Reproducible images by supplying latents to pipeline (#247)

* Accept latents as input for StableDiffusionPipeline.

* Notebook to demonstrate reusable seeds (latents).

* More accurate type annotation

Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Review comments: move to device, raise instead of assert.

* Actually commit the test notebook.

I had mistakenly pushed an empty file instead.

* Adapt notebook to Colab.

* Update examples readme.

* Move notebook to personal repo.

Co-authored-by: Suraj Patil <surajp815@gmail.com>

examples/inference/readme.md

@@ -48,3 +48,7 @@ with autocast("cuda"):
 images[0].save("fantasy_landscape.png")
 ```
 You can also run this example on colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/Notebooks/blob/master/image_2_image_using_diffusers.ipynb)
+
+## 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).

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -46,6 +46,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
         guidance_scale: Optional[float] = 7.5,
         eta: Optional[float] = 0.0,
         generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
         output_type: Optional[str] = "pil",
         **kwargs,
     ):
@@ -98,12 +99,18 @@ class StableDiffusionPipeline(DiffusionPipeline):
             # to avoid doing two forward passes
             text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
 
-        # get the intial random noise
+        # get the initial random noise unless the user supplied it
+        latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
+        if latents is None:
             latents = torch.randn(
-            (batch_size, self.unet.in_channels, height // 8, width // 8),
+                latents_shape,
                 generator=generator,
                 device=self.device,
             )
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
 
         # set timesteps
         accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())