Merge branch 'master' into master

README.md

@@ -70,6 +70,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
 - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
 - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
+- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. 
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.

modules/extras.py

@@ -39,9 +39,12 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         if input_dir == '':
             return outputs, "Please select an input directory.", ''
-        image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
+        image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)]
         for img in image_list:
-            image = Image.open(img)
+            try:
+                image = Image.open(img)
+            except Exception:
+                continue
             imageArr.append(image)
             imageNameArr.append(img)
     else:
@@ -118,10 +121,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         while len(cached_images) > 2:
             del cached_images[next(iter(cached_images.keys()))]
+        
+        if opts.use_original_name_batch and image_name != None:
+            basename = os.path.splitext(os.path.basename(image_name))[0]
+        else:
+            basename = ''
 
-        images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,
-                          forced_filename=image_name if opts.use_original_name_batch else None)
+        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo

modules/hypernetworks/hypernetwork.py

@@ -22,16 +22,26 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False, activation_func=None):
         super().__init__()
 
-        assert layer_structure is not None, "layer_structure mut not be None"
+        assert layer_structure is not None, "layer_structure must not be None"
         assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
         assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
 
         linears = []
         for i in range(len(layer_structure) - 1):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
+
+            if activation_func == "relu":
+                linears.append(torch.nn.ReLU())
+            elif activation_func == "leakyrelu":
+                linears.append(torch.nn.LeakyReLU())
+            elif activation_func == 'linear' or activation_func is None:
+                pass
+            else:
+                raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')
+
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
@@ -42,8 +52,9 @@ class HypernetworkModule(torch.nn.Module):
             self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                layer.weight.data.normal_(mean=0.0, std=0.01)
-                layer.bias.data.zero_()
+                if type(layer) == torch.nn.Linear:
+                    layer.weight.data.normal_(mean=0.0, std=0.01)
+                    layer.bias.data.zero_()
 
         self.to(devices.device)
 
@@ -69,7 +80,8 @@ class HypernetworkModule(torch.nn.Module):
     def trainables(self):
         layer_structure = []
         for layer in self.linear:
-            layer_structure += [layer.weight, layer.bias]
+            if type(layer) == torch.nn.Linear:
+                layer_structure += [layer.weight, layer.bias]
         return layer_structure
 
 
@@ -81,7 +93,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False, activation_func=None):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -90,11 +102,12 @@ class Hypernetwork:
         self.sd_checkpoint_name = None
         self.layer_structure = layer_structure
         self.add_layer_norm = add_layer_norm
+        self.activation_func = activation_func
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm),
-                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm),
+                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func),
+                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func),
             )
 
     def weights(self):
@@ -117,6 +130,7 @@ class Hypernetwork:
         state_dict['name'] = self.name
         state_dict['layer_structure'] = self.layer_structure
         state_dict['is_layer_norm'] = self.add_layer_norm
+        state_dict['activation_func'] = self.activation_func
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
 
@@ -131,12 +145,13 @@ class Hypernetwork:
 
         self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
+        self.activation_func = state_dict.get('activation_func', None)
 
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm, self.activation_func),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm, self.activation_func),
                 )
 
         self.name = state_dict.get('name', self.name)

modules/hypernetworks/ui.py

@@ -10,7 +10,7 @@ from modules import sd_hijack, shared, devices
 from modules.hypernetworks import hypernetwork
 
