Create app.py

app.py

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+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from diffusers import UniPCMultistepScheduler
+from diffusers.utils import load_image
+import gradio as gr
+import torch
+
+# Constants
+low_threshold = 100
+high_threshold = 200
+
+# Models
+pose_model = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
+controlnet = ControlNetModel.from_pretrained(
+    "lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16
+)
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "andite/anything-v4.0", controlnet=controlnet, torch_dtype=torch.float16
+)
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+# This command loads the individual model components on GPU on-demand. So, we don't
+# need to explicitly call pipe.to("cuda").
+pipe.enable_model_cpu_offload()
+
+# Generator seed,
+generator = torch.manual_seed(0)
+
+def get_pose(image):
+    return pose_model(image) 
+
+
+def generate_images(image, prompt):
+    pose = get_pose(image)
+    output = pipe(
+        prompt,
+        pose,
+        generator=generator,
+        num_images_per_prompt=3
+    )
+    return output.images
+
+
+gr.Interface(
+    generate_images,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Textbox(
+            label="Enter your prompt",
+            max_lines=1,
+            placeholder="best quality, extremely detailed",
+        ),
+    ],
+    outputs=gr.Gallery().style(grid=[2], height="auto"),
+    title="Generate controlled outputs with ControlNet and Stable Diffusion. ",
+    description="This Space uses pose estimated lines as the additional conditioning.",
+    examples=[["yoga1.jpeg", "best quality, extremely detailed"]],
+    allow_flagging=False,
+).launch(enable_queue=True)
+
+