cleanup scripts

scripts/img2imgalt.py

@@ -1,19 +1,15 @@
 from collections import namedtuple
-
 import numpy as np
 from tqdm import trange
-
-import modules.scripts as scripts
-import gradio as gr
-
-from modules import processing, shared, sd_samplers, sd_samplers_common
-
 import torch
 import k_diffusion as K
+import gradio as gr
+import modules.scripts as scripts
+from modules import processing, shared, sd_samplers, sd_samplers_common
+
 
 def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
     x = p.init_latent
-
     s_in = x.new_ones([x.shape[0]])
     if shared.sd_model.parameterization == "v":
         dnw = K.external.CompVisVDenoiser(shared.sd_model)
@@ -22,40 +18,29 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
         dnw = K.external.CompVisDenoiser(shared.sd_model)
         skip = 0
     sigmas = dnw.get_sigmas(steps).flip(0)
-
     shared.state.sampling_steps = steps
 
     for i in trange(1, len(sigmas)):
         shared.state.sampling_step += 1
-
         x_in = torch.cat([x] * 2)
         sigma_in = torch.cat([sigmas[i] * s_in] * 2)
         cond_in = torch.cat([uncond, cond])
-
         image_conditioning = torch.cat([p.image_conditioning] * 2)
         cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
-
         c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
         t = dnw.sigma_to_t(sigma_in)
-
         eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
         denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
-
         denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
-
         d = (x - denoised) / sigmas[i]
         dt = sigmas[i] - sigmas[i - 1]
-
         x = x + d * dt
-
         sd_samplers_common.store_latent(x)
-
         # This shouldn't be necessary, but solved some VRAM issues
         del x_in, sigma_in, cond_in, c_out, c_in, t,
         del eps, denoised_uncond, denoised_cond, denoised, d, dt
 
     shared.state.nextjob()
-
     return x / x.std()
 
 
@@ -65,7 +50,6 @@ Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "origina
 # Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736
 def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
     x = p.init_latent
-
     s_in = x.new_ones([x.shape[0]])
     if shared.sd_model.parameterization == "v":
         dnw = K.external.CompVisVDenoiser(shared.sd_model)
@@ -79,42 +63,31 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
 
     for i in trange(1, len(sigmas)):
         shared.state.sampling_step += 1
-
         x_in = torch.cat([x] * 2)
         sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
         cond_in = torch.cat([uncond, cond])
-
         image_conditioning = torch.cat([p.image_conditioning] * 2)
         cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
-
         c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
-
         if i == 1:
             t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
         else:
             t = dnw.sigma_to_t(sigma_in)
-
         eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
         denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
-
         denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
-
         if i == 1:
             d = (x - denoised) / (2 * sigmas[i])
         else:
             d = (x - denoised) / sigmas[i - 1]
-
         dt = sigmas[i] - sigmas[i - 1]
         x = x + d * dt
-
         sd_samplers_common.store_latent(x)
-
         # This shouldn't be necessary, but solved some VRAM issues
         del x_in, sigma_in, cond_in, c_out, c_in, t,
         del eps, denoised_uncond, denoised_cond, denoised, d, dt
 
     shared.state.nextjob()
-
     return x / sigmas[-1]
 
 
@@ -123,7 +96,7 @@ class Script(scripts.Script):
         self.cache = None
 
     def title(self):
-        return "img2img alternative test"
+        return "Alternative"
 
     def show(self, is_img2img):
         return is_img2img
@@ -132,24 +105,19 @@ class Script(scripts.Script):
         info = gr.Markdown('''
         * `CFG Scale` should be 2 or lower.
         ''')
-
         override_sampler = gr.Checkbox(label="Override `Sampling method` to Euler?(this method is built for it)", value=True, elem_id=self.elem_id("override_sampler"))
-
         override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=True, elem_id=self.elem_id("override_prompt"))
         original_prompt = gr.Textbox(label="Original prompt", lines=1, elem_id=self.elem_id("original_prompt"))
         original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1, elem_id=self.elem_id("original_negative_prompt"))
-
         override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True, elem_id=self.elem_id("override_steps"))
         st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50, elem_id=self.elem_id("st"))
-
         override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True, elem_id=self.elem_id("override_strength"))
-
         cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0, elem_id=self.elem_id("cfg"))
         randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0, elem_id=self.elem_id("randomness"))
         sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False, elem_id=self.elem_id("sigma_adjustment"))
 
         return [
-            info, 
+            info,
             override_sampler,
             override_prompt, original_prompt, original_negative_prompt, 
             override_steps, st,
@@ -171,13 +139,11 @@ class Script(scripts.Script):
 
         def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
             lat = (p.init_latent.cpu().numpy() * 10).astype(int)
-
             same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \
                                 and self.cache.original_prompt == original_prompt \
                                 and self.cache.original_negative_prompt == original_negative_prompt \
                                 and self.cache.sigma_adjustment == sigma_adjustment
             same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
-
             if same_everything:
                 rec_noise = self.cache.noise
             else:
@@ -191,28 +157,20 @@ class Script(scripts.Script):
                 self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
 
             rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
-            
             combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
-            
             sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
-
             sigmas = sampler.model_wrap.get_sigmas(p.steps)
-            
             noise_dt = combined_noise - (p.init_latent / sigmas[0])
-            
             p.seed = p.seed + 1
-            
             return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)
 
         p.sample = sample_extra
-
         p.extra_generation_params["Decode prompt"] = original_prompt
         p.extra_generation_params["Decode negative prompt"] = original_negative_prompt
         p.extra_generation_params["Decode CFG scale"] = cfg
         p.extra_generation_params["Decode steps"] = st
         p.extra_generation_params["Randomness"] = randomness
         p.extra_generation_params["Sigma Adjustment"] = sigma_adjustment
-
         processed = processing.process_images(p)
 
         return processed

scripts/outpainting_mk_2.py

@@ -120,7 +120,7 @@ def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.0
 
 class Script(scripts.Script):
     def title(self):
-        return "Outpainting mk2"
+        return "Outpainting"
 
     def show(self, is_img2img):
         return is_img2img

scripts/poor_mans_outpainting.py

@@ -11,7 +11,7 @@ from modules.shared import opts, cmd_opts, state
 
 class Script(scripts.Script):
     def title(self):
-        return "Poor man's outpainting"
+        return "Outpainting alternative"
 
     def show(self, is_img2img):
         return is_img2img

scripts/prompts_from_file.py

@@ -109,7 +109,7 @@ def load_prompt_file(file):
 
 class Script(scripts.Script):
     def title(self):
-        return "Prompts from file or textbox"
+        return "Prompts from file"
 
     def ui(self, is_img2img):       
         checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False, elem_id=self.elem_id("checkbox_iterate"))