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-rw-r--r--javascript/dragdrop.js5
-rw-r--r--javascript/imageParams.js19
-rw-r--r--modules/devices.py2
-rw-r--r--modules/images.py20
-rw-r--r--modules/interrogate.py14
-rw-r--r--modules/processing.py71
-rw-r--r--modules/safe.py9
-rw-r--r--modules/shared.py6
-rw-r--r--modules/txt2img.py5
-rw-r--r--modules/ui.py89
10 files changed, 178 insertions, 62 deletions
diff --git a/javascript/dragdrop.js b/javascript/dragdrop.js
index 5aac57f7..fe0185a5 100644
--- a/javascript/dragdrop.js
+++ b/javascript/dragdrop.js
@@ -43,7 +43,7 @@ function dropReplaceImage( imgWrap, files ) {
window.document.addEventListener('dragover', e => {
const target = e.composedPath()[0];
const imgWrap = target.closest('[data-testid="image"]');
- if ( !imgWrap ) {
+ if ( !imgWrap && target.placeholder != "Prompt") {
return;
}
e.stopPropagation();
@@ -53,6 +53,9 @@ window.document.addEventListener('dragover', e => {
window.document.addEventListener('drop', e => {
const target = e.composedPath()[0];
+ if (target.placeholder === "Prompt") {
+ return;
+ }
const imgWrap = target.closest('[data-testid="image"]');
if ( !imgWrap ) {
return;
diff --git a/javascript/imageParams.js b/javascript/imageParams.js
new file mode 100644
index 00000000..4a7b0900
--- /dev/null
+++ b/javascript/imageParams.js
@@ -0,0 +1,19 @@
+window.onload = (function(){
+ window.addEventListener('drop', e => {
+ const target = e.composedPath()[0];
+ const idx = selected_gallery_index();
+ if (target.placeholder != "Prompt") return;
+
+ let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
+
+ e.stopPropagation();
+ e.preventDefault();
+ const imgParent = gradioApp().getElementById(prompt_target);
+ const files = e.dataTransfer.files;
+ const fileInput = imgParent.querySelector('input[type="file"]');
+ if ( fileInput ) {
+ fileInput.files = files;
+ fileInput.dispatchEvent(new Event('change'));
+ }
+ });
+});
diff --git a/modules/devices.py b/modules/devices.py
index 03ef58f1..eb422583 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -34,7 +34,7 @@ def enable_tf32():
errors.run(enable_tf32, "Enabling TF32")
-device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
+device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
dtype = torch.float16
dtype_vae = torch.float16
diff --git a/modules/images.py b/modules/images.py
index c0a90676..68cdbc93 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -1,4 +1,5 @@
import datetime
+import io
import math
import os
from collections import namedtuple
@@ -463,3 +464,22 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
txt_fullfn = None
return fullfn, txt_fullfn
+
+
+def image_data(data):
+ try:
+ image = Image.open(io.BytesIO(data))
+ textinfo = image.text["parameters"]
+ return textinfo, None
+ except Exception:
+ pass
+
+ try:
+ text = data.decode('utf8')
+ assert len(text) < 10000
+ return text, None
+
+ except Exception:
+ pass
+
+ return '', None
diff --git a/modules/interrogate.py b/modules/interrogate.py
index af858cc0..9263d65a 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -55,7 +55,7 @@ class InterrogateModels:
model, preprocess = clip.load(clip_model_name)
model.eval()
- model = model.to(shared.device)
+ model = model.to(devices.device_interrogate)
return model, preprocess
@@ -65,14 +65,14 @@ class InterrogateModels:
if not shared.cmd_opts.no_half:
self.blip_model = self.blip_model.half()
- self.blip_model = self.blip_model.to(shared.device)
+ self.blip_model = self.blip_model.to(devices.device_interrogate)
if self.clip_model is None:
self.clip_model, self.clip_preprocess = self.load_clip_model()
if not shared.cmd_opts.no_half:
self.clip_model = self.clip_model.half()
- self.clip_model = self.clip_model.to(shared.device)
+ self.clip_model = self.clip_model.to(devices.device_interrogate)
self.dtype = next(self.clip_model.parameters()).dtype
@@ -99,11 +99,11 @@ class InterrogateModels:
text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
top_count = min(top_count, len(text_array))
- text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(shared.device)
+ text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(devices.device_interrogate)
text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
text_features /= text_features.norm(dim=-1, keepdim=True)
- similarity = torch.zeros((1, len(text_array))).to(shared.device)
+ similarity = torch.zeros((1, len(text_array))).to(devices.device_interrogate)
for i in range(image_features.shape[0]):
similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
similarity /= image_features.shape[0]
@@ -116,7 +116,7 @@ class InterrogateModels:
transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
- ])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+ ])(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
with torch.no_grad():
caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length)
@@ -140,7 +140,7 @@ class InterrogateModels:
res = caption
- clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+ clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
with torch.no_grad(), precision_scope("cuda"):
diff --git a/modules/processing.py b/modules/processing.py
index d5172f00..100a259f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -506,11 +506,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
firstphase_width_truncated = 0
firstphase_height_truncated = 0
- def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs):
+ def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, **kwargs):
super().__init__(**kwargs)
self.enable_hr = enable_hr
- self.scale_latent = scale_latent
self.denoising_strength = denoising_strength
