diff options
| -rw-r--r-- | modules/img2img.py | 11 | ||||
| -rw-r--r-- | modules/processing.py | 55 | ||||
| -rw-r--r-- | modules/scripts.py | 82 | ||||
| -rw-r--r-- | modules/sd_samplers.py | 32 | ||||
| -rw-r--r-- | modules/shared.py | 1 | ||||
| -rw-r--r-- | modules/txt2img.py | 27 | ||||
| -rw-r--r-- | modules/ui.py | 60 | ||||
| -rw-r--r-- | script.js | 27 | ||||
| -rw-r--r-- | style.css | 12 | ||||
| -rw-r--r-- | webui.py | 26 |
10 files changed, 181 insertions, 152 deletions
diff --git a/modules/img2img.py b/modules/img2img.py index f2817ba8..f14226f7 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -7,8 +7,9 @@ import modules.shared as shared import modules.processing as processing
from modules.ui import plaintext_to_html
import modules.images as images
+import modules.scripts
-def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, prompt_matrix, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_name: str, upscale_overlap: int, inpaint_full_res: bool):
+def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_name: str, upscale_overlap: int, inpaint_full_res: bool, *args):
is_inpaint = mode == 1
is_loopback = mode == 2
is_upscale = mode == 3
@@ -35,7 +36,6 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index cfg_scale=cfg_scale,
width=width,
height=height,
- prompt_matrix=prompt_matrix,
use_GFPGAN=use_GFPGAN,
init_images=[image],
mask=mask,
@@ -128,6 +128,11 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index processed = Processed(p, [combined_image], initial_seed, initial_info)
else:
- processed = process_images(p)
+
+ processed = modules.scripts.run(p, *args)
+
+ if processed is None:
+ processed = process_images(p)
+
return processed.images, processed.js(), plaintext_to_html(processed.info)
diff --git a/modules/processing.py b/modules/processing.py index faf56c9c..cab79b4c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -28,11 +28,12 @@ def torch_gc(): class StableDiffusionProcessing:
- def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
+ def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
self.sd_model = sd_model
self.outpath_samples: str = outpath_samples
self.outpath_grids: str = outpath_grids
self.prompt: str = prompt
+ self.prompt_for_display: str = None
self.negative_prompt: str = (negative_prompt or "")
self.seed: int = seed
self.sampler_index: int = sampler_index
@@ -42,7 +43,6 @@ class StableDiffusionProcessing: self.cfg_scale: float = cfg_scale
self.width: int = width
self.height: int = height
- self.prompt_matrix: bool = prompt_matrix
self.use_GFPGAN: bool = use_GFPGAN
self.do_not_save_samples: bool = do_not_save_samples
self.do_not_save_grid: bool = do_not_save_grid
@@ -71,8 +71,8 @@ class Processed: def js(self):
obj = {
- "prompt": self.prompt,
- "seed": int(self.seed),
+ "prompt": self.prompt if type(self.prompt) != list else self.prompt[0],
+ "seed": int(self.seed if type(self.seed) != list else self.seed[0]),
"width": self.width,
"height": self.height,
"sampler": self.sampler,
@@ -105,35 +105,22 @@ def process_images(p: StableDiffusionProcessing) -> Processed: assert p.prompt is not None
torch_gc()
- seed = int(random.randrange(4294967294) if p.seed == -1 else p.seed)
+ seed = int(random.randrange(4294967294)) if p.seed == -1 else p.seed
os.makedirs(p.outpath_samples, exist_ok=True)
os.makedirs(p.outpath_grids, exist_ok=True)
comments = []
- prompt_matrix_parts = []
- if p.prompt_matrix:
- all_prompts = []
- prompt_matrix_parts = prompt.split("|")
- combination_count = 2 ** (len(prompt_matrix_parts) - 1)
- for combination_num in range(combination_count):
- selected_prompts = [text.strip().strip(',') for n, text in enumerate(prompt_matrix_parts[1:]) if combination_num & (1 << n)]
-
- if opts.prompt_matrix_add_to_start:
- selected_prompts = selected_prompts + [prompt_matrix_parts[0]]
- else:
- selected_prompts = [prompt_matrix_parts[0]] + selected_prompts
-
- all_prompts.append(", ".join(selected_prompts))
-
- p.n_iter = math.ceil(len(all_prompts) / p.batch_size)
- all_seeds = len(all_prompts) * [seed]
-
- print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
+ if type(prompt) == list:
+ all_prompts = prompt
else:
all_prompts = p.batch_size * p.n_iter * [prompt]
- all_seeds = [seed + x for x in range(len(all_prompts))]
+
+ if type(seed) == list:
+ all_seeds = seed
+ else:
+ all_seeds = [int(seed + x) for x in range(len(all_prompts))]
def infotext(iteration=0, position_in_batch=0):
generation_params = {