 
-def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False):
+def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False, activation_func=None):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
@@ -22,6 +22,7 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm
         enable_sizes=[int(x) for x in enable_sizes],
         layer_structure=layer_structure,
         add_layer_norm=add_layer_norm,
+        activation_func=activation_func,
     )
     hypernet.save(fn)
 

modules/processing.py

@@ -540,17 +540,37 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
+    def create_dummy_mask(self, x, width=None, height=None):
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
+            height = height or self.height
+            width = width or self.width
+
+            # The "masked-image" in this case will just be all zeros since the entire image is masked.
+            image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+            image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
+
+            # Add the fake full 1s mask to the first dimension.
+            image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+            image_conditioning = image_conditioning.to(x.dtype)
+
+        else:
+            # Dummy zero conditioning if we're not using inpainting model.
+            # Still takes up a bit of memory, but no encoder call.
+            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+            image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+
+        return image_conditioning
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
@@ -587,7 +607,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=self.create_dummy_mask(samples))
 
         return samples
 
@@ -613,6 +633,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.inpainting_mask_invert = inpainting_mask_invert
         self.mask = None
         self.nmask = None
+        self.image_conditioning = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
@@ -714,10 +735,39 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
+            if self.image_mask is not None:
+                conditioning_mask = np.array(self.image_mask.convert("L"))
+                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+                conditioning_mask = torch.round(conditioning_mask)
+            else:
+                conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
+
+            # Create another latent image, this time with a masked version of the original input.
+            conditioning_mask = conditioning_mask.to(image.device)
+            conditioning_image = image * (1.0 - conditioning_mask)
+            conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+
+            # Create the concatenated conditioning tensor to be fed to `c_concat`
+            conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
+            conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+            self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+            self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
+        else:
+            self.image_conditioning = torch.zeros(
+                self.init_latent.shape[0], 5, 1, 1,
+                dtype=self.init_latent.dtype,
+                device=self.init_latent.device
+            )
+
+
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
-        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)
+        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
 
         if self.mask is not None:
             samples = samples * self.nmask + self.init_latent * self.mask

