Merge branch 'main' of github.com:piEsposito/diffusers into main

Author: piEsposito

src/diffusers/configuration_utils.py

@@ -323,7 +323,7 @@ def extract_init_dict(cls, config_dict, **kwargs):
 
         # remove attributes from orig class that cannot be expected
         orig_cls_name = config_dict.pop("_class_name", cls.__name__)
-        if orig_cls_name != cls.__name__:
+        if orig_cls_name != cls.__name__ and hasattr(diffusers_library, orig_cls_name):
             orig_cls = getattr(diffusers_library, orig_cls_name)
             unexpected_keys_from_orig = cls._get_init_keys(orig_cls) - expected_keys
             config_dict = {k: v for k, v in config_dict.items() if k not in unexpected_keys_from_orig}

src/diffusers/models/attention.py

@@ -244,7 +244,9 @@ def __init__(
         self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
         self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
 
-        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
+        self.to_out = nn.ModuleList([])
+        self.to_out.append(nn.Linear(inner_dim, query_dim))
+        self.to_out.append(nn.Dropout(dropout))
 
     def reshape_heads_to_batch_dim(self, tensor):
         batch_size, seq_len, dim = tensor.shape
@@ -283,7 +285,11 @@ def forward(self, hidden_states, context=None, mask=None):
         else:
             hidden_states = self._sliced_attention(query, key, value, sequence_length, dim)
 
-        return self.to_out(hidden_states)
+        # linear proj
+        hidden_states = self.to_out[0](hidden_states)
+        # dropout
+        hidden_states = self.to_out[1](hidden_states)
+        return hidden_states
 
     def _attention(self, query, key, value):
         # TODO: use baddbmm for better performance
@@ -354,12 +360,19 @@ def __init__(
         super().__init__()
         inner_dim = int(dim * mult)
         dim_out = dim_out if dim_out is not None else dim
-        project_in = GEGLU(dim, inner_dim)
+        self.net = nn.ModuleList([])
 
-        self.net = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
+        # project in
+        self.net.append(GEGLU(dim, inner_dim))
+        # project dropout
+        self.net.append(nn.Dropout(dropout))
+        # project out
+        self.net.append(nn.Linear(inner_dim, dim_out))
 
     def forward(self, hidden_states):
-        return self.net(hidden_states)
+        for module in self.net:
+            hidden_states = module(hidden_states)
+        return hidden_states
 
 
 # feedforward

src/diffusers/models/unet_2d_blocks.py

@@ -1053,7 +1053,6 @@ def __init__(
         cross_attention_dim=1280,
         attention_type="default",
         output_scale_factor=1.0,
-        downsample_padding=1,
         add_upsample=True,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -91,17 +91,18 @@ def __init__(
             new_config["steps_offset"] = 1
             scheduler._internal_dict = FrozenDict(new_config)
 
-        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+        if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
             deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
-                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
-                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
-                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
-                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration"
+                " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
+                " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
+                " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
+                " Hub, it would be very nice if you could open a Pull request for the"
+                " `scheduler/scheduler_config.json` file"
             )
-            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            deprecate("skip_prk_steps not set", "1.0.0", deprecation_message, standard_warn=False)
             new_config = dict(scheduler.config)
-            new_config["clip_sample"] = False
+            new_config["skip_prk_steps"] = True
             scheduler._internal_dict = FrozenDict(new_config)
 
         if safety_checker is None:

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -640,13 +640,14 @@ def test_stable_diffusion(self):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
     def test_stable_diffusion_fast_ddim(self):
-        sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1", device_map="auto")
+        scheduler = DDIMScheduler.from_config("CompVis/stable-diffusion-v1-1", subfolder="scheduler")
+
+        sd_pipe = StableDiffusionPipeline.from_pretrained(
+            "CompVis/stable-diffusion-v1-1", scheduler=scheduler, device_map="auto"
+        )
         sd_pipe = sd_pipe.to(torch_device)
         sd_pipe.set_progress_bar_config(disable=None)
 
-        scheduler = DDIMScheduler.from_config("CompVis/stable-diffusion-v1-1", subfolder="scheduler")
-        sd_pipe.scheduler = scheduler
-
         prompt = "A painting of a squirrel eating a burger"
         generator = torch.Generator(device=torch_device).manual_seed(0)