update

options/Banner_Model/Image2Image_2.py

@@ -1,10 +1,7 @@
 import torch
 from controlnet_aux import LineartDetector
 from diffusers import ControlNetModel,UniPCMultistepScheduler,FluxPipeline
-from huggingface_hub import login
 from PIL import Image
-import os
-login(token=os.getenv("TOKEN"))
 
 device= "cuda" if torch.cuda.is_available() else "cpu"
 print("Using device for I2I_2:", device)
@@ -13,7 +10,7 @@ processor = LineartDetector.from_pretrained("lllyasviel/Annotators")
 checkpoint = "ControlNet-1-1-preview/control_v11p_sd15_lineart"
 controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16).to(device)
 pipe = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", controlnet=controlnet, torch_dtype=torch.float16
+    "benjamin-paine/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
 ).to(device)
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 # pipe.enable_model_cpu_offload()
@@ -25,5 +22,4 @@ def I2I_2(image, prompt,size,num_inference_steps):
     image=processor(image)
     generator = torch.Generator(device=device).manual_seed(0)
     image = pipe(prompt, num_inference_steps=num_inference_steps, generator=generator, image=image).images[0]
-    return image
-
+    return image

options/Video_model/Model.py

@@ -1,6 +1,6 @@
 import torch
 from diffusers import StableVideoDiffusionPipeline
-from diffusers.utils import load_image, save_video
+from diffusers.utils import load_image
 from PIL import Image
 from tdd_svd_scheduler import TDDSVDStochasticIterativeScheduler
 from utils import load_lora_weights, save_video