_NAME"])
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if os.path.exists(cache_path):
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print(f"The directory '{cache_path}' exists.")
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else:
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print(f"The directory '{cache_path}' does not exist. Download models...")
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download_models()
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torch.backends.cuda.matmul.allow_tf32 = True
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vae = diffusers.AutoencoderKL.from_pretrained(
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cache_path,
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subfolder="vae",
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)
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scheduler = diffusers.EulerAncestralDiscreteScheduler.from_pretrained(
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cache_path,
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subfolder="scheduler",
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)
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self.pipe = diffusers.StableDiffusionPipeline.from_pretrained(
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cache_path,
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scheduler=scheduler,
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vae=vae,
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custom_pipeline="lpw_stable_diffusion",
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torch_dtype=torch.float16,
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).to("cuda")
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self.pipe.enable_xformers_memory_efficient_attention()
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@method()
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def run_inference(self, inputs: dict[str, int | str]) -> 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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with torch.inference_mode():
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with torch.autocast("cuda"):
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base_images = self.pipe(
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[inputs["prompt"]] * int(inputs["batch_size"]),
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negative_prompt=[inputs["n_prompt"]] * int(inputs["batch_size"]),
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height=inputs["height"],
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width=inputs["width"],
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num_inference_steps=inputs["steps"],
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guidance_scale=7.5,
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max_embeddings_multiples=inputs["max_embeddings_multiples"],
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).images
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if inputs["upscaler"] != "":
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uplcaled_images = self.upscale(
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base_images=base_images,
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model_name="RealESRGAN_x4plus",
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scale_factor=4,
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half_precision=False,
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tile=700,
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)
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base_images.extend(uplcaled_images)
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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 upscale(
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self,
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base_images: list[Image.Image],
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model_name: str = "RealESRGAN_x4plus",
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scale_factor: float = 4,
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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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) -> list[Image.Image]:
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"""
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Upscales the given images using the given model.
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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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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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torch.cuda.empty_cache()
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upscaled_imgs = []
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with tqdm(total=len(base_images)) as progress_bar:
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for i, img in enumerate(base_images):
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img = numpy.array(img)
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enhance_result = upsampler.enhance(img)[0]
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upscaled_imgs.append(Image.fromarray(enhance_result))
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progress_bar.update(1)
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torch.cuda.empty_cache()
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return upscaled_imgs
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@stub.local_entrypoint()
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def entrypoint(
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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 = 5,
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batch_size: int = 1,
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steps: int = 20,
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upscaler: str = "",
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):
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"""
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This function is the entrypoint for the Runway CLI.
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The function pass the given prompt to StableDiffusion on Modal,
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gets back a list of images and outputs images to local.
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"""
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inputs: dict[str, int | str] = {
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"prompt": prompt,
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"n_prompt": n_prompt,
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"height": height,
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"width": width,
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"samples": samples,
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"batch_size": batch_size,
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"steps": steps,
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"upscaler": upscaler, # sd_x2_latent_upscaler, sd_x4_upscaler
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# seed=-1
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}
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inputs["max_embeddings_multiples"] = util.count_token(p=prompt, n=n_prompt)
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directory = util.make_directory()
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sd = StableDiffusion()
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for i in range(samples):
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start_time = time.time()
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images = sd.run_inference.call(inputs)
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util.save_images(directory, images, i)
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total_time = time.time() - start_time
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print(f"Sample {i} took {total_time:.3f}s ({(total_time)/len(images):.3f}s / image).")
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util.save_prompts(inputs)
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