Commit 05ec128c authored by Alex "mcmonkey" Goodwin's avatar Alex "mcmonkey" Goodwin
Browse files

fix img2img alt for SD v2.x

parent a9fed7c3
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+14 −4
Original line number Diff line number Diff line
@@ -22,7 +22,12 @@ 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 +42,7 @@ 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 +74,12 @@ 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 +94,7 @@ 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))