| import modules.scripts as scripts |
| import modules.processing as processing |
| import gradio as gr |
|
|
| from modules.processing import process_images, slerp |
| from modules import devices, shared |
| import torch |
|
|
|
|
| global_seeds = '' |
|
|
|
|
| def advanced_creator (shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None): |
| global global_seeds |
|
|
| parsed = [] |
|
|
| for one in global_seeds.split(","): |
| parts = one.split(":") |
| parsed.append((int(parts[0]), float(parts[1]) if len(parts) > 1 else 1)) |
|
|
| noises = list(map(lambda e: (devices.randn(e[0], shape), e[1]), parsed)) |
| while True: |
| cur = noises[0] |
| rest = noises[1:] |
| if len(rest) <= 0: |
| break |
| noises = list( |
| map(lambda r: (slerp(r[1] / (r[1] + cur[1]), cur[0], r[0]), r[1] * cur[1]), rest)) |
|
|
| return torch.stack([noises[0][0]]).to(shared.device) |
|
|
|
|
| class Script(scripts.Script): |
| def title(self): |
| return "Advanced Seed Blending" |
|
|
| def ui(self, is_img2img): |
| seeds = gr.Textbox(label='Seeds', value="") |
|
|
| return [seeds] |
|
|
| def run(self, p, seeds): |
| real_creator = processing.create_random_tensors |
| try: |
| processing.create_random_tensors = advanced_creator |
| global global_seeds |
| global_seeds = seeds |
| return process_images(p) |
| finally: |
| processing.create_random_tensors = real_creator |
|
|
