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from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import Optional
import uuid
from lcm.lcm_scheduler import LCMScheduler
from lcm.lcm_pipeline import LatentConsistencyModelPipeline
from lcm.lcm_i2i_pipeline import LatentConsistencyModelImg2ImgPipeline, LCMSchedulerWithTimestamp
from diffusers.image_processor import PipelineImageInput
# import modules.scripts as scripts
# import modules.shared
# from modules import script_callbacks
import os
import random
import time
import numpy as np
import gradio as gr
from PIL import Image, PngImagePlugin
import torch
scheduler = LCMScheduler.from_pretrained( "charliebaby2023/cybrpny", subfolder="scheduler")
pipe = LatentConsistencyModelPipeline.from_pretrained(
"charliebaby2023/cybrpny", scheduler = scheduler, safety_checker = None)
# "SimianLuo/LCM_Dreamshaper_v7", scheduler = scheduler, safety_checker = None)
DESCRIPTION = '''# Latent Consistency Model
Running [LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) | [Project Page](https://latent-consistency-models.github.io) | [Extension Page](https://github.com/0xbitches/sd-webui-lcm)
'''
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "768"))
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
def save_image(img, metadata: dict):
save_dir = './outputs/LCM-txt2img/'
Path(save_dir).mkdir(exist_ok=True, parents=True)
seed = metadata["seed"]
unique_id = uuid.uuid4()
filename = save_dir + f"{unique_id}-{seed}" + ".png"
meta_tuples = [(k, str(v)) for k, v in metadata.items()]
png_info = PngImagePlugin.PngInfo()
for k, v in meta_tuples:
png_info.add_text(k, v)
img.save(filename, pnginfo=png_info)
return filename
def save_images(image_array, metadata: dict):
paths = []
with ThreadPoolExecutor() as executor:
paths = list(executor.map(save_image, image_array,
[metadata]*len(image_array)))
return paths
def generate(
prompt: str,
seed: int = 0,
width: int = 512,
height: int = 512,
guidance_scale: float = 8.0,
num_inference_steps: int = 4,
num_images: int = 4,
randomize_seed: bool = False,
use_fp16: bool = True,
use_torch_compile: bool = False,
use_cpu: bool = False,
progress=gr.Progress(track_tqdm=True)
) -> Image.Image:
seed = randomize_seed_fn(seed, randomize_seed)
torch.manual_seed(seed)
selected_device = 'cuda'
if use_cpu:
selected_device = "cpu"
if use_fp16:
use_fp16 = False
print("LCM warning: running on CPU, overrode FP16 with FP32")
global pipe, scheduler
pipe = LatentConsistencyModelPipeline(
vae= pipe.vae,
text_encoder = pipe.text_encoder,
tokenizer = pipe.tokenizer,
unet = pipe.unet,
scheduler = scheduler,
safety_checker = pipe.safety_checker,
feature_extractor = pipe.feature_extractor,
)
# pipe = LatentConsistencyModelPipeline.from_pretrained(
# "SimianLuo/LCM_Dreamshaper_v7", scheduler = scheduler, safety_checker = None)
if use_fp16:
pipe.to(torch_device=selected_device, torch_dtype=torch.float16)
else:
pipe.to(torch_device=selected_device, torch_dtype=torch.float32)
# Windows does not support torch.compile for now
if os.name != 'nt' and use_torch_compile:
pipe.unet = torch.compile(pipe.unet, mode='max-autotune')
start_time = time.time()
result = pipe(
prompt=prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
num_images_per_prompt=num_images,
original_inference_steps=50,
output_type="pil",
device = selected_device
).images
paths = save_images(result, metadata={"prompt": prompt, "seed": seed, "width": width,
"height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps})
elapsed_time = time.time() - start_time
print("LCM inference time: ", elapsed_time, "seconds")
return paths, seed
examples = [ "" ]
with gr.Blocks() as lcm:
with gr.Tab("LCM txt2img"):
gr.Markdown("Latent Consistency Models: Synthesizing High-Resolution Images with Few-step Inference")
gr.Markdown("Try the guide on Colab's free tier [](https://colab.research.google.com/github/R3gm/InsightSolver-Colab/blob/main/Latent_Consistency_Models.ipynb)")
with gr.Row():
prompt = gr.Textbox(label="Prompt",
show_label=False,
lines=3,
placeholder="Prompt",
elem_classes=["prompt"])
run_button = gr.Button("Run", scale=0)
with gr.Row():
result = gr.Gallery(
label="Generated images", show_label=False, elem_id="gallery", grid=[2], preview=True
)
with gr.Accordion("Advanced options", open=False):
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
randomize=True
)
randomize_seed = gr.Checkbox(
label="Randomize seed across runs", value=True)
use_fp16 = gr.Checkbox(
label="Run LCM in fp16 (for lower VRAM)", value=False)
use_torch_compile = gr.Checkbox(
label="Run LCM with torch.compile (currently not supported on Windows)", value=False)
use_cpu = gr.Checkbox(label="Run LCM on CPU", value=True)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale for base",
minimum=2,
maximum=14,
step=0.1,
value=8.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps for base",
minimum=1,
maximum=8,
step=1,
value=4,
)
with gr.Row():
num_images = gr.Slider(
label="Number of images (batch count)",
minimum=1,
maximum=int(os.getenv("MAX_NUM_IMAGES")),
step=1,
value=1,
)
gr.Examples(
examples=examples,
inputs=prompt,
outputs=result,
fn=generate
)
run_button.click(
fn=generate,
inputs=[
prompt,
seed,
width,
height,
guidance_scale,
num_inference_steps,
num_images,
randomize_seed,
use_fp16,
use_torch_compile,
use_cpu
],
outputs=[result, seed],
)
if __name__ == "__main__":
lcm.queue().launch() |