Update app.py

app.py

@@ -52,19 +52,6 @@ models = [
      Model("TrinArt v2", "naclbit/trinart_stable_diffusion_v2"),
      Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "),
   ]
-     
-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:
@@ -76,23 +63,20 @@ 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, safety_checker=lambda images, clip_input: (images, False))
-
-else: # download all models
-  pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16, scheduler=scheduler)
-  # print(f"{datetime.datetime.now()} Downloading vae...")
-  # vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16)
-  # for model in models:
-  #   try:
-  #       print(f"{datetime.datetime.now()} Downloading {model.name} model...")
-  #       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 Exception as e:
-  #       print(f"{datetime.datetime.now()} Failed to load model " + model.name + ": " + str(e))
-  #       models.remove(model)
-  # pipe = models[0].pipe_t2i
-  
+  pipe = StableDiffusionPipeline.from_pretrained(
+      current_model.path,
+      torch_dtype=torch.float16,
+      scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
+      safety_checker=lambda images, clip_input: (images, False)
+      )
+
+else:
+  pipe = StableDiffusionPipeline.from_pretrained(
+      current_model.path,
+      torch_dtype=torch.float16,
+      scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
+      )
+    
 if torch.cuda.is_available():
   pipe = pipe.to("cuda")
 
@@ -113,7 +97,7 @@ def on_model_change(model_name):
 
   return gr.update(visible = model_name == models[0].name), gr.update(placeholder=prefix)
 
-def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
+def inference(model_name, prompt, guidance, steps, n_images=1, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
 
   print(psutil.virtual_memory()) # print memory usage
 
@@ -127,13 +111,13 @@ def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0
 
   try:
     if img is not None:
-      return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
+      return img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator), None
     else:
-      return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator), None
+      return txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator), None
   except Exception as e:
     return None, error_str(e)
 
-def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator):
+def txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator):
 
     print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")
 
@@ -144,9 +128,18 @@ def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, g
         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, safety_checker=lambda images, clip_input: (images, False))
+          pipe = StableDiffusionPipeline.from_pretrained(
+              current_model_path,
+              torch_dtype=torch.float16,
+              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
+              safety_checker=lambda images, clip_input: (images, False)
+              )
         else:
-          pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler)
+          pipe = StableDiffusionPipeline.from_pretrained(
+              current_model_path,
+              torch_dtype=torch.float16,
+              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
+              )
           # pipe = pipe.to("cpu")
           # pipe = current_model.pipe_t2i
 
@@ -158,7 +151,7 @@ def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, g
     result = pipe(
       prompt,
       negative_prompt = neg_prompt,
-      # num_images_per_prompt=n_images,
+      num_images_per_prompt=n_images,
       num_inference_steps = int(steps),
       guidance_scale = guidance,
       width = width,
@@ -167,7 +160,7 @@ def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, g
     
     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, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator):
 
     print(f"{datetime.datetime.now()} img_to_img, model: {model_path}")
 
@@ -178,9 +171,18 @@ def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, w
         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, safety_checker=lambda images, clip_input: (images, False))
+          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+              current_model_path,
+              torch_dtype=torch.float16,
+              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
+              safety_checker=lambda images, clip_input: (images, False)
+              )
         else:
-          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler)
+          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+              current_model_path,
+              torch_dtype=torch.float16,
+              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
+              )
           # pipe = pipe.to("cpu")
           # pipe = current_model.pipe_i2i
         
@@ -194,13 +196,13 @@ def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, w
     result = pipe(
         prompt,
         negative_prompt = neg_prompt,
-        # num_images_per_prompt=n_images,
+        num_images_per_prompt=n_images,
         init_image = img,
         num_inference_steps = int(steps),
         strength = strength,
         guidance_scale = guidance,
-        width = width,
-        height = height,
+        # width = width,
+        # height = height,
         generator = generator)
         
     return replace_nsfw_images(result)
@@ -208,12 +210,12 @@ def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, w
 def replace_nsfw_images(results):
 
     if is_colab:
-      return results.images[0]
+      return results.images
       
     for i in range(len(results.images)):
       if results.nsfw_content_detected[i]:
         results.images[i] = Image.open("nsfw.png")
-    return results.images[0]
+    return results.images
 
