Update comment out fusing

app-diffusers.py

@@ -14,7 +14,7 @@ except ModuleNotFoundError:
 import PIL
 from diffusers import DiffusionPipeline
 
-ldm = DiffusionPipeline.from_pretrained("fusing/latent-diffusion-text2im-large")
+ldm = DiffusionPipeline.from_pretrained("fu sing/latent-diffusion-text2im-large")
 
 generator = torch.manual_seed(42)
 

app.py

@@ -25,7 +25,11 @@ def generate_images(phrase: str, steps: int = 125):
     num_images = 1
     diversity = 6
 
-    image_bytes = image_gen(generated_text, steps, width, height, num_images, diversity)
+    try:
+        image_bytes = image_gen(generated_text, steps, width, height, num_images, diversity)
+    except Exception as exc:
+        logger.error(exc)
+        return img, f"phrase: {phrase}, errors: str(exc). Try again."
 
     # Algo from spaces/Gradio-Blocks/latent_gpt2_story/blob/main/app.py
     # generated_images = []

example.py

@@ -7,8 +7,8 @@ import tqdm
 torch_device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # 1. Load models
-scheduler = DDIMScheduler.from_config("fusing/ddpm-celeba-hq", tensor_format="pt")
-unet = UNetUnconditionalModel.from_pretrained("fusing/ddpm-celeba-hq", ddpm=True).to(torch_device)
+scheduler = DDIMScheduler.from_config("fu sing/ddpm-celeba-hq", tensor_format="pt")
+unet = UNetUnconditionalModel.from_pretrained("fu sing/ddpm-celeba-hq", ddpm=True).to(torch_device)
 
 # 2. Sample gaussian noise
 generator = torch.manual_seed(23)

example1.py

@@ -11,11 +11,11 @@ https://github.com/CompVis/latent-diffusion/blob/main/scripts/txt2img.py
 https://medium.com/tag/diffusion-models
 
  !pip install einops
-    
+
 """
 from diffusers import DiffusionPipeline
 
-ldm = DiffusionPipeline.from_pretrained("fusing/latent-diffusion-text2im-large")
+ldm = DiffusionPipeline.from_pretrained("fu sing/latent-diffusion-text2im-large")
 
 generator = torch.manual_seed(42)