change StableDiffusionProcessing to internally use sampler name instead of sampler index
This commit is contained in:
AUTOMATIC
parent
d9fd4525a5
commit
cdc8020d13
11 changed files with 49 additions and 54 deletions
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@ -6,9 +6,9 @@ from threading import Lock
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from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
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from fastapi import APIRouter, Depends, FastAPI, HTTPException
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import modules.shared as shared
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from modules import sd_samplers
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from modules.api.models import *
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
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from modules.sd_samplers import all_samplers
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from modules.extras import run_extras, run_pnginfo
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from PIL import PngImagePlugin
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from modules.sd_models import checkpoints_list
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@ -25,8 +25,12 @@ def upscaler_to_index(name: str):
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raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
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sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
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def validate_sampler_name(name):
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config = sd_samplers.all_samplers_map.get(name, None)
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if config is None:
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raise HTTPException(status_code=404, detail="Sampler not found")
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return name
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def setUpscalers(req: dict):
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reqDict = vars(req)
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@ -82,14 +86,9 @@ class Api:
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self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
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def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
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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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"sampler_name": validate_sampler_name(txt2imgreq.sampler_index),
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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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@ -109,12 +108,6 @@ class Api:
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return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
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def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
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sampler_index = sampler_to_index(img2imgreq.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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init_images = img2imgreq.init_images
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if init_images is None:
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raise HTTPException(status_code=404, detail="Init image not found")
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@ -123,10 +116,9 @@ class Api:
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if mask:
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mask = decode_base64_to_image(mask)
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populate = img2imgreq.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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"sampler_name": validate_sampler_name(img2imgreq.sampler_index),
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"do_not_save_samples": True,
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"do_not_save_grid": True,
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"mask": mask
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@ -272,7 +264,7 @@ class Api:
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return vars(shared.cmd_opts)
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def get_samplers(self):
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return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers]
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return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
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def get_upscalers(self):
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upscalers = []
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@ -12,7 +12,7 @@ import torch
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import tqdm
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from einops import rearrange, repeat
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from ldm.util import default
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from modules import devices, processing, sd_models, shared
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from modules import devices, processing, sd_models, shared, sd_samplers
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from modules.textual_inversion import textual_inversion
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from modules.textual_inversion.learn_schedule import LearnRateScheduler
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from torch import einsum
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@ -535,7 +535,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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p.prompt = preview_prompt
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p.negative_prompt = preview_negative_prompt
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p.steps = preview_steps
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p.sampler_index = preview_sampler_index
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p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
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p.cfg_scale = preview_cfg_scale
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p.seed = preview_seed
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p.width = preview_width
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@ -303,7 +303,7 @@ class FilenameGenerator:
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'width': lambda self: self.image.width,
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'height': lambda self: self.image.height,
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'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
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'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),
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'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
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'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
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'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
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'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
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@ -6,7 +6,7 @@ import traceback
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import numpy as np
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from PIL import Image, ImageOps, ImageChops
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from modules import devices
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from modules import devices, sd_samplers
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from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
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from modules.shared import opts, state
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import modules.shared as shared
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@ -99,7 +99,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
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seed_resize_from_h=seed_resize_from_h,
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seed_resize_from_w=seed_resize_from_w,
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seed_enable_extras=seed_enable_extras,
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sampler_index=sampler_index,
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sampler_index=sd_samplers.samplers_for_img2img[sampler_index].name,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=steps,
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@ -2,6 +2,7 @@ import json
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import math
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import os
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import sys
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import warnings
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import torch
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import numpy as np
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@ -66,19 +67,15 @@ def apply_overlay(image, paste_loc, index, overlays):
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return image
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def get_correct_sampler(p):
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if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
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return sd_samplers.samplers
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elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
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return sd_samplers.samplers_for_img2img
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elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI):
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return sd_samplers.samplers
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class StableDiffusionProcessing():
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"""
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The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
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"""
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_index: int = 0, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None):
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, sampler_index: int = None):
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if sampler_index is not None:
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warnings.warn("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name")
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self.sd_model = sd_model
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self.outpath_samples: str = outpath_samples
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self.outpath_grids: str = outpath_grids
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@ -91,7 +88,7 @@ class StableDiffusionProcessing():
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self.subseed_strength: float = subseed_strength
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self.seed_resize_from_h: int = seed_resize_from_h
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self.seed_resize_from_w: int = seed_resize_from_w
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self.sampler_index: int = sampler_index
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self.sampler_name: str = sampler_name
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self.batch_size: int = batch_size
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self.n_iter: int = n_iter
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self.steps: int = steps
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@ -210,8 +207,7 @@ class Processed:
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self.info = info
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self.width = p.width
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self.height = p.height
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self.sampler_index = p.sampler_index
