111 lines
3.5 KiB
Python
111 lines
3.5 KiB
Python
import numpy as np
|
|
from tqdm import trange
|
|
|
|
import modules.scripts as scripts
|
|
import gradio as gr
|
|
|
|
from modules import processing, shared, sd_samplers
|
|
from modules.processing import Processed
|
|
from modules.sd_samplers import samplers
|
|
from modules.shared import opts, cmd_opts, state
|
|
|
|
import torch
|
|
import k_diffusion as K
|
|
|
|
from PIL import Image
|
|
from torch import autocast
|
|
from einops import rearrange, repeat
|
|
|
|
|
|
def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
|
|
x = p.init_latent
|
|
|
|
s_in = x.new_ones([x.shape[0]])
|
|
dnw = K.external.CompVisDenoiser(shared.sd_model)
|
|
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])
|
|
|
|
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
|
|
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.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()
|
|
|
|
cache = [None, None, None, None, None]
|
|
|
|
class Script(scripts.Script):
|
|
def title(self):
|
|
return "img2img alternative test"
|
|
|
|
def show(self, is_img2img):
|
|
return is_img2img
|
|
|
|
def ui(self, is_img2img):
|
|
original_prompt = gr.Textbox(label="Original prompt", lines=1)
|
|
cfg = gr.Slider(label="Decode CFG scale", minimum=0.1, maximum=3.0, step=0.1, value=1.0)
|
|
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
|
|
|
|
return [original_prompt, cfg, st]
|
|
|
|
def run(self, p, original_prompt, cfg, st):
|
|
p.batch_size = 1
|
|
p.batch_count = 1
|
|
|
|
def sample_extra(x, conditioning, unconditional_conditioning):
|
|
lat = tuple([int(x*10) for x in p.init_latent.cpu().numpy().flatten().tolist()])
|
|
|
|
if cache[0] is not None and cache[1] == cfg and cache[2] == st and len(cache[3]) == len(lat) and sum(np.array(cache[3])-np.array(lat)) < 100 and cache[4] == original_prompt:
|
|
noise = cache[0]
|
|
else:
|
|
shared.state.job_count += 1
|
|
cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
|
|
noise = find_noise_for_image(p, cond, unconditional_conditioning, cfg, st)
|
|
cache[0] = noise
|
|
cache[1] = cfg
|
|
cache[2] = st
|
|
cache[3] = lat
|
|
cache[4] = original_prompt
|
|
|
|
sampler = samplers[p.sampler_index].constructor(p.sd_model)
|
|
|
|
samples_ddim = sampler.sample(p, noise, conditioning, unconditional_conditioning)
|
|
return samples_ddim
|
|
|
|
p.sample = sample_extra
|
|
|
|
p.extra_generation_params = {
|
|
"Decode prompt": original_prompt,
|
|
"Decode CFG scale": cfg,
|
|
"Decode steps": st,
|
|
}
|
|
|
|
processed = processing.process_images(p)
|
|
|
|
return processed
|
|
|