214 lines
8.4 KiB
Python
214 lines
8.4 KiB
Python
# Prediction interface for Cog ⚙️
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# https://github.com/replicate/cog/blob/main/docs/python.md
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import os
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import subprocess
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import time
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from omegaconf import OmegaConf
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from PIL import Image
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from cog import BasePredictor, Input, Path
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from SUPIR.util import (
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create_SUPIR_model,
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PIL2Tensor,
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Tensor2PIL,
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convert_dtype,
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)
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from llava.llava_agent import LLavaAgent
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import CKPT_PTH
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SUPIR_v0Q_URL = "https://weights.replicate.delivery/default/SUPIR-v0Q.ckpt"
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SUPIR_v0F_URL = "https://weights.replicate.delivery/default/SUPIR-v0F.ckpt"
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LLAVA_URL = "https://weights.replicate.delivery/default/llava-v1.5-13b.tar"
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LLAVA_CLIP_URL = (
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"https://weights.replicate.delivery/default/clip-vit-large-patch14-336.tar"
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)
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SDXL_URL = "https://weights.replicate.delivery/default/stable-diffusion-xl-base-1.0/sd_xl_base_1.0_0.9vae.safetensors"
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SDXL_CLIP1_URL = "https://weights.replicate.delivery/default/clip-vit-large-patch14.tar"
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SDXL_CLIP2_URL = (
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"https://weights.replicate.delivery/default/CLIP-ViT-bigG-14-laion2B-39B-b160k.tar"
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)
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MODEL_CACHE = "/opt/data/private/AIGC_pretrain/" # Follow the default in CKPT_PTH.py
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LLAVA_CLIP_PATH = CKPT_PTH.LLAVA_CLIP_PATH
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LLAVA_MODEL_PATH = CKPT_PTH.LLAVA_MODEL_PATH
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SDXL_CLIP1_PATH = CKPT_PTH.SDXL_CLIP1_PATH
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SDXL_CLIP2_CACHE = f"{MODEL_CACHE}/models--laion--CLIP-ViT-bigG-14-laion2B-39B-b160k"
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SDXL_CKPT = f"{MODEL_CACHE}/SDXL_cache/sd_xl_base_1.0_0.9vae.safetensors"
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SUPIR_CKPT_F = f"{MODEL_CACHE}/SUPIR_cache/SUPIR-v0F.ckpt"
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SUPIR_CKPT_Q = f"{MODEL_CACHE}/SUPIR_cache/SUPIR-v0Q.ckpt"
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def download_weights(url, dest, extract=True):
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start = time.time()
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print("downloading url: ", url)
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print("downloading to: ", dest)
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args = ["pget"]
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if extract:
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args.append("-x")
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subprocess.check_call(args + [url, dest], close_fds=False)
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print("downloading took: ", time.time() - start)
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class Predictor(BasePredictor):
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def setup(self) -> None:
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"""Load the model into memory to make running multiple predictions efficient"""
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for model_dir in [
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MODEL_CACHE,
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f"{MODEL_CACHE}/SUPIR_cache",
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f"{MODEL_CACHE}/SDXL_cache",
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]:
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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if not os.path.exists(SUPIR_CKPT_Q):
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download_weights(SUPIR_v0Q_URL, SUPIR_CKPT_Q, extract=False)
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if not os.path.exists(SUPIR_CKPT_F):
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download_weights(SUPIR_v0F_URL, SUPIR_CKPT_F, extract=False)
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if not os.path.exists(LLAVA_MODEL_PATH):
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download_weights(LLAVA_URL, LLAVA_MODEL_PATH)
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if not os.path.exists(LLAVA_CLIP_PATH):
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download_weights(LLAVA_CLIP_URL, LLAVA_CLIP_PATH)
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if not os.path.exists(SDXL_CLIP1_PATH):
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download_weights(SDXL_CLIP1_URL, SDXL_CLIP1_PATH)
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if not os.path.exists(SDXL_CKPT):
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download_weights(SDXL_URL, SDXL_CKPT, extract=False)
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if not os.path.exists(SDXL_CKPT):
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download_weights(SDXL_CLIP2_URL, SDXL_CKPT)
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self.supir_device = "cuda:0"
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self.llava_device = "cuda:0"
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ae_dtype = "bf16" # Inference data type of AutoEncoder
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diff_dtype = "bf16" # Inference data type of Diffusion
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self.models = {
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k: create_SUPIR_model("options/SUPIR_v0.yaml", SUPIR_sign=k).to(
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self.supir_device
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)
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for k in ["Q", "F"]
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}
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for k in ["Q", "F"]:
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self.models[k].ae_dtype = convert_dtype(ae_dtype)
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self.models[k].model.dtype = convert_dtype(diff_dtype)
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# load LLaVA
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self.llava_agent = LLavaAgent(LLAVA_MODEL_PATH, device=self.llava_device)
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def predict(
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self,
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model_name: str = Input(
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description="Choose a model. SUPIR-v0Q is the default training settings with paper. SUPIR-v0F is high generalization and high image quality in most cases. Training with light degradation settings. Stage1 encoder of SUPIR-v0F remains more details when facing light degradations.",
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choices=["SUPIR-v0Q", "SUPIR-v0F"],
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default="SUPIR-v0Q",
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),
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image: Path = Input(description="Low quality input image."),
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upscale: int = Input(
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description="Upsampling ratio of given inputs.", default=1
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),
