takuma104

from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
from torch
from PIL import Image

def image_grid(imgs, rows, cols):
    assert len(imgs) == rows*cols

    w, h = imgs[0].size
    grid = Image.new('RGB', size=(cols*w, rows*h))
    grid_w, grid_h = grid.size

takuma104

import torch
import safetensors.torch
import sys

def dump_keys(parent, suffix=''):
    for k in sorted(parent.keys()):
        if isinstance(parent[k], torch.Tensor):
            print(f'{suffix}{k} {list(parent[k].shape)}')
        else:
            dump_keys(parent[k], f'{suffix}{k}.')

takuma104

takuma104

takuma104

import torch
import torch.nn


def test_monkey_patch():
    x = torch.randn((2, 2)).cuda()
    target = torch.nn.Linear(2, 2).cuda()
    with torch.no_grad():
        y = target(x)
        assert y.shape == (2, 2)

takuma104

takuma104

takuma104

import torch
import safetensors.torch
import sys

def dump_keys(parent, suffix=''):
    for k in sorted(parent.keys()):
        if isinstance(parent[k], torch.Tensor):
            print(f'{suffix}{k} {list(parent[k].shape)} mean={torch.mean(parent[k]):.3g} std={torch.std(parent[k]):.3g}')
        else:
            dump_keys(parent[k], f'{suffix}{k}.')

takuma104

import torch
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler

def create_pipeline():
    pipe = StableDiffusionPipeline.from_pretrained(
        "gsdf/Counterfeit-V2.5", torch_dtype=torch.float16, safety_checker=None
    ).to("cuda")
    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)
    pipe.enable_xformers_memory_efficient_attention()
    return pipe

takuma104

import torch
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler

def create_pipeline():
    pipe = StableDiffusionPipeline.from_pretrained(
        "gsdf/Counterfeit-V2.5", torch_dtype=torch.float16, safety_checker=None
    ).to("cuda")
    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)
    pipe.enable_xformers_memory_efficient_attention()
    return pipe