Spaces:
Running on Zero
Running on Zero
File size: 4,627 Bytes
e5a560a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | _base_ = ['../piflux/_data_test.py']
name = 'flux_turbo_test'
model = dict(
type='LatentDiffusionTextImage',
vae=dict(
type='PretrainedVAEDecoder',
from_pretrained='black-forest-labs/FLUX.1-dev',
subfolder='vae',
freeze=True,
torch_dtype='bfloat16'),
diffusion=dict(
type='GaussianFlow',
denoising=dict(
type='FluxTransformer2DModel',
patch_size=2,
pretrained='huggingface://black-forest-labs/FLUX.1-dev/transformer/diffusion_pytorch_model.safetensors.index.json',
pretrained_lora='huggingface://alimama-creative/FLUX.1-Turbo-Alpha/diffusion_pytorch_model.safetensors',
pretrained_lora_scale=1.0,
in_channels=64,
num_layers=19,
num_single_layers=38,
attention_head_dim=128,
num_attention_heads=24,
joint_attention_dim=4096,
pooled_projection_dim=768,
guidance_embeds=True,
torch_dtype='bfloat16'),
num_timesteps=1,
timestep_sampler=dict(
type='ContinuousTimeStepSampler',
shift=3.0,
logit_normal_enable=False,
use_dynamic_shifting=True,
base_seq_len=256 * 4,
max_seq_len=4096 * 4,
base_logshift=0.5,
max_logshift=1.15),
denoising_mean_mode='U'))
work_dir = f'work_dirs/{name}'
# yapf: disable
train_cfg = dict()
test_cfg = dict()
data = dict(
workers_per_gpu=1,
test_dataloader=dict(samples_per_gpu=1),
persistent_workers=True,
prefetch_factor=2
)
distilled_guidance_scale = 3.5
steps = [8]
evaluation = []
for step in steps:
for data_split in ['test', 'test2']:
prefix = f'step{step}'
num_images = None
metrics = []
if data_split == 'test':
num_images = 3200
metrics.extend([
dict(
type='InceptionMetrics',
num_images=num_images,
resize=True,
use_kid=False,
use_pr=False,
use_is=False,
reference_pkl='huggingface://Lakonik/inception_feats/flux_hpsv2_inception.pkl'),
dict(
type='InceptionMetrics',
num_images=num_images,
center_crop=True,
resize=False,
use_kid=False,
use_pr=False,
use_is=False,
prefix='patch',
reference_pkl='huggingface://Lakonik/inception_feats/flux_hpsv2_patch_inception.pkl'),
])
elif data_split == 'test2':
num_images = 10000
metrics.extend([
dict(
type='InceptionMetrics',
num_images=num_images,
resize=True,
use_kid=False,
use_pr=False,
use_is=False,
reference_pkl='huggingface://Lakonik/inception_feats/coco10k_inception.pkl'),
dict(
type='InceptionMetrics',
num_images=num_images,
center_crop=True,
resize=False,
use_kid=False,
use_pr=False,
use_is=False,
prefix='patch',
reference_pkl='huggingface://Lakonik/inception_feats/coco10k_patch_inception.pkl'),
])
metrics.extend([
dict(
type='HPSv2',
num_images=num_images,
hps_version='v2.1'),
dict(
type='VQAScore',
num_images=num_images),
dict(
type='CLIPSimilarity',
num_images=num_images),
])
evaluation.append(
dict(
type='GenerativeEvalHook',
data=data_split,
prefix=prefix,
sample_kwargs=dict(
test_cfg_override=dict(
sampler='FlowEulerODE',
num_timesteps=step,
distilled_guidance_scale=distilled_guidance_scale,
)),
metrics=metrics,
viz_dir=f'viz/{name}/{data_split}_{prefix}',
save_best_ckpt=False))
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
cudnn_benchmark = True
mp_start_method = 'fork'
|