Create app.py

app.py

@@ -0,0 +1,30 @@
+import torch, os, gc, random
+import gradio as gr
+from PIL import Image
+from diffusers.utils import load_image
+from accelerate import Accelerator
+from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
+accelerator = Accelerator(cpu=True)
+
+pipe = accelerator.prepare(StableDiffusionXLPipeline.from_single_file("https://huggingface.co/lllyasviel/fav_models/fav/realisticStockPhoto_v10.safetensors", torch_dtype=torch.bfloat16, use_safetensors=True, variant=None, safety_checker=False))
+##pipe.scheduler = accelerator.prepare(EulerDiscreteScheduler.from_config(pipe.scheduler.config))
+##pipe.unet.to(memory_format=torch.channels_last)
+pipe.to("cpu")
+apol=[]
+def plex(prompt,neg_prompt,stips,nut):
+    apol=[]
+    if nut == 0:
+        nm = random.randint(1, 2147483616)
+        while nm % 32 != 0:
+            nm = random.randint(1, 2147483616)
+    else:
+        nm=nut
+    generator = torch.Generator(device="cpu").manual_seed(nm)
+    image = pipe(prompt=prompt, negative_prompt=neg_prompt, denoising_end=1.0,num_inference_steps=stips, output_type="pil",generator=generator)
+    for i, imge in enumerate(image["images"]):
+        apol.append(imge)
+    return apol
+
+iface = gr.Interface(fn=plex, inputs=[gr.Textbox(label="prompt"),gr.Textbox(label="negative prompt",value="ugly, blurry, poor quality"), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=10, value=6),gr.Slider(label="manual seed (leave 0 for random)", minimum=0,step=32,maximum=2147483616,value=0)], outputs=gr.Gallery(label="out", columns=1),description="Running on cpu, very slow! by JoPmt.")
+iface.queue(max_size=1,api_open=False)
+iface.launch(max_threads=1)