update space

README.md

@@ -4,7 +4,7 @@ emoji: 🎮
 colorFrom: gray
 colorTo: green
 sdk: gradio
-sdk_version: 3.9
+sdk_version: 3.6
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -1,4 +1,4 @@
-from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
+from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
 import gradio as gr
 import torch
 from PIL import Image
@@ -17,23 +17,43 @@ class Model:
 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("Beksinski", "s3nh/beksinski-style-stable-diffusion", "beksinski style "),
+     Model("Robo Diffusion", "nousr/robo-diffusion", ""),
+     Model("Guohua", "Langboat/Guohua-Diffusion", "guohua style ")
+  ]
+
+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,
+)
+
+custom_model = None
+if is_colab:
+  models.insert(0, Model("Custom model", "", ""))
+  custom_model = models[0]
 
 last_mode = "txt2img"
-current_model = models[0]
+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)
+  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[0:]:
+  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)
-        model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, 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
@@ -71,8 +91,8 @@ 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 == models[0]:
-          pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
+        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
@@ -81,7 +101,7 @@ def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, g
           pipe = pipe.to("cuda")
         last_mode = "txt2img"
 
-    prompt = current_model.prefix + prompt
+    prompt = current_model.prefix + prompt  
     result = pipe(
       prompt,
       negative_prompt = neg_prompt,
@@ -102,8 +122,8 @@ 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 == models[0]:
-          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
+        if is_colab or current_model == custom_model:
+          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler)
         else:
           pipe.to("cpu")
           pipe = current_model.pipe_i2i
@@ -135,43 +155,12 @@ def replace_nsfw_images(results):
         results.images[i] = Image.open("nsfw.png")
     return results.images[0]
 
-css = """
-  <style>
-  .finetuned-diffusion-div {
-      text-align: center;
-      max-width: 700px;
-      margin: 0 auto;
-    }
-    .finetuned-diffusion-div div {
-      display: inline-flex;
-      align-items: center;
-      gap: 0.8rem;
-      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: 0px;
-      margin-bottom: 0px;
-    }
-    #gallery {
-      min-height: 20rem;
-    }
-  </style>
+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:
     gr.HTML(
         f"""
-            <div class="finetuned-diffusion-div">
+            <div class="playground-diffusion-div">
               <div>
                 <h1>Playground Diffusion</h1>
               </div>
@@ -191,7 +180,8 @@ 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)              
+              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))
@@ -211,7 +201,7 @@ with gr.Blocks(css=css) as demo:
 
               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)
+                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)
@@ -224,18 +214,22 @@ with gr.Blocks(css=css) as demo:
                 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]
     prompt.submit(inference, inputs=inputs, outputs=image_out)
     generate.click(inference, inputs=inputs, outputs=image_out)
 
     ex = gr.Examples([
         [models[0].name, "Neon techno-magic robot with spear pierces an ancient beast, hyperrealism, no blur, 4k resolution, ultra detailed", 7.5, 50],
+        [models[0].name, "halfturn portrait of a big crystal face of a beautiful abstract ancient Egyptian elderly shaman woman, made of iridescent golden crystals, half - turn, bottom view, ominous, intricate, studio, art by anthony macbain and greg rutkowski and alphonse mucha, concept art, 4k, sharp focus", 7.5, 25],
     ], [model_name, prompt, guidance, steps, seed], image_out, inference, cache_examples=False)
 
-    gr.Markdown('''
-      Models by [@riccardogiorato](https://huggingface.co/riccardogiorato) <br>
-      Space by: [![Twitter Follow](https://img.shields.io/twitter/follow/riccardogiorato?style=social)](https://twitter.com/riccardogiorato)
-    ''')
+    gr.HTML("""
+      <p>Models by <a href="https://huggingface.co/riccardogiorato">@riccardogiorato</a><br></p>
+    """)
 
 if not is_colab:
   demo.queue(concurrency_count=1)

requirements.txt

@@ -1,6 +1,7 @@
 --extra-index-url https://download.pytorch.org/whl/cu113
 torch
-diffusers
+git+https://github.com/huggingface/diffusers.git
 transformers
 scipy
-ftfy
+ftfy
+accelerate