A difusão estável da I.A. cria a rapariga do biquíni. Experimente os vários modelos.

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A difusão estável da I.A. cria a rapariga do biquíni. Experimente os vários modelos.

by bikinigirlsdiary

Hoje vou fazer experiências de combinação de models, schedulers, loras, and lora_multipliers

O ponto de partida é o meu prompt

base_prompt = "a white girl in pink bikini at the beach"

models é uma tupla de model_name, and model_path

models = [
("stable-diffusion-v1-5", "runwayml/stable-diffusion-v1-5"),  # Reference bad
("majicmixRealistic_v6", "../ai_directory/majicmixRealistic_v6"),  # Quite good
("realisticVisionV40_v40VAE", "../ai_directory/realisticVisionV40_v40VAE"),
("MeinaV10", "../ai_directory/MeinaV10"),  # Anime
("perfectWorld_v4Baked", "../ai_directory/perfectWorld_v4Baked"),  # Ordinary
("chilloutmix_NiPrunedFp32Fix", "../ai_directory/chilloutmix_NiPrunedFp32Fix"),
("henmixrealV10_henmixrealV10", "../ai_directory/henmixrealV10_henmixrealV10"),
("realisticVisionV40_v40VAE", "../ai_directory/realisticVisionV40_v40VAE"),
("realisticVisionV50_v50VAE", "../ai_directory/realisticVisionV50_v50VAE"),
]

schedulers

schedulers = [
("LMSDiscreteScheduler", diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler),
("DDIMScheduler", diffusers.schedulers.scheduling_ddim.DDIMScheduler),
("DPMSolverMultistepScheduler", diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler),
("EulerDiscreteScheduler", diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler),
("PNDMScheduler", diffusers.schedulers.scheduling_pndm.PNDMScheduler),
("DDPMScheduler", diffusers.schedulers.scheduling_ddpm.DDPMScheduler),
("EulerAncestralDiscreteScheduler",
diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler)
]

loras

loras = [
(None, None),
("virginie_efira_v01", "../ai_files/loras/virginie_efira_v01.safetensors"),
("Night_scene_20230715120543", "../ai_files/loras/Night_scene_20230715120543.safetensors"),
("Oldsaigon-v1.0", "../ai_files/loras/Oldsaigon-v1.0.safetensors"),
]

Vamos saltar para o código completo.

