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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 | |
| def create_key(seed=0): | |
| return jax.random.PRNGKey(seed) | |
| def canny_filter(image): | |
| gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0) | |
| edges_image = cv2.Canny(blurred_image, 50, 200) | |
| return edges_image | |
| # load control net and stable diffusion v1-5 | |
| controlnet, controlnet_params = FlaxControlNetModel.from_pretrained( | |
| "tsungtao/controlnet-mlsd-202305011046", from_flax=True, dtype=jnp.bfloat16 | |
| ) | |
| #controlnet.save_pretrained("tsungtao/controlnet-mlsd-202305011046",params=controlnet_params) | |
| 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 = canny_filter(image) | |
| canny_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([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:]))) | |
| return output_images | |
| title = "ControlNet MLSD" | |
| description = "This is a demo on ControlNet MLSD." | |
| examples = [["living room with TV", "fan", "image_01.jpg"], | |
| ["a living room with hardwood floors and a flat screen tv", "sea", "image_02.jpg"], | |
| ["a living room with a fireplace and a view of the ocean", "pendant", "image_03.jpg"] | |
| ] | |
| with gr.Blocks() as demo: | |
| gr.Interface(infer, inputs=["text", "text", "image"], outputs="gallery", title = title, description = description, examples = examples, theme='gradio/soft') | |
| gr.Markdown( | |
| """ | |
| * * * | |
| * [Dataset](https://huggingface.co/datasets/tsungtao/diffusers-testing) | |
| * [Diffusers model](https://huggingface.co/runwayml/stable-diffusion-v1-5) | |
| * [Training Report](https://wandb.ai/tsungtao0311/controlnet-mlsd-202305011046/runs/ezfn6bkz?workspace=user-tsungtao0311) | |
| """) | |
| # with gr.Accordion("Open for More!"): | |
| # gr.Markdown("Team:https://huggingface.co/ellljoy, https://huggingface.co/zenkig, https://huggingface.co/aze555, https://huggingface.co/tsungtao, https://huggingface.co/Mayyu") | |
| gr.Markdown("* * *") | |
| # gr.Markdown(""" <img src='https://huggingface.co/spaces/tsungtao/tsungtao-controlnet-mlsd-202305011046/blob/main/test.png' /> """) | |
| demo.launch() |