segmind-vegart-api

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import numpy as np
import PIL.Image
import torch
from typing import List
from diffusers.utils import numpy_to_pil
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS
from fastapi import FastAPI
import uvicorn
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import RedirectResponse, StreamingResponse
import io
import os
from pathlib import Path
from db import Database
import uuid
import logging
logging.basicConfig(level=os.environ.get("LOGLEVEL", "INFO"))

MAX_SEED = np.iinfo(np.int32).max
USE_TORCH_COMPILE = os.environ.get("USE_TORCH_COMPILE", "0") == "1"
SPACE_ID = os.environ.get('SPACE_ID', '')

DB_PATH = Path("/data/cache") if SPACE_ID else Path("./cache")
IMGS_PATH = DB_PATH / "imgs"
DB_PATH.mkdir(exist_ok=True, parents=True)
IMGS_PATH.mkdir(exist_ok=True, parents=True)

database = Database(DB_PATH)

with database() as db:
    cursor = db.cursor()
    cursor.execute("SELECT * FROM cache")
    print(list(cursor.fetchall()))

dtype = torch.bfloat16
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    prior_pipeline = StableCascadePriorPipeline.from_pretrained(
        "stabilityai/stable-cascade-prior", torch_dtype=dtype
    )  # .to(device)
    decoder_pipeline = StableCascadeDecoderPipeline.from_pretrained(
        "stabilityai/stable-cascade", torch_dtype=dtype
    )  # .to(device)
    prior_pipeline.to(device)
    decoder_pipeline.to(device)

    if USE_TORCH_COMPILE:
        prior_pipeline.prior = torch.compile(
            prior_pipeline.prior, mode="reduce-overhead", fullgraph=True
        )
        decoder_pipeline.decoder = torch.compile(
            decoder_pipeline.decoder, mode="max-autotune", fullgraph=True
        )


def generate(
    prompt: str,
    negative_prompt: str = "",
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    prior_num_inference_steps: int = 20,
    prior_guidance_scale: float = 4.0,
    decoder_num_inference_steps: int = 10,
    decoder_guidance_scale: float = 0.0,
    num_images_per_prompt: int = 2,
) -> PIL.Image.Image:

    generator = torch.Generator().manual_seed(seed)
    prior_output = prior_pipeline(
        prompt=prompt,
        height=height,
        width=width,
        num_inference_steps=prior_num_inference_steps,
        timesteps=DEFAULT_STAGE_C_TIMESTEPS,
        negative_prompt=negative_prompt,
        guidance_scale=prior_guidance_scale,
        num_images_per_prompt=num_images_per_prompt,
        generator=generator,
    )
    decoder_output = decoder_pipeline(
        image_embeddings=prior_output.image_embeddings,
        prompt=prompt,
        num_inference_steps=decoder_num_inference_steps,
        # timesteps=decoder_timesteps,
        guidance_scale=decoder_guidance_scale,
        negative_prompt=negative_prompt,
        generator=generator,
        output_type="pil",
    ).images

    return decoder_output[0]


app = FastAPI()
origins = [
    "http://huggingface.co",
]

app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.get("/image")
async def generate_image(prompt: str, negative_prompt: str, seed: int = 2134213213):
    cached_img = database.check(prompt, negative_prompt, seed)
    if cached_img:
        logging.info(f"Image found in cache: {cached_img[0]}")
        return StreamingResponse(open(cached_img[0], "rb"), media_type="image/jpeg")

    logging.info(f"Image not found in cache, generating new image")
    pil_image = generate(prompt, negative_prompt, seed)
    img_id = str(uuid.uuid4())
    img_path = IMGS_PATH / f"{img_id}.jpg"
    pil_image.save(img_path)
    img_io = io.BytesIO()
    pil_image.save(img_io, "JPEG")
    img_io.seek(0)
    database.insert(prompt, negative_prompt, str(img_path), seed)

    return StreamingResponse(img_io, media_type="image/jpeg")


@app.get("/")
async def main():
    # redirect to https://huggingface.co/spaces/multimodalart/stable-cascade
    return RedirectResponse(
        "https://multimodalart-stable-cascade.hf.space/?__theme=system"
    )


if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)