modules/sd_hijack_inpainting.py

@@ -0,0 +1,331 @@
+import torch
+
+from einops import repeat
+from omegaconf import ListConfig
+
+import ldm.models.diffusion.ddpm
+import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
+
+from ldm.models.diffusion.ddpm import LatentDiffusion
+from ldm.models.diffusion.plms import PLMSSampler
+from ldm.models.diffusion.ddim import DDIMSampler, noise_like
+
+# =================================================================================================
+# Monkey patch DDIMSampler methods from RunwayML repo directly.
+# Adapted from:
+# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
+# =================================================================================================
+@torch.no_grad()
+def sample_ddim(self,
+            S,
+            batch_size,
+            shape,
+            conditioning=None,
+            callback=None,
+            normals_sequence=None,
+            img_callback=None,
+            quantize_x0=False,
+            eta=0.,
+            mask=None,
+            x0=None,
+            temperature=1.,
+            noise_dropout=0.,
+            score_corrector=None,
+            corrector_kwargs=None,
+            verbose=True,
+            x_T=None,
+            log_every_t=100,
+            unconditional_guidance_scale=1.,
+            unconditional_conditioning=None,
+            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+            **kwargs
+            ):
+    if conditioning is not None:
+        if isinstance(conditioning, dict):
+            ctmp = conditioning[list(conditioning.keys())[0]]
+            while isinstance(ctmp, list):
+                ctmp = ctmp[0]
+            cbs = ctmp.shape[0]
+            if cbs != batch_size:
+                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+        else:
+            if conditioning.shape[0] != batch_size:
+                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+    # sampling
+    C, H, W = shape
+    size = (batch_size, C, H, W)
+    print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+
+    samples, intermediates = self.ddim_sampling(conditioning, size,
+                                                callback=callback,
+                                                img_callback=img_callback,
+                                                quantize_denoised=quantize_x0,
+                                                mask=mask, x0=x0,
+                                                ddim_use_original_steps=False,
+                                                noise_dropout=noise_dropout,
+                                                temperature=temperature,
+                                                score_corrector=score_corrector,
+                                                corrector_kwargs=corrector_kwargs,
+                                                x_T=x_T,
+                                                log_every_t=log_every_t,
+                                                unconditional_guidance_scale=unconditional_guidance_scale,
+                                                unconditional_conditioning=unconditional_conditioning,
+                                                )
+    return samples, intermediates
+
+@torch.no_grad()
+def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                    unconditional_guidance_scale=1., unconditional_conditioning=None):
+    b, *_, device = *x.shape, x.device
+
+    if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+        e_t = self.model.apply_model(x, t, c)
+    else:
+        x_in = torch.cat([x] * 2)
+        t_in = torch.cat([t] * 2)
+        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)
+
+    if score_corrector is not None:
+        assert self.model.parameterization == "eps"
+        e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+    # select parameters corresponding to the currently considered timestep
+    a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+    a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+    sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+    sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+    # current prediction for x_0
+    pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+    if quantize_denoised:
+        pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+    # direction pointing to x_t
+    dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+    noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+    if noise_dropout > 0.:
+        noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+    x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+    return x_prev, pred_x0
+
+
+# =================================================================================================
+# Monkey patch PLMSSampler methods.
+# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes.
+# Adapted from:
+# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py
+# =================================================================================================
+@torch.no_grad()
+def sample_plms(self,
+            S,
+            batch_size,
+            shape,
+            conditioning=None,
+            callback=None,
+            normals_sequence=None,
+            img_callback=None,
+            quantize_x0=False,
+            eta=0.,
+            mask=None,
+            x0=None,
+            temperature=1.,
+            noise_dropout=0.,
+            score_corrector=None,
+            corrector_kwargs=None,
+            verbose=True,
+            x_T=None,
+            log_every_t=100,
+            unconditional_guidance_scale=1.,
+            unconditional_conditioning=None,
+            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+            **kwargs
+            ):
+    if conditioning is not None:
+        if isinstance(conditioning, dict):
+            ctmp = conditioning[list(conditioning.keys())[0]]
+            while isinstance(ctmp, list):
+                ctmp = ctmp[0]
+            cbs = ctmp.shape[0]
+            if cbs != batch_size:
+                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+        else:
+            if conditioning.shape[0] != batch_size:
+                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+    # sampling
+    C, H, W = shape
+    size = (batch_size, C, H, W)
+    print(f'Data shape for PLMS sampling is {size}')
+
+    samples, intermediates = self.plms_sampling(conditioning, size,
+                                                callback=callback,
+                                                img_callback=img_callback,
+                                                quantize_denoised=quantize_x0,
+                                                mask=mask, x0=x0,
+                                                ddim_use_original_steps=False,
+                                                noise_dropout=noise_dropout,
+                                                temperature=temperature,
+                                                score_corrector=score_corrector,
+                                                corrector_kwargs=corrector_kwargs,
+                                                x_T=x_T,
+                                                log_every_t=log_every_t,
+                                                unconditional_guidance_scale=unconditional_guidance_scale,
+                                                unconditional_conditioning=unconditional_conditioning,
+                                                )
+    return samples, intermediates
+
+
+@torch.no_grad()
+def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                    unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
+    b, *_, device = *x.shape, x.device
+
+    def get_model_output(x, t):
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            e_t = self.model.apply_model(x, t, c)
+        else:
+            x_in = torch.cat([x] * 2)
+            t_in = torch.cat([t] * 2)
+            
+            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)
+
+        if score_corrector is not None:
+            assert self.model.parameterization == "eps"
+            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+        return e_t
+
+    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+    def get_x_prev_and_pred_x0(e_t, index):
+        # select parameters corresponding to the currently considered timestep
+        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+        # current prediction for x_0
+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        if quantize_denoised:
+            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+        # direction pointing to x_t
+        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+        return x_prev, pred_x0
+
+    e_t = get_model_output(x, t)
+    if len(old_eps) == 0:
+        # Pseudo Improved Euler (2nd order)
+        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+        e_t_next = get_model_output(x_prev, t_next)
+        e_t_prime = (e_t + e_t_next) / 2
+    elif len(old_eps) == 1:
+        # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (3 * e_t - old_eps[-1]) / 2
+    elif len(old_eps) == 2:
+        # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+    elif len(old_eps) >= 3:
+        # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+    x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+    return x_prev, pred_x0, e_t
+    
+# =================================================================================================
+# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
+# Adapted from:
+# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py
+# =================================================================================================
+
+@torch.no_grad()
+def get_unconditional_conditioning(self, batch_size, null_label=None):
+    if null_label is not None:
+        xc = null_label
+        if isinstance(xc, ListConfig):
+            xc = list(xc)
+        if isinstance(xc, dict) or isinstance(xc, list):
+            c = self.get_learned_conditioning(xc)
+        else:
+            if hasattr(xc, "to"):
+                xc = xc.to(self.device)
+            c = self.get_learned_conditioning(xc)
+    else:
+        # todo: get null label from cond_stage_model
+        raise NotImplementedError()
+    c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
+    return c
+
+
+class LatentInpaintDiffusion(LatentDiffusion):
+    def __init__(
+        self,
+        concat_keys=("mask", "masked_image"),
+        masked_image_key="masked_image",
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.masked_image_key = masked_image_key
+        assert self.masked_image_key in concat_keys
+        self.concat_keys = concat_keys
+
+
+def should_hijack_inpainting(checkpoint_info):
+    return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml")
+
+
+def do_inpainting_hijack():
+    ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
+    ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
+
+    ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
+    ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
+
+    ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
+    ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms

