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app.py

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+from PIL import Image
+import gradio as gr
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
+import torch
+torch.backends.cuda.matmul.allow_tf32 = True
+
+controlnet = ControlNetModel.from_pretrained("JFoz/dog-cat-pose", torch_dtype=torch.float16, use_safetensors=True)
+
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    controlnet=controlnet,
+    torch_dtype=torch.float16,
+    safety_checker=None,
+)
+
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+pipe.enable_xformers_memory_efficient_attention()
+pipe.enable_model_cpu_offload()
+pipe.enable_attention_slicing()
+
+def infer(
+        prompt,
+        negative_prompt,
+        conditioning_image,
+        num_inference_steps=30,
+        size=768,
+        guidance_scale=7.0,
+        seed=1234,
+):
+
+    conditioning_image_raw = Image.fromarray(conditioning_image)
+    #conditioning_image = conditioning_image_raw.convert('L')
+
+    g_cpu = torch.Generator()
+
+    if seed == -1:
+        generator = g_cpu.manual_seed(g_cpu.seed())
+    else:
+        generator = g_cpu.manual_seed(seed)
+
+    output_image = pipe(
+        prompt,
+        conditioning_image,
+        height=size,
+        width=size,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        controlnet_conditioning_scale=1.0,
+    ).images[0]
+
+    #del conditioning_image, conditioning_image_raw
+    #gc.collect()
+
+    return output_image
+
+with gr.Blocks(theme=gr.themes.Default(font=[gr.themes.GoogleFont("Inconsolata"), "Arial", "sans-serif"])) as demo:
+    gr.Markdown(
+        """
+    # Animal Pose Control Net
+    # This is a demo of Animal Pose Control Net, which is a model trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Prompt",
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+            )
+            conditioning_image = gr.Image(
+                label="Conditioning Image",
+            )
+            with gr.Accordion('Advanced options', open=False):
+                with gr.Row():
+                    num_inference_steps = gr.Slider(
+                        10, 40, 20,
+                        step=1,
+                        label="Steps",
+                    )
+                    size = gr.Slider(
+                        256, 768, 512,
+                        step=128,
+                        label="Size",
+                    )
+                with gr.Row():
+                    guidance_scale = gr.Slider(
+                        label='Guidance Scale',
+                        minimum=0.1,
+                        maximum=30.0,
+                        value=7.0,
+                        step=0.1
+                    )
+                    seed = gr.Slider(
+                        label='Seed',
+                        value=-1,
+                        minimum=-1,
+                        maximum=2147483647,
+                        step=1,
+                        # randomize=True
+                    )
+            submit_btn = gr.Button(
+                value="Submit",
+                variant="primary"
+            )
+        with gr.Column(min_width=300):
+            output = gr.Image(
+                label="Result",
+            )
+
+    submit_btn.click(
+        fn=infer,
+        inputs=[
+            prompt, negative_prompt, conditioning_image, num_inference_steps, size, guidance_scale, seed
+            #prompt, size, seed
+        ],
+        outputs=output
+    )
+    gr.Examples(
+        examples=[
+            #["a tortoiseshell cat is sitting on a cushion"],
+            #["a yellow dog standing on a lawn"],
+            ["a tortoiseshell cat is sitting on a cushion",  "https://huggingface.co/JFoz/dog-cat-pose/blob/main/images_0.png"],
+            ["a yellow dog standing on a lawn", "https://huggingface.co/JFoz/dog-cat-pose/blob/main/images_1.png"],
+        ],
+        inputs=[
+            #prompt, negative_prompt, conditioning_image
+            prompt
+        ],
+        outputs=output,
+        fn=infer,
+        cache_examples=True,
+    )
+    gr.Markdown(
+        """
+    * [Dataset](https://huggingface.co/datasets/JFoz/dog-poses-controlnet-dataset)
+    * [Diffusers model](), [Web UI model](https://huggingface.co/JFoz/dog-pose)
+    * [Training Report](https://wandb.ai/john-fozard/dog-cat-pose/runs/kmwcvae5))
+    """)
+
+#gr.Interface(infer, inputs=["text"], outputs=[output], title=title, description=description, examples=examples).queue().launch()
+demo.launch()