Update app.py

app.py

@@ -114,14 +114,14 @@ normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
                                  std=[0.26862954, 0.26130258, 0.27577711])
 lpips_model = lpips.LPIPS(net='vgg').to(device)
     
-def inference(text, init_image, skip_timesteps, clip_guidance_scale):
+def inference(text, init_image, skip_timesteps, clip_guidance_scale, tv_scale, range_scale, init_scale, seed):
     all_frames = []
     prompts = [text]
     image_prompts = []
     batch_size = 1
     clip_guidance_scale = clip_guidance_scale  # Controls how much the image should look like the prompt.
-    tv_scale = 150              # Controls the smoothness of the final output.
-    range_scale = 50            # Controls how far out of range RGB values are allowed to be.
+    tv_scale = tv_scale             # Controls the smoothness of the final output.
+    range_scale = range_scale            # Controls how far out of range RGB values are allowed to be.
     cutn = 16
     n_batches = 1
     if init_image:
@@ -130,8 +130,8 @@ def inference(text, init_image, skip_timesteps, clip_guidance_scale):
         init_image = None   # This can be an URL or Colab local path and must be in quotes.
     skip_timesteps = skip_timesteps  # This needs to be between approx. 200 and 500 when using an init image.
                         # Higher values make the output look more like the init.
-    init_scale = 0      # This enhances the effect of the init image, a good value is 1000.
-    seed = 0
+    init_scale = init_scale      # This enhances the effect of the init image, a good value is 1000.
+    seed = seed
    
     if seed is not None:
         torch.manual_seed(seed)
@@ -217,6 +217,6 @@ def inference(text, init_image, skip_timesteps, clip_guidance_scale):
 title = "CLIP Guided Diffusion HQ"
 description = "Gradio demo for CLIP Guided Diffusion. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
 article = "<p style='text-align: center'> By Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings). It uses OpenAI's 256x256 unconditional ImageNet diffusion model (https://github.com/openai/guided-diffusion) together with CLIP (https://github.com/openai/CLIP) to connect text prompts with images. | <a href='https://colab.research.google.com/drive/12a_Wrfi2_gwwAuN3VvMTwVMz9TfqctNj' target='_blank'>Colab</a></p>"
-iface = gr.Interface(inference, inputs=["text",gr.inputs.Image(type="file", label='initial image (optional)', optional=True),gr.inputs.Slider(minimum=0, maximum=45, step=1, default=0, label="skip_timesteps"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=700, label="clip guidance scale (Controls how much the image should look like the prompt.)")], outputs=["image","video"], title=title, description=description, article=article, examples=[["coral reef city by artistation artists"]],
+iface = gr.Interface(inference, inputs=["text",gr.inputs.Image(type="file", label='initial image (optional)', optional=True),gr.inputs.Slider(minimum=0, maximum=45, step=1, default=0, label="skip_timesteps"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=700, label="clip guidance scale (Controls how much the image should look like the prompt)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=150, label="tv_scale (Controls the smoothness of the final output)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=50, label="range_scale (Controls how far out of range RGB values are allowed to be)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, label="init_scale (This enhances the effect of the init image, a good value is 1000)"), gr.inputs.Number(default=0, label="Seed") ], outputs=["image","video"], title=title, description=description, article=article, examples=[["coral reef city by artistation artists", None, 0, 1000, 150, 50, 0, 0]],
     enable_queue=True)
 iface.launch()