modules/sd_models.py

@@ -9,6 +9,7 @@ from ldm.util import instantiate_from_config
 
 from modules import shared, modelloader, devices
 from modules.paths import models_path
+from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
@@ -203,14 +204,26 @@ def load_model_weights(model, checkpoint_info):
     model.sd_checkpoint_info = checkpoint_info
 
 
-def load_model():
+def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
-    checkpoint_info = select_checkpoint()
+    checkpoint_info = checkpoint_info or select_checkpoint()
 
     if checkpoint_info.config != shared.cmd_opts.config:
         print(f"Loading config from: {checkpoint_info.config}")
 
     sd_config = OmegaConf.load(checkpoint_info.config)
+    
+    if should_hijack_inpainting(checkpoint_info):
+        # Hardcoded config for now...
+        sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
+        sd_config.model.params.use_ema = False
+        sd_config.model.params.conditioning_key = "hybrid"
+        sd_config.model.params.unet_config.params.in_channels = 9
+
+        # Create a "fake" config with a different name so that we know to unload it when switching models.
+        checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
+
+    do_inpainting_hijack()
     sd_model = instantiate_from_config(sd_config.model)
     load_model_weights(sd_model, checkpoint_info)
 
@@ -234,9 +247,9 @@ def reload_model_weights(sd_model, info=None):
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
-    if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
+    if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         checkpoints_loaded.clear()
-        shared.sd_model = load_model()
+        shared.sd_model = load_model(checkpoint_info)
         return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:

modules/sd_samplers.py

@@ -117,6 +117,8 @@ class VanillaStableDiffusionSampler:
         self.config = None
         self.last_latent = None
 
+        self.conditioning_key = sd_model.model.conditioning_key
+
     def number_of_needed_noises(self, p):
         return 0
 
@@ -136,6 +138,12 @@ class VanillaStableDiffusionSampler:
         if self.stop_at is not None and self.step > self.stop_at:
             raise InterruptedException
 
+        # Have to unwrap the inpainting conditioning here to perform pre-processing
+        image_conditioning = None
+        if isinstance(cond, dict):
+            image_conditioning = cond["c_concat"][0]
+            cond = cond["c_crossattn"][0]
+            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
 
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
@@ -157,6 +165,12 @@ class VanillaStableDiffusionSampler:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)
             x_dec = img_orig * self.mask + self.nmask * x_dec
 
+        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
+        # Note that they need to be lists because it just concatenates them later.
+        if image_conditioning is not None:
+            cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
         res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
 
         if self.mask is not None:
@@ -182,7 +196,7 @@ class VanillaStableDiffusionSampler:
         self.mask = p.mask if hasattr(p, 'mask') else None
         self.nmask = p.nmask if hasattr(p, 'nmask') else None
 