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self.sampler = sd_samplers.samplers[p.sampler_index].name
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self.sampler_name = p.sampler_name
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self.cfg_scale = p.cfg_scale
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self.steps = p.steps
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self.batch_size = p.batch_size
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@ -256,8 +252,7 @@ class Processed:
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"subseed_strength": self.subseed_strength,
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"width": self.width,
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"height": self.height,
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"sampler_index": self.sampler_index,
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"sampler": self.sampler,
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"sampler_name": self.sampler_name,
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"cfg_scale": self.cfg_scale,
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"steps": self.steps,
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"batch_size": self.batch_size,
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@ -384,7 +379,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
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generation_params = {
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"Steps": p.steps,
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"Sampler": get_correct_sampler(p)[p.sampler_index].name,
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"Sampler": p.sampler_name,
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"CFG scale": p.cfg_scale,
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"Seed": all_seeds[index],
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"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
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@ -645,7 +640,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
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self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
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self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
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if not self.enable_hr:
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x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
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@ -706,7 +701,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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shared.state.nextjob()
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self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
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self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
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noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
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@ -743,7 +738,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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self.image_conditioning = None
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def init(self, all_prompts, all_seeds, all_subseeds):
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self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
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self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
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crop_region = None
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if self.image_mask is not None:
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@ -46,16 +46,23 @@ all_samplers = [
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SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
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SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
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]
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all_samplers_map = {x.name: x for x in all_samplers}
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samplers = []
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samplers_for_img2img = []
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def create_sampler_with_index(list_of_configs, index, model):
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config = list_of_configs[index]
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def create_sampler(name, model):
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if name is not None:
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config = all_samplers_map.get(name, None)
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else:
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config = all_samplers[0]
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assert config is not None, f'bad sampler name: {name}'
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sampler = config.constructor(model)
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sampler.config = config
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return sampler
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@ -10,7 +10,7 @@ import csv
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from PIL import Image, PngImagePlugin
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from modules import shared, devices, sd_hijack, processing, sd_models, images
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from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers
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import modules.textual_inversion.dataset
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from modules.textual_inversion.learn_schedule import LearnRateScheduler
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@ -345,7 +345,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
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p.prompt = preview_prompt
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p.negative_prompt = preview_negative_prompt
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p.steps = preview_steps
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p.sampler_index = preview_sampler_index
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p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
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p.cfg_scale = preview_cfg_scale
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p.seed = preview_seed
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p.width = preview_width
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@ -1,4 +1,5 @@
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import modules.scripts
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from modules import sd_samplers
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from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
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StableDiffusionProcessingImg2Img, process_images
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from modules.shared import opts, cmd_opts
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@ -21,7 +22,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
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seed_resize_from_h=seed_resize_from_h,
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seed_resize_from_w=seed_resize_from_w,
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seed_enable_extras=seed_enable_extras,
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sampler_index=sampler_index,
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sampler_name=sd_samplers.samplers[sampler_index].name,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=steps,
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@ -142,7 +142,7 @@ def save_files(js_data, images, do_make_zip, index):
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filenames.append(os.path.basename(txt_fullfn))
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fullfns.append(txt_fullfn)
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writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
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writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
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# Make Zip
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if do_make_zip:
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@ -157,7 +157,7 @@ class Script(scripts.Script):
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def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
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# Override
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if override_sampler:
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p.sampler_index = [sampler.name for sampler in sd_samplers.samplers].index("Euler")
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p.sampler_name = "Euler"
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if override_prompt:
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p.prompt = original_prompt
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p.negative_prompt = original_negative_prompt
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@ -191,7 +191,7 @@ class Script(scripts.Script):
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combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
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sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)
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sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
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sigmas = sampler.model_wrap.get_sigmas(p.steps)
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@ -10,9 +10,9 @@ import numpy as np
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import modules.scripts as scripts
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import gradio as gr
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from modules import images
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from modules import images, sd_samplers
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from modules.hypernetworks import hypernetwork
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from modules.processing import process_images, Processed, get_correct_sampler, StableDiffusionProcessingTxt2Img
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from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
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from modules.shared import opts, cmd_opts, state
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import modules.shared as shared
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import modules.sd_samplers
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@ -60,9 +60,9 @@ def apply_order(p, x, xs):
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p.prompt = prompt_tmp + p.prompt
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def build_samplers_dict(p):
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def build_samplers_dict():
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samplers_dict = {}
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for i, sampler in enumerate(get_correct_sampler(p)):
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for i, sampler in enumerate(sd_samplers.all_samplers):
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samplers_dict[sampler.name.lower()] = i
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||||
for alias in sampler.aliases:
|
||||
samplers_dict[alias.lower()] = i
|
||||
|
|
@ -70,7 +70,7 @@ def build_samplers_dict(p):
|
|||
|
||||
|
||||
def apply_sampler(p, x, xs):
|
||||
sampler_index = build_samplers_dict(p).get(x.lower(), None)
|
||||
sampler_index = build_samplers_dict().get(x.lower(), None)
|
||||
if sampler_index is None:
|
||||
raise RuntimeError(f"Unknown sampler: {x}")
|
||||
|
||||
|
|
@ -78,7 +78,7 @@ def apply_sampler(p, x, xs):
|
|||
|
||||
|
||||
def confirm_samplers(p, xs):
|
||||
samplers_dict = build_samplers_dict(p)
|
||||
samplers_dict = build_samplers_dict()
|
||||
for x in xs:
|
||||
if x.lower() not in samplers_dict.keys():
|
||||
raise RuntimeError(f"Unknown sampler: {x}")
|
||||
|
|
|
|||
Loading…
Reference in a new issue