import gradio as gr import jax.numpy as jnp import jax 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(prompt, 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. """) 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"], ] ) gr.Interface(fn = infer, inputs = ["text", "text", "image"], outputs = "image", title = title, description = description, examples = gr.examples, theme='gradio/soft').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)) """)