 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%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
 """
@@ -231,7 +233,8 @@ with gr.Blocks(css=css) as demo:
               <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>
                Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
               </p>
-              <p>You can also duplicate this space and upgrade to gpu by going to settings: <a style="display:inline-block" href="https://huggingface.co/spaces/anzorq/finetuned_diffusion?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>
+              <p>You can also duplicate this space and upgrade to gpu by going to settings:<br>
+              <a style="display:inline-block" href="https://huggingface.co/spaces/anzorq/finetuned_diffusion?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>
             </div>
         """
     )
@@ -249,10 +252,9 @@ with gr.Blocks(css=css) as demo:
                 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")
+              # image_out = gr.Image(height=512)
+              gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
+
           error_output = gr.Markdown()
 
         with gr.Column(scale=45):
@@ -260,7 +262,7 @@ with gr.Blocks(css=css) as demo:
             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)
+              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)
@@ -282,27 +284,29 @@ with gr.Blocks(css=css) as demo:
       custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
     # 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]
-    outputs = [image_out, error_output]
+    inputs = [model_name, prompt, guidance, steps, n_images, width, height, seed, image, strength, neg_prompt]
+    outputs = [gallery, error_output]
     prompt.submit(inference, inputs=inputs, outputs=outputs)
     generate.click(inference, inputs=inputs, outputs=outputs)
 
     ex = gr.Examples([
-        [models[7].name, "tiny cute and adorable kitten adventurer dressed in a warm overcoat with survival gear on a winters day", 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],
-    ], inputs=[model_name, prompt, guidance, steps, seed], outputs=outputs, fn=inference, cache_examples=False)
+        [models[7].name, "tiny cute and adorable kitten adventurer dressed in a warm overcoat with survival gear on a winters day", 7.5, 25],
+        [models[4].name, "portrait of dwayne johnson", 7.0, 35],
+        [models[5].name, "portrait of a beautiful alyx vance half life", 10, 25],
+        [models[6].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 30],
+        [models[5].name, "fantasy portrait painting, digital art", 4.0, 20],
+    ], inputs=[model_name, prompt, guidance, steps], outputs=outputs, fn=inference, cache_examples=False)
 
     gr.HTML("""
     <div style="border-top: 1px solid #303030;">
       <br>
       <p>Models by <a href="https://huggingface.co/nitrosocke">@nitrosocke</a>, <a href="https://twitter.com/haruu1367">@haruu1367</a>, <a href="https://twitter.com/DGSpitzer">@Helixngc7293</a>, <a href="https://twitter.com/dal_mack">@dal_mack</a>, <a href="https://twitter.com/prompthero">@prompthero</a> and others. ❤️</p>
-      <p>This space uses the <a href="https://github.com/LuChengTHU/dpm-solver">DPM-Solver++</a> sampler by <a href="https://arxiv.org/abs/2206.00927">Cheng Lu, et al.</a>.</p><br>
-<p>Space by: <a href="https://twitter.com/hahahahohohe"><img src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social" alt="Twitter Follow"></a></p><br>
-<a href="https://www.buymeacoffee.com/anzorq" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 45px !important;width: 162px !important;" ></a><br><br>
-<p><img src="https://visitor-badge.glitch.me/badge?page_id=anzorq.finetuned_diffusion" alt="visitors"></p>
+      <p>This space uses the <a href="https://github.com/LuChengTHU/dpm-solver">DPM-Solver++</a> sampler by <a href="https://arxiv.org/abs/2206.00927">Cheng Lu, et al.</a>.</p>
+      <p>Space by:<br>
+      <a href="https://twitter.com/hahahahohohe"><img src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social" alt="Twitter Follow"></a><br>
+      <a href="https://github.com/qunash"><img alt="GitHub followers" src="https://img.shields.io/github/followers/qunash?style=social" alt="Github Follow"></a></p><br><br>
+      <a href="https://www.buymeacoffee.com/anzorq" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 45px !important;width: 162px !important;" ></a><br><br>
+      <p><img src="https://visitor-badge.glitch.me/badge?page_id=anzorq.finetuned_diffusion" alt="visitors"></p>
     </div>
     """)