unconditional guidance with general s!=1.0

Example: "negative weights"

Author: rromb

ldm/models/diffusion/ddim.py

@@ -168,7 +168,7 @@ def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=F
         b, *_, device = *x.shape, x.device
         e_t = self.model.apply_model(x, t, c)
 
-        if unconditional_guidance_scale > 1.:
+        if unconditional_guidance_scale != 1.:
             assert unconditional_conditioning is not None
             e_t_uncond =  self.model.apply_model(x, t, unconditional_conditioning)
             e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)