merve HF staff pcuenq HF staff commited on
Commit
7a74ef3
1 Parent(s): 6ff2199

Use bfloat16, 1 image, no grid (#1)

Browse files

- Use bfloat16, 1 image, no grid (75ba64df6af4017799c72e44f57a7e60ac010e68)


Co-authored-by: Pedro Cuenca <pcuenq@users.noreply.huggingface.co>

Files changed (1) hide show
  1. app.py +9 -25
app.py CHANGED
@@ -7,48 +7,30 @@ 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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- import os
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-
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-
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-
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-
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- def image_grid(imgs, rows, cols):
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- w, h = imgs[0].size
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- grid = Image.new("RGB", size=(cols * w, rows * h))
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- for i, img in enumerate(imgs):
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- grid.paste(img, box=(i % cols * w, i // cols * h))
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- return grid
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-
22
 
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  def create_key(seed=0):
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  return jax.random.PRNGKey(seed)
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-
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-
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-
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  def canny_filter(image):
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  gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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-
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  blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)
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  edges_image = cv2.Canny(blurred_image, 50, 150)
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  return edges_image
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  # load control net and stable diffusion v1-5
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  controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
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- "jax-diffusers-event/canny-coyo1m", dtype=jnp.float32
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  )
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  pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
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- "runwayml/stable-diffusion-v1-5", from_pt=True, controlnet=controlnet, dtype=jnp.float32
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  )
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  def infer(prompts, negative_prompts, image):
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-
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-
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  params["controlnet"] = controlnet_params
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- num_samples = jax.device_count("gpu")
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  rng = create_key(0)
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- rng = jax.random.split(rng, jax.device_count("gpu"))
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  im = canny_filter(image)
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  canny_image = Image.fromarray(im)
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@@ -56,12 +38,15 @@ def infer(prompts, negative_prompts, image):
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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([canny_image] * num_samples)
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-
 
 
 
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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=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,
@@ -69,7 +54,6 @@ def infer(prompts, negative_prompts, image):
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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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- output_images = image_grid(output_images, num_samples // 4, 4)
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  return output_images
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  gr.Interface(infer, inputs=["text", "text", "image"], outputs="gallery").launch()
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  from PIL import Image
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  from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel
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  import cv2
 
 
 
 
 
 
 
 
 
 
 
 
10
 
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  def create_key(seed=0):
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  return jax.random.PRNGKey(seed)
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  def canny_filter(image):
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  gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
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  blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)
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  edges_image = cv2.Canny(blurred_image, 50, 150)
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  return edges_image
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  # load control net and stable diffusion v1-5
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  controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
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+ "jax-diffusers-event/canny-coyo1m", 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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  )
27
 
28
  def infer(prompts, negative_prompts, image):
 
 
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  params["controlnet"] = controlnet_params
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+ num_samples = 1 #jax.device_count()
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  rng = create_key(0)
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+ rng = jax.random.split(rng, jax.device_count())
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  im = canny_filter(image)
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  canny_image = Image.fromarray(im)
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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([canny_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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46
  output = pipe(
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  prompt_ids=prompt_ids,
48
  image=processed_image,
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+ params=p_params,
50
  prng_seed=rng,
51
  num_inference_steps=50,
52
  neg_prompt_ids=negative_prompt_ids,
54
  ).images
55
 
56
  output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
 
57
  return output_images
58
 
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  gr.Interface(infer, inputs=["text", "text", "image"], outputs="gallery").launch()