Implement Styles Preset!
- Implement #7 - Finally separate logics into their own scripts - Version bump - Lint codepull/8/head
Haoming
parent
6baa27b97c
commit
b0da134bf5
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@ -53,6 +53,13 @@ refer to the parameters and sample images below and play around with the values.
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- **Reset:** Revert all settings to the default values
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- **Randomize:** Randomize `Brightness`, `Contrast`, `Saturation`, `R`, `G`, `B`
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#### Styles Preset
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- Use the `Dropdown` to select a Style to apply
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- To save a Style, enter a name in the `Textbox` then click **Save Style**
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- To delete a Style, enter the name in the `Textbox` then click **Delete Style**
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- *Deleted Style is still in the `styles.json` in case you wish to retrieve it*
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- Click **Manual Refresh** to update the `Dropdown` the if you edited the `styles.json` directly
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#### Advanced Settings
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- **Process Hires. fix:** By default, this Extension only functions during the **txt2img** phase, so that **Hires. fix** may "fix" the artifacts introduced during **txt2img**. Enable this to process **Hires. fix** phase too.
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@ -128,6 +135,7 @@ refer to the parameters and sample images below and play around with the values.
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- [X] Add Support for **X/Y/Z Plot**
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- [X] Implement different Noise functions
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- [X] Add Randomize functions
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- [X] Styles Preset
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- [ ] Implement a better scaling algorithm
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- [ ] Fix the Brightness issues
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- [ ] Add Gradient feature
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@ -1,64 +1,19 @@
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from modules.sd_samplers_kdiffusion import KDiffusionSampler
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import modules.scripts as scripts
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from modules import devices
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from modules import shared
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import gradio as gr
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import random
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import torch
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import json
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VERSION = 'v1.2.0'
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from scripts.cc_version import *
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from scripts.cc_noise import *
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from scripts.cc_style import StyleManager
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def clean_outdated(EXT_NAME:str):
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with open(scripts.basedir() + '/' + 'ui-config.json', 'r') as json_file:
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configs = json.loads(json_file.read())
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cleaned_configs = {key: value for key, value in configs.items() if EXT_NAME not in key}
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with open(scripts.basedir() + '/' + 'ui-config.json', 'w') as json_file:
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json.dump(cleaned_configs, json_file)
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def ones(latent):
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return torch.ones_like(latent)
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def gaussian_noise(latent):
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return torch.rand_like(latent)
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def normal_noise(latent):
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return torch.randn_like(latent)
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def multires_noise(latent, use_zero:bool, iterations=8, discount=0.4):
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"""
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Reference: https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2
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Credit: Kohya_SS
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"""
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noise = torch.zeros_like(latent) if use_zero else ones(latent)
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batchSize = noise.size(0)
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height = noise.size(2)
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width = noise.size(3)
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device = devices.get_optimal_device()
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upsampler = torch.nn.Upsample(size=(height, width), mode="bilinear").to(device)
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for b in range(batchSize):
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for i in range(iterations):
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r = random.random() * 2 + 2
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wn = max(1, int(width / (r**i)))
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hn = max(1, int(height / (r**i)))
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for c in range(4):
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noise[b, c] += upsampler(torch.randn(1, 1, hn, wn).to(device))[0, 0] * discount**i
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if wn == 1 or hn == 1:
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break
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return noise / noise.std()
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style_manager = StyleManager()
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class VectorscopeCC(scripts.Script):
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def __init__(self):
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clean_outdated('cc.py')
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style_manager.load_styles()
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global og_callback
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og_callback = KDiffusionSampler.callback_state
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@ -73,7 +28,7 @@ class VectorscopeCC(scripts.Script):
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return _
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def choices_bool():
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return ["True", "False"]
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return ["False", "True"]
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def choices_method():
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return ["Disabled", "Straight", "Straight Abs.", "Cross", "Cross Abs.", "Ones", "N.Random", "U.Random", "Multi-Res", "Multi-Res Abs."]
