Update app.py

app.py

@@ -9,60 +9,6 @@ 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)
-
-canny_image = load_image(
-    "https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/blog_post_cell_10_output_0.jpeg"
-)
-
-prompts = "a living room with tv, sea, window"
-negative_prompts = "fan "
-
-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")
 
 
 #gr.Interface.load("models/tsungtao/controlnet-mlsd-202305011046").launch()