+ self.firstphase_width = firstphase_width
+ self.firstphase_height = firstphase_height
def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr:
@@ -519,15 +520,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
else:
state.job_count = state.job_count * 2
- desired_pixel_count = 512 * 512
- actual_pixel_count = self.width * self.height
- scale = math.sqrt(desired_pixel_count / actual_pixel_count)
-
- self.firstphase_width = math.ceil(scale * self.width / 64) * 64
- self.firstphase_height = math.ceil(scale * self.height / 64) * 64
- self.firstphase_width_truncated = int(scale * self.width)
- self.firstphase_height_truncated = int(scale * self.height)
-
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
@@ -536,39 +528,46 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
return samples
+ self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
+
x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_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)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
- truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f
- truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f
+ truncate_x = 0
+ truncate_y = 0
+ width_ratio = self.width/self.firstphase_width
+ height_ratio = self.height/self.firstphase_height
+ if width_ratio > height_ratio:
+ truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
+
+ elif width_ratio < height_ratio:
+ truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
+
samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
- if self.scale_latent:
- samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+ decoded_samples = decode_first_stage(self.sd_model, samples)
+
+ if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
+ decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
else:
- decoded_samples = decode_first_stage(self.sd_model, samples)
+ lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
- if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
- decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
- else:
- lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
- batch_images = []
- for i, x_sample in enumerate(lowres_samples):
- x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
- x_sample = x_sample.astype(np.uint8)
- image = Image.fromarray(x_sample)
- image = images.resize_image(0, image, self.width, self.height)
- image = np.array(image).astype(np.float32) / 255.0
- image = np.moveaxis(image, 2, 0)
- batch_images.append(image)
-
- decoded_samples = torch.from_numpy(np.array(batch_images))
- decoded_samples = decoded_samples.to(shared.device)
- decoded_samples = 2. * decoded_samples - 1.
-
- samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
+ batch_images = []
+ for i, x_sample in enumerate(lowres_samples):
+ x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+ x_sample = x_sample.astype(np.uint8)
+ image = Image.fromarray(x_sample)
+ image = images.resize_image(0, image, self.width, self.height)
+ image = np.array(image).astype(np.float32) / 255.0
+ image = np.moveaxis(image, 2, 0)
+ batch_images.append(image)
+
+ decoded_samples = torch.from_numpy(np.array(batch_images))
+ decoded_samples = decoded_samples.to(shared.device)
+ decoded_samples = 2. * decoded_samples - 1.
+
+ samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
shared.state.nextjob()
diff --git a/modules/safe.py b/modules/safe.py
index 20be16a5..399165a1 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -96,11 +96,18 @@ def load(filename, *args, **kwargs):
if not shared.cmd_opts.disable_safe_unpickle:
check_pt(filename)
+ except pickle.UnpicklingError:
+ print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+ print(f"-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
+ print(f"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
+ return None
+
except Exception:
print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
- print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr)
+ print(f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
return None
return unsafe_torch_load(filename, *args, **kwargs)
diff --git a/modules/shared.py b/modules/shared.py
index 5901e605..b6a5c1a8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -54,7 +54,7 @@ parser.add_argument("--opt-split-attention", action='store_true', help="force-en
parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
-parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[])
+parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -76,8 +76,8 @@ parser.add_argument("--disable-safe-unpickle", action='store_true', help="disabl
cmd_opts = parser.parse_args()
-devices.device, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
-(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'])
+devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
+(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])
device = devices.device
diff --git a/modules/txt2img.py b/modules/txt2img.py
index e985242b..2381347f 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -6,7 +6,7 @@ import modules.processing as processing
from modules.ui import plaintext_to_html
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):
p = StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -30,8 +30,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
restore_faces=restore_faces,
tiling=tiling,
enable_hr=enable_hr,
- scale_latent=scale_latent if enable_hr else None,
denoising_strength=denoising_strength if enable_hr else None,
+ firstphase_width=firstphase_width if enable_hr else None,
+ firstphase_height=firstphase_height if enable_hr else None,
)
if cmd_opts.enable_console_prompts:
