Update app.py

app.py

@@ -6,21 +6,26 @@ from transformers import pipeline
 from diffusers.utils import load_image
 from diffusers import DiffusionPipeline, DDPMScheduler
 
+from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline
+from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS, DDPMWuerstchenScheduler
+
+
+
 accelerator = Accelerator(cpu=True)
-warp_prior = accelerator.prepare(DiffusionPipeline.from_pretrained("warp-ai/wuerstchen-prior", torch_dtype=torch.bfloat16, use_safetensors=True, safety_cheker=None))
-warp_prior.scheduler = DDPMScheduler.from_config(warp_prior.scheduler.config)
+warp_prior = accelerator.prepare(WuerstchenPriorPipeline.from_pretrained("warp-ai/wuerstchen-prior", torch_dtype=torch.bfloat16, use_safetensors=True, safety_cheker=None))
+warp_prior.scheduler = DDPMWuerstchenScheduler.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, use_safetensors=True, safety_checker=None))
-warp.scheduler = DDPMScheduler.from_config(warp.scheduler.config)
+warp = accelerator.prepare(WuerstchenDecoderPipeline.from_pretrained("warp-ai/wuerstchen", torch_dtype=torch.bfloat16, use_safetensors=True, safety_checker=None))
+warp.scheduler = DDPMWuerstchenScheduler.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):
     ###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,output_type="pt")
+    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]
+    imas = warp(warp_out.image_embbedings, 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="Inference steps",minimum=1,step=1,maximum=10,value=5), gr.Slider(label="Prior guidance scale",minimum=4.1,step=0.1,maximum=19.9,value=4.1)], outputs=gr.Image(), title="Txt2Img Wrstchn SD", description="Txt2Img Wrstchn SD")