Create app.py

app.py

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+from PIL import Image
+import cv2
+import gradio as gr
+import numpy as np
+import torch, os, random
+from accelerate import Accelerator
+from transformers import pipeline
+from diffusers.utils import load_image
+from diffusers import DiffusionPipeline, DDPMScheduler
+from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS
+
+accelerator = Accelerator(cpu=True)
+warp_prior = accelerator.prepare(DiffusionPipeline.from_pretrained("warp-ai/wuerstchen-prior", torch_dtype=torch.bfloat16))
+warp_prior.scheduler = DDPMScheduler.from_config(warp_prior.scheduler.config)
+warp_prior = warp_prior.to("cpu")
+warp = accelerator.prepare(DiffusionPipeline.from_pretrained("warp-ai/wuerstchen", torch_dtype=torch.bfloat16))
+warp.scheduler = DDPMScheduler.from_config(warp.scheduler.config)
+warp = warp.to("cpu")
+generator = torch.Generator(device="cpu").manual_seed(random.randint(1, 4876364))
+
+def plex(cook, one, two, three):
+    ###goof = load_image(img).resize((512, 512))
+    negative_prompt = "lowres,text,bad quality,low quality,jpeg artifacts,ugly,bad hands,bad face,blurry,bad eyes,watermark,signature"
+    warp_out = warp_prior(prompt=cook, height=512,width=512,negative_prompt=negative_prompt,guidance_scale=4.0, num_inference_steps=5,generator=generator)
+    primpt = ""
+    imas = warp(**warp_out, height=512, width=512, num_inference_steps=5, prompt=cook,negative_prompt=primpt,guidance_scale=0.0,output_type="pil",generator=generator).images[0]
+    return imas
+
+iface = gr.Interface(fn=plex,inputs=[gr.Textbox(label="prompt"), gr.Slider(label="Text Guide",minimum=0.01,step=0.01,maximum=0.99,value=0.5), gr.Slider(label="Your Image Guide",minimum=0.01,step=0.01,maximum=0.99,value=0.5),gr.Slider(label="Generated Image Guide",minimum=0.01,step=0.01,maximum=0.99,value=0.3)], outputs=gr.Image(), title="Txt2Img Wrstchn Interp", description="Txt2Img Wrstchn Interp")
+iface.queue(max_size=1)
+iface.launch(max_threads=1)