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@ -107,6 +62,7 @@ class VectorscopeCC(scripts.Script):
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return scripts.AlwaysVisible
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def ui(self, is_img2img):
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with gr.Accordion(f"Vectorscope CC {VERSION}", open=False):
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with gr.Row():
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@ -124,6 +80,25 @@ class VectorscopeCC(scripts.Script):
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g = gr.Slider(label="G", info='Magenta | Green',minimum=-2.5, maximum=2.5, step=0.05, value=0.0)
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b = gr.Slider(label="B", info='Yellow | Blue',minimum=-2.5, maximum=2.5, step=0.05, value=0.0)
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with gr.Accordion("Styles", open=False):
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with gr.Row():
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with gr.Column():
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style_choice = gr.Dropdown(label="Apply Style", choices=style_manager.list_style())
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style_name = gr.Textbox(label="Style Name")
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style_choice.input(fn=style_manager.get_style, inputs=style_choice, outputs=[latent, bri, con, sat, r, g, b])
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with gr.Column(variant="compact"):
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save_btn = gr.Button(value="Save Style")
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delete_btn = gr.Button(value="Delete Style")
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refresh_btn = gr.Button(value="Manual Refresh")
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save_btn.click(fn=lambda *args: gr.update(choices=style_manager.save_style(*args)), inputs=[style_name, latent, bri, con, sat, r, g, b], outputs=style_choice)
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delete_btn.click(fn=lambda name: gr.update(choices=style_manager.delete_style(name)), inputs=style_name, outputs=style_choice)
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refresh_btn.click(fn=lambda _: gr.update(choices=style_manager.list_style()), outputs=style_choice)
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with gr.Accordion("Advanced Settings", open=False):
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doHR = gr.Checkbox(label="Process Hires. fix")
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method = gr.Radio(["Straight", "Straight Abs.", "Cross", "Cross Abs.", "Ones", "N.Random", "U.Random", "Multi-Res", "Multi-Res Abs."], label="Noise Settings", value="Straight Abs.")
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@ -139,19 +114,19 @@ class VectorscopeCC(scripts.Script):
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def register_reset(self, reset_btn, enable, latent, bri, con, sat, early, r, g, b, doHR, method):
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for component in [enable, latent, doHR]:
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reset_btn.click(fn=lambda x: gr.update(value=False), outputs=component)
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reset_btn.click(fn=lambda _: gr.update(value=False), outputs=component)
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for component in [early, bri, r, g, b]:
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reset_btn.click(fn=lambda x: gr.update(value=0.0), outputs=component)
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reset_btn.click(fn=lambda _: gr.update(value=0.0), outputs=component)
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for component in [con, sat]:
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reset_btn.click(fn=lambda x: gr.update(value=1.0), outputs=component)
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reset_btn.click(fn=lambda _: gr.update(value=1.0), outputs=component)
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reset_btn.click(fn=lambda x: gr.update(value='Straight Abs.'), outputs=method)
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reset_btn.click(fn=lambda _: gr.update(value='Straight Abs.'), outputs=method)
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def register_random(self, random_btn, bri, con, sat, r, g, b):
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for component in [bri, r, g, b]:
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random_btn.click(fn=lambda x: gr.update(value=round(random.uniform(-2.5, 2.5), 2)), outputs=component)
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random_btn.click(fn=lambda _: gr.update(value=round(random.uniform(-2.5, 2.5), 2)), outputs=component)
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for component in [con, sat]:
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random_btn.click(fn=lambda x: gr.update(value=round(random.uniform(0.5, 1.5), 2)), outputs=component)
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random_btn.click(fn=lambda _: gr.update(value=round(random.uniform(0.5, 1.5), 2)), outputs=component)
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def parse_bool(self, string:str):
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if string.lower() == "true":
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@ -218,7 +193,7 @@ class VectorscopeCC(scripts.Script):
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stop = steps * (1.0 - early)
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if not cc_seed == None:
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if cc_seed is not None:
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random.seed(cc_seed)
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bri = round(random.uniform(-2.5, 2.5), 2)
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@ -292,7 +267,7 @@ class VectorscopeCC(scripts.Script):
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target = gaussian_noise(d[mode])
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if 'Abs' in method:
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target = torch.abs(target)
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abs_cvt(target)
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batchSize = d[mode].size(0)
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@ -0,0 +1,43 @@
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from modules import devices
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import torch
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def abs_cvt(latent):
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return torch.abs(latent)
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def ones(latent):
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return torch.ones_like(latent)
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def gaussian_noise(latent):
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return torch.rand_like(latent)
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def normal_noise(latent):
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return torch.randn_like(latent)
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def multires_noise(latent, use_zero:bool, iterations=8, discount=0.4):
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"""
