stable-diffusion-webui/modules/extras.py

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from __future__ import annotations
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import math
import os
import sys
import traceback
import numpy as np
from PIL import Image
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import torch
import tqdm
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from typing import Callable, List, OrderedDict, Tuple
from functools import partial
from dataclasses import dataclass
from modules import processing, shared, images, devices, sd_models, sd_samplers
from modules.shared import opts
import modules.gfpgan_model
from modules.ui import plaintext_to_html
import modules.codeformer_model
import piexif
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import piexif.helper
import gradio as gr
import safetensors.torch
class LruCache(OrderedDict):
@dataclass(frozen=True)
class Key:
image_hash: int
info_hash: int
args_hash: int
@dataclass
class Value:
image: Image.Image
info: str
def __init__(self, max_size: int = 5, *args, **kwargs):
super().__init__(*args, **kwargs)
self._max_size = max_size
def get(self, key: LruCache.Key) -> LruCache.Value:
ret = super().get(key)
if ret is not None:
self.move_to_end(key) # Move to end of eviction list
return ret
def put(self, key: LruCache.Key, value: LruCache.Value) -> None:
self[key] = value
while len(self) > self._max_size:
self.popitem(last=False)
cached_images: LruCache = LruCache(max_size=5)
def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool):
devices.torch_gc()
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imageArr = []
# Also keep track of original file names
imageNameArr = []
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outputs = []
if extras_mode == 1:
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#convert file to pillow image
for img in image_folder:
image = Image.open(img)
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imageArr.append(image)
imageNameArr.append(os.path.splitext(img.orig_name)[0])
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elif extras_mode == 2:
assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
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if input_dir == '':
return outputs, "Please select an input directory.", ''
image_list = shared.listfiles(input_dir)
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for img in image_list:
try:
image = Image.open(img)
except Exception:
continue
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imageArr.append(image)
imageNameArr.append(img)
else:
imageArr.append(image)
imageNameArr.append(None)
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if extras_mode == 2 and output_dir != '':
outpath = output_dir
else:
outpath = opts.outdir_samples or opts.outdir_extras_samples
# Extra operation definitions
def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
res = Image.fromarray(restored_img)
if gfpgan_visibility < 1.0:
res = Image.blend(image, res, gfpgan_visibility)
info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
return (res, info)
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def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
res = Image.fromarray(restored_img)
if codeformer_visibility < 1.0:
res = Image.blend(image, res, codeformer_visibility)
info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
return (res, info)
def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
upscaler = shared.sd_upscalers[scaler_index]
res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
if mode == 1 and crop:
cropped = Image.new("RGB", (resize_w, resize_h))
cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2))
res = cropped
return res
def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
# Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text
nonlocal upscaling_resize
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if resize_mode == 1:
upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
crop_info = " (crop)" if upscaling_crop else ""
info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
return (image, info)
@dataclass
class UpscaleParams:
upscaler_idx: int
blend_alpha: float
def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
blended_result: Image.Image = None
image_hash: str = hash(np.array(image.getdata()).tobytes())
for upscaler in params:
upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,
upscaling_resize_w, upscaling_resize_h, upscaling_crop)
cache_key = LruCache.Key(image_hash=image_hash,
info_hash=hash(info),
args_hash=hash(upscale_args))
cached_entry = cached_images.get(cache_key)
if cached_entry is None:
res = upscale(image, *upscale_args)
info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
cached_images.put(cache_key, LruCache.Value(image=res, info=info))
else:
res, info = cached_entry.image, cached_entry.info
if blended_result is None:
blended_result = res
else:
blended_result = Image.blend(blended_result, res, upscaler.blend_alpha)
return (blended_result, info)
# Build a list of operations to run
facefix_ops: List[Callable] = []
facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else []
facefix_ops += [run_codeformer] if codeformer_visibility > 0 else []
upscale_ops: List[Callable] = []
upscale_ops += [run_prepare_crop] if resize_mode == 1 else []
if upscaling_resize != 0:
step_params: List[UpscaleParams] = []
step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_1, blend_alpha=1.0))
if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility))
upscale_ops.append(partial(run_upscalers_blend, step_params))
extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)
for image, image_name in zip(imageArr, imageNameArr):
if image is None:
return outputs, "Please select an input image.", ''
existing_pnginfo = image.info or {}
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image = image.convert("RGB")
info = ""
# Run each operation on each image
for op in extras_ops:
image, info = op(image, info)
if opts.use_original_name_batch and image_name != None:
basename = os.path.splitext(os.path.basename(image_name))[0]
else:
basename = ''
images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
if opts.enable_pnginfo:
image.info = existing_pnginfo
image.info["extras"] = info
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if extras_mode != 2 or show_extras_results :
outputs.append(image)
devices.torch_gc()
return outputs, plaintext_to_html(info), ''
def clear_cache():
cached_images.clear()
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def run_pnginfo(image):
if image is None:
return '', '', ''
geninfo, items = images.read_info_from_image(image)
items = {**{'parameters': geninfo}, **items}
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info = ''
for key, text in items.items():
info += f"""
<div>
<p><b>{plaintext_to_html(str(key))}</b></p>
<p>{plaintext_to_html(str(text))}</p>
</div>
""".strip()+"\n"
if len(info) == 0:
message = "Nothing found in the image."
