Small fixes

app.py

@@ -0,0 +1,80 @@
+import gradio as gr
+import jax
+import jax.numpy as jnp
+import numpy as np
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+from PIL import Image
+from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel
+import cv2
+
+def create_key(seed=0):
+    return jax.random.PRNGKey(seed)
+
+controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
+    "JFoz/dog-cat-pose", dtype=jnp.bfloat16
+)
+pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.bfloat16
+)
+
+def infer(prompts, negative_prompts, image):
+    params["controlnet"] = controlnet_params
+    
+    num_samples = 1 #jax.device_count()
+    rng = create_key(0)
+    rng = jax.random.split(rng, jax.device_count())
+    im = image
+    image = Image.fromarray(im)
+    
+    prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
+    negative_prompt_ids = pipe.prepare_text_inputs([negative_prompts] * num_samples)
+    processed_image = pipe.prepare_image_inputs([image] * num_samples)
+    
+    p_params = replicate(params)
+    prompt_ids = shard(prompt_ids)
+    negative_prompt_ids = shard(negative_prompt_ids)
+    processed_image = shard(processed_image)
+    
+    output = pipe(
+        prompt_ids=prompt_ids,
+        image=processed_image,
+        params=p_params,
+        prng_seed=rng,
+        num_inference_steps=50,
+        neg_prompt_ids=negative_prompt_ids,
+        jit=True,
+    ).images
+    
+    output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
+    return output_images
+
+#gr.Interface(infer, inputs=["text", "text", "image"], outputs="gallery").launch()
+
+title = "Animal Pose Control Net"
+description = "This is a demo of Animal Pose ControlNet, which is a model trained on runwayml/stable-diffusion-v1-5 with new type of conditioning."
+
+#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.
+    #""")
+
+theme = gr.themes.Default(primary_hue="green").set(
+    button_primary_background_fill="*primary_200",
+    button_primary_background_fill_hover="*primary_300",
+)
+
+gr.Interface(fn = infer, inputs = ["text", "text", "image"], outputs = "gallery",
+             title = title, description = description, theme='gradio/soft',
+             examples=[["a Labrador crossing the road", "low quality", "pose.jpg"]]
+).launch()
+
+
+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))
+    """)