bugfix

app.py

@@ -8,14 +8,15 @@ from diffusers import AutoPipelineForText2Image
 from diffusers import AutoPipelineForImage2Image
 from diffusers.utils import load_image, export_to_video
 from diffusers import StableVideoDiffusionPipeline
-
-
-
+pipeline_image2video = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt",).to("cuda")
+pipeline_text2image = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo").to("cuda")
+pipeline_image2image = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")
+pipeline_image2video.enable_model_cpu_offload()
 
 
 
 def img2video(image,seed="",fps=7,outfile=""):
-    pipelineVideo = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt",).to("cuda")
+    
     if seed=="":
         seed=random.randint(0, 5000)
     
@@ -29,19 +30,18 @@ def img2video(image,seed="",fps=7,outfile=""):
     image = load_image(image)
     image = image.resize((1024, 576))
     generator = torch.manual_seed(seed)
-    frames = pipelineVideo(image, decode_chunk_size=8, generator=generator).frames[0]
+    frames = pipeline_image2video(image, decode_chunk_size=8, generator=generator).frames[0]
     export_to_video(frames, outfile, fps=fps)
     time.time(30)
     return outfile
 
 def text2img(prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe.",guidance_scale=0.0, num_inference_steps=1):
-    pipeline_text2image = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo").to("cuda")
+    
     image = pipeline_text2image(prompt=prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
     return image
 
 def img2img(image,prompt="A cinematic shot of a baby racoon wearing an intricate italian priest robe.", guidance_scale=0.0, num_inference_steps=1,strength=0.5):
-    pipeline_text2image = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo").to("cuda")
-    pipeline_image2image = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")
+
     init_image = load_image(image)
     init_image = init_image.resize((512, 512))
     image = pipeline_image2image(prompt, image=init_image, strength=strength, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]