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import os
from fastapi import FastAPI, File, UploadFile, Form
from fastapi.responses import StreamingResponse
import torch
from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler, DPMSolverSinglestepScheduler
from diffusers.pipelines import StableDiffusionInpaintPipeline
from huggingface_hub import hf_hub_download
import numpy as np
import random
from PIL import Image
import io
app = FastAPI()
MAX_SEED = np.iinfo(np.int32).max
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Load HF token from environment variable
HF_TOKEN = os.getenv("HF_TOKEN")
# Dictionary to store loaded pipelines
loaded_pipelines = {}
# Function to load pipeline dynamically
def load_pipeline(model_name: str):
if model_name in loaded_pipelines:
return loaded_pipelines[model_name]
if model_name == "Fluently XL Final":
pipe = StableDiffusionXLPipeline.from_single_file(
hf_hub_download(repo_id="fluently/Fluently-XL-Final", filename="FluentlyXL-Final.safetensors", token=HF_TOKEN),
torch_dtype=torch.float16,
use_safetensors=True,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
elif model_name == "Fluently Anime":
pipe = StableDiffusionPipeline.from_pretrained(
"fluently/Fluently-anime",
torch_dtype=torch.float16,
use_safetensors=True,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
elif model_name == "Fluently Epic":
pipe = StableDiffusionPipeline.from_pretrained(
"fluently/Fluently-epic",
torch_dtype=torch.float16,
use_safetensors=True,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
elif model_name == "Fluently XL v4":
pipe = StableDiffusionXLPipeline.from_pretrained(
"fluently/Fluently-XL-v4",
torch_dtype=torch.float16,
use_safetensors=True,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
elif model_name == "Fluently XL v3 Lightning":
pipe = StableDiffusionXLPipeline.from_pretrained(
"fluently/Fluently-XL-v3-lightning",
torch_dtype=torch.float16,
use_safetensors=True,
)
pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=False, timestep_spacing="trailing", lower_order_final=True)
elif model_name == "Fluently v4 inpaint":
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"fluently/Fluently-v4-inpainting",
torch_dtype=torch.float16,
use_safetensors=True,
)
else:
raise ValueError(f"Unknown model: {model_name}")
pipe.to(device)
loaded_pipelines[model_name] = pipe
return pipe
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
@app.post("/generate")
async def generate(
model: str = Form(...),
prompt: str = Form(...),
negative_prompt: str = Form(""),
use_negative_prompt: bool = Form(False),
seed: int = Form(0),
width: int = Form(1024),
height: int = Form(1024),
guidance_scale: float = Form(3),
randomize_seed: bool = Form(False),
inpaint_image: UploadFile = File(None),
mask_image: UploadFile = File(None),
blur_factor: float = Form(1.0),
strength: float = Form(0.75)
):
seed = int(randomize_seed_fn(seed, randomize_seed))
if not use_negative_prompt:
negative_prompt = ""
inpaint_image_pil = Image.open(io.BytesIO(await inpaint_image.read())) if inpaint_image else None
mask_image_pil = Image.open(io.BytesIO(await mask_image.read())) if mask_image else None
pipe = load_pipeline(model)
if model in ["Fluently v4 inpaint"]:
blurred_mask = pipe.mask_processor.blur(mask_image_pil, blur_factor=blur_factor)
images = pipe(
prompt=prompt,
image=inpaint_image_pil,
mask_image=blurred_mask,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=30,
strength=strength,
num_images_per_prompt=1,
output_type="pil",
).images
else:
images = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=25 if model == "Fluently XL Final" else 30,
num_images_per_prompt=1,
output_type="pil",
).images
img = images[0]
img_byte_arr = io.BytesIO()
img.save(img_byte_arr, format='PNG')
img_byte_arr.seek(0)
return StreamingResponse(img_byte_arr, media_type="image/png")
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)
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