Create app.py

app.py

@@ -0,0 +1,33 @@
+from diffusers import AutoPipelineForText2Image, DiffusionPipeline, UniPCMultistepScheduler, EulerAncestralDiscreteScheduler
+import torch
+import gradio as gr
+from PIL import Image
+import os, random
+import PIL.Image
+from transformers import pipeline
+from diffusers.utils import load_image
+from accelerate import Accelerator
+
+accelerator = Accelerator()
+apol=[]
+pipe = accelerator.prepare(DiffusionPipeline.from_single_file("https://huggingface.co/lllyasviel/fav_models/fav/DreamShaper_8_pruned.safetensors",torch_dtype=torch.float32, variant=None, use_safetensors=True, safety_checker=None))
+##pipe.scheduler = accelerator.prepare(EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config))
+pipe.unet.to(memory_format=torch.channels_last)
+pipe = accelerator.prepare(pipe.to("cpu"))
+def plex(prompt,neg_prompt,stips,scaly,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, generator=generator, num_inference_steps=stips, guidance_scale=scaly)
+    for a, imze in enumerate(image["images"]):
+        apol.append(imze)
+    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="num inference steps",minimum=1,step=1,maximum=20,value=15), gr.Slider(label="guidance_scale",minimum=1,step=1,maximum=10,value=7),gr.Slider(label="manual seed (leave 0 for random)",minimum=0,step=32,maximum=2147483616,value=0)],outputs=gr.Gallery(label="Generated Output Image", columns=1), title="Txt2Img_DrmDrp_v1_SD",description="Running on cpu, very slow!")
+iface.queue(max_size=1,api_open=False)
+iface.launch(max_threads=1)