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[feat] support negative_prompt #13

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[feat] support negative_prompt #13

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11 changes: 7 additions & 4 deletions sdxl-turbo/entrypoint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
import os
import sys
import traceback

import uvicorn

from fastapi import FastAPI, HTTPException, Response, status
Expand All @@ -16,7 +16,7 @@

if MODEL_NAME is None or CACHED_MODEL_PATH is None:
logging.error("Environment variables MODEL_NAME and CACHED_MODEL_PATH must be set. See Dockerfile for values.")
sys.exit(1)
sys.exit(1)

app = FastAPI()

Expand All @@ -40,6 +40,7 @@ async def generate_t2i(request: SdxlTurboRequest):
try:
image = pipe_t2i(
prompt=request.prompt,
negative_prompt=request.negative_prompt,
strength=request.strength,
guidance_scale=request.guidance_scale,
num_images_per_prompt=request.num_images_per_prompt,
Expand All @@ -57,17 +58,19 @@ async def generate_t2i(request: SdxlTurboRequest):
logging.error(traceback.format_exc())
raise HTTPException(status_code=500, detail=f"Error generating image: {str(e)}")


# sdxl turbo image to image endpoint
@app.post("/sdxl-turbo-i2i")
async def generate_i2i(request: SdxlTurboRequest):
try:
init_image = request.image
base64_decoded = base64.b64decode(init_image)
input_image = Image.frombytes("RGB", (512, 512), base64_decoded, "raw")

image = pipe_i2i(
image=input_image,
prompt=request.prompt,
negative_prompt=request.negative_prompt,
strength=request.strength,
guidance_scale=request.guidance_scale,
num_images_per_prompt=request.num_images_per_prompt,
Expand All @@ -87,4 +90,4 @@ async def generate_i2i(request: SdxlTurboRequest):

if __name__ == "__main__":
port = int(sys.argv[1]) if len(sys.argv) > 1 else 8080
uvicorn.run("entrypoint:app", host="0.0.0.0", port=port)
uvicorn.run("entrypoint:app", host="0.0.0.0", port=port)
4 changes: 3 additions & 1 deletion sdxl-turbo/sdxl_turbo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,16 +2,18 @@
from pydantic import BaseModel, Field
import torch
from typing import Optional


class SdxlTurboRequest(BaseModel):
prompt: str
negative_prompt: Optional[str] = None
num_inference_steps: int = Field(default=4)
guidance_scale: float = Field(default=0.0)
strength: float = Field(default=1.0)
num_images_per_prompt: int = Field(default=1)
image: Optional[str] = None


def setup_pipeline(model_name: str, cached_model_path):
pipe_t2i = AutoPipelineForText2Image.from_pretrained(
model_name,
Expand Down