aboutsummaryrefslogtreecommitdiff
path: root/modules
diff options
context:
space:
mode:
Diffstat (limited to 'modules')
-rw-r--r--modules/images.py1
-rw-r--r--modules/processing.py18
-rw-r--r--modules/sd_samplers.py28
-rw-r--r--modules/shared.py10
-rw-r--r--modules/ui.py28
5 files changed, 65 insertions, 20 deletions
diff --git a/modules/images.py b/modules/images.py
index ae0e6304..9458bf8d 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -295,6 +295,7 @@ def apply_filename_pattern(x, p, seed, prompt):
x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
x = x.replace("[date]", datetime.date.today().isoformat())
+ x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
x = x.replace("[job_timestamp]", shared.state.job_timestamp)
if cmd_opts.hide_ui_dir_config:
diff --git a/modules/processing.py b/modules/processing.py
index 0246e094..8d043f4d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -78,7 +78,14 @@ class StableDiffusionProcessing:
self.paste_to = None
self.color_corrections = None
self.denoising_strength: float = 0
-
+
+ self.ddim_eta = opts.ddim_eta
+ self.ddim_discretize = opts.ddim_discretize
+ self.s_churn = opts.s_churn
+ self.s_tmin = opts.s_tmin
+ self.s_tmax = float('inf') # not representable as a standard ui option
+ self.s_noise = opts.s_noise
+
if not seed_enable_extras:
self.subseed = -1
self.subseed_strength = 0
@@ -117,6 +124,13 @@ class Processed:
self.extra_generation_params = p.extra_generation_params
self.index_of_first_image = index_of_first_image
+ self.ddim_eta = p.ddim_eta
+ self.ddim_discretize = p.ddim_discretize
+ self.s_churn = p.s_churn
+ self.s_tmin = p.s_tmin
+ self.s_tmax = p.s_tmax
+ self.s_noise = p.s_noise
+
self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
@@ -406,7 +420,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
index_of_first_image = 1
if opts.grid_save:
- images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p)
+ images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
devices.torch_gc()
return Processed(p, output_images, all_seeds[0], infotext(), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 1fc9d18c..666ee1ee 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -37,6 +37,11 @@ samplers = [
]
samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
+sampler_extra_params = {
+ 'sample_euler':['s_churn','s_tmin','s_tmax','s_noise'],
+ 'sample_heun' :['s_churn','s_tmin','s_tmax','s_noise'],
+ 'sample_dpm_2':['s_churn','s_tmin','s_tmax','s_noise'],
+}
def setup_img2img_steps(p, steps=None):
if opts.img2img_fix_steps or steps is not None:
@@ -120,9 +125,9 @@ class VanillaStableDiffusionSampler:
# existing code fails with cetain step counts, like 9
try:
- self.sampler.make_schedule(ddim_num_steps=steps, verbose=False)
+ self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
except Exception:
- self.sampler.make_schedule(ddim_num_steps=steps+1, verbose=False)
+ self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
@@ -149,9 +154,9 @@ class VanillaStableDiffusionSampler:
# existing code fails with cetin step counts, like 9
try:
- samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
+ samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.ddim_eta)
except Exception:
- samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
+ samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.ddim_eta)
return samples_ddim
@@ -224,6 +229,7 @@ class KDiffusionSampler:
self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
self.funcname = funcname
self.func = getattr(k_diffusion.sampling, self.funcname)
+ self.extra_params = sampler_extra_params.get(funcname,[])
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
self.sampler_noises = None
self.sampler_noise_index = 0
@@ -269,7 +275,12 @@ class KDiffusionSampler:
if self.sampler_noises is not None:
k_diffusion.sampling.torch = TorchHijack(self)
- return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
+ extra_params_kwargs = {}
+ for val in self.extra_params:
+ if hasattr(p,val):
+ extra_params_kwargs[val] = getattr(p,val)
+
+ return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
steps = steps or p.steps
@@ -286,7 +297,12 @@ class KDiffusionSampler:
if self.sampler_noises is not None:
k_diffusion.sampling.torch = TorchHijack(self)
- samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
+ extra_params_kwargs = {}
+ for val in self.extra_params:
+ if hasattr(p,val):
+ extra_params_kwargs[val] = getattr(p,val)
+
+ samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
return samples
diff --git a/modules/shared.py b/modules/shared.py
index c32da110..870fb3b9 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -66,7 +66,7 @@ class State:
job = ""
job_no = 0
job_count = 0
- job_timestamp = 0
+ job_timestamp = '0'
sampling_step = 0
sampling_steps = 0
current_latent = None
@@ -80,6 +80,7 @@ class State:
self.job_no += 1
self.sampling_step = 0
self.current_image_sampling_step = 0
+
def get_job_timestamp(self):
return datetime.datetime.now().strftime("%Y%m%d%H%M%S")
@@ -219,6 +220,13 @@ options_templates.update(options_section(('ui', "User interface"), {
"js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
}))
+options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
+ "ddim_eta": OptionInfo(0.0, "DDIM eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform','quad']}),
+ 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+}))
class Options:
data = None
diff --git a/modules/ui.py b/modules/ui.py
index 6525676c..5476c32f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -564,13 +564,13 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
with gr.TabItem('Inpaint', id='inpaint'):
init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA")
- init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False)
- init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False)
+ init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
+ init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
with gr.Row():
- mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask")
+ mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index")
@@ -998,6 +998,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
for interface, label, ifid in interfaces:
with gr.TabItem(label, id=ifid):
interface.render()
+
+ if os.path.exists(os.path.join(script_path, "notification.mp3")):
+ audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
settings_submit.click(
@@ -1006,18 +1009,21 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
outputs=[result, text_settings],
)
+ paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Seed', 'Size-1', 'Size-2']
+ txt2img_fields = [field for field,name in txt2img_paste_fields if name in paste_field_names]
+ img2img_fields = [field for field,name in img2img_paste_fields if name in paste_field_names]
send_to_img2img.click(
- fn=lambda x: (image_from_url_text(x)),
- _js="extract_image_from_gallery_img2img",
- inputs=[txt2img_gallery],
- outputs=[init_img],
+ fn=lambda img, *args: (image_from_url_text(img),*args),
+ _js="(gallery, ...args) => [extract_image_from_gallery_img2img(gallery), ...args]",
+ inputs=[txt2img_gallery] + txt2img_fields,
+ outputs=[init_img] + img2img_fields,
)
send_to_inpaint.click(
- fn=lambda x: (image_from_url_text(x)),
- _js="extract_image_from_gallery_inpaint",
- inputs=[txt2img_gallery],
- outputs=[init_img_with_mask],
+ fn=lambda x, *args: (image_from_url_text(x), *args),
+ _js="(gallery, ...args) => [extract_image_from_gallery_inpaint(gallery), ...args]",
+ inputs=[txt2img_gallery] + txt2img_fields,
+ outputs=[init_img_with_mask] + img2img_fields,
)
img2img_send_to_img2img.click(
generated by cgit v1.2.1 (git 2.18.0) at 2025-05-28 09:25:16 +0000