Use bfloat16, 1 image, no grid

app.py

@@ -7,48 +7,30 @@ from flax.training.common_utils import shard
 from PIL import Image
 from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel
 import cv2
-import os
-
-
-
-
-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 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, 150)
     return edges_image
 
 # load control net and stable diffusion v1-5
 controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
-    "jax-diffusers-event/canny-coyo1m", dtype=jnp.float32
+    "jax-diffusers-event/canny-coyo1m", dtype=jnp.bfloat16
 )
 pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", from_pt=True, controlnet=controlnet, dtype=jnp.float32
+    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.bfloat16
 )
 
 def infer(prompts, negative_prompts, image):
-    
-    
     params["controlnet"] = controlnet_params
     
-    num_samples = jax.device_count("gpu")
+    num_samples = 1 #jax.device_count()
     rng = create_key(0)
-    rng = jax.random.split(rng, jax.device_count("gpu"))
+    rng = jax.random.split(rng, jax.device_count())
     im = canny_filter(image)
     canny_image = Image.fromarray(im)
     
@@ -56,12 +38,15 @@ def infer(prompts, negative_prompts, image):
     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=params,
+        params=p_params,
         prng_seed=rng,
         num_inference_steps=50,
         neg_prompt_ids=negative_prompt_ids,
@@ -69,7 +54,6 @@ def infer(prompts, negative_prompts, image):
     ).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)
     return output_images
 
 gr.Interface(infer, inputs=["text", "text", "image"], outputs="gallery").launch()