Update app.py

app.py

@@ -2,50 +2,33 @@ from PIL import Image
 import cv2
 import gradio as gr
 import numpy as np
-import torch
+import torch, os, random
 from accelerate import Accelerator
 from transformers import pipeline
 from diffusers.utils import load_image
 from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline
 
 accelerator = Accelerator(cpu=True)
-
-generator = torch.Generator(device="cpu").manual_seed(4096)
 pope_prior = accelerator.prepare(KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float32))
 pope_prior = pope_prior.to("cpu")
-    
 pope = accelerator.prepare(KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float32))
 pope = pope.to("cpu")
+generator = torch.Generator(device="cpu").manual_seed(random.randint(1, 4876364))
 
 def plex(img, cook, one, two, three):
     goof = load_image(img).resize((512, 512))
-    # We pass the prompt and negative prompt through the prior to generate image embeddings
     prompt = cook
     negative_prior_prompt = "lowres,text,bad quality,low quality,jpeg artifacts,ugly,bad hands,bad face,blurry,bad eyes,watermark,signature"
     img_emb = pope_prior(prompt=prompt, guidance_scale=0.85, num_inference_steps=5, generator=generator)
     negative_emb = pope_prior(prompt=negative_prior_prompt, guidance_scale=1, num_inference_steps=5, generator=generator)
-
-    # run text2img pipeline
-    imags = pope(
-    image_embeds=img_emb.image_embeds,
-    negative_image_embeds=negative_emb.image_embeds,
-    num_inference_steps=10,
-    generator=generator,
-    height=512,
-    width=512,
-    ).images[0]
-
-    ## return imags
+    imags = pope(image_embeds=img_emb.image_embeds,negative_image_embeds=negative_emb.image_embeds,num_inference_steps=10,generator=generator,height=512,width=512,).images[0]
     images_texts = [cook, goof, imags]
-
-    # specify the weights for each condition in images_texts
     weights = [one, two, three]
-
-    # We can leave the prompt empty
     primpt = ""
     prior_out = pope_prior.interpolate(images_texts, weights, num_inference_steps=5)
     imas = pope(**prior_out, height=512, width=512, num_inference_steps=25).images[0]
     return imas
 
 iface = gr.Interface(fn=plex,inputs=[gr.Image(label="drop", type="pil"), gr.Textbox(label="prompt"), gr.Slider(label="Text Guide",minimum=0.01,step=0.01,maximum=1,value=0.5), gr.Slider(label="Your Image Guide",minimum=0.01,step=0.01,maximum=1,value=0.5),gr.Slider(label="Generated Image Guide",minimum=0.01,step=0.01,maximum=1,value=0.3)], outputs=gr.Image(), title="Ksky22 Cntrl Gdd Interp", description="ksky22 Cntrl Gdd Interp")
-iface.launch()
+iface.queue(max_size=1)
+iface.launch(max_threads=1)