Update app.py

app.py

@@ -8,8 +8,6 @@ os.system('pip install -e ./diffusion')
 os.system('pip install lpips')
 os.system("curl -OL 'https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt'")
 
-
-
 import io
 import math
 import sys
@@ -221,6 +219,6 @@ def inference(text, init_image, skip_timesteps, clip_guidance_scale, tv_scale, r
     
 title = "CLIP Guided Diffusion Model"
 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 YuanFu Yang (https://github.com/Yfyangd/diffusion). 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. </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=10, label="skip_timesteps"), gr.inputs.Slider(minimum=0, maximum=3000, step=1, default=600, label="clip guidance scale (Controls how much the image should look like the prompt)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, label="tv_scale (Controls the smoothness of the final output)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, 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)"), gr.inputs.Number(default=0, label="Seed"), gr.inputs.Image(type="file", label='image prompt (optional)', optional=True), gr.inputs.Slider(minimum=50, maximum=500, step=1, default=50, label="timestep respacing"),gr.inputs.Slider(minimum=1, maximum=64, step=1, default=32, label="cutn")], outputs=["image","video"], title=title, description=description, article=article, examples=[["little girl with cat on bed", "feifei.jpg", 0, 1000, 150, 50, 0, 0, "feifei.jpg", 90, 32]])
+#article = "<p style='text-align: center'> By YuanFu Yang (https://github.com/Yfyangd/diffusion). 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. </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=10, label="skip_timesteps"), gr.inputs.Slider(minimum=0, maximum=3000, step=1, default=600, label="clip guidance scale (Controls how much the image should look like the prompt)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, label="tv_scale (Controls the smoothness of the final output)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, 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)"), gr.inputs.Number(default=0, label="Seed"), gr.inputs.Image(type="file", label='image prompt (optional)', optional=True), gr.inputs.Slider(minimum=50, maximum=500, step=1, default=50, label="timestep respacing"),gr.inputs.Slider(minimum=1, maximum=64, step=1, default=32, label="cutn")], outputs=["image","video"], title=title, description=description, examples=[["little girl with cat on bed", "feifei.jpg", 0, 1000, 150, 50, 0, 0, "feifei.jpg", 90, 32]])
 iface.launch()