import itertools
import math
import os.path
import random
import diffusers
import numpy as np
import torch
# !pip install transformers accelerate
from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, StableDiffusionPipeline
from diffusers.utils import load_image
from tqdm import tqdm
from set_seed import seed_everything
from read_lora import load_lora_weights_orig
from solve_77_limits import get_pipeline_embeds
human_name: str = "bell"
out_dir: str = f"{human_name}"
seed: int = 8811
seed_everything(seed)
base_prompt = "a white girl in pink bikini at the beach"
additional_prompts = [
"(masterpiece:1.2), best quality,PIXIV",
]
negative_prompt: str = "(low quality, worst quality:1.4),"
strengths = [1, ]
# guidance_scales = [round(0.1 * _, 3) for _ in range(70, 252, 2)]
guidance_scales = [30, ]
# eta_list = [0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.5, 2.0, 4, 6, 8, 10]
eta_list = [1]
models = [
("stable-diffusion-v1-5", "runwayml/stable-diffusion-v1-5"),  # Reference bad
("majicmixRealistic_v6", "../ai_directory/majicmixRealistic_v6"),  # Quite good
("realisticVisionV40_v40VAE", "../ai_directory/realisticVisionV40_v40VAE"),
("MeinaV10", "../ai_directory/MeinaV10"),  # Anime
("perfectWorld_v4Baked", "../ai_directory/perfectWorld_v4Baked"),  # Ordinary
("chilloutmix_NiPrunedFp32Fix", "../ai_directory/chilloutmix_NiPrunedFp32Fix"),
("henmixrealV10_henmixrealV10", "../ai_directory/henmixrealV10_henmixrealV10"),
("realisticVisionV40_v40VAE", "../ai_directory/realisticVisionV40_v40VAE"),
("realisticVisionV50_v50VAE", "../ai_directory/realisticVisionV50_v50VAE"),
]
schedulers = [
("LMSDiscreteScheduler", diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler),
("DDIMScheduler", diffusers.schedulers.scheduling_ddim.DDIMScheduler),
("DPMSolverMultistepScheduler", diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler),
("EulerDiscreteScheduler", diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler),
("PNDMScheduler", diffusers.schedulers.scheduling_pndm.PNDMScheduler),
("DDPMScheduler", diffusers.schedulers.scheduling_ddpm.DDPMScheduler),
("EulerAncestralDiscreteScheduler",
diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler)
]
loras = [
(None, None),
("virginie_efira_v01", "../ai_files/loras/virginie_efira_v01.safetensors"),
("Night_scene_20230715120543", "../ai_files/loras/Night_scene_20230715120543.safetensors"),
("Oldsaigon-v1.0", "../ai_files/loras/Oldsaigon-v1.0.safetensors"),
]
lora_multipliers = [0.5, 1.0, 1.5, 2.0]
combined_list = list(itertools.product(
models, schedulers, loras, lora_multipliers, strengths, guidance_scales, eta_list, additional_prompts)
)
# Shuffle the combined list
random.shuffle(combined_list)
for item in tqdm(combined_list, total=len(combined_list)):
(model_name, model_dir), (scheduler_name, scheduler), (lora_name, lora_file), lora_multiplier, strength, guidance_scale, eta, add_prompt = item
pipe = StableDiffusionPipeline.from_pretrained(
model_dir,
requires_safety_checker=False,
safety_checker=None
)
pipe.requires_safety_check = False
pipe.safety_checker = None
if lora_name != None:
# TypeError: Trying to convert BFloat16 to the MPS backend, but it does not have support for that dtype.
device: str = "cpu"
print(f"{lora_name} : {device}")
pipe = pipe.to(device)
pipe = load_lora_weights_orig(pipe, lora_file, lora_multiplier, device, torch.float32)
else:
device: str = "mps" if torch.backends.mps.is_available() else "cpu"
# device: str = "cpu"  # With this LoRA it can't run with mps. It raises RuntimeError: Invalid buffer size: 58.07 GB
print(f"No lora : {device}")
pipe = pipe.to(device)
pipe.scheduler = scheduler.from_config(pipe.scheduler.config)
generator = torch.Generator(device=device).manual_seed(seed)
prompt: str = f"{base_prompt} {add_prompt}"
prompt_embeds, negative_prompt_embeds = get_pipeline_embeds(pipe, prompt, negative_prompt, device)
filename: str = f"{out_dir}/{model_name}_{scheduler_name}_{lora_name}_{lora_multiplier}_{strength}_{guidance_scale}_{eta}_{base_prompt}_{add_prompt[:20]}.png"
print(f"{filename} is running")
if not os.path.exists(filename):
# generate image
image = pipe(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
num_inference_steps=500,
generator=generator,
eta=eta,
guidance_scale=guidance_scale,
).images[0].save(filename)
else:
print(f"{filename} is exists")
chilloutmix_NiPrunedFp32Fix_DDPMScheduler_Oldsaigon-v1.0_0.5_1_30_1_uma rapariga branca em biquíni cor-de-rosa na praia_(obra-prima:1.2), b
henmixrealV10_henmixrealV10_EulerAncestralDiscreteScheduler_Oldsaigon-v1.0_0.5_1_30_1_uma rapariga branca de biquíni cor-de-rosa na praia_(obra-prima:1.2), b
MeinaV10_EulerDiscreteScheduler_None_0.5_1_30_1_uma rapariga branca de biquíni cor-de-rosa na praia_(obra-prima:1.2), b
MeinaV10_EulerDiscreteScheduler_None_1.0_1_30_1_uma rapariga branca em biquíni cor-de-rosa na praia_(obra-prima:1.2), b
perfectWorld_v4Baked_LMSDiscreteScheduler_None_1.5_1_30_1_uma rapariga branca em biquíni cor-de-rosa na praia_(obra-prima:1.2), b
realisticVisionV50_v50VAE_PNDMScheduler_None_1.0_1_30_1_uma rapariga branca em biquíni cor-de-rosa na praia_(obra-prima:1.2), b
stable-diffusion-v1-5_DPMSolverMultistepScheduler_None_2.0_1_30_1_uma rapariga branca de biquíni cor-de-rosa na praia_(obra-prima:1.2), b

As minhas experiências ainda não terminaram. Na minha corrida M2 com mps são necessárias ~250 horas para terminar todas as combinações. O consumo médio de energia do meu portátil é de 50 watts. No entanto, em alguns casos que não suportam mps é lora . Demora cerca de 30 minutos a terminá-lo.

No próximo artigo irá afinar o resultado para que fique mais nítido.

Website https://bikinigirlsdiary.com

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