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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-29 19:05:05 +08:00
fix img2img alt for SD v2.x
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a9fed7c364
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@ -22,7 +22,12 @@ 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 +42,7 @@ 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 +74,12 @@ 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 +94,7 @@ 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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