Merge branch 'main' of https://github.com/huggingface/diffusers

README.md

@@ -152,7 +152,7 @@ images[0].save("cat_on_bench.png")
 
 ### Tweak prompts reusing seeds and latents
 
-You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. [This notebook](stable-diffusion-seeds.ipynb) shows how to do it step by step. You can also run it in Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb).
+You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. [This notebook](https://github.com/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) shows how to do it step by step. You can also run it in Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb).
 
 
 For more details, check out [the Stable Diffusion notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb)

src/diffusers/models/resnet.py

@@ -328,39 +328,39 @@ class ResnetBlock2D(nn.Module):
         if self.use_nin_shortcut:
             self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
 
-    def forward(self, x, temb, hey=False):
-        h = x
+    def forward(self, x, temb):
+        hidden_states = x
 
         # make sure hidden states is in float32
         # when running in half-precision
-        h = self.norm1(h.float()).type(h.dtype)
-        h = self.nonlinearity(h)
+        hidden_states = self.norm1(hidden_states.float()).type(hidden_states.dtype)
+        hidden_states = self.nonlinearity(hidden_states)
 
         if self.upsample is not None:
             x = self.upsample(x)
-            h = self.upsample(h)
+            hidden_states = self.upsample(hidden_states)
         elif self.downsample is not None:
             x = self.downsample(x)
-            h = self.downsample(h)
+            hidden_states = self.downsample(hidden_states)
 
-        h = self.conv1(h)
+        hidden_states = self.conv1(hidden_states)
 
         if temb is not None:
             temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
-            h = h + temb
+            hidden_states = hidden_states + temb
 
         # make sure hidden states is in float32
         # when running in half-precision
-        h = self.norm2(h.float()).type(h.dtype)
-        h = self.nonlinearity(h)
+        hidden_states = self.norm2(hidden_states.float()).type(hidden_states.dtype)
+        hidden_states = self.nonlinearity(hidden_states)
 
-        h = self.dropout(h)
-        h = self.conv2(h)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.conv2(hidden_states)
 
         if self.conv_shortcut is not None:
             x = self.conv_shortcut(x)
 
-        out = (x + h) / self.output_scale_factor
+        out = (x + hidden_states) / self.output_scale_factor
 
         return out
 

src/diffusers/pipelines/README.md

@@ -77,9 +77,7 @@ all of our pipelines to be  **self-contained**, **easy-to-tweak**, **beginner-fr
 - **Easy-to-use**: Pipelines should be extremely easy to use - one should be able to load the pipeline and 
 use it for its designated task, *e.g.* text-to-image generation, in just a couple of lines of code. Most 
 logic including pre-processing, an unrolled diffusion loop, and post-processing should all happen inside the `__call__` method.
-- **Easy-to-tweak**: Certain pipelines will not be able to handle all use cases and tasks that you might like them to. If you want to use a certain pipeline for a specific use case that is not yet supported, you might have to copy the pipeline file and tweak the code to your needs.
-
-We try to make the pipeline code as readable as possible so that each part –from pre-processing to diffusing to post-processing– can easily be adapted. If you would like the community to benefit from your customized pipeline, we would love to see a contribution to our [community-examples](https://github.com/huggingface/diffusers/tree/main/examples/commmunity). If you feel that an important pipeline should be part of the official pipelines but isn't, a contribution to the [official pipelines](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines) would be even better.
+- **Easy-to-tweak**: Certain pipelines will not be able to handle all use cases and tasks that you might like them to. If you want to use a certain pipeline for a specific use case that is not yet supported, you might have to copy the pipeline file and tweak the code to your needs. We try to make the pipeline code as readable as possible so that each part –from pre-processing to diffusing to post-processing– can easily be adapted. If you would like the community to benefit from your customized pipeline, we would love to see a contribution to our [community-examples](https://github.com/huggingface/diffusers/tree/main/examples/commmunity). If you feel that an important pipeline should be part of the official pipelines but isn't, a contribution to the [official pipelines](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines) would be even better.
 - **One-purpose-only**: Pipelines should be used for one task and one task only. Even if two tasks are very similar from a modeling point of view, *e.g.* image2image translation and in-painting, pipelines shall be used for one task only to keep them *easy-to-tweak* and *readable*.
 
 ## Examples