Merge pull request #8772 from mcmonkey4eva/img2img-alt-sd2-fix
Fix img2img-alternative-test script for SD v2.x
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commit
983d48a921
@ -6,23 +6,21 @@ from tqdm import trange
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import modules.scripts as scripts
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import gradio as gr
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from modules import processing, shared, sd_samplers, prompt_parser, sd_samplers_common
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from modules.processing import Processed
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from modules.shared import opts, cmd_opts, state
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from modules import processing, shared, sd_samplers, sd_samplers_common
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import torch
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import k_diffusion as K
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from PIL import Image
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from torch import autocast
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from einops import rearrange, repeat
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def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
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x = p.init_latent
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s_in = x.new_ones([x.shape[0]])
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dnw = K.external.CompVisDenoiser(shared.sd_model)
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if shared.sd_model.parameterization == "v":
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dnw = K.external.CompVisVDenoiser(shared.sd_model)
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skip = 1
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else:
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dnw = K.external.CompVisDenoiser(shared.sd_model)
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skip = 0
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sigmas = dnw.get_sigmas(steps).flip(0)
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shared.state.sampling_steps = steps
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@ -37,7 +35,7 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
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image_conditioning = torch.cat([p.image_conditioning] * 2)
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cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
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c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
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c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
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t = dnw.sigma_to_t(sigma_in)
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eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
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@ -69,7 +67,12 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
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x = p.init_latent
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s_in = x.new_ones([x.shape[0]])
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dnw = K.external.CompVisDenoiser(shared.sd_model)
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if shared.sd_model.parameterization == "v":
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dnw = K.external.CompVisVDenoiser(shared.sd_model)
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skip = 1
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else:
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dnw = K.external.CompVisDenoiser(shared.sd_model)
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skip = 0
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sigmas = dnw.get_sigmas(steps).flip(0)
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shared.state.sampling_steps = steps
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@ -84,7 +87,7 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
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image_conditioning = torch.cat([p.image_conditioning] * 2)
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cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
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c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
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c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
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if i == 1:
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t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
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@ -125,7 +128,7 @@ class Script(scripts.Script):
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def show(self, is_img2img):
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return is_img2img
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def ui(self, is_img2img):
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def ui(self, is_img2img):
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info = gr.Markdown('''
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* `CFG Scale` should be 2 or lower.
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''')
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@ -213,4 +216,3 @@ class Script(scripts.Script):
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processed = processing.process_images(p)
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return processed
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