Update app.py

app.py

@@ -7,9 +7,6 @@ import utils
 
 is_colab = utils.is_google_colab()
 
-max_width = 832
-max_height = 832
-
 class Model:
     def __init__(self, name, path, prefix):
         self.name = name
@@ -26,13 +23,13 @@ models = [
      Model("Classic Disney", "nitrosocke/classic-anim-diffusion", ""),
      Model("Waifu", "hakurei/waifu-diffusion", ""),
      Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""),
-     Model("Fuyuko Waifu", "yuk/fuyuko-waifu-diffusion", ""),
      Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""),
      Model("Robo Diffusion", "nousr/robo-diffusion", ""),
      Model("Cyberpunk Anime", "DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style "),
-     Model("Hergé Style", "sd-dreambooth-library/herge-style", "herge_style "),
+     Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy")
 ]
 
+last_mode = "txt2img"
 current_model = models[1]
 current_model_path = current_model.path
 pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16)
@@ -63,56 +60,66 @@ def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0
 
 def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator=None):
 
+    global last_mode
     global pipe
     global current_model_path
-    if model_path != current_model_path:
+    if model_path != current_model_path or last_mode != "txt2img":
         current_model_path = model_path
 
         pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
         if torch.cuda.is_available():
           pipe = pipe.to("cuda")
+        last_mode = "txt2img"
 
     prompt = current_model.prefix + prompt
-    results = pipe(
+    result = pipe(
       prompt,
-      negative_prompt=neg_prompt,
-      num_inference_steps=int(steps),
-      guidance_scale=guidance,
-      width=width,
-      height=height,
-      generator=generator)
+      negative_prompt = neg_prompt,
+      # num_images_per_prompt=n_images,
+      num_inference_steps = int(steps),
+      guidance_scale = guidance,
+      width = width,
+      height = height,
+      generator = generator)
     
-    image = results.images[0] if not results.nsfw_content_detected[0] else Image.open("nsfw.png")
-    return image
+    return replace_nsfw_images(result)
 
-def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
+def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator=None):
 
+    global last_mode
     global pipe
     global current_model_path
-    if model_path != current_model_path:
+    if model_path != current_model_path or last_mode != "img2img":
         current_model_path = model_path
 
         pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
         
         if torch.cuda.is_available():
               pipe = pipe.to("cuda")
+        last_mode = "img2img"
 
     prompt = current_model.prefix + prompt
-    ratio = min(max_height / img.height, max_width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)))
-    results = pipe(
+    ratio = min(height / img.height, width / img.width)
+    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
+    result = pipe(
         prompt,
-        negative_prompt=neg_prompt,
-        init_image=img,
-        num_inference_steps=int(steps),
-        strength=strength,
-        guidance_scale=guidance,
-        width=width,
-        height=height,
-        generator=generator)
+        negative_prompt = neg_prompt,
+        # num_images_per_prompt=n_images,
+        init_image = img,
+        num_inference_steps = int(steps),
+        strength = strength,
+        guidance_scale = guidance,
+        width = width,
+        height = height,
+        generator = generator)
         
-    image = results.images[0] if not results.nsfw_content_detected[0] else Image.open("nsfw.png")
-    return image
+    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")
+    return results.images[0]
 
 css = """
   <style>
@@ -138,6 +145,13 @@ css = """
     .finetuned-diffusion-div p a {
       text-decoration: underline;
     }
+    .tabs {
+      margin-top: 0px;
+      margin-bottom: 0px;
+    }
+    #gallery {
+      min-height: 20rem;
+    }
   </style>
 """
 with gr.Blocks(css=css) as demo:
@@ -151,7 +165,7 @@ with gr.Blocks(css=css) as demo:
                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">Spiderverse</a>, <a href="https://huggingface.co/nitrosocke/modern-disney-diffusion">Modern Disney</a>, <a href="https://huggingface.co/hakurei/waifu-diffusion">Waifu</a>, <a href="https://huggingface.co/lambdalabs/sd-pokemon-diffusers">Pokemon</a>, <a href="https://huggingface.co/yuk/fuyuko-waifu-diffusion">Fuyuko Waifu</a>, <a href="https://huggingface.co/AstraliteHeart/pony-diffusion">Pony</a>, <a href="https://huggingface.co/sd-dreambooth-library/herge-style">Hergé (Tintin)</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo</a>, <a href="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion">Cyberpunk Anime</a> + any other custom Diffusers 🧨 SD model hosted on HuggingFace 🤗.
               </p>
-              <p>Don't want to wait in queue? ➡️ <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>Don't want to wait in queue? <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>
               </p>
             </div>
@@ -159,42 +173,58 @@ with gr.Blocks(css=css) as demo:
     )
     with gr.Row():
         
-        with gr.Column():
+        with gr.Group():
             model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
             custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", visible=False, interactive=True)
-            prompt = gr.Textbox(label="Prompt", placeholder="Style prefix is applied automatically")
-            run = gr.Button(value="Run")
-
-            with gr.Tab("Options"):
-                neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
-                steps = gr.Slider(label="Steps", value=50, maximum=100, minimum=2, step=1)
-                width = gr.Slider(label="Width", value=512, maximum=max_width, minimum=64, step=8)
-                height = gr.Slider(label="Height", value=512, maximum=max_height, minimum=64, step=8)
-                seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-                
-            with gr.Tab("Image to image"):
-                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
-                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
-        with gr.Column():
+            
+            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)
-            log = gr.Textbox()
+            # gallery = gr.Gallery(
+            #     label="Generated images", show_label=False, elem_id="gallery"
+            # ).style(grid=[1], height="auto")
+
+        with gr.Tab("Options"):
+          with gr.Group():
+            neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
+
+            # n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
+
+            with gr.Row():
+              guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
+              steps = gr.Slider(label="Steps", value=50, minimum=2, maximum=100, step=1)
+
+            with gr.Row():
+              width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
+              height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
+
+            seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
+
+        with gr.Tab("Image to image"):
+            with gr.Group():
+              image = gr.Image(label="Image", height=256, tool="editor", type="pil")
+              strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
 
     model_name.change(lambda x: gr.update(visible = x == models[0].name), inputs=model_name, outputs=custom_model_path)
-    custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=log)
+    custom_model_path.change(custom_model_changed, inputs=custom_model_path)
+    # n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)
+
     inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
-    prompt.submit(inference, inputs=inputs, outputs=image_out, scroll_to_output=True)
-    run.click(inference, inputs=inputs, outputs=image_out, scroll_to_output=True)
-  
-    gr.Examples([
+    prompt.submit(inference, inputs=inputs, outputs=image_out)
+    generate.click(inference, inputs=inputs, outputs=image_out)
+
+    ex = gr.Examples([
         [models[1].name, "jason bateman disassembling the demon core", 7.5, 50],
         [models[4].name, "portrait of dwayne johnson", 7.0, 75],
         [models[5].name, "portrait of a beautiful alyx vance half life", 10, 50],
         [models[6].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 45],
         [models[5].name, "fantasy portrait painting, digital art", 4.0, 30],
     ], [model_name, prompt, guidance, steps, seed], image_out, inference, cache_examples=not is_colab and torch.cuda.is_available())
-  
+    # ex.dataset.headers = [""]
+
     gr.Markdown('''
       Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. ❤️<br>
       Space by: [![Twitter Follow](https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social)](https://twitter.com/hahahahohohe)