@@ -149,7 +136,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
- return f"{prompt}\n{generation_params_text}".strip() + "".join(["\n\n" + x for x in comments])
+ return f"{p.prompt_for_display or prompt}\n{generation_params_text}".strip() + "".join(["\n\n" + x for x in comments])
if os.path.exists(cmd_opts.embeddings_dir):
model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)
@@ -218,25 +205,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
return_grid = opts.return_grid
- if p.prompt_matrix:
- grid = images.image_grid(output_images, p.batch_size, rows=1 << ((len(prompt_matrix_parts)-1)//2))
-
- try:
- grid = images.draw_prompt_matrix(grid, p.width, p.height, prompt_matrix_parts)
- except Exception:
- import traceback
- print("Error creating prompt_matrix text:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- return_grid = True
- else:
- grid = images.image_grid(output_images, p.batch_size)
+ grid = images.image_grid(output_images, p.batch_size)
if return_grid:
output_images.insert(0, grid)
if opts.grid_save:
- images.save_image(grid, p.outpath_grids, "grid", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
+ images.save_image(grid, p.outpath_grids, "grid", seed, all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
torch_gc()
return Processed(p, output_images, seed, infotext())
diff --git a/modules/scripts.py b/modules/scripts.py index 20f489ce..be348a70 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -2,19 +2,33 @@ import os import sys
import traceback
+import modules.ui as ui
import gradio as gr
+from modules.processing import StableDiffusionProcessing
+
class Script:
filename = None
+ args_from = None
+ args_to = None
def title(self):
raise NotImplementedError()
+ def ui(self, is_img2img):
+ pass
+
+ def run(self, *args):
+ raise NotImplementedError()
+
+ def describe(self):
+ return ""
+
scripts = []
-def load_scripts(basedir, globs):
+def load_scripts(basedir):
for filename in os.listdir(basedir):
path = os.path.join(basedir, filename)
@@ -27,27 +41,73 @@ def load_scripts(basedir, globs): from types import ModuleType
compiled = compile(text, path, 'exec')
module = ModuleType(filename)
- module.__dict__.update(globs)
exec(compiled, module.__dict__)
- for key, item in module.__dict__.items():
- if type(item) == type and issubclass(item, Script):
- item.filename = path
+ for key, script_class in module.__dict__.items():
+ if type(script_class) == type and issubclass(script_class, Script):
+ obj = script_class()
+ obj.filename = path
- scripts.append(item)
+ scripts.append(obj)
def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
try:
- res = func()
+ res = func(*args, **kwargs)
return res
except Exception:
- print(f"Error calling: {filename/funcname}", file=sys.stderr)
+ print(f"Error calling: {filename}/{funcname}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
return default
-def setup_ui():
- titles = [wrap_call(script.title, script.filename, "title") for script in scripts]
- gr.Dropdown(options=[""] + titles, value="", type="index")
+def setup_ui(is_img2img):
+ titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in scripts]
+
+ dropdown = gr.Dropdown(label="Script", choices=["None"] + titles, value="None", type="index")
+
+ inputs = [dropdown]
+
+ for script in scripts:
+ script.args_from = len(inputs)
+ controls = script.ui(is_img2img)
+
+ for control in controls:
+ control.visible = False
+
+ inputs += controls
+ script.args_to = len(inputs)
+
+ def select_script(index):
+ if index > 0:
+ script = scripts[index-1]
+ args_from = script.args_from
+ args_to = script.args_to
+ else:
+ args_from = 0
+ args_to = 0
+
+ return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
+
+ dropdown.change(
+ fn=select_script,
+ inputs=[dropdown],
+ outputs=inputs
+ )
+
+ return inputs
+
+
+def run(p: StableDiffusionProcessing, *args):
+ script_index = args[0] - 1
+
+ if script_index < 0 or script_index >= len(scripts):
+ return None
+
+ script = scripts[script_index]
+
+ script_args = args[script.args_from:script.args_to]
+ processed = script.run(p, *script_args)
+
+ return processed
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 54c5fd7c..6f028f5f 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -9,18 +9,28 @@ from ldm.models.diffusion.plms import PLMSSampler from modules.shared import opts, cmd_opts, state
import modules.shared as shared
-SamplerData = namedtuple('SamplerData', ['name', 'constructor'])
+
+SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases'])
+
+samplers_k_diffusion = [