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min_size: float = Input(
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description="Minimum resolution of output images.", default=1024
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),
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edm_steps: int = Input(
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description="Number of steps for EDM Sampling Schedule.",
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ge=1,
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le=500,
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default=50,
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),
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use_llava: bool = Input(
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description="Use LLaVA model to get captions.", default=True
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),
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a_prompt: str = Input(
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description="Additive positive prompt for the inputs.",
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default="Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed photo - realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, skin pore detailing, hyper sharpness, perfect without deformations.",
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),
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n_prompt: str = Input(
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description="Negative prompt for the inputs.",
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default="painting, oil painting, illustration, drawing, art, sketch, oil painting, cartoon, CG Style, 3D render, unreal engine, blurring, dirty, messy, worst quality, low quality, frames, watermark, signature, jpeg artifacts, deformed, lowres, over-smooth",
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),
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color_fix_type: str = Input(
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description="Color Fixing Type..",
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choices=["None", "AdaIn", "Wavelet"],
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default="Wavelet",
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),
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s_stage1: int = Input(
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description="Control Strength of Stage1 (negative means invalid).",
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default=-1,
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),
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s_churn: float = Input(
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description="Original churn hy-param of EDM.", default=5
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),
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s_noise: float = Input(
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description="Original noise hy-param of EDM.", default=1.003
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),
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s_cfg: float = Input(
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description=" Classifier-free guidance scale for prompts.",
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ge=1,
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le=20,
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default=7.5,
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),
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s_stage2: float = Input(description="Control Strength of Stage2.", default=1.0),
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linear_CFG: bool = Input(
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description="Linearly (with sigma) increase CFG from 'spt_linear_CFG' to s_cfg.",
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default=False,
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),
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linear_s_stage2: bool = Input(
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description="Linearly (with sigma) increase s_stage2 from 'spt_linear_s_stage2' to s_stage2.",
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default=False,
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),
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spt_linear_CFG: float = Input(
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description="Start point of linearly increasing CFG.", default=1.0
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),
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spt_linear_s_stage2: float = Input(
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description="Start point of linearly increasing s_stage2.", default=0.0
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),
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seed: int = Input(
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description="Random seed. Leave blank to randomize the seed", default=None
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),
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) -> Path:
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"""Run a single prediction on the model"""
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if seed is None:
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seed = int.from_bytes(os.urandom(2), "big")
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print(f"Using seed: {seed}")
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model = self.models["Q"] if model_name == "SUPIR-v0Q" else self.models["F"]
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lq_img = Image.open(str(image))
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lq_img, h0, w0 = PIL2Tensor(lq_img, upsacle=upscale, min_size=min_size)
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lq_img = lq_img.unsqueeze(0).to(self.supir_device)[:, :3, :, :]
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# step 1: Pre-denoise for LLaVA)
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clean_imgs = model.batchify_denoise(lq_img)
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clean_PIL_img = Tensor2PIL(clean_imgs[0], h0, w0)
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# step 2: LLaVA
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captions = [""]
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if use_llava:
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captions = self.llava_agent.gen_image_caption([clean_PIL_img])
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print(f"Captions from LLaVA: {captions}")
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# step 3: Diffusion Process
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samples = model.batchify_sample(
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lq_img,
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captions,
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num_steps=edm_steps,
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restoration_scale=s_stage1,
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s_churn=s_churn,
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s_noise=s_noise,
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cfg_scale=s_cfg,
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control_scale=s_stage2,
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seed=seed,
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num_samples=1,
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p_p=a_prompt,
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n_p=n_prompt,
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color_fix_type=color_fix_type,
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use_linear_CFG=linear_CFG,
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use_linear_control_scale=linear_s_stage2,
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cfg_scale_start=spt_linear_CFG,
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control_scale_start=spt_linear_s_stage2,
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)
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out_path = "/tmp/out.png"
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Tensor2PIL(samples[0], h0, w0).save(out_path)
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return Path(out_path)
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