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on
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Running
on
Zero
import spaces | |
import torch | |
from io import BytesIO | |
import PIL.Image | |
import pillow_heif | |
from pathlib import Path | |
import gradio as gr | |
from gradio_imageslider import ImageSlider | |
from huggingface_hub import hf_hub_download | |
from PIL import Image | |
from refiners.fluxion.utils import manual_seed | |
from refiners.foundationals.latent_diffusion import Solver, solvers | |
import requests | |
from enhancer import ESRGANUpscaler, ESRGANUpscalerCheckpoints | |
import time | |
import boto3 | |
from datetime import datetime | |
import json | |
pillow_heif.register_heif_opener() | |
pillow_heif.register_avif_opener() | |
TITLE = """ | |
Image Enhancer | |
""" | |
CHECKPOINTS = ESRGANUpscalerCheckpoints( | |
unet=Path( | |
hf_hub_download( | |
repo_id="refiners/juggernaut.reborn.sd1_5.unet", | |
filename="model.safetensors", | |
revision="347d14c3c782c4959cc4d1bb1e336d19f7dda4d2", | |
) | |
), | |
clip_text_encoder=Path( | |
hf_hub_download( | |
repo_id="refiners/juggernaut.reborn.sd1_5.text_encoder", | |
filename="model.safetensors", | |
revision="744ad6a5c0437ec02ad826df9f6ede102bb27481", | |
) | |
), | |
lda=Path( | |
hf_hub_download( | |
repo_id="refiners/juggernaut.reborn.sd1_5.autoencoder", | |
filename="model.safetensors", | |
revision="3c1aae3fc3e03e4a2b7e0fa42b62ebb64f1a4c19", | |
) | |
), | |
controlnet_tile=Path( | |
hf_hub_download( | |
repo_id="refiners/controlnet.sd1_5.tile", | |
filename="model.safetensors", | |
revision="48ced6ff8bfa873a8976fa467c3629a240643387", | |
) | |
), | |
esrgan=Path( | |
hf_hub_download( | |
repo_id="philz1337x/upscaler", | |
filename="4x-UltraSharp.pth", | |
revision="011deacac8270114eb7d2eeff4fe6fa9a837be70", | |
) | |
), | |
negative_embedding=Path( | |
hf_hub_download( | |
repo_id="philz1337x/embeddings", | |
filename="JuggernautNegative-neg.pt", | |
revision="203caa7e9cc2bc225031a4021f6ab1ded283454a", | |
) | |
), | |
negative_embedding_key="string_to_param.*", | |
loras={ | |
"more_details": Path( | |
hf_hub_download( | |
repo_id="philz1337x/loras", | |
filename="more_details.safetensors", | |
revision="a3802c0280c0d00c2ab18d37454a8744c44e474e", | |
) | |
), | |
"sdxl_render": Path( | |
hf_hub_download( | |
repo_id="philz1337x/loras", | |
filename="SDXLrender_v2.0.safetensors", | |
revision="a3802c0280c0d00c2ab18d37454a8744c44e474e", | |
) | |
), | |
}, | |
) | |
LORA_SCALES = { | |
"more_details": 0.5, | |
"sdxl_render": 1.0, | |
} | |
# initialize the enhancer, on the cpu | |
DEVICE_CPU = torch.device("cpu") | |
DTYPE = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32 | |
enhancer = ESRGANUpscaler(checkpoints=CHECKPOINTS, device=DEVICE_CPU, dtype=DTYPE) | |
# "move" the enhancer to the gpu, this is handled by Zero GPU | |
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
enhancer.to(device=DEVICE, dtype=DTYPE) | |
def upload_image_to_r2(image, account_id, access_key, secret_key, bucket_name): | |
print("upload_image_to_s3", account_id, access_key, secret_key, bucket_name) | |
connectionUrl = f"https://{account_id}.r2.cloudflarestorage.com" | |
s3 = boto3.client( | |
's3', | |
endpoint_url=connectionUrl, | |
region_name='auto', | |
aws_access_key_id=access_key, | |
aws_secret_access_key=secret_key | |
) | |
current_time = datetime.now().strftime("%Y%m%d_%H%M%S") | |
image_file = f"generated_images/{current_time}_{random.randint(0, MAX_SEED)}.png" | |
buffer = BytesIO() | |
image.save(buffer, "PNG") | |
buffer.seek(0) | |
s3.upload_fileobj(buffer, bucket_name, image_file) | |
print("upload finish", image_file) | |
return image_file | |
class calculateDuration: | |
def __init__(self, activity_name=""): | |
self.activity_name = activity_name | |
def __enter__(self): | |
self.start_time = time.time() | |
return self | |
def __exit__(self, exc_type, exc_value, traceback): | |
self.end_time = time.time() | |
self.elapsed_time = self.end_time - self.start_time | |
if self.activity_name: | |
print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds") | |
else: | |
print(f"Elapsed time: {self.elapsed_time:.6f} seconds") | |
def process( | |
input_image: Image.Image, | |
image_url:str, | |
prompt: str = "masterpiece, best quality, highres", | |
negative_prompt: str = "worst quality, low quality, normal quality", | |
seed: int = 42, | |
upscale_factor: int = 2, | |
controlnet_scale: float = 0.6, | |
controlnet_decay: float = 1.0, | |
condition_scale: int = 6, | |
