Update app.py

app.py

@@ -1,47 +1,27 @@
-import gradio as gr
 from diffusers import LDMTextToImagePipeline
-import torch
-import numpy as np
-import PIL
-import cv2
-
+import gradio as gr
 import PIL.Image
+import numpy as np
 import random
+import torch
 
-print('\nDEBUG:   Version: 3')
-
-#pipeline = LDMTextToImagePipeline.from_pretrained("fusing/latent-diffusion-text2im-large")
 ldm_pipeline = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
 
-FOOTER = '<img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=milyiyo.testing-diffusers" />'
-
-def greet(name):
-    return "Hello " + name + "!!"
-    
-def genimage(prompt, iterations):
-  image = pipeline([prompt], generator=generator, eta=0.3, guidance_scale=6.0, num_inference_steps=iterations)["sample"]
-  return image[0]
-
-  #image_processed = image.cpu().permute(0, 2, 3, 1)
-  #image_processed = image_processed  * 255.
-  #image_processed = image_processed.numpy().astype(np.uint8)
-  #image_pil = PIL.Image.fromarray(image_processed[0])
-
-  # save image
-  #file_name = "test.png"
-  #image_pil.save(file_name)
-  #img = cv2.imread(file_name)
-  ##cv2_imshow(img)
-  #return img
-  
-def predict(prompt, steps=100):
-  torch.cuda.empty_cache()
-  generator = torch.manual_seed(42)
-  images = ldm_pipeline([prompt], generator=generator, num_inference_steps=steps, eta=0.3, guidance_scale=6.0)["sample"]
-  return images[0]
-
-iface = gr.Interface(
-  fn=predict, 
-  inputs=["text", "number"], 
-  outputs=gr.Image(shape=[256,256], type="pil", elem_id="output_image"))
-iface.launch()
+def predict(prompt, steps=100, seed=42, guidance_scale=6.0):
+    torch.cuda.empty_cache()
+    generator = torch.manual_seed(seed)
+    images = ldm_pipeline([prompt], generator=generator, num_inference_steps=steps, eta=0.3, guidance_scale=guidance_scale)["sample"]
+    return images[0]
+
+random_seed = random.randint(0, 2147483647)
+gr.Interface(
+    predict,
+    inputs=[
+        gr.inputs.Textbox(label='Prompt', default='a chalk pastel drawing of a llama wearing a wizard hat'),
+        gr.inputs.Slider(1, 100, label='Inference Steps', default=50, step=1),
+        gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),
+        gr.inputs.Slider(1.0, 20.0, label='Guidance Scale - how much the prompt will influence the results', default=6.0, step=0.1),
+    ],
+    outputs=gr.Image(shape=[256,256], type="pil", elem_id="output_image"),
+    css="#output_image{width: 256px}",
+).launch()