bug and warning message fixes

- txt2img2img back to using DDIM as img2img sampler; results produced
  by some k* samplers are just not reliable enough for good user
  experience
- img2img progress message clarifies why img2img steps taken != steps requested
- warn of potential problems when user tries to run img2img on a small init image

ldm/dream/generator/inpaint.py

@@ -27,7 +27,7 @@ class Inpaint(Img2Img):
         # klms samplers not supported yet, so ignore previous sampler
         if isinstance(sampler,KSampler):
             print(
-                f">> sampler '{sampler.__class__.__name__}' is not yet supported for inpainting, using DDIMSampler instead."
+                f">> Using recommended DDIM sampler for inpainting."
             )
             sampler = DDIMSampler(self.model, device=self.model.device)
         

ldm/dream/generator/txt2img2img.py

@@ -64,7 +64,7 @@ class Txt2Img2Img(Generator):
             )
             
             print(
-                  f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height}"
+                  f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
                  )
             
             # resizing
@@ -75,17 +75,19 @@ class Txt2Img2Img(Generator):
             )
 
             t_enc = int(strength * steps)
+            ddim_sampler = DDIMSampler(self.model, device=self.model.device)
+            ddim_sampler.make_schedule(
+                    ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
+            )
 
-            x = self.get_noise(width,height,False)
-
-            z_enc = sampler.stochastic_encode(
+            z_enc = ddim_sampler.stochastic_encode(
                 samples,
                 torch.tensor([t_enc]).to(self.model.device),
-                noise=x
+                noise=self.get_noise(width,height,False)
             )
 
             # decode it
-            samples = sampler.decode(
+            samples = ddim_sampler.decode(
                 z_enc,
                 c,
                 t_enc,

ldm/generate.py

@@ -417,7 +417,8 @@ class Generate:
                 generator = self._make_txt2img()
 
             generator.set_variation(
-                self.seed, variation_amount, with_variations)
+                self.seed, variation_amount, with_variations
+            )
             results = generator.generate(
                 prompt,
                 iterations=iterations,
@@ -626,18 +627,14 @@ class Generate:
             height,
         )
 
+        if image.width < self.width and image.height < self.height:
+            print(f'>> WARNING: img2img and inpainting may produce unexpected results with initial images smaller than {self.width}x{self.height} in both dimensions')
+
         # if image has a transparent area and no mask was provided, then try to generate mask
-        if self._has_transparency(image) and not mask:
-            print(
-                '>> Initial image has transparent areas. Will inpaint in these regions.')
-            if self._check_for_erasure(image):
-                print(
-                    '>> WARNING: Colors underneath the transparent region seem to have been erased.\n',
-                    '>>          Inpainting will be suboptimal. Please preserve the colors when making\n',
-                    '>>          a transparency mask, or provide mask explicitly using --init_mask (-M).'
-                )
+        if self._has_transparency(image):
+            self._transparency_check_and_warning(image, mask)
             # this returns a torch tensor
-            init_mask = self._create_init_mask(image,width,height,fit=fit)
+            init_mask = self._create_init_mask(image, width, height, fit=fit)
             
         if (image.width * image.height) > (self.width * self.height):
             print(">> This input is larger than your defaults. If you run out of memory, please use a smaller image.")
@@ -953,6 +950,17 @@ class Generate:
                         colored += 1
         return colored == 0
 
+    def _transparency_check_and_warning(image, mask):
+        if not mask:
+            print(
+                '>> Initial image has transparent areas. Will inpaint in these regions.')
+            if self._check_for_erasure(image):
+                print(
+                    '>> WARNING: Colors underneath the transparent region seem to have been erased.\n',
+                    '>>          Inpainting will be suboptimal. Please preserve the colors when making\n',
+                    '>>          a transparency mask, or provide mask explicitly using --init_mask (-M).'
+                )
+
     def _squeeze_image(self, image):
         x, y, resize_needed = self._resolution_check(image.width, image.height)
         if resize_needed:

ldm/models/diffusion/ksampler.py

@@ -51,8 +51,9 @@ class KSampler(Sampler):
             schedule,
             steps=model.num_timesteps,
         )
-        self.ds    = None
-        self.s_in  = None
+        self.sigmas = None
+        self.ds     = None
+        self.s_in   = None
 
         def forward(self, x, sigma, uncond, cond, cond_scale):
             x_in = torch.cat([x] * 2)
@@ -140,7 +141,7 @@ class KSampler(Sampler):
             'uncond': unconditional_conditioning,
             'cond_scale': unconditional_guidance_scale,
         }
-        print(f'>> Sampling with k_{self.schedule}')
+        print(f'>> Sampling with k_{self.schedule} starting at step {len(self.sigmas)-S-1} of {len(self.sigmas)-1} ({S} new sampling steps)')
         return (
             K.sampling.__dict__[f'sample_{self.schedule}'](
                 model_wrap_cfg, x, sigmas, extra_args=extra_args,
@@ -149,6 +150,8 @@ class KSampler(Sampler):
             None,
         )
 
+    # this code will support inpainting if and when ksampler API modified or
+    # a workaround is found.
     @torch.no_grad()
     def p_sample(
             self,

ldm/models/diffusion/sampler.py

@@ -39,6 +39,7 @@ class Sampler(object):
             ddim_eta=0.0,
             verbose=False,
     ):
+        self.total_steps = ddim_num_steps
         self.ddim_timesteps = make_ddim_timesteps(
             ddim_discr_method=ddim_discretize,
             num_ddim_timesteps=ddim_num_steps,
@@ -211,6 +212,7 @@ class Sampler(object):
             if ddim_use_original_steps
             else np.flip(timesteps)
         )
+
         total_steps=steps
 
         iterator = tqdm(
@@ -305,7 +307,7 @@ class Sampler(object):
 
         time_range = np.flip(timesteps)
         total_steps = timesteps.shape[0]
-        print(f'>> Running {self.__class__.__name__} Sampling with {total_steps} timesteps')
+        print(f'>> Running {self.__class__.__name__} sampling starting at step {self.total_steps - t_start} of {self.total_steps} ({total_steps} new sampling steps)')
 
         iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
         x_dec    = x_latent