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Reference: https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2
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Credit: Kohya_SS
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"""
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noise = torch.zeros_like(latent) if use_zero else ones(latent)
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batchSize = noise.size(0)
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height = noise.size(2)
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width = noise.size(3)
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device = devices.get_optimal_device()
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upsampler = torch.nn.Upsample(size=(height, width), mode="bilinear").to(device)
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for b in range(batchSize):
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for i in range(iterations):
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r = torch.rand(1).item() * 2 + 2
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wn = max(1, int(width / (r**i)))
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hn = max(1, int(height / (r**i)))
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for c in range(4):
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noise[b, c] += upsampler(torch.randn(1, 1, hn, wn).to(device))[0, 0] * discount**i
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if wn == 1 or hn == 1:
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break
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return noise / noise.std()
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@ -0,0 +1,71 @@
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import modules.scripts as scripts
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import json
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STYLE_FILE = scripts.basedir() + '/' + 'styles.json'
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EMPTY_STYLE = {
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'styles' : {},
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'deleted' : {}
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}
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class StyleManager():
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def __init__(self):
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self.STYLE_SHEET = None
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def load_styles(self):
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if self.STYLE_SHEET is not None:
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return
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try:
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with open(STYLE_FILE, 'r') as json_file:
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self.STYLE_SHEET = json.loads(json_file.read())
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print('[Vec. CC] Style Sheet Loaded...')
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except IOError:
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with open(STYLE_FILE, 'w+') as json_file:
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self.STYLE_SHEET = EMPTY_STYLE
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json_file.write(json.dumps(self.STYLE_SHEET))
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print('[Vec. CC] Creating Empty Style Sheet...')
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def list_style(self):
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return list(self.STYLE_SHEET['styles'].keys())
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def get_style(self, style_name):
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style = self.STYLE_SHEET['styles'][style_name]
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return style['alt'], style['brightness'], style['contrast'], style['saturation'], style['rgb'][0], style['rgb'][1], style['rgb'][2]
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def save_style(self, style_name, latent, bri, con, sat, r, g, b):
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if style_name in self.STYLE_SHEET['styles'].keys():
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print(f'\n[Warning] Duplicated Style Name "{style_name}" Detected! Values are not saved!\n')
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return self.list_style()
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style = {
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'alt' : latent,
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'brightness' : bri,
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'contrast' : con,
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'saturation' : sat,
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'rgb' : [r, g, b]
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}
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self.STYLE_SHEET['styles'].update({style_name:style})
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with open(STYLE_FILE, 'w+') as json_file:
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json_file.write(json.dumps(self.STYLE_SHEET))
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print(f'\nStyle of Name "{style_name}" Saved!\n')
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return self.list_style()
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def delete_style(self, style_name):
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try:
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style = self.STYLE_SHEET['styles'][style_name]
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del self.STYLE_SHEET['styles'][style_name]
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except KeyError:
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print(f'\n[Warning] No Style of Name "{style_name}" Found!\n')
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return self.list_style()
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self.STYLE_SHEET['deleted'].update({style_name:style})
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with open(STYLE_FILE, 'w+') as json_file:
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json_file.write(json.dumps(self.STYLE_SHEET))
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print(f'\nStyle of Name "{style_name}" Deleted!\n')
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return self.list_style()
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@ -0,0 +1,13 @@
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import modules.scripts as scripts
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import json
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VERSION = 'v1.3.0'
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def clean_outdated(EXT_NAME:str):
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with open(scripts.basedir() + '/' + 'ui-config.json', 'r') as json_file:
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configs = json.loads(json_file.read())
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cleaned_configs = {key: value for key, value in configs.items() if EXT_NAME not in key}
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with open(scripts.basedir() + '/' + 'ui-config.json', 'w') as json_file:
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json.dump(cleaned_configs, json_file)
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