Create app.py

app.py

@@ -0,0 +1,34 @@
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from diffusers import DiffusionPipeline
+import torch
+from controlnet_aux import OpenposeDetector
+from diffusers import UniPCMultistepScheduler
+from PIL import Image
+import requests
+from io import BytesIO
+import gradio as gr
+
+model = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
+controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-openpose", torch_dtype=torch.float16)
+model_id = "Jccqqqqq/Personajes"
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    model_id,
+    controlnet=controlnet,
+    torch_dtype=torch.float16,
+)
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+pipe.enable_model_cpu_offload()
+generators = []
+def generate(prompt, inputimage, seed, steps, negative):
+  generator = torch.Generator(device="cpu").manual_seed(seed)
+  response = requests.get(inputimage)
+  img = Image.open(BytesIO(response.content))
+  img = model(img)
+  img.save("/content/test.png")
+  image = pipe(prompt,img,negative_prompt=negative,generator=generator,num_inference_steps=steps).images[0]
+  return image
+demo = gr.Interface(fn=generate, inputs=[gr.Textbox(placeholder="Prompt"),gr.Textbox(placeholder="Image"), gr.Number(precision=0, label="seed"), gr.Number(precision=0, label="steps", value=20),gr.Textbox(placeholder="Negative Prompt",value="monochrome, lowres, bad anatomy, worst quality, low quality")], outputs=gr.Image(type="pil"), title="test")
+
+if __name__ == "__main__":
+    demo.queue()
+    demo.launch(inline=False, debug=True)