load_ckpt.py

#!/usr/bin/env python3
from diffusers import StableDiffusionPipeline, KDPM2DiscreteScheduler, StableDiffusionImg2ImgPipeline, HeunDiscreteScheduler, KDPM2AncestralDiscreteScheduler, DDIMScheduler
import time
import os
from huggingface_hub import HfApi
# from compel import Compel
import torch
import sys
from pathlib import Path
import requests
from PIL import Image
from io import BytesIO

path = sys.argv[1]

api = HfApi()
start_time = time.time()
pipe = StableDiffusionPipeline.from_ckpt(path, torch_dtype=torch.float16)
import ipdb; ipdb.set_trace()

pipe = pipe.to("cuda")

prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k"

# rompts = ["a cat playing with a ball++ in the forest", "a cat playing with a ball++ in the forest", "a cat playing with a ball-- in the forest"]

# prompt_embeds = torch.cat([compel.build_conditioning_tensor(prompt) for prompt in prompts])

# generator = [torch.Generator(device="cuda").manual_seed(0) for _ in range(prompt_embeds.shape[0])]
#
# url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
# 
# response = requests.get(url)
# image = Image.open(BytesIO(response.content)).convert("RGB")
# image.thumbnail((768, 768))
#

for TIMESTEP_TYPE in ["trailing", "leading"]:
    for RESCALE_BETAS_ZEROS_SNR in [True, False]:
        for GUIDANCE_RESCALE in [0,0, 0.7]:

            pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing=TIMESTEP_TYPE, rescale_betas_zero_snr=RESCALE_BETAS_ZEROS_SNR)
            generator = torch.Generator(device="cpu").manual_seed(0)
            images = pipe(prompt=prompt, generator=generator, num_images_per_prompt=4, num_inference_steps=40, guidance_rescale=GUIDANCE_RESCALE).images

            for i, image in enumerate(images):
                file_name = f"bb_{i}_{TIMESTEP_TYPE}_{str(int(RESCALE_BETAS_ZEROS_SNR))}_{GUIDANCE_RESCALE}"
                path = os.path.join(Path.home(), "images", f"{file_name}.png")
                image.save(path)

                api.upload_file(
                    path_or_fileobj=path,
                    path_in_repo=path.split("/")[-1],
                    repo_id="patrickvonplaten/images",
                    repo_type="dataset",
                )
                print(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/{file_name}.png")