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import sys
import os
from pathlib import Path
import gc
# Add the StableCascade and CSD directories to the Python path
app_dir = Path(__file__).parent
sys.path.extend([
str(app_dir),
str(app_dir / "third_party" / "StableCascade"),
str(app_dir / "third_party" / "CSD")
])
import yaml
import torch
from tqdm import tqdm
from accelerate.utils import set_module_tensor_to_device
import torch.nn.functional as F
import torchvision.transforms as T
from lang_sam import LangSAM
from inference.utils import *
from core.utils import load_or_fail
from train import WurstCoreC, WurstCoreB
from gdf_rbm import RBM
from stage_c_rbm import StageCRBM
from utils import WurstCoreCRBM
from gdf.schedulers import CosineSchedule
from gdf import VPScaler, CosineTNoiseCond, DDPMSampler, P2LossWeight, AdaptiveLossWeight
from gdf.targets import EpsilonTarget
import PIL
# Device configuration
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
# Flag for low VRAM usage
low_vram = True
# Function definition for low VRAM usage
def models_to(model, device="cpu", excepts=None):
"""
Change the device of nn.Modules within a class, skipping specified attributes.
"""
for attr_name in dir(model):
if attr_name.startswith('__') and attr_name.endswith('__'):
continue # skip special attributes
attr_value = getattr(model, attr_name, None)
if isinstance(attr_value, torch.nn.Module):
if excepts and attr_name in excepts:
print(f"Except '{attr_name}'")
continue
print(f"Change device of '{attr_name}' to {device}")
attr_value.to(device)
torch.cuda.empty_cache()
# Stage C model configuration
config_file = 'third_party/StableCascade/configs/inference/stage_c_3b.yaml'
with open(config_file, "r", encoding="utf-8") as file:
loaded_config = yaml.safe_load(file)
core = WurstCoreCRBM(config_dict=loaded_config, device=device, training=False)
# Stage B model configuration
config_file_b = 'third_party/StableCascade/configs/inference/stage_b_3b.yaml'
with open(config_file_b, "r", encoding="utf-8") as file:
config_file_b = yaml.safe_load(file)
core_b = WurstCoreB(config_dict=config_file_b, device=device, training=False)
# Setup extras and models for Stage C
extras = core.setup_extras_pre()
gdf_rbm = RBM(
schedule=CosineSchedule(clamp_range=[0.0001, 0.9999]),
input_scaler=VPScaler(), target=EpsilonTarget(),
noise_cond=CosineTNoiseCond(),
loss_weight=AdaptiveLossWeight(),
)
sampling_configs = {
"cfg": 5,
"sampler": DDPMSampler(gdf_rbm),
"shift": 1,
"timesteps": 20
}
extras = core.Extras(
gdf=gdf_rbm,
sampling_configs=sampling_configs,
transforms=extras.transforms,
effnet_preprocess=extras.effnet_preprocess,
clip_preprocess=extras.clip_preprocess
)
models = core.setup_models(extras)
models.generator.eval().requires_grad_(False)
# Setup extras and models for Stage B
extras_b = core_b.setup_extras_pre()
models_b = core_b.setup_models(extras_b, skip_clip=True)
models_b = WurstCoreB.Models(
**{**models_b.to_dict(), 'tokenizer': models.tokenizer, 'text_model': models.text_model}
)
models_b.generator.bfloat16().eval().requires_grad_(False)
# Off-load old generator (low VRAM mode)
if low_vram:
models.generator.to("cpu")
torch.cuda.empty_cache()
# Load and configure new generator
generator_rbm = StageCRBM()
for param_name, param in load_or_fail(core.config.generator_checkpoint_path).items():
set_module_tensor_to_device(generator_rbm, param_name, "cpu", value=param)
generator_rbm = generator_rbm.to(getattr(torch, core.config.dtype)).to(device)
generator_rbm = core.load_model(generator_rbm, 'generator')
# Create models_rbm instance
models_rbm = core.Models(
effnet=models.effnet,
previewer=models.previewer,
generator=generator_rbm,
generator_ema=models.generator_ema,
tokenizer=models.tokenizer,
text_model=models.text_model,
image_model=models.image_model,
stage_a=models.stage_a,
stage_b=models.stage_b,
)
def reset_inference_state():
global models_rbm, models_b, extras, extras_b, device, core, core_b
# Reset sampling configurations
extras.sampling_configs['cfg'] = 5
extras.sampling_configs['shift'] = 1
extras.sampling_configs['timesteps'] = 20
extras.sampling_configs['t_start'] = 1.0
extras_b.sampling_configs['cfg'] = 1.1
extras_b.sampling_configs['shift'] = 1
extras_b.sampling_configs['timesteps'] = 10
extras_b.sampling_configs['t_start'] = 1.0
# Move models to CPU to free up GPU memory
models_to(models_rbm, device="cpu")
