Update app.py

app.py

@@ -20,7 +20,9 @@ class Model:
 
 models = [
      Model("Arcane", "nitrosocke/Arcane-Diffusion", "arcane style"),
+     Model("Dreamlike Diffusion 1.0", "dreamlike-art/dreamlike-diffusion-1.0", "dreamlikeart "),
      Model("Archer", "nitrosocke/archer-diffusion", "archer style"),
+     Model("Anything V3", "Linaqruf/anything-v3.0", ""),
      Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style"),
      Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style"),
      Model("Modern Disney", "nitrosocke/modern-disney-diffusion", "modern disney style"),
@@ -46,6 +48,8 @@ models = [
      Model("Avatar", "riccardogiorato/avatar-diffusion", "avatartwow style "),
      Model("Poolsuite", "prompthero/poolsuite", "poolsuite style "),
      Model("Loving Vincent (Van Gogh)", "dallinmackay/Van-Gogh-diffusion", "lvngvncnt "),
+     Model("Wavyfusion", "wavymulder/wavyfusion", "wa-vy style "),
+     Model("Analog Diffusion", "wavymulder/Analog-Diffusion", "analog style "),
      Model("Redshift renderer (Cinema4D)", "nitrosocke/redshift-diffusion", "redshift style "),
      Model("Midjourney v4 style", "prompthero/midjourney-v4-diffusion", "mdjrny-v4 style "),
      Model("TrinArt v2", "naclbit/trinart_stable_diffusion_v2"),
@@ -78,6 +82,7 @@ else:
     
 if torch.cuda.is_available():
   pipe = pipe.to("cuda")
+  pipe.enable_xformers_memory_efficient_attention()
 
 device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
 
@@ -144,6 +149,7 @@ def txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width,
 
         if torch.cuda.is_available():
           pipe = pipe.to("cuda")
+          pipe.enable_xformers_memory_efficient_attention()
         last_mode = "txt2img"
 
     prompt = current_model.prefix + prompt  
@@ -187,6 +193,7 @@ def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance
         
         if torch.cuda.is_available():
           pipe = pipe.to("cuda")
+          pipe.enable_xformers_memory_efficient_attention()
         last_mode = "img2img"
 
     prompt = current_model.prefix + prompt
@@ -196,7 +203,7 @@ def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance
         prompt,
         negative_prompt = neg_prompt,
         num_images_per_prompt=n_images,
-        init_image = img,
+        image = img,
         num_inference_steps = int(steps),
         strength = strength,
         guidance_scale = guidance,