tile_width: int = 112, | |
tile_height: int = 144, | |
denoise_strength: float = 0.35, | |
num_inference_steps: int = 18, | |
solver: str = "DDIM", | |
upload_to_r2: bool = True, | |
account_id: str = "", | |
access_key: str = "", | |
secret_key: str = "", | |
bucket_name: str = "" | |
) -> tuple[tuple[Image.Image, Image.Image], str]: | |
manual_seed(seed) | |
if image_url: | |
# fetch image from url | |
with calculateDuration("Download Image"): | |
print("start to fetch image from url", image_url) | |
response = requests.get(image_url) | |
response.raise_for_status() | |
input_image = PIL.Image.open(BytesIO(response.content)) | |
print("fetch image success") | |
print("start", prompt, upscale_factor) | |
solver_type: type[Solver] = getattr(solvers, solver) | |
with calculateDuration("enhancer"): | |
enhanced_image = enhancer.upscale( | |
image=input_image, | |
prompt=prompt, | |
negative_prompt=negative_prompt, | |
upscale_factor=upscale_factor, | |
controlnet_scale=controlnet_scale, | |
controlnet_scale_decay=controlnet_decay, | |
condition_scale=condition_scale, | |
tile_size=(tile_height, tile_width), | |
denoise_strength=denoise_strength, | |
num_inference_steps=num_inference_steps, | |
loras_scale=LORA_SCALES, | |
solver_type=solver_type, | |
) | |
print("enhancer finish") | |
if upload_to_r2: | |
url = upload_image_to_r2(enhanced_image, account_id, access_key, secret_key, bucket_name) | |
result = {"status": "success", "url": url} | |
else: | |
result = {"status": "success", "message": "Image generated but not uploaded"} | |
return [input_image, enhanced_image], json.dumps(result) | |
with gr.Blocks() as demo: | |
gr.HTML(TITLE) | |
with gr.Row(): | |
with gr.Column(): | |
input_image = gr.Image(type="pil", label="Input Image") | |
image_url = gr.Textbox(label="Image Url", placeholder="Enter image URL here (optional)") | |
run_button = gr.ClearButton(components=None, value="Enhance Image") | |
with gr.Column(): | |
output_slider = ImageSlider(label="Generate image", type="pil", slider_color="pink") | |
logs = gr.Textbox(label="logs") | |
run_button.add(output_slider) | |
with gr.Accordion("Advanced Options", open=False): | |
prompt = gr.Textbox( | |
label="Prompt", | |
placeholder="masterpiece, best quality, highres", | |
) | |
negative_prompt = gr.Textbox( | |
label="Negative Prompt", | |
placeholder="worst quality, low quality, normal quality", | |
) | |
seed = gr.Slider( | |
minimum=0, | |
maximum=10_000, | |
value=42, | |
step=1, | |
label="Seed", | |
) | |
upscale_factor = gr.Slider( | |
minimum=1, | |
maximum=4, | |
value=2, | |
step=0.2, | |
label="Upscale Factor", | |
) | |
controlnet_scale = gr.Slider( | |
minimum=0, | |
maximum=1.5, | |
value=0.6, | |
step=0.1, | |
label="ControlNet Scale", | |
) | |
controlnet_decay = gr.Slider( | |
minimum=0.5, | |
maximum=1, | |
value=1.0, | |
step=0.025, | |
label="ControlNet Scale Decay", | |
) | |
condition_scale = gr.Slider( | |
minimum=2, | |
maximum=20, | |
value=6, | |
step=1, | |
label="Condition Scale", | |
) | |
tile_width = gr.Slider( | |
minimum=64, | |
maximum=200, | |
value=112, | |
step=1, | |
label="Latent Tile Width", | |
) | |
tile_height = gr.Slider( | |
minimum=64, | |
maximum=200, | |
value=144, | |
step=1, | |
label="Latent Tile Height", | |
) | |
denoise_strength = gr.Slider( | |
minimum=0, | |
maximum=1, | |
value=0.35, | |
step=0.1, | |
label="Denoise Strength", | |
) | |
num_inference_steps = gr.Slider( | |
minimum=1, | |
maximum=30, | |
value=18, | |
step=1, | |
label="Number of Inference Steps", | |
) | |
solver = gr.Radio( | |
choices=["DDIM", "DPMSolver"], | |
value="DDIM", | |
label="Solver", | |
) | |
upload_to_r2 = gr.Checkbox(label="Upload generated image to R2", value=False) | |
account_id = gr.Textbox(label="Account Id", placeholder="Enter R2 account id") | |
access_key = gr.Textbox(label="Access Key", placeholder="Enter R2 access key here") | |
secret_key = gr.Textbox(label="Secret Key", placeholder="Enter R2 secret key here") | |
bucket = gr.Textbox(label="Bucket Name", placeholder="Enter R2 bucket name here") | |
run_button.click( | |
fn=process, | |
inputs=[ | |
input_image, | |
image_url, | |
prompt, | |
negative_prompt, | |
seed, | |
upscale_factor, | |
controlnet_scale, | |
controlnet_decay, | |
condition_scale, | |
tile_width, | |
tile_height, | |
denoise_strength, | |
num_inference_steps, | |
solver, | |
upload_to_r2, | |
account_id, | |
access_key, | |
secret_key, | |
bucket | |
], | |
outputs=[output_slider, logs] | |
) | |
demo.launch(share=False) | |