+ ('Euler a', 'sample_euler_ancestral', ['k_euler_a']),
+ ('Euler', 'sample_euler', ['k_euler']),
+ ('LMS', 'sample_lms', ['k_lms']),
+ ('Heun', 'sample_heun', ['k_heun']),
+ ('DPM2', 'sample_dpm_2', ['k_dpm_2']),
+ ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
+]
+
+samplers_data_k_diffusion = [
+ SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)
+ for label, funcname, aliases in samplers_k_diffusion
+ if hasattr(k_diffusion.sampling, funcname)
+]
+
samplers = [
- *[SamplerData(x[0], lambda model, funcname=x[1]: KDiffusionSampler(funcname, model)) for x in [
- ('Euler a', 'sample_euler_ancestral'),
- ('Euler', 'sample_euler'),
- ('LMS', 'sample_lms'),
- ('Heun', 'sample_heun'),
- ('DPM2', 'sample_dpm_2'),
- ('DPM2 a', 'sample_dpm_2_ancestral'),
- ] if hasattr(k_diffusion.sampling, x[1])],
- SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(DDIMSample, model)),
- SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(PLMSSampler, model)),
+ *samplers_data_k_diffusion,
+ SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(DDIMSampler, model), []),
+ SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(PLMSSampler, model), []),
]
samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
diff --git a/modules/shared.py b/modules/shared.py index 33a08559..b02c9e36 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -76,7 +76,6 @@ class Options: "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
"font": OptionInfo("arial.ttf", "Font for image grids that have text"),
- "prompt_matrix_add_to_start": OptionInfo(True, "In prompt matrix, add the variable combination of text to the start of the prompt, rather than the end"),
"enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text text and [text] to make it pay less attention"),
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
diff --git a/modules/txt2img.py b/modules/txt2img.py index d03a29f2..f5ac0540 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -1,4 +1,4 @@ -
+import modules.scripts
from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, cmd_opts
import modules.shared as shared
@@ -6,7 +6,7 @@ import modules.processing as processing from modules.ui import plaintext_to_html
-def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, code: str):
+def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, *args):
p = StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -21,30 +21,13 @@ def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, u cfg_scale=cfg_scale,
width=width,
height=height,
- prompt_matrix=prompt_matrix,
use_GFPGAN=use_GFPGAN
)
- if code != '' and cmd_opts.allow_code:
- p.do_not_save_grid = True
- p.do_not_save_samples = True
-
- display_result_data = [[], -1, ""]
-
- def display(imgs, s=display_result_data[1], i=display_result_data[2]):
- display_result_data[0] = imgs
- display_result_data[1] = s
- display_result_data[2] = i
-
- from types import ModuleType
- compiled = compile(code, '', 'exec')
- module = ModuleType("testmodule")
- module.__dict__.update(globals())
- module.p = p
- module.display = display
- exec(compiled, module.__dict__)
+ processed = modules.scripts.run(p, *args)
- processed = Processed(p, *display_result_data)
+ if processed is not None:
+ pass
else:
processed = process_images(p)
diff --git a/modules/ui.py b/modules/ui.py index 5223179f..c6ded678 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -12,6 +12,7 @@ from PIL import Image import gradio as gr
import gradio.utils
+import gradio.routes
from modules.paths import script_path
from modules.shared import opts, cmd_opts
@@ -19,6 +20,7 @@ import modules.shared as shared from modules.sd_samplers import samplers, samplers_for_img2img
import modules.gfpgan_model as gfpgan
import modules.realesrgan_model as realesrgan
+import modules.scripts
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
mimetypes.init()
@@ -131,7 +133,7 @@ def wrap_gradio_call(func): return f
-def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
+def create_ui(txt2img, img2img, run_extras, run_pnginfo):
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
with gr.Row():
@@ -145,8 +147,7 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
with gr.Row():
- use_GFPGAN = gr.Checkbox(label='GFPGAN', value=False, visible=gfpgan.have_gfpgan)
- prompt_matrix = gr.Checkbox(label='Prompt matrix', value=False)
+ use_gfpgan = gr.Checkbox(label='GFPGAN', value=False, visible=gfpgan.have_gfpgan)
with gr.Row():
batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
