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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 diffusers.utils import load_image
from PIL import Image
from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel
def image_grid(imgs, rows, cols):
w, h = imgs[0].size
grid = Image.new("RGB", size=(cols * w, rows * h))
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
def create_key(seed=0):
return jax.random.PRNGKey(seed)
rng = create_key(0)
def canny_filter(image):
## TODO: Implement canny filter here
return canny_image
def infer(prompts, negative_prompts, image):
# load control net and stable diffusion v1-5
controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
"jax-diffusers-event/canny-coyo1m", from_pt=True, dtype=jnp.float32
)
pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, from_pt=True, dtype=jnp.float32
)
params["controlnet"] = controlnet_params
num_samples = jax.device_count()
rng = jax.random.split(rng, jax.device_count())
canny_image = canny_filter(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([canny_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:])))
output_images = image_grid(output_images, num_samples // 4, 4)
return output_images
gr.Interface(pipeline, inputs=["text", "text", "image"], outputs="gallery").launch()
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