diff options
Diffstat (limited to 'scripts/img2imgalt.py')
| -rw-r--r-- | scripts/img2imgalt.py | 43 |
1 files changed, 35 insertions, 8 deletions
diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index 0ef137f7..d438175c 100644 --- a/scripts/img2imgalt.py +++ b/scripts/img2imgalt.py @@ -8,7 +8,6 @@ import gradio as gr from modules import processing, shared, sd_samplers, prompt_parser
from modules.processing import Processed
-from modules.sd_samplers import samplers
from modules.shared import opts, cmd_opts, state
import torch
@@ -121,17 +120,45 @@ class Script(scripts.Script): return is_img2img
def ui(self, is_img2img):
+ 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)
+
+ override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=True)
original_prompt = gr.Textbox(label="Original prompt", lines=1)
original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1)
- cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
+
+ override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True)
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
+
+ override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True)
+
+ cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False)
- return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
- def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
- p.batch_size = 1
- p.batch_count = 1
+ return [
+ info,
+ override_sampler,
+ override_prompt, original_prompt, original_negative_prompt,
+ override_steps, st,
+ override_strength,
+ cfg, randomness, sigma_adjustment,
+ ]
+
+ def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
+ # Override
+ if override_sampler:
+ p.sampler_index = [sampler.name for sampler in sd_samplers.samplers].index("Euler")
+ if override_prompt:
+ p.prompt = original_prompt
+ p.negative_prompt = original_negative_prompt
+ if override_steps:
+ p.steps = st
+ if override_strength:
+ p.denoising_strength = 1.0
def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
@@ -155,11 +182,11 @@ class Script(scripts.Script): rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
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:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
+ 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 = samplers[p.sampler_index].constructor(p.sd_model)
+ sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)
sigmas = sampler.model_wrap.get_sigmas(p.steps)
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