Create app.py

app.py

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+from diffusers import KandinskyV22CombinedPipeline
+import gradio as gr
+from accelerate import Accelerator
+import torch
+from transformers import pipeline
+from PIL import Image
+
+accelerator = Accelerator()
+pipe = accelerator.prepare(KandinskyV22CombinedPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float32))
+pipe.unet.to(memory_format=torch.channels_last)
+pipe.to("cpu")
+apol=[]
+def plex(prompt,negative_prompt,stips,stups,uno):
+    image = pipe([prompt]*2, [negative_prompt]*2,num_inference_steps=stips, num_prior_inference_steps=stups,prior_guidance_scale=uno, height=512, width=512)
+    for i, igs in enumerate(image["images"]):
+        apol.append(igs)
+    return apol
+
+iface = gr.Interface(fn=plex,inputs=[gr.Textbox(label="prompt"),gr.Textbox(label="negative prompt", value="low quality, bad quality"), gr.Slider(label="inference_steps",minimum=1,step=1,maximum=10,value=5),gr.Slider(label="inference_steps_prior",minimum=1,step=1,maximum=10,value=5),gr.Slider(label="prior_guidance_scale",minimum=0.1,step=0.1,maximum=1.0,value=0.5)],outputs=gr.Gallery(columns=2), title="Txt2Img_KndskyV22_Cmbnd by JoPmt", description="Running on CPU, very slow!")
+iface.queue(max_size=1)
+iface.launch(max_threads=1)