Create app.py

app.py

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+import gradio as gr
+import jax
+import numpy as np
+import jax.numpy as jnp
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+from PIL import Image
+from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel
+import cv2
+
+# load control net and stable diffusion v1-5
+controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
+    "Nahrawy/controlnet-VIDIT-FAID", dtype=jnp.bfloat16, revision="615ba4a457b95a0eba813bcc8caf842c03a4f7bd"
+)
+pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.bfloat16
+)
+
+def create_key(seed=0):
+    return jax.random.PRNGKey(seed)
+
+def process_mask(image):
+    mask = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    mask = cv2.resize(mask,(512,512))
+    return mask
+
+
+
+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 = process_mask(image)
+    mask = 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([mask] * num_samples)
+    
+    p_params = replicate(params)
+    prompt_ids = shard(prompt_ids)
+    negative_prompt_ids = shard(negative_prompt_ids)
+    processed_image = shard(processed_image)
+    print(processed_image[0].shape)
+    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
+
+e_images = ['examples/0.png',
+          'examples/1.png'
+          'examples/2.png']
+e_prompts = ['a dog in the middle of the road, shadow on the ground,light direction north-east',
+           'a skyscraper in the middle of an intersection, shadow on the ground, light direction east',
+           'a red rural house, light temperature 5500, shadow on the ground, light direction south-west']
+e_negative_prompts = ['monochromatic, unrealistic, bad looking, full of glitches'*3]
+examples = []
+for image, prompt, negative_prompt in zip(e_images, e_prompts, e_negative_prompts):
+  examples.append([prompt, negative_prompt, image])
+
+with gr.Blocks() as demo:
+    gr.Markdown(title)
+    prompts = gr.Textbox(label='prompts')
+    negative_prompts = gr.Textbox(label='negative_prompts')
+    with gr.Row():
+      with gr.Column():
+        in_image = gr.Image(label="Depth Map Conditioning")
+      with gr.Column():
+        out_image = gr.Image(label="Generated Image")
+    with gr.Row():
+        btn = gr.Button("Run")
+        gr.Examples(examples=examples,
+                   inputs=[prompts,negative_prompts, in_image],
+                   outputs=out_image,
+                   cache_examples=True)
+    btn.click(fn=infer, inputs=[prompts,negative_prompts, in_image] , outputs=out_image)
+    
+demo.launch()