diff --git a/configs/stable-diffusion/txt2img-upscale-clip-encoder-f16-1024.yaml b/configs/stable-diffusion/txt2img-upscale-clip-encoder-f16-1024.yaml
index e6b9db5..2f1ab09 100644
--- a/configs/stable-diffusion/txt2img-upscale-clip-encoder-f16-1024.yaml
+++ b/configs/stable-diffusion/txt2img-upscale-clip-encoder-f16-1024.yaml
@@ -24,7 +24,7 @@ model:
         linear_start: 0.00085
         linear_end: 0.0120
         timesteps: 1000
-        max_noise_level: 250
+        max_noise_level: 100
         output_size: 64
         model_config:
           target: ldm.models.autoencoder.AutoencoderKL
diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
index 3d01f60..1e495ee 100644
--- a/ldm/models/diffusion/ddim.py
+++ b/ldm/models/diffusion/ddim.py
@@ -179,7 +179,20 @@ class DDIMSampler(object):
         else:
             x_in = torch.cat([x] * 2)
             t_in = torch.cat([t] * 2)
-            c_in = torch.cat([unconditional_conditioning, c])
+            if isinstance(c, dict):
+                assert isinstance(unconditional_conditioning, dict)
+                c_in = dict()
+                for k in c:
+                    if isinstance(c[k], list):
+                        c_in[k] = [torch.cat([
+                            unconditional_conditioning[k][i],
+                            c[k][i]]) for i in range(len(c[k]))]
+                    else:
+                        c_in[k] = torch.cat([
+                                unconditional_conditioning[k],
+                                c[k]])
+            else:
+                c_in = torch.cat([unconditional_conditioning, c])
             e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
             e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
 
diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py
index 6aa8b86..299b060 100644
--- a/ldm/models/diffusion/ddpm.py
+++ b/ldm/models/diffusion/ddpm.py
@@ -1543,7 +1543,18 @@ class LatentUpscaleDiffusion(LatentDiffusion):
                 log["denoise_row"] = denoise_grid
 
         if unconditional_guidance_scale > 1.0:
-            uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+            uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+            # TODO explore better "unconditional" choices for the other keys
+            uc = dict()
+            for k in c:
+                if k == "c_crossattn":
+                    assert isinstance(c[k], list) and len(c[k]) == 1
+                    uc[k] = [uc_tmp]
+                elif isinstance(c[k], list):
+                    uc[k] = [torch.zeros_like(c[k][i]) for i in range(len(c[k]))]
+                else:
+                    uc[k] = torch.zeros_like(c[k])
+
             with ema_scope("Sampling with classifier-free guidance"):
                 samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
                                                  ddim_steps=ddim_steps, eta=ddim_eta,