Spaces:
Runtime error
Runtime error
#!/usr/bin/env python | |
from __future__ import annotations | |
import os | |
import random | |
import time | |
import gradio as gr | |
import numpy as np | |
import PIL.Image | |
import torch | |
from lcm_pipeline import LatentConsistencyModelPipeline | |
from lcm_scheduler import LCMScheduler | |
from diffusers import AutoencoderKL, UNet2DConditionModel | |
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker | |
from transformers import CLIPTokenizer, CLIPTextModel, CLIPImageProcessor | |
import os | |
import torch | |
from tqdm import tqdm | |
from safetensors.torch import load_file | |
from huggingface_hub import hf_hub_download | |
DESCRIPTION = '''# Latent Consistency Model | |
Distilled from Dreamshaper v7 fine-tune of [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). [Project page](https://latent-consistency-models.github.io) | |
''' | |
if not torch.cuda.is_available(): | |
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
MAX_SEED = np.iinfo(np.int32).max | |
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1" | |
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "768")) | |
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1" | |
DTYPE = torch.float32 # torch.float16 works as well, but pictures seem to be a bit worse | |
model_id = "digiplay/DreamShaper_7" | |
# Initalize Diffusers Model: | |
vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae") | |
text_encoder = CLIPTextModel.from_pretrained(model_id, subfolder="text_encoder") | |
tokenizer = CLIPTokenizer.from_pretrained(model_id, subfolder="tokenizer") | |
config = UNet2DConditionModel.load_config(model_id, subfolder="unet") | |
config["time_cond_proj_dim"] = 256 | |
unet = UNet2DConditionModel.from_config(config) | |
safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_id, subfolder="safety_checker") | |
feature_extractor = CLIPImageProcessor.from_pretrained(model_id, subfolder="feature_extractor") | |
# Initalize Scheduler: | |
scheduler = LCMScheduler(beta_start=0.00085, beta_end=0.0120, beta_schedule="scaled_linear", prediction_type="epsilon") | |
HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
if torch.cuda.is_available(): | |
# Replace the unet with LCM: | |
# lcm_unet_ckpt = hf_hub_download("SimianLuo/LCM_Dreamshaper_v7", filename="LCM_Dreamshaper_v7_4k.safetensors", token=HF_TOKEN) | |
lcm_unet_ckpt = "./LCM_Dreamshaper_v7_4k.safetensors" | |
ckpt = load_file(lcm_unet_ckpt) | |
m, u = unet.load_state_dict(ckpt, strict=False) | |
if len(m) > 0: | |
print("missing keys:") | |
print(m) | |
if len(u) > 0: | |
print("unexpected keys:") | |
print(u) | |
# LCM Pipeline: | |
pipe = LatentConsistencyModelPipeline(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor) | |
pipe = pipe.to(torch_device="cuda", torch_dtype=DTYPE) | |
if USE_TORCH_COMPILE: | |
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) | |
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
return seed | |
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, | |
progress = gr.Progress(track_tqdm=True) | |
) -> PIL.Image.Image: | |
seed = randomize_seed_fn(seed, randomize_seed) | |
torch.manual_seed(seed) | |
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, | |
lcm_origin_steps=50, | |
output_type="pil", | |
).images | |
print(time.time() - start_time) | |
return result, seed | |
examples = [ | |
"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography", | |
"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k", | |
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", | |
"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece", | |
] | |
with gr.Blocks(css="style.css") as demo: | |
gr.Markdown(DESCRIPTION) | |
gr.DuplicateButton( | |
value="Duplicate Space for private use", | |
elem_id="duplicate-button", | |
visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", | |
) | |
with gr.Group(): | |
with gr.Row(): | |
prompt = gr.Text( | |
label="Prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your prompt", | |
container=False, | |
) | |
run_button = gr.Button("Run", scale=0) | |
result = gr.Gallery( | |
label="Generated images", show_label=False, elem_id="gallery", grid=[2] | |
) | |
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) | |
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" | |
minimum=1, | |
maximum=8, | |
step=1, | |
value=4, | |
visible=False, | |
) | |
gr.Examples( | |
examples=examples, | |
inputs=prompt, | |
outputs=result, | |
fn=generate, | |
cache_examples=CACHE_EXAMPLES, | |
) | |
gr.on( | |
triggers=[ | |
prompt.submit, | |
run_button.click, | |
], | |
fn=generate, | |
inputs=[ | |
prompt, | |
seed, | |
width, | |
height, | |
guidance_scale, | |
num_inference_steps, | |
num_images, | |
randomize_seed | |
], | |
outputs=[result, seed], | |
api_name="run", | |
) | |
if __name__ == "__main__": | |
demo.queue(api_open=False) | |
# demo.queue(max_size=20).launch() | |
demo.launch() | |