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
 
         self.initialize(p)
@@ -199,11 +213,17 @@ class VanillaStableDiffusionSampler:
         self.last_latent = x
         self.step = 0
 
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        if image_conditioning is not None:
+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+            
+            
         samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
 
         return samples
 
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         self.initialize(p)
 
         self.init_latent = None
@@ -212,6 +232,11 @@ class VanillaStableDiffusionSampler:
 
         steps = steps or p.steps
 
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        if image_conditioning is not None:
+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
         # existing code fails with certain step counts, like 9
         try:
             samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
@@ -230,7 +255,7 @@ class CFGDenoiser(torch.nn.Module):
         self.init_latent = None
         self.step = 0
 
-    def forward(self, x, sigma, uncond, cond, cond_scale):
+    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
         if state.interrupted or state.skipped:
             raise InterruptedException
 
@@ -241,28 +266,29 @@ class CFGDenoiser(torch.nn.Module):
         repeats = [len(conds_list[i]) for i in range(batch_size)]
 
         x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
 
         if tensor.shape[1] == uncond.shape[1]:
             cond_in = torch.cat([tensor, uncond])
 
             if shared.batch_cond_uncond:
-                x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
+                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
             else:
                 x_out = torch.zeros_like(x_in)
                 for batch_offset in range(0, x_out.shape[0], batch_size):
                     a = batch_offset
                     b = a + batch_size
-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
         else:
             x_out = torch.zeros_like(x_in)
             batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
             for batch_offset in range(0, tensor.shape[0], batch_size):
                 a = batch_offset
                 b = min(a + batch_size, tensor.shape[0])
-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})
 
-            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)
+            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
 
         denoised_uncond = x_out[-uncond.shape[0]:]
         denoised = torch.clone(denoised_uncond)
@@ -308,6 +334,8 @@ class KDiffusionSampler:
         self.config = None
         self.last_latent = None
 
+        self.conditioning_key = sd_model.model.conditioning_key
+
     def callback_state(self, d):
         step = d['i']
         latent = d["denoised"]
@@ -363,7 +391,7 @@ class KDiffusionSampler:
 
         return extra_params_kwargs
 
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
 
         if p.sampler_noise_scheduler_override:
@@ -392,11 +420,16 @@ class KDiffusionSampler:
         self.model_wrap_cfg.init_latent = x
         self.last_latent = x
 
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
+            'cond': conditioning, 
+            'image_cond': image_conditioning, 
+            'uncond': unconditional_conditioning, 
+            'cond_scale': p.cfg_scale
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples
 
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
         steps = steps or p.steps
 
         if p.sampler_noise_scheduler_override:
@@ -418,7 +451,12 @@ class KDiffusionSampler:
             extra_params_kwargs['sigmas'] = sigmas
 
         self.last_latent = x
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+            'cond': conditioning, 
+            'image_cond': image_conditioning, 
+            'uncond': unconditional_conditioning, 
+            'cond_scale': p.cfg_scale
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples
 

modules/ui.py

@@ -1224,6 +1224,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"])
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1308,6 +1309,7 @@ def create_ui(wrap_gradio_gpu_call):
                 new_hypernetwork_sizes,
                 new_hypernetwork_layer_structure,
                 new_hypernetwork_add_layer_norm,
+                new_hypernetwork_activation_func,
             ],
             outputs=[
                 train_hypernetwork_name,

scripts/xy_grid.py

@@ -89,6 +89,7 @@ def apply_checkpoint(p, x, xs):
     if info is None:
         raise RuntimeError(f"Unknown checkpoint: {x}")
     modules.sd_models.reload_model_weights(shared.sd_model, info)
+    p.sd_model = shared.sd_model
 
 
 def confirm_checkpoints(p, xs):