@@ -160,7 +161,8 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): seed = gr.Number(label='Seed', value=-1)
- code = gr.Textbox(label="Python script", visible=cmd_opts.allow_code, lines=1)
+ with gr.Group():
+ custom_inputs = modules.scripts.setup_ui(is_img2img=False)
with gr.Column(variant='panel'):
with gr.Group():
@@ -185,16 +187,14 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): negative_prompt,
steps,
sampler_index,
- use_GFPGAN,
- prompt_matrix,
+ use_gfpgan,
batch_count,
batch_size,
cfg_scale,
seed,
height,
width,
- code
- ],
+ ] + custom_inputs,
outputs=[
txt2img_gallery,
generation_info,
@@ -244,8 +244,7 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): inpainting_fill = gr.Radio(label='Msked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
with gr.Row():
- use_GFPGAN = gr.Checkbox(label='GFPGAN', value=False, visible=gfpgan.have_gfpgan)
- prompt_matrix = gr.Checkbox(label='Prompt matrix', value=False)
+ use_gfpgan = gr.Checkbox(label='GFPGAN', value=False, visible=gfpgan.have_gfpgan)
inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=True, visible=False)
with gr.Row():
@@ -266,6 +265,10 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): seed = gr.Number(label='Seed', value=-1)
+ with gr.Group():
+ custom_inputs = modules.scripts.setup_ui(is_img2img=False)
+
+
with gr.Column(variant='panel'):
with gr.Group():
img2img_gallery = gr.Gallery(label='Output', elem_id='img2img_gallery')
@@ -291,11 +294,10 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): init_img_with_mask: gr_show(is_inpaint),
mask_blur: gr_show(is_inpaint),
inpainting_fill: gr_show(is_inpaint),
- prompt_matrix: gr_show(is_classic),
batch_count: gr_show(not is_upscale),
batch_size: gr_show(not is_loopback),
sd_upscale_upscaler_name: gr_show(is_upscale),
- sd_upscale_overlap:gr_show(is_upscale),
+ sd_upscale_overlap: gr_show(is_upscale),
inpaint_full_res: gr_show(is_inpaint),
}
@@ -307,7 +309,6 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): init_img_with_mask,
mask_blur,
inpainting_fill,
- prompt_matrix,
batch_count,
batch_size,
sd_upscale_upscaler_name,
@@ -326,8 +327,7 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): sampler_index,
mask_blur,
inpainting_fill,
- use_GFPGAN,
- prompt_matrix,
+ use_gfpgan,
switch_mode,
batch_count,
batch_size,
@@ -340,7 +340,7 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): sd_upscale_upscaler_name,
sd_upscale_overlap,
inpaint_full_res,
- ],
+ ] + custom_inputs,
outputs=[
img2img_gallery,
generation_info,
@@ -384,9 +384,6 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): outputs=[init_img_with_mask],
)
-
-
-
with gr.Blocks(analytics_enabled=False) as extras_interface:
with gr.Row().style(equal_height=False):
with gr.Column(variant='panel'):
@@ -420,7 +417,6 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): submit.click(**extras_args)
-
send_to_extras.click(
fn=lambda x: image_from_url_text(x),
_js="extract_image_from_gallery",
@@ -435,7 +431,6 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): outputs=[image],
)
-
pnginfo_interface = gr.Interface(
wrap_gradio_call(run_pnginfo),
inputs=[
@@ -450,7 +445,6 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): analytics_enabled=False,
)
-
def create_setting_component(key):
def fun():
return opts.data[key] if key in opts.data else opts.data_labels[key].default
@@ -520,20 +514,16 @@ def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo): return demo
-with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as file:
- javascript = file.read()
-
-def inject_gradio_html(javascript):
- import gradio.routes
+with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
+ javascript = jsfile.read()
- def template_response(*args, **kwargs):
- res = gradio_routes_templates_response(*args, **kwargs)
- res.body = res.body.replace(b'</head>', f'<script>{javascript}</script></head>'.encode("utf8"))
- res.init_headers()
- return res
- gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
- gradio.routes.templates.TemplateResponse = template_response
+def template_response(*args, **kwargs):
+ res = gradio_routes_templates_response(*args, **kwargs)
+ res.body = res.body.replace(b'</head>', f'<script>{javascript}</script></head>'.encode("utf8"))
+ res.init_headers()
+ return res
-inject_gradio_html(javascript)
+gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
+gradio.routes.templates.TemplateResponse = template_response
