update stuff

.gitignore

@@ -0,0 +1,2 @@
+
+node_modules

app.py

@@ -6,6 +6,7 @@ import utils
 
 is_colab = utils.is_google_colab()
 
+
 class Model:
     def __init__(self, name, path, prefix):
         self.name = name
@@ -14,15 +15,16 @@ class Model:
         self.pipe_t2i = None
         self.pipe_i2i = None
 
+
 models = [
-     Model("Beeple", "riccardogiorato/beeple-diffusion", "beeple style "),
-     Model("Avatar", "riccardogiorato/avatar-diffusion", "avatartwow style "),
-     Model("Beksinski", "s3nh/beksinski-style-stable-diffusion", "beksinski style "),
-     Model("Poolsuite","prompthero/poolsuite","poolsuite style "),
-     Model("Robo Diffusion", "nousr/robo-diffusion", ""),
-     Model("Guohua", "Langboat/Guohua-Diffusion", "guohua style ")
-     Model("JWST", "dallinmackay/JWST-Deep-Space-diffusion", "JWST ")
-  ]
+    Model("Beeple", "riccardogiorato/beeple-diffusion", "beeple style "),
+    Model("Avatar", "riccardogiorato/avatar-diffusion", "avatartwow style "),
+    Model("Beksinski", "s3nh/beksinski-style-stable-diffusion", "beksinski style "),
+    Model("Poolsuite", "prompthero/poolsuite", "poolsuite style "),
+    Model("Robo Diffusion", "nousr/robo-diffusion", ""),
+    Model("Guohua", "Langboat/Guohua-Diffusion", "guohua style "),
+    Model("JWST", "dallinmackay/JWST-Deep-Space-diffusion", "JWST ")
+]
 
 scheduler = DPMSolverMultistepScheduler(
     beta_start=0.00085,
@@ -37,53 +39,51 @@ scheduler = DPMSolverMultistepScheduler(
     lower_order_final=True,
 )
 
-custom_model = None
-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 = 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:
-    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[0].pipe_t2i
-  
+    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:
+        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[0].pipe_t2i
+
 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=""):
 
-  global current_model
-  for model in models:
-    if model.name == model_name:
-      current_model = model
-      model_path = current_model.path
+    global current_model
+    for model in models:
+        if model.name == model_name:
+            current_model = model
+            model_path = current_model.path
+
+    generator = torch.Generator('cuda').manual_seed(
+        seed) if seed != 0 else None
 
-  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
+    if img is not None:
+        return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator)
+    else:
+        return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator)
 
-  if img is not None:
-    return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator)
-  else:
-    return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator)
 
 def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator=None):
 
@@ -93,29 +93,31 @@ def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, g
     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)
+        if is_colab:
+            pipe = StableDiffusionPipeline.from_pretrained(
+                current_model_path, torch_dtype=torch.float16, scheduler=scheduler)
         else:
-          pipe.to("cpu")
-          pipe = current_model.pipe_t2i
+            pipe.to("cpu")
+            pipe = current_model.pipe_t2i
 
         if torch.cuda.is_available():
-          pipe = pipe.to("cuda")
+            pipe = pipe.to("cuda")
         last_mode = "txt2img"
 
-    prompt = current_model.prefix + prompt  
+    prompt = current_model.prefix + prompt
     result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      # num_images_per_prompt=n_images,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator)
-    
+        prompt,
+        negative_prompt=neg_prompt,
+        # num_images_per_prompt=n_images,
+        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):
 
     global last_mode
@@ -124,39 +126,43 @@ def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, w
     if model_path != current_model_path or last_mode != "img2img":
         current_model_path = model_path
 
-        if is_colab or current_model == custom_model:
-          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler)
+        if is_colab:
+            pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+                current_model_path, torch_dtype=torch.float16, scheduler=scheduler)
         else:
-          pipe.to("cpu")
-          pipe = current_model.pipe_i2i
-        
+            pipe.to("cpu")
+            pipe = current_model.pipe_i2i
+
         if torch.cuda.is_available():
-          pipe = pipe.to("cuda")
+            pipe = pipe.to("cuda")
         last_mode = "img2img"
 
     prompt = current_model.prefix + prompt
     ratio = min(height / img.height, width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
+    img = img.resize(
+        (int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
     result = pipe(
         prompt,
-        negative_prompt = neg_prompt,
+        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)
-        
+        init_image=img,
+        num_inference_steps=int(steps),
+        strength=strength,
+        guidance_scale=guidance,
+        width=width,
+        height=height,
+        generator=generator)
+
     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 = """.playground-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.playground-diffusion-div div h1{font-weight:900;margin-bottom:7px}.playground-diffusion-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
 """
 with gr.Blocks(css=css) as demo:
@@ -179,48 +185,55 @@ with gr.Blocks(css=css) as demo:
         """
     )
     with gr.Row():
-        
-        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.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"
-              # ).style(grid=[1], height="auto")
-
-        with gr.Column(scale=45):
-          with gr.Tab("Options"):
+        with gr.Column(scale=55):
             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=25, minimum=2, maximum=75, step=1)
+                model_name = gr.Dropdown(label="Model", choices=[
+                                         m.name for m in models], value=current_model.name)
 
-              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)
+                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))
 
-              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
+                image_out = gr.Image(height=512)
+                # gallery = gr.Gallery(
+                #     label="Generated images", show_label=False, elem_id="gallery"
+                # ).style(grid=[1], height="auto")
 
-          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)
-
-    if is_colab:
-      model_name.change(lambda x: gr.update(visible = x == models[0].name), inputs=model_name, outputs=custom_model_group)
-    # 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]
+        with gr.Column(scale=45):
+            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=25, minimum=2, maximum=75, 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)
+
+    inputs = [model_name, prompt, guidance, steps,
+              width, height, seed, image, strength, neg_prompt]
     prompt.submit(inference, inputs=inputs, outputs=image_out)
     generate.click(inference, inputs=inputs, outputs=image_out)
 
@@ -234,5 +247,5 @@ with gr.Blocks(css=css) as demo:
     """)
 
 if not is_colab:
-  demo.queue(concurrency_count=1)
-demo.launch(debug=is_colab, share=is_colab)
+    demo.queue(concurrency_count=1)
+demo.launch(debug=is_colab, share=is_colab)

package.json

@@ -0,0 +1,9 @@
+{
+  "name": "playground_diffusion",
+  "version": "1.0.0",
+  "repository": "https://huggingface.co/spaces/riccardogiorato/playground_diffusion",
+  "license": "MIT",
+  "scripts": {
+    "install": "pip install -r requirements.txt"
+  }
+}

yarn.lock

@@ -0,0 +1,4 @@
+# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
+# yarn lockfile v1
+
+