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| import gradio as gr | |
| from datasets import load_dataset | |
| 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 diffusers.utils.testing_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) | |
| def infer(prompt, negative_prompt, image): | |
| rng = create_key(0) | |
| # canny_image = load_image( | |
| # "https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/blog_post_cell_10_output_0.jpeg" | |
| # ) | |
| canny_image = load_image(image) | |
| #prompts = "a living room fan" | |
| prompts = prompt | |
| negative_prompts = negative_prompt | |
| controlnet, controlnet_params = FlaxControlNetModel.from_pretrained( | |
| "tsungtao/controlnet-mlsd-202305011046", from_flax=True, dtype=jnp.float32 | |
| ) | |
| pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.float32 | |
| ) | |
| params["controlnet"] = controlnet_params | |
| num_samples = jax.device_count() | |
| rng = jax.random.split(rng, jax.device_count()) | |
| 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) | |
| #output_images.save("tao/image.png") | |
| #dataset = load_dataset('imagefolder', data_dir='tao') | |
| #dataset.push_to_hub('tsungtao/tmp') | |
| return output_images | |
| #infer('','','') | |
| def infer2(prompt, negative_prompt, image): | |
| output_image = infer(prompt, negative_prompt, image) | |
| #output_image = "https://datasets-server.huggingface.co/assets/tsungtao/tmp/--/tsungtao--tmp/train/0/image/image.jpg" | |
| return output_image | |
| title = "ControlNet on MLSD Filter" | |
| description = "This is a demo on ControlNet based on mlsd filter." | |
| #examples = [["living room with TV", "fan", "https://datasets-server.huggingface.co/assets/tsungtao/diffusers-testing/--/tsungtao--diffusers-testing/train/0/images/image.jpg"]] | |
| interface = gr.Interface(fn = infer2, inputs = ["text", "text", "text"], outputs = "image",title = title, description = description, theme='gradio/soft') | |
| interface.launch(enable_queue=True) |