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Merge pull request #8772 from mcmonkey4eva/img2img-alt-sd2-fix

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Fix img2img-alternative-test script for SD v2.x
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AUTOMATIC1111 2023-03-25 12:16:09 +03:00 committed by GitHub
commit 983d48a921
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1 changed files with 16 additions and 14 deletions
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30
scripts/img2imgalt.py
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@ -6,23 +6,21 @@ from tqdm import trange
import modules.scripts as scripts import modules.scripts as scripts
import gradio as gr import gradio as gr
from modules import processing, shared, sd_samplers, prompt_parser, sd_samplers_common from modules import processing, shared, sd_samplers, sd_samplers_common
from modules.processing import Processed
from modules.shared import opts, cmd_opts, state
import torch import torch
import k_diffusion as K 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): def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
x = p.init_latent x = p.init_latent
s_in = x.new_ones([x.shape[0]]) s_in = x.new_ones([x.shape[0]])
dnw = K.external.CompVisDenoiser(shared.sd_model) if shared.sd_model.parameterization == "v":
dnw = K.external.CompVisVDenoiser(shared.sd_model)
skip = 1
else:
dnw = K.external.CompVisDenoiser(shared.sd_model)
skip = 0
sigmas = dnw.get_sigmas(steps).flip(0) sigmas = dnw.get_sigmas(steps).flip(0)
shared.state.sampling_steps = steps shared.state.sampling_steps = steps
@ -37,7 +35,7 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
image_conditioning = torch.cat([p.image_conditioning] * 2) image_conditioning = torch.cat([p.image_conditioning] * 2)
cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]} 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)] 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) t = dnw.sigma_to_t(sigma_in)
eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in) eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
@ -69,7 +67,12 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
x = p.init_latent x = p.init_latent
s_in = x.new_ones([x.shape[0]]) s_in = x.new_ones([x.shape[0]])
dnw = K.external.CompVisDenoiser(shared.sd_model) if shared.sd_model.parameterization == "v":
dnw = K.external.CompVisVDenoiser(shared.sd_model)
skip = 1
else:
dnw = K.external.CompVisDenoiser(shared.sd_model)
skip = 0
sigmas = dnw.get_sigmas(steps).flip(0) sigmas = dnw.get_sigmas(steps).flip(0)
shared.state.sampling_steps = steps shared.state.sampling_steps = steps
@ -84,7 +87,7 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
image_conditioning = torch.cat([p.image_conditioning] * 2) image_conditioning = torch.cat([p.image_conditioning] * 2)
cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]} 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)] c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
if i == 1: if i == 1:
t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2)) t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
@ -125,7 +128,7 @@ class Script(scripts.Script):
def show(self, is_img2img): def show(self, is_img2img):
return is_img2img return is_img2img
def ui(self, is_img2img): def ui(self, is_img2img):
info = gr.Markdown(''' info = gr.Markdown('''
* `CFG Scale` should be 2 or lower. * `CFG Scale` should be 2 or lower.
''') ''')
@ -213,4 +216,3 @@ class Script(scripts.Script):
processed = processing.process_images(p) processed = processing.process_images(p)
return processed return processed