68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
from modules.api.processing import StableDiffusionProcessingAPI
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from modules.processing import StableDiffusionProcessingTxt2Img, process_images
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from modules.sd_samplers import samplers_k_diffusion
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import modules.shared as shared
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import uvicorn
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from fastapi import Body, APIRouter, HTTPException
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from fastapi.responses import JSONResponse
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from pydantic import BaseModel, Field, Json
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import json
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import io
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import base64
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sampler_to_index = lambda name: next(filter(lambda row: name in row[1][2], enumerate(samplers_k_diffusion)), None)
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class TextToImageResponse(BaseModel):
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images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: Json
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info: Json
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class Api:
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def __init__(self, app, queue_lock):
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self.router = APIRouter()
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self.app = app
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self.queue_lock = queue_lock
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self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
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def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
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sampler_index = sampler_to_index(txt2imgreq.sampler_index)
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if sampler_index is None:
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raise HTTPException(status_code=404, detail="Sampler not found")
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populate = txt2imgreq.copy(update={ # Override __init__ params
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"sd_model": shared.sd_model,
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"sampler_index": sampler_index[0],
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"do_not_save_samples": True,
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"do_not_save_grid": True
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}
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)
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p = StableDiffusionProcessingTxt2Img(**vars(populate))
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# Override object param
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with self.queue_lock:
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processed = process_images(p)
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b64images = []
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for i in processed.images:
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buffer = io.BytesIO()
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i.save(buffer, format="png")
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b64images.append(base64.b64encode(buffer.getvalue()))
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return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
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def img2imgendoint(self):
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raise NotImplementedError
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def extrasendoint(self):
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raise NotImplementedError
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def pnginfoendoint(self):
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raise NotImplementedError
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def launch(self, server_name, port):
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self.app.include_router(self.router)
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uvicorn.run(self.app, host=server_name, port=port)
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