Update app.py

app.py

@@ -5,13 +5,17 @@ import modin.pandas as pd
 from PIL import Image
 #from diffusers import DiffusionPipeline
 from huggingface_hub import hf_hub_download
-from diffusers import StableCascadeCombinedPipeline
+from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
 
 device = 'cuda' #if torch.cuda.is_available() else 'cpu'
 torch.cuda.max_memory_allocated(device=device)
 torch.cuda.empty_cache()
-pipe = StableCascadeCombinedPipeline.from_pretrained("stabilityai/stable-cascade", variant="bf16", torch_dtype=torch.bfloat16)
-pipe.enable_xformers_memory_efficient_attention()
+prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", variant="bf16", torch_dtype=torch.bfloat16)
+prior.enable_model_cpu_offload()
+decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", variant="bf16", torch_dtype=torch.float16)
+decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", variant="bf16", torch_dtype=torch.float16)
+
+#pipe.enable_xformers_memory_efficient_attention()
 #pipe = pipe.to(device)
 torch.cuda.empty_cache()
             
@@ -20,13 +24,21 @@ def genie (Prompt, negative_prompt, height, width, scale, steps, seed, upscale):
     generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
     torch.cuda.empty_cache()
     pipe.enable_model_cpu_offload()
-    image=pipe(prompt=Prompt,
-    negative_prompt=negative_prompt,
-    num_inference_steps=15,
-    prior_num_inference_steps=steps,
-    prior_guidance_scale=scale,
+    prior_image=prior(
+    prompt=Prompt,
+    height=height,
     width=width,
-    height=height,).images[0]
+    negative_prompt=negative_prompt,
+    guidance_scale=scale,
+    num_images_per_prompt=1,
+    num_inference_steps=steps)
+    image=decoder(
+    image_embeddings=prior_output.image_embeddings.to(torch.float16),
+    prompt=Prompt,
+    negative_prompt=negative_prompt,
+    guidance_scale=0.0,
+    output_type="pil",
+    num_inference_steps=10).images[0]
     #int_image = pipe(prompt=Prompt, negative_prompt=negative_prompt, num_inference_steps=steps, guidance_scale=scale, width=width, height=height, output_type="latent").images #
     #torch.cuda.empty_cache()
     #image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=.99).images[0]