Support one-string prompts and custom image size in LDM

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -1,5 +1,5 @@
 import inspect
-from typing import Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -24,20 +24,30 @@ def __init__(self, vqvae, bert, tokenizer, unet, scheduler):
     @torch.no_grad()
     def __call__(
         self,
-        prompt,
-        batch_size=1,
-        generator=None,
-        torch_device=None,
-        eta=0.0,
-        guidance_scale=1.0,
-        num_inference_steps=50,
-        output_type="pil",
+        prompt: Union[str, List[str]],
+        height: Optional[int] = 256,
+        width: Optional[int] = 256,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 1.0,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        torch_device: Optional[Union[str, torch.device]] = None,
+        output_type: Optional[str] = "pil",
     ):
         # eta corresponds to η in paper and should be between [0, 1]
 
         if torch_device is None:
             torch_device = "cuda" if torch.cuda.is_available() else "cpu"
-        batch_size = len(prompt)
+
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
 
         self.unet.to(torch_device)
         self.vqvae.to(torch_device)
@@ -53,7 +63,7 @@ def __call__(
         text_embeddings = self.bert(text_input.input_ids.to(torch_device))[0]
 
         latents = torch.randn(
-            (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
+            (batch_size, self.unet.in_channels, height // 8, width // 8),
             generator=generator,
         )
         latents = latents.to(torch_device)

tests/test_modeling_utils.py

@@ -854,7 +854,7 @@ def test_ldm_text2img_fast(self):
 
         prompt = "A painting of a squirrel eating a burger"
         generator = torch.manual_seed(0)
-        image = ldm([prompt], generator=generator, num_inference_steps=1, output_type="numpy")["sample"]
+        image = ldm(prompt, generator=generator, num_inference_steps=1, output_type="numpy")["sample"]
 
         image_slice = image[0, -3:, -3:, -1]