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import gradio as gr |
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import jax |
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import jax.numpy as jnp |
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import numpy as np |
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from flax.jax_utils import replicate |
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from flax.training.common_utils import shard |
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from PIL import Image |
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from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel |
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import cv2 |
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with open("test.html") as f: |
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lines = f.readlines() |
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def create_key(seed=0): |
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return jax.random.PRNGKey(seed) |
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def wandb_report(url): |
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iframe = f'<iframe src ={url} style="border:none;height:1024px;width:100%"/frame>' |
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return gr.HTML(iframe) |
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report_url = 'https://wandb.ai/john-fozard/dog-cat-pose/runs/kmwcvae5' |
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control_img = 'myimage.jpg' |
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controlnet, controlnet_params = FlaxControlNetModel.from_pretrained( |
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"JFoz/dog-cat-pose", dtype=jnp.bfloat16 |
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) |
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pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained( |
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"runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.bfloat16 |
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) |
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def infer(prompts, negative_prompts, image): |
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params["controlnet"] = controlnet_params |
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num_samples = 1 |
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rng = create_key(0) |
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rng = jax.random.split(rng, jax.device_count()) |
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image = Image.fromarray(image) |
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prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples) |
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negative_prompt_ids = pipe.prepare_text_inputs([negative_prompts] * num_samples) |
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processed_image = pipe.prepare_image_inputs([image] * num_samples) |
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p_params = replicate(params) |
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prompt_ids = shard(prompt_ids) |
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negative_prompt_ids = shard(negative_prompt_ids) |
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processed_image = shard(processed_image) |
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output = pipe( |
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prompt_ids=prompt_ids, |
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image=processed_image, |
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params=p_params, |
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prng_seed=rng, |
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num_inference_steps=50, |
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neg_prompt_ids=negative_prompt_ids, |
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jit=True, |
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).images |
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output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:]))) |
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return output_images |
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with gr.Blocks(theme='kfahn/AnimalPose') as demo: |
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gr.Markdown( |
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""" |
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# Animal Pose Control Net |
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## This is a demo of Animal Pose ControlNet, which is a model trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. |
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[Dataset](https://huggingface.co/datasets/JFoz/dog-poses-controlnet-dataset) |
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[Diffusers model](https://huggingface.co/JFoz/dog-pose) |
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[Github](https://github.com/fi4cr/animalpose) |
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[Training Report](https://wandb.ai/john-fozard/AP10K-pose/runs/wn89ezaw) |
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""") |
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with gr.Row(): |
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with gr.Column(): |
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prompts = gr.Textbox(label="Prompt", placeholder="black cocker spaniel sitting on a lawn, best quality) |
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negative_prompts = gr.Textbox(label="Negative Prompt", value="lowres, bad anatomy, missing ears, missing paws") |
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conditioning_image = gr.Image(label="Conditioning Image") |
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run_btn = gr.Button("Run") |
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with gr.Column(): |
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#keypoint_tool = addp5sketch(sketch_url) |
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keypoint_tool = gr.HTML(lines) |
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run_btn.click(fn=infer, inputs = ["prompts", "negative_prompts", "conditioning_image"], outputs = "gallery") |
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#gr.Interface(fn=infer, inputs = ["text", "text", "image"], outputs = "gallery", |
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#examples=[["a Labrador crossing the road", "low quality", "myimage.jpg"]]) |
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#with gr.Row(): |
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# report = wandb_report(report_url) |
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demo.launch() |
