Merge pull request #8772 from mcmonkey4eva/img2img-alt-sd2-fix (983d48a9) · Commits · github_fork / Stable Diffusion Webui

scripts/img2imgalt.py

+16 −14

Original line number	Diff line number	Diff line
		@@ -6,23 +6,21 @@ from tqdm import trange
		import modules.scripts as scripts
		import gradio as gr

		from modules import processing, shared, sd_samplers, prompt_parser, sd_samplers_common
		from modules.processing import Processed
		from modules.shared import opts, cmd_opts, state
		from modules import processing, shared, sd_samplers, sd_samplers_common

		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]])
		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)

		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)
		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)

		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

		s_in = x.new_ones([x.shape[0]])
		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)

		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)
		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:
		t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
		@@ -213,4 +216,3 @@ class Script(scripts.Script):
		processed = processing.process_images(p)

		return processed

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