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import gradio as gr
import torch
import random
from PIL import Image
import os
import argparse
import shutil
import gc
import importlib
import json
from multiprocessing import cpu_count
import cv2
import numpy as np
from pathlib import Path
from diffusers import (
StableDiffusionControlNetPipeline,
StableDiffusionPipeline,
ControlNetModel,
AutoencoderKL,
)
from src.controlnet_pipe import ControlNetPipe as StableDiffusionControlNetPipeline
from src.lab import Lab
from src.ui_shared import (
default_scheduler,
scheduler_dict,
model_ids,
controlnet_ids,
is_hfspace,
)
CONTROLNET_REPO = "lint/anime_control"
_xformers_available = importlib.util.find_spec("xformers") is not None
device = "cuda" if torch.cuda.is_available() else "cpu"
# device = 'cpu'
dtype = torch.float16 if device == "cuda" else torch.float32
pipe = None
loaded_model_id = ""
loaded_controlnet_id = ""
def load_pipe(model_id, controlnet_id, scheduler_name):
global pipe, loaded_model_id, loaded_controlnet_id
scheduler = scheduler_dict[scheduler_name]
reload_pipe = False
if pipe:
new_weights = pipe.components
else:
new_weights = {}
if model_id != loaded_model_id:
new_pipe = StableDiffusionPipeline.from_pretrained(
model_id,
vae=AutoencoderKL.from_pretrained("lint/anime_vae", torch_dtype=dtype),
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
use_safetensors=False,
torch_dtype=dtype,
)
loaded_model_id = model_id
new_weights.update(new_pipe.components)
new_weights["scheduler"] = scheduler.from_pretrained(model_id, subfolder="scheduler")
reload_pipe = True
if controlnet_id != loaded_controlnet_id:
controlnet = ControlNetModel.from_pretrained(
CONTROLNET_REPO,
subfolder=controlnet_id,
torch_dtype=dtype,
)
loaded_controlnet_id = controlnet_id
new_weights["controlnet"] = controlnet
reload_pipe = True
if reload_pipe:
pipe = StableDiffusionControlNetPipeline(
**new_weights,
requires_safety_checker=False,
)
if device == "cuda":
for component in pipe.components.values():
if isinstance(component, torch.nn.Module):
component.to("cuda", torch.float16)
if _xformers_available:
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_attention_slicing()
pipe.enable_vae_tiling()
return pipe
# initialize with preloaded pipe
if is_hfspace:
pipe = load_pipe(model_ids[0], controlnet_ids[0], default_scheduler)
def extract_canny(image):
CANNY_THRESHOLD = (100, 200)
image_array = np.asarray(image)
canny_image = cv2.Canny(image_array, *CANNY_THRESHOLD)
canny_image = canny_image[:, :, None]
canny_image = np.concatenate([canny_image]*3, axis=2)
return Image.fromarray(canny_image)
@torch.no_grad()
def generate(
model_name,
guidance_image,
controlnet_name,
scheduler_name,
prompt,
guidance,
steps,
n_images=1,
width=512,
height=512,
seed=0,
neg_prompt="",
controlnet_prompt=None,
controlnet_negative_prompt=None,
controlnet_cond_scale=1.0,
progress=gr.Progress(),
):
if seed == -1:
seed = random.randint(0, 2147483647)
if guidance_image:
guidance_image = extract_canny(guidance_image)
else:
guidance_image = torch.zeros(n_images, 3, height, width)
generator = torch.Generator(device).manual_seed(seed)
pipe = load_pipe(
model_id=model_name,
controlnet_id=controlnet_name,
scheduler_name=scheduler_name,
)
status_message = f"Prompt: '{prompt}' | Seed: {seed} | Guidance: {guidance} | Scheduler: {scheduler_name} | Steps: {steps}"
# pass None so pipeline uses base prompt as controlnet_prompt
if controlnet_prompt == "":
controlnet_prompt = None #
if controlnet_negative_prompt == "":
controlnet_negative_prompt = None
if controlnet_prompt:
controlnet_prompt_embeds = pipe._encode_prompt(
controlnet_prompt,
device,
n_images,
do_classifier_free_guidance = guidance > 1.0,
negative_prompt = controlnet_negative_prompt,
prompt_embeds=None,
negative_prompt_embeds=None,
)
else:
controlnet_prompt_embeds = None
result = pipe(
prompt,
image=guidance_image,
height=height,
width=width,
num_inference_steps=int(steps),
guidance_scale=guidance,
negative_prompt=neg_prompt,
num_images_per_prompt=n_images,
generator=generator,
controlnet_conditioning_scale = float(controlnet_cond_scale),
controlnet_prompt_embeds = controlnet_prompt_embeds,
)
return result.images, status_message
def run_training(
model_name,
controlnet_weights_path,
train_data_dir,
valid_data_dir,
train_batch_size,
train_whole_controlnet,
gradient_accumulation_steps,
num_train_epochs,
train_learning_rate,
output_dir,
checkpointing_steps,
image_logging_steps,
save_whole_pipeline,
progress=gr.Progress(),
):
global pipe
if device == "cpu":
raise gr.Error("Training not supported on CPU")
pathobj = Path(controlnet_weights_path)
controlnet_path = str(Path().joinpath(*pathobj.parts[:-1]))
subfolder = str(pathobj.parts[-1])
controlnet = ControlNetModel.from_pretrained(
controlnet_path,
subfolder=subfolder,
low_cpu_mem_usage=False,
device_map=None,
)
pipe.components["controlnet"] = controlnet
pipe = StableDiffusionControlNetPipeline(
**pipe.components,
requires_safety_checker=False,
)
training_args = argparse.Namespace(
# start training from preexisting models
pretrained_model_name_or_path=None,
controlnet_weights_path=None,
# dataset args
train_data_dir=train_data_dir,
valid_data_dir=valid_data_dir,
resolution=512,
from_hf_hub = train_data_dir == "lint/anybooru",
controlnet_hint_key="canny",
# training args
# options are ["zero convolutions", "input hint blocks"], trains whole controlnet by default
training_stage="" if train_whole_controlnet else "zero convolutions",
learning_rate=float(train_learning_rate),
num_train_epochs=int(num_train_epochs),
seed=3434554,
max_grad_norm=1.0,
gradient_accumulation_steps=int(gradient_accumulation_steps),
# VRAM args
batch_size=train_batch_size,
mixed_precision="fp16", # set to "fp16" for mixed-precision training.
gradient_checkpointing=True, # set this to True to lower the memory usage.
use_8bit_adam=False, # use 8bit optimizer from bitsandbytes
enable_xformers_memory_efficient_attention=True,
allow_tf32=True,
dataloader_num_workers=cpu_count(),
# logging args
output_dir=output_dir,
report_to="tensorboard",
image_logging_steps=image_logging_steps, # disabled when 0. costs additional VRAM to log images
save_whole_pipeline=save_whole_pipeline,
checkpointing_steps=checkpointing_steps,
)
try:
lab = Lab(training_args, pipe)
lab.train(training_args.num_train_epochs, gr_progress=progress)
except Exception as e:
raise gr.Error(e)
for component in pipe.components.values():
if isinstance(component, torch.nn.Module):
component.to(device, dtype=dtype)
gc.collect()
torch.cuda.empty_cache()
return f"Finished training! Check the {training_args.output_dir} directory for saved model weights"
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