info = f"<div><p>{message}<p></div>"
return '', geninfo, info
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def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
def weighted_sum(theta0, theta1, alpha):
return ((1 - alpha) * theta0) + (alpha * theta1)
def get_difference(theta1, theta2):
return theta1 - theta2
def add_difference(theta0, theta1_2_diff, alpha):
return theta0 + (alpha * theta1_2_diff)
primary_model_info = sd_models.checkpoints_list[primary_model_name]
secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
tertiary_model_info = sd_models.checkpoints_list.get(tertiary_model_name, None)
result_is_inpainting_model = False
theta_funcs = {
"Weighted sum": (None, weighted_sum),
"Add difference": (get_difference, add_difference),
}
theta_func1, theta_func2 = theta_funcs[interp_method]
if theta_func1 and not tertiary_model_info:
return ["Failed: Interpolation method requires a tertiary model."] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
print(f"Loading {secondary_model_info.filename}...")
theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
if theta_func1:
print(f"Loading {tertiary_model_info.filename}...")
theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
for key in tqdm.tqdm(theta_1.keys()):
if 'model' in key:
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if key in theta_2:
t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
theta_1[key] = theta_func1(theta_1[key], t2)
else:
theta_1[key] = torch.zeros_like(theta_1[key])
del theta_2
print(f"Loading {primary_model_info.filename}...")
theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
print("Merging...")
for key in tqdm.tqdm(theta_0.keys()):
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if 'model' in key and key in theta_1:
a = theta_0[key]
b = theta_1[key]
# this enables merging an inpainting model (A) with another one (B);
# where normal model would have 4 channels, for latenst space, inpainting model would
# have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9
if a.shape != b.shape and a.shape[0:1] + a.shape[2:] == b.shape[0:1] + b.shape[2:]:
if a.shape[1] == 4 and b.shape[1] == 9:
raise RuntimeError("When merging inpainting model with a normal one, A must be the inpainting model.")
assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
result_is_inpainting_model = True
else:
theta_0[key] = theta_func2(a, b, multiplier)
if save_as_half:
theta_0[key] = theta_0[key].half()
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# I believe this part should be discarded, but I'll leave it for now until I am sure
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for key in theta_1.keys():
if 'model' in key and key not in theta_0:
theta_0[key] = theta_1[key]
if save_as_half:
theta_0[key] = theta_0[key].half()
del theta_1
ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
filename = \
primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + \
secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + \
interp_method.replace(" ", "_") + \
'-merged.' + \
("inpainting." if result_is_inpainting_model else "") + \
checkpoint_format
filename = filename if custom_name == '' else (custom_name + '.' + checkpoint_format)
output_modelname = os.path.join(ckpt_dir, filename)
print(f"Saving to {output_modelname}...")
_, extension = os.path.splitext(output_modelname)
if extension.lower() == ".safetensors":
safetensors.torch.save_file(theta_0, output_modelname, metadata={"format": "pt"})
else:
torch.save(theta_0, output_modelname)
sd_models.list_models()
print("Checkpoint saved.")
return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]