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from cog import BasePredictor, Input, Path |
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import os |
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import sys |
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import signal |
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import time |
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import re |
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from typing import Dict, List, Any |
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import logging |
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logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage()) |
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from modules import errors |
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from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call |
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import torch |
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if ".dev" in torch.__version__ or "+git" in torch.__version__: |
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torch.__long_version__ = torch.__version__ |
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torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0) |
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from modules import shared, devices, ui_tempdir |
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from modules.api.api import encode_pil_to_base64 |
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import modules.codeformer_model as codeformer |
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import modules.face_restoration |
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import modules.gfpgan_model as gfpgan |
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import modules.img2img |
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import modules.lowvram |
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import modules.paths |
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import modules.scripts |
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import modules.sd_hijack |
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import modules.sd_models |
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import modules.sd_vae |
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import modules.txt2img |
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import modules.script_callbacks |
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import modules.textual_inversion.textual_inversion |
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import modules.progress |
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import modules.ui |
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from modules import modelloader |
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from modules.shared import cmd_opts, opts |
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import modules.hypernetworks.hypernetwork |
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images |
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def initialize(): |
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modelloader.cleanup_models() |
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modules.sd_models.setup_model() |
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codeformer.setup_model(cmd_opts.codeformer_models_path) |
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gfpgan.setup_model(cmd_opts.gfpgan_models_path) |
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modelloader.list_builtin_upscalers() |
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modelloader.load_upscalers() |
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modules.sd_vae.refresh_vae_list() |
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try: |
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modules.sd_models.load_model() |
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except Exception as e: |
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errors.display(e, "loading stable diffusion model") |
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print("", file=sys.stderr) |
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print("Stable diffusion model failed to load, exiting", file=sys.stderr) |
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exit(1) |
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shared.opts.data["sd_model_checkpoint"] = shared.sd_model.sd_checkpoint_info.title |
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shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights())) |
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shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False) |
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shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False) |
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shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed) |
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class Predictor(BasePredictor): |
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def setup(self): |
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"""Load the model into memory to make running multiple predictions efficient""" |
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initialize() |
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def predict( |
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self, |
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prompt: str = Input(description="prompt en", default="lora:koreanDollLikeness_v15:0.66, best quality, ultra high res, (photorealistic:1.4), 1girl, beige sweater, black choker, smile, laughing, bare shoulders, solo focus, ((full body), (brown hair:1), looking at viewer"), |
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negative_prompt: str = Input(description="negative prompt", default="paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, glans, (ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, 3hands,4fingers,3arms, bad anatomy, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts,poorly drawn face,mutation,deformed"), |
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sampler_name: str = Input(description="sampler name", default="DPM++ SDE Karras", choices=["DPM++ SDE Karras", "DPM++ 2M Karras", "DPM++ 2S a Karras", "DPM2 a Karras", "DPM2 Karras", "LMS Karras", "DPM adaptive", "DPM fast", "DPM++ SDE", "DPM++ 2M", "DPM++ 2S a", "DPM2 a", "DPM2", "Heun", "LMS", "Euler", "Euler a"]), |
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steps: int = Input(description="steps", default=20), |
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cfg_scale: int = Input(description="cfg scale", default=8), |
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width: int = Input(description="width", default=512), |
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height: int = Input(description="height", default=768), |
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seed: int = Input(description="seed", default=-1), |
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) -> Path: |
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"""Run a single prediction on the model""" |
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args = { |
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"do_not_save_samples": True, |
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"do_not_save_grid": True, |
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"outpath_samples": "./output", |
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"prompt": prompt, |
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"negative_prompt": negative_prompt, |
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"sampler_name": sampler_name, |
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"steps": steps, |
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"cfg_scale": cfg_scale, |
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"width": width, |
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"height": height, |
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"seed": seed, |
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} |
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p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args) |
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processed = process_images(p) |
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filename = str(int(time.time())) + ".png" |
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processed.images[0].save(fp=filename, format="PNG") |
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return Path(filename) |
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class PredictorOld(BasePredictor): |
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def setup(self): |
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"""Load the model into memory to make running multiple predictions efficient""" |
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initialize() |
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self.shared = shared |
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def predict( |
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self, |
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prompt: str = Input(description="prompt en"), |
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) -> Dict[str, Any]: |
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"""Run a single prediction on the model""" |
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args = { |
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"do_not_save_samples": True, |
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"do_not_save_grid": True, |
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"outpath_samples": "./output", |
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"prompt": "lora:koreanDollLikeness_v15:0.66, best quality, ultra high res, (photorealistic:1.4), 1girl, beige sweater, black choker, smile, laughing, bare shoulders, solo focus, ((full body), (brown hair:1), looking at viewer", |
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"negative_prompt": "paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, glans, (ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, 3hands,4fingers,3arms, bad anatomy, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts,poorly drawn face,mutation,deformed", |
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"sampler_name": "DPM++ SDE Karras", |
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"steps": 20, |
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"cfg_scale": 8, |
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"width": 512, |
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"height": 768, |
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"seed": -1, |
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} |
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if len(prompt) > 0: |
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print("get prompt from request: ", prompt) |
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args["prompt"] = prompt |
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p = StableDiffusionProcessingTxt2Img(sd_model=self.shared.sd_model, **args) |
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processed = process_images(p) |
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single_image_b64 = encode_pil_to_base64(processed.images[0]).decode('utf-8') |
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return { |
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"img_data": single_image_b64, |
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"parameters": processed.images[0].info.get('parameters', ""), |
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} |
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