Update app.py

app.py

@@ -1,4 +1,4 @@
-from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
+from diffusers import CycleDiffusionPipeline, DDIMScheduler
 import gradio as gr
 import torch
 from PIL import Image
@@ -6,76 +6,17 @@ import utils
 
 is_colab = utils.is_google_colab()
 
-class Model:
-    def __init__(self, name, path, prefix):
-        self.name = name
-        self.path = path
-        self.prefix = prefix
-        self.pipe_t2i = None
-        self.pipe_i2i = None
-
-models = [
-     Model("Arcane", "nitrosocke/Arcane-Diffusion", "arcane style "),
-     Model("Archer", "nitrosocke/archer-diffusion", "archer style "),
-     Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
-     Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
-     Model("Modern Disney", "nitrosocke/mo-di-diffusion", "modern disney style "),
-     Model("Classic Disney", "nitrosocke/classic-anim-diffusion", "classic disney style "),
-     Model("Loving Vincent (Van Gogh)", "dallinmackay/Van-Gogh-diffusion", "lvngvncnt "),
-     Model("Redshift renderer (Cinema4D)", "nitrosocke/redshift-diffusion", "redshift style "),
-     Model("Midjourney v4 style", "prompthero/midjourney-v4-diffusion", "mdjrny-v4 style "),
-     Model("Waifu", "hakurei/waifu-diffusion", ""),
-     Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""),
-     Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""),
-     Model("Robo Diffusion", "nousr/robo-diffusion", ""),
-     Model("Cyberpunk Anime", "DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style "),
-     Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy ")
-  ]
-
-scheduler = DPMSolverMultistepScheduler(
-    beta_start=0.00085,
-    beta_end=0.012,
-    beta_schedule="scaled_linear",
-    num_train_timesteps=1000,
-    trained_betas=None,
-    predict_epsilon=True,
-    thresholding=False,
-    algorithm_type="dpmsolver++",
-    solver_type="midpoint",
-    lower_order_final=True,
-)
-
-if is_colab:
-  models.insert(0, Model("Custom model", "", ""))
-  custom_model = models[0]
-
-last_mode = "txt2img"
-current_model = models[1] if is_colab else models[0]
-current_model_path = current_model.path
-
-if is_colab:
-  pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16, scheduler=scheduler)
-
-else: # download all models
-  vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16)
-  for model in models[1:]:
-    try:
-        unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=torch.float16)
-        model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
-        model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
-    except:
-        models.remove(model)
-  pipe = models[1].pipe_t2i
-  
+scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
+                          num_train_timesteps=1000, clip_sample=False, set_alpha_to_one=False)
+
+model_id_or_path = "CompVis/stable-diffusion-v1-4"
+pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler).to("cuda")
+
 if torch.cuda.is_available():
-  pipe = pipe.to("cuda")
+    pipe = pipe.to("cuda")
 
 device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
 
-def custom_model_changed(path):
-  models[0].path = path
-  global current_model
-  current_model = models[0]
 
 def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", inpaint_image=None):
 
@@ -92,43 +33,6 @@ def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0
   else:
     return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator, inpaint_image)
 
-def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator=None, inpaint_image=None):
-
-    global last_mode
-    global pipe
-    global current_model_path
-    if model_path != current_model_path or last_mode != "txt2img":
-        current_model_path = model_path
-
-        if is_colab or current_model == custom_model:
-          pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler)
-        else:
-          pipe.to("cpu")
-          pipe = current_model.pipe_t2i
-
-        if torch.cuda.is_available():
-          pipe = pipe.to("cuda")
-        last_mode = "txt2img"
-
-    prompt = current_model.prefix + prompt
-
-    if inpaint_image is not None:
-      init_image = inpaint_image["image"].convert("RGB").resize((width, height))
-      mask = inpaint_image["mask"].convert("RGB").resize((width, height))
-  
-    result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      # num_images_per_prompt=n_images,
-      image = init_image,
-      mask_image = mask,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator)
-    
-    return replace_nsfw_images(result)
 
 def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator=None):
 
@@ -165,12 +69,14 @@ def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, w
         
     return replace_nsfw_images(result)
 
+
 def replace_nsfw_images(results):
     for i in range(len(results.images)):
-      if results.nsfw_content_detected[i]:
-        results.images[i] = Image.open("nsfw.png")
+        if results.nsfw_content_detected[i]:
+            results.images[i] = Image.open("nsfw.png")
     return results.images[0]
 
+
 css = """.finetuned-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.finetuned-diffusion-div div h1{font-weight:900;margin-bottom:7px}.finetuned-diffusion-div p{margin-bottom:10px;font-size:94%}.finetuned-diffusion-div p a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
 """
 with gr.Blocks(css=css) as demo:
@@ -178,13 +84,12 @@ with gr.Blocks(css=css) as demo:
         f"""
             <div class="finetuned-diffusion-div">
               <div>
-                <h1>Finetuned Diffusion</h1>
+                <h1>CycleDiffusion with Stable Diffusion</h1>
               </div>
               <p>
-               Demo for multiple fine-tuned Stable Diffusion models, trained on different styles: <br>
-               <a href="https://huggingface.co/nitrosocke/Arcane-Diffusion">Arcane</a>, <a href="https://huggingface.co/nitrosocke/archer-diffusion">Archer</a>, <a href="https://huggingface.co/nitrosocke/elden-ring-diffusion">Elden Ring</a>, <a href="https://huggingface.co/nitrosocke/spider-verse-diffusion">Spider-Verse</a>, <a href="https://huggingface.co/nitrosocke/modern-disney-diffusion">Modern Disney</a>, <a href="https://huggingface.co/nitrosocke/classic-anim-diffusion">Classic Disney</a>, <a href="https://huggingface.co/hakurei/waifu-diffusion">Waifu</a>, <a href="https://huggingface.co/lambdalabs/sd-pokemon-diffusers">Pokémon</a>, <a href="https://huggingface.co/AstraliteHeart/pony-diffusion">Pony Diffusion</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo Diffusion</a>, <a href="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion">Cyberpunk Anime</a>, <a href="https://huggingface.co/dallinmackay/Tron-Legacy-diffusion">Tron Legacy</a> + any other custom Diffusers 🧨 SD model hosted on HuggingFace 🤗.
+               Demo for CycleDiffusion with Stable Diffusion, built with Diffusers 🧨 by HuggingFace 🤗.
               </p>
-              <p>You can skip the queue and load custom models in the colab: <a href="https://colab.research.google.com/gist/qunash/42112fb104509c24fd3aa6d1c11dd6e0/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
+              <p>You can skip the queue in the colab: <a href="https://colab.research.google.com/gist/qunash/42112fb104509c24fd3aa6d1c11dd6e0/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
                Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
               </p>
             </div>
@@ -194,16 +99,11 @@ with gr.Blocks(css=css) as demo:
         
         with gr.Column(scale=55):
           with gr.Group():
-              model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
-              with gr.Box(visible=False) as custom_model_group:
-                custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True)
-                gr.HTML("<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>")
-              
+
               with gr.Row():
                 prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
                 generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
 
-
               image_out = gr.Image(height=512)
               # gallery = gr.Gallery(
               #     label="Generated images", show_label=False, elem_id="gallery"
@@ -259,5 +159,5 @@ with gr.Blocks(css=css) as demo:
     ''')
 
 if not is_colab:
-  demo.queue(concurrency_count=1)
+    demo.queue(concurrency_count=1)
 demo.launch(debug=is_colab, share=is_colab)