diff --git a/modules/ui.py b/modules/ui.py
index d2cbfe50..828bfeea 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -22,7 +22,7 @@ import gradio as gr
import gradio.utils
import gradio.routes
-from modules import sd_hijack
+from modules import sd_hijack, sd_models
from modules.paths import script_path
from modules.shared import opts, cmd_opts
if cmd_opts.deepdanbooru:
@@ -434,7 +434,6 @@ def create_toprow(is_img2img):
with gr.Column(scale=80):
with gr.Row():
prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2)
-
with gr.Column(scale=1, elem_id="roll_col"):
roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
paste = gr.Button(value=paste_symbol, elem_id="paste")
@@ -509,13 +508,40 @@ def setup_progressbar(progressbar, preview, id_part, textinfo=None):
)
+def apply_setting(key, value):
+ if value is None:
+ return gr.update()
+
+ if key == "sd_model_checkpoint":
+ ckpt_info = sd_models.get_closet_checkpoint_match(value)
+
+ if ckpt_info is not None:
+ value = ckpt_info.title
+ else:
+ return gr.update()
+
+ comp_args = opts.data_labels[key].component_args
+ if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
+ return
+
+ valtype = type(opts.data_labels[key].default)
+ oldval = opts.data[key]
+ opts.data[key] = valtype(value) if valtype != type(None) else value
+ if oldval != value and opts.data_labels[key].onchange is not None:
+ opts.data_labels[key].onchange()
+
+ opts.save(shared.config_filename)
+ return value
+
+
def create_ui(wrap_gradio_gpu_call):
import modules.img2img
import modules.txt2img
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
- txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
+ txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
dummy_component = gr.Label(visible=False)
+ txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
with gr.Row(elem_id='txt2img_progress_row'):
with gr.Column(scale=1):
@@ -541,10 +567,11 @@ def create_ui(wrap_gradio_gpu_call):
enable_hr = gr.Checkbox(label='Highres. fix', value=False)
with gr.Row(visible=False) as hr_options:
- scale_latent = gr.Checkbox(label='Scale latent', value=False)
+ firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
+ firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
- with gr.Row():
+ with gr.Row(equal_height=True):
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
@@ -603,8 +630,9 @@ def create_ui(wrap_gradio_gpu_call):
height,
width,
enable_hr,
- scale_latent,
denoising_strength,
+ firstphase_width,
+ firstphase_height,
] + custom_inputs,
outputs=[
txt2img_gallery,
@@ -617,6 +645,17 @@ def create_ui(wrap_gradio_gpu_call):
txt2img_prompt.submit(**txt2img_args)
submit.click(**txt2img_args)
+ txt_prompt_img.change(
+ fn=modules.images.image_data,
+ inputs=[
+ txt_prompt_img
+ ],
+ outputs=[
+ txt2img_prompt,
+ txt_prompt_img
+ ]
+ )
+
enable_hr.change(
fn=lambda x: gr_show(x),
inputs=[enable_hr],
@@ -669,14 +708,17 @@ def create_ui(wrap_gradio_gpu_call):
(denoising_strength, "Denoising strength"),
(enable_hr, lambda d: "Denoising strength" in d),
(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
+ (firstphase_width, "First pass size-1"),
+ (firstphase_height, "First pass size-2"),
]
- modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
with gr.Blocks(analytics_enabled=False) as img2img_interface:
- img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
+ img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
with gr.Row(elem_id='img2img_progress_row'):
+ img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
+
with gr.Column(scale=1):
pass
@@ -771,6 +813,17 @@ def create_ui(wrap_gradio_gpu_call):
connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+ img2img_prompt_img.change(
+ fn=modules.images.image_data,
+ inputs=[
+ img2img_prompt_img
+ ],
+ outputs=[
+ img2img_prompt,
+ img2img_prompt_img
+ ]
+ )
+
mask_mode.change(
lambda mode, img: {
init_img_with_mask: gr_show(mode == 0),
@@ -911,7 +964,6 @@ def create_ui(wrap_gradio_gpu_call):
(seed_resize_from_h, "Seed resize from-2"),
(denoising_strength, "Denoising strength"),
]
- modules.generation_parameters_copypaste.connect_paste(paste, img2img_paste_fields, img2img_prompt)
token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
with gr.Blocks(analytics_enabled=False) as extras_interface:
@@ -959,6 +1011,7 @@ def create_ui(wrap_gradio_gpu_call):
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else ''
open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
+
submit.click(
fn=wrap_gradio_gpu_call(modules.extras.run_extras),
_js="get_extras_tab_index",
@@ -1560,8 +1613,22 @@ Requested path was: {f}
outputs=[extras_image],
)
- modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields, generation_info, 'switch_to_txt2img')
- modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields, generation_info, 'switch_to_img2img_img2img')
+ settings_map = {
+ 'sd_hypernetwork': 'Hypernet',
+ 'CLIP_stop_at_last_layers': 'Clip skip',
+ 'sd_model_checkpoint': 'Model hash',
+ }
+
+ settings_paste_fields = [
+ (component_dict[k], lambda d, k=k, v=v: apply_setting(k, d.get(v, None)))
+ for k, v in settings_map.items()
+ ]
+
+ modules.generation_parameters_copypaste.connect_paste(txt2img_paste, txt2img_paste_fields + settings_paste_fields, txt2img_prompt)
+ modules.generation_parameters_copypaste.connect_paste(img2img_paste, img2img_paste_fields + settings_paste_fields, img2img_prompt)
+
+ modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_txt2img')
+ modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_img2img_img2img')
ui_config_file = cmd_opts.ui_config_file
ui_settings = {}
generated by cgit v1.2.3 (git 2.25.1) at 2025-04-30 15:27:08 +0000