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import os
import random
import uuid
import numpy as np
from PIL import Image
import spaces
import torch
from diffusers import DiffusionPipeline
from flask import Flask, flash, request
from flask_session import Session
app = Flask(__name__)
app.config["SESSION_PERMANENT"] = False
app.config["SESSION_TYPE"] = "filesystem"
Session(app)
DESCRIPTION = """# Playground v2.5"""
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1536"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
NUM_IMAGES_PER_PROMPT = 1
if torch.cuda.is_available():
pipe = DiffusionPipeline.from_pretrained(
"playgroundai/playground-v2.5-1024px-aesthetic",
torch_dtype=torch.float16,
use_safetensors=True,
add_watermarker=False,
variant="fp16"
)
if ENABLE_CPU_OFFLOAD:
pipe.enable_model_cpu_offload()
else:
pipe.to(device)
print("Loaded on Device!")
if USE_TORCH_COMPILE:
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
print("Model Compiled!")
def save_image(img):
unique_name = str(uuid.uuid4()) + ".png"
img.save(unique_name)
return unique_name
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
@spaces.GPU(enable_queue=True)
def generate(
prompt: str,
negative_prompt: str = "",
use_negative_prompt: bool = False,
seed: int = 0,
width: int = 1024,
height: int = 1024,
guidance_scale: float = 3,
randomize_seed: bool = False,
use_resolution_binning: bool = True,
):
pipe = DiffusionPipeline.from_pretrained(
"playgroundai/playground-v2.5-1024px-aesthetic",
torch_dtype=torch.float16,
use_safetensors=True,
add_watermarker=False,
variant="fp16"
)
if ENABLE_CPU_OFFLOAD:
pipe.enable_model_cpu_offload()
else:
pipe.to(device)
print("Loaded on Device!")
if USE_TORCH_COMPILE:
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
print("Model Compiled!")
pipe.to('cpu')
seed = int(randomize_seed_fn(seed, randomize_seed))
generator = torch.Generator().manual_seed(seed)
if not use_negative_prompt:
negative_prompt = None # type: ignore
images = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=25,
generator=generator,
num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
use_resolution_binning=use_resolution_binning,
output_type="pil",
).images
image_paths = [save_image(img) for img in images]
print(image_paths)
return image_paths, seed
examples = [
"neon holography crystal cat",
"a cat eating a piece of cheese",
"an astronaut riding a horse in space",
"a cartoon of a boy playing with a tiger",
"a cute robot artist painting on an easel, concept art",
"a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone"
]
@app.route("/", methods=['GET', 'POST'])
def index():
if request.method == 'POST':
if 'file' and 'file1' not in request.files:
flash('No file part')
return {"status": "Failed", "message": "Please Provide file name(file)."}
file = request.files['file']
file1 = request.files['file1']
image = Image.open(file)
image1 = Image.open(file1)
preprocess_image = generate('a boy playing with basketball')
# print(preprocess_image)
return {"status": "Success", "message": "You can download the 3D model.", "data": preprocess_image}
else:
return {
"status": "Success",
"message":"You can upload an image file to get the 3D model."
}
if "__main__" == __name__:
app.run(debug=True, port=7860, host="0.0.0.0") |