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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())
image = Image.fromarray(image)
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"], outputs = "image",
# title = title, description = description, theme='gradio/soft').launch()
control_image = "https://huggingface.co/spaces/kfahn/Animal_Pose_Control_Net/blob/main/image_control.png"
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", control_image]]
).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))
""")
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