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import json
import numpy as np
import math
import csv
import random
import argparse
import torch
import os
import torch.distributed as dist
from PIL import Image
from torch.nn.parallel import DistributedDataParallel as DDP
from accelerate.utils import set_seed
from diffusion_pipeline.sd35_pipeline import StableDiffusion3Pipeline, FlowMatchEulerInverseScheduler
from diffusion_pipeline.sdxl_pipeline import StableDiffusionXLPipeline
from diffusers import BitsAndBytesConfig, SD3Transformer2DModel
from diffusers import FlowMatchEulerDiscreteScheduler, DDIMInverseScheduler, DDIMScheduler
device = torch.device('cuda')
def get_args():
# pick: test_unique_caption_zh.csv draw: drawbench.csv
parser = argparse.ArgumentParser()
parser.add_argument("--model", default='sd35', choices=['sdxl', 'sd35'], type=str)
parser.add_argument("--inference-step", default=30, type=int)
parser.add_argument("--size", default=1024, type=int)
parser.add_argument("--seed", default=33, type=int)
parser.add_argument("--cfg", default=3.5, type=float)
# hyperparameters for Z-Sampling
parser.add_argument("--inv-cfg", default=0.5, type=float)
# hyperparameters for Z-Core^2
parser.add_argument("--w2s-guidance", default=1.5, type=float)
parser.add_argument("--end_timesteps", default=28, type=int) # equal to inference step - 2 or inference step
parser.add_argument("--prompt", default='Mickey Mouse painting by Frank Frazetta.', type=str)
parser.add_argument("--method", default='standard', choices=['standard', 'core', 'zigzag', 'z-core'], type=str)
args = parser.parse_args()
return args
if __name__ == '__main__':
torch.cuda.empty_cache()
dtype = torch.float16
args = get_args()
print("args.seed: ", args.seed)
set_seed(args.seed)
# TODO: load pipeline
if args.model == 'sd35':
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
model_nf4 = SD3Transformer2DModel.from_pretrained(
"stabilityai/stable-diffusion-3.5-large",
subfolder="transformer",
quantization_config=nf4_config,
torch_dtype=torch.bfloat16
)
pipe = StableDiffusion3Pipeline.from_pretrained(
"stabilityai/stable-diffusion-3.5-large",
transformer=model_nf4,
torch_dtype=torch.bfloat16,
)
pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe.scheduler.config)
inverse_scheduler = FlowMatchEulerInverseScheduler.from_pretrained("stabilityai/stable-diffusion-3.5-large",
subfolder='scheduler')
pipe.inv_scheduler = inverse_scheduler
elif args.model == "sdxl":
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
).to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
inverse_scheduler = DDIMInverseScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
subfolder='scheduler')
pipe.inv_scheduler = inverse_scheduler
pipe.to(device)
pipe.enable_model_cpu_offload()
# TODO: load noise model
if args.method == 'core' or args.method == 'z-core':
from diffusion_pipeline.refine_model import PromptSD35Net, PromptSDXLNet
from diffusion_pipeline.lora import replace_linear_with_lora, lora_true
if args.model == 'sd35':
refine_model = PromptSD35Net()
replace_linear_with_lora(refine_model, rank=64, alpha=1.0, number_of_lora=28)
lora_true(refine_model, lora_idx=0)
checkpoint = torch.load('./weights/sd35_noise_model.pth', map_location='cpu')
refine_model.load_state_dict(checkpoint)
elif args.model == 'sdxl':
refine_model = PromptSDXLNet()
replace_linear_with_lora(refine_model, rank=48, alpha=1.0, number_of_lora=50)
lora_true(refine_model, lora_idx=0)
checkpoint = torch.load('./weights/sdxl_noise_model.pth', map_location='cpu')
refine_model.load_state_dict(checkpoint)
print("Load Lora Success")
refine_model = refine_model.to(device)
refine_model = refine_model.to(torch.bfloat16)
# TODO: load hyperparameters
size = args.size
if args.model == 'sdxl':
shape = (1, 4, size // 8, size // 8)
else:
shape = (1, 16, size // 8, size // 8)
num_steps = args.inference_step
end_timesteps = args.end_timesteps
guidance_scale = args.cfg
w2s_guidance = args.w2s_guidance
inv_cfg = args.inv_cfg
prompt = args.prompt
print("pass this prompt: ", prompt)
start_latents = torch.randn(shape, dtype=dtype).to(device)
if args.model == 'sdxl':
if args.method == 'core':
output = pipe.core(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_steps,
latents=start_latents,
return_dict=False,
refine_model=refine_model,
lora_true=lora_true,
end_timesteps=end_timesteps,
w2s_guidance=w2s_guidance)[0][0]
elif args.method == 'zigzag':
output = pipe.zigzag(
prompt=prompt,
guidance_scale=guidance_scale,
latents=start_latents,
return_dict=False,
num_inference_steps=num_steps,
inv_cfg=inv_cfg)[0][0]
elif args.method == 'z-core':
output = pipe.z_core(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_steps,
latents=start_latents,
return_dict=False,
refine_model=refine_model,
lora_true=lora_true,
end_timesteps=end_timesteps,
w2s_guidance=w2s_guidance,
inv_cfg=inv_cfg)[0][0]
elif args.method == 'standard':
output = pipe(
prompt=prompt,
guidance_scale=guidance_scale,
latents=start_latents,
return_dict=False,
num_inference_steps=num_steps)[0][0]
else:
raise ValueError("Invalid method")
output.save(f'{args.model}_{args.method}.png')
else:
if args.method == 'core':
output = pipe.core(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_steps,
latents=start_latents,
max_sequence_length=512,
return_dict=False,
refine_model=refine_model,
lora_true=lora_true,
end_timesteps=end_timesteps,
w2s_guidance=w2s_guidance)[0][0]
elif args.method == 'zigzag':
output = pipe.zigzag(
prompt=prompt,
max_sequence_length=512,
guidance_scale=guidance_scale,
latents=start_latents,
return_dict=False,
num_inference_steps=num_steps,
inv_cfg=inv_cfg)[0][0]
elif args.method == 'z-core':
output = pipe.z_core(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_steps,
latents=start_latents,
return_dict=False,
max_sequence_length=512,
refine_model=refine_model,
lora_true=lora_true,
end_timesteps=end_timesteps,
w2s_guidance=w2s_guidance)[0][0]
elif args.method == 'standard':
output = pipe(
prompt=prompt,
guidance_scale=guidance_scale,
latents=start_latents,
return_dict=False,
max_sequence_length=512,
num_inference_steps=num_steps)[0][0]
else:
raise ValueError("Invalid method")
output.save(f'{args.model}_{args.method}.png')
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