models_b.generator.to("cpu")
# Clear CUDA cache
torch.cuda.empty_cache()
gc.collect()
# Move necessary models back to the correct device
if low_vram:
models_to(models_rbm, device="cpu", excepts=["generator", "previewer"])
models_rbm.generator.to(device)
models_rbm.previewer.to(device)
else:
models_to(models_rbm, device=device)
models_b.generator.to("cpu") # Keep Stage B generator on CPU for now
# Ensure effnet is on the correct device
models_rbm.effnet.to(device)
# Reset model states
models_rbm.generator.eval().requires_grad_(False)
models_b.generator.bfloat16().eval().requires_grad_(False)
# Clear CUDA cache again
torch.cuda.empty_cache()
gc.collect()
def infer(style_description, ref_style_file, caption):
global models_rbm, models_b
try:
height=1024
width=1024
batch_size=1
output_file='output.png'
stage_c_latent_shape, stage_b_latent_shape = calculate_latent_sizes(height, width, batch_size=batch_size)
extras.sampling_configs['cfg'] = 4
extras.sampling_configs['shift'] = 2
extras.sampling_configs['timesteps'] = 20
extras.sampling_configs['t_start'] = 1.0
extras_b.sampling_configs['cfg'] = 1.1
extras_b.sampling_configs['shift'] = 1
extras_b.sampling_configs['timesteps'] = 10
extras_b.sampling_configs['t_start'] = 1.0
ref_style = resize_image(PIL.Image.open(ref_style_file).convert("RGB")).unsqueeze(0).expand(batch_size, -1, -1, -1).to(device)
batch = {'captions': [caption] * batch_size}
batch['style'] = ref_style
# Ensure models are on the correct device before inference
if low_vram:
models_to(models_rbm, device=device, excepts=["generator", "previewer"])
else:
models_to(models_rbm, device=device)
models_b.generator.to(device)
# Ensure effnet is on the correct device
models_rbm.effnet.to(device)
x0_style_forward = models_rbm.effnet(extras.effnet_preprocess(ref_style))
conditions = core.get_conditions(batch, models_rbm, extras, is_eval=True, is_unconditional=False, eval_image_embeds=True, eval_style=True, eval_csd=False)
unconditions = core.get_conditions(batch, models_rbm, extras, is_eval=True, is_unconditional=True, eval_image_embeds=False)
conditions_b = core_b.get_conditions(batch, models_b, extras_b, is_eval=True, is_unconditional=False)
unconditions_b = core_b.get_conditions(batch, models_b, extras_b, is_eval=True, is_unconditional=True)
if low_vram:
# The sampling process uses more vram, so we offload everything except two modules to the cpu.
models_to(models_rbm, device="cpu", excepts=["generator", "previewer"])
# Stage C reverse process.
sampling_c = extras.gdf.sample(
models_rbm.generator, conditions, stage_c_latent_shape,
unconditions, device=device,
**extras.sampling_configs,
x0_style_forward=x0_style_forward,
apply_pushforward=False, tau_pushforward=8,
num_iter=3, eta=0.1, tau=20, eval_csd=True,
extras=extras, models=models_rbm,
lam_style=1, lam_txt_alignment=1.0,
use_ddim_sampler=True,
)
for (sampled_c, _, _) in tqdm(sampling_c, total=extras.sampling_configs['timesteps']):
sampled_c = sampled_c
# Stage B reverse process.
with torch.no_grad(), torch.cuda.amp.autocast(dtype=torch.bfloat16):
conditions_b['effnet'] = sampled_c
unconditions_b['effnet'] = torch.zeros_like(sampled_c)
sampling_b = extras_b.gdf.sample(
models_b.generator, conditions_b, stage_b_latent_shape,
unconditions_b, device=device, **extras_b.sampling_configs,
)
for (sampled_b, _, _) in tqdm(sampling_b, total=extras_b.sampling_configs['timesteps']):
sampled_b = sampled_b
sampled = models_b.stage_a.decode(sampled_b).float()
sampled = torch.cat([
torch.nn.functional.interpolate(ref_style.cpu(), size=(height, width)),
sampled.cpu(),
], dim=0)
# Remove the batch dimension and keep only the generated image
sampled = sampled[1] # This selects the generated image, discarding the reference style image
# Ensure the tensor is in [C, H, W] format
if sampled.dim() == 3 and sampled.shape[0] == 3:
sampled_image = T.ToPILImage()(sampled) # Convert tensor to PIL image
sampled_image.save(output_file) # Save the image as a PNG
else:
raise ValueError(f"Expected tensor of shape [3, H, W] but got {sampled.shape}")
return output_file # Return the path to the saved image
finally:
# Reset the state after inference, regardless of success or failure
reset_inference_state()
import gradio as gr
gr.Interface(
fn = infer,
inputs=[gr.Textbox(label="style description"), gr.Image(label="Ref Style File", type="filepath"), gr.Textbox(label="caption")],
outputs=[gr.Image()]
).launch() |