@@ -4,7 +4,7 @@ titles = { "GFPGAN": "Restore low quality faces using GFPGAN neural network", "Euler a": "Euler Ancestral - very creative, each can get acompletely different pictures depending on step count, setting seps tohigher than 30-40 does not help", "DDIM": "Denoising Diffusion Implicit Models - best at inpainting", - "Prompt matrix": "Separate prompts into part using vertical pipe character (|) and the script will create a picture for every combination of them (except for first part, which will be present in all combinations)", + "Batch count": "How many batches of images to create", "Batch size": "How many image to create in a single batch", "CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results", @@ -30,6 +30,16 @@ titles = { "Interrupt": "Stop processing images and return any results accumulated so far.", "Save": "Write image to a directory (default - log/images) and generation parameters into csv file.", + + "X values": "Separate values for X axis using commas.", + "Y values": "Separate values for Y axis using commas.", + + "None": "Do not do anything special", + "Prompt matrix": "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)", + "X/Y Plot": "Create a grid where images will have different parameters. Use inputs below to specify which parameterswill be shared by columns and rows", + "Custom code": "Run python code. Advanced user only. Must run program with --allow-code for this to work", + + "Prompt S/R": "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others", } function gradioApp(){ @@ -37,12 +47,25 @@ function gradioApp(){ } function addTitles(root){ - root.querySelectorAll('span, button').forEach(function(span){ + root.querySelectorAll('span, button, select').forEach(function(span){ tooltip = titles[span.textContent]; + + if(!tooltip){ + tooltip = titles[span.value]; + } + if(tooltip){ span.title = tooltip; } }) + + root.querySelectorAll('select').forEach(function(select){ + if (select.onchange != null) return; + + select.onchange = function(){ + select.title = titles[select.value] || ""; + } + }) } document.addEventListener("DOMContentLoaded", function() { @@ -22,22 +22,24 @@ button{ overflow: visible !important;
}
-fieldset span.text-gray-500, .gr-panel div.flex-col div.justify-between label, label.block span{
+#x_type, #y_type{
+ max-width: 10em;
+}
+
+fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block span{
position: absolute;
top: -0.6em;
line-height: 1.2em;
padding: 0 0.5em;
margin: 0;
-}
-fieldset span.text-gray-500, .gr-panel div.flex-col div.justify-between label, label.block span{
background-color: white;
border-top: 1px solid #eee;
border-left: 1px solid #eee;
border-right: 1px solid #eee;
}
-.dark fieldset span.text-gray-500, .dark .gr-panel div.flex-col div.justify-between label, .dark label.block span{
+.dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{
background-color: rgb(31, 41, 55);
border-top: 1px solid rgb(55 65 81);
border-left: 1px solid rgb(55 65 81);
@@ -63,4 +65,4 @@ input[type="range"]{ #txt2img_sampling label{
padding-left: 0.6em;
padding-right: 0.6em;
-}
\ No newline at end of file +}
@@ -55,23 +55,6 @@ def load_model_from_config(config, ckpt, verbose=False): return model
-def draw_xy_grid(xs, ys, x_label, y_label, cell):
- res = []
-
- ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
- hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
-
- for y in ys:
- for x in xs:
- state.job = f"{x + y * len(xs)} out of {len(xs) * len(ys)}"
- res.append(cell(x, y))
-
- grid = images.image_grid(res, rows=len(ys))
- grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
-
- return grid
-
-
def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_index):
processing.torch_gc()
@@ -138,7 +121,6 @@ try: except Exception:
pass
-
sd_config = OmegaConf.load(cmd_opts.config)
shared.sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
shared.sd_model = (shared.sd_model if cmd_opts.no_half else shared.sd_model.half())
@@ -150,18 +132,18 @@ else: modules.sd_hijack.model_hijack.hijack(shared.sd_model)
+modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
+
# make the program just exit at ctrl+c without waiting for anything
-def sigint_handler(signal, frame):
- print('Interrupted')
+def sigint_handler(sig, frame):
+ print(f'Interrupted with singal {sig} in {frame}')
os._exit(0)
signal.signal(signal.SIGINT, sigint_handler)
demo = modules.ui.create_ui(
- opts=opts,
- cmd_opts=cmd_opts,
txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
run_extras=wrap_gradio_gpu_call(run_extras),
|