# else:
#     prior_pipeline = None
#     decoder_pipeline = None


# 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,
#     negative_prompt: str = "",
#     seed: int = 0,
#     width: int = 1024,
#     height: int = 1024,
#     prior_num_inference_steps: int = 30,
#     # prior_timesteps: List[float] = None,
#     prior_guidance_scale: float = 4.0,
#     decoder_num_inference_steps: int = 12,
#     # decoder_timesteps: List[float] = None,
#     decoder_guidance_scale: float = 0.0,
#     num_images_per_prompt: int = 2,
#     progress=gr.Progress(track_tqdm=True),
# ) -> PIL.Image.Image:

#     generator = torch.Generator().manual_seed(seed)
#     prior_output = prior_pipeline(
#         prompt=prompt,
#         height=height,
#         width=width,
#         num_inference_steps=prior_num_inference_steps,
#         timesteps=DEFAULT_STAGE_C_TIMESTEPS,
#         negative_prompt=negative_prompt,
#         guidance_scale=prior_guidance_scale,
#         num_images_per_prompt=num_images_per_prompt,
#         generator=generator,
#     )
#     decoder_output = decoder_pipeline(
#         image_embeddings=prior_output.image_embeddings,
#         prompt=prompt,
#         num_inference_steps=decoder_num_inference_steps,
#         # timesteps=decoder_timesteps,
#         guidance_scale=decoder_guidance_scale,
#         negative_prompt=negative_prompt,
#         generator=generator,
#         output_type="pil",
#     ).images

#     return decoder_output[0]


# examples = [
#     "An astronaut riding a green horse",
#     "A mecha robot in a favela by Tarsila do Amaral",
#     "The sprirt of a Tamagotchi wandering in the city of Los Angeles",
#     "A delicious feijoada ramen dish"
# ]

# with gr.Blocks() 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.Image(label="Result", show_label=False)
#     with gr.Accordion("Advanced options", open=False):
#         negative_prompt = gr.Text(
#             label="Negative prompt",
#             max_lines=1,
#             placeholder="Enter a Negative Prompt",
#         )

#         seed = gr.Slider(
#             label="Seed",
#             minimum=0,
#             maximum=MAX_SEED,
#             step=1,
#             value=0,
#         )
#         randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
#         with gr.Row():
#             width = gr.Slider(
#                 label="Width",
#                 minimum=1024,
#                 maximum=1536,
#                 step=512,
#                 value=1024,
#             )
#             height = gr.Slider(
#                 label="Height",
#                 minimum=1024,
#                 maximum=1536,
#                 step=512,
#                 value=1024,
#             )
#             num_images_per_prompt = gr.Slider(
#                 label="Number of Images",
#                 minimum=1,
#                 maximum=2,
#                 step=1,
#                 value=1,
#             )
#         with gr.Row():
#             prior_guidance_scale = gr.Slider(
#                 label="Prior Guidance Scale",
#                 minimum=0,
#                 maximum=20,
#                 step=0.1,
#                 value=4.0,
#             )
#             prior_num_inference_steps = gr.Slider(
#                 label="Prior Inference Steps",
#                 minimum=10,
#                 maximum=30,
#                 step=1,
#                 value=20,
#             )

#             decoder_guidance_scale = gr.Slider(
#                 label="Decoder Guidance Scale",
#                 minimum=0,
#                 maximum=0,
#                 step=0.1,
#                 value=0.0,
#             )
#             decoder_num_inference_steps = gr.Slider(
#                 label="Decoder Inference Steps",
#                 minimum=4,
#                 maximum=12,
#                 step=1,
#                 value=10,
#             )

#     gr.Examples(
#         examples=examples,
#         inputs=prompt,
#         outputs=result,
#         fn=generate,
#         cache_examples=False,
#     )

#     inputs = [
#         prompt,
#         negative_prompt,
#         seed,
#         width,
#         height,
#         prior_num_inference_steps,
#         # prior_timesteps,
#         prior_guidance_scale,
#         decoder_num_inference_steps,
#         # decoder_timesteps,
#         decoder_guidance_scale,
#         num_images_per_prompt,
#     ]
#     gr.on(
#         triggers=[prompt.submit, negative_prompt.submit, run_button.click],
#         fn=randomize_seed_fn,
#         inputs=[seed, randomize_seed],
#         outputs=seed,
#         queue=False,
#         api_name=False,
#     ).then(
#         fn=generate,
#         inputs=inputs,
#         outputs=result,
#         api_name="run",
#     )


# if __name__ == "__main__":
#     demo.queue(max_size=20).launch()