




'''simple docstring'''


import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard





@slow
@require_flax
class    __lowercase						(  unittest.TestCase ):







   def 		UpperCamelCase__			(	self      )				->    Any:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()







   def 		UpperCamelCase__			(	self      )				->    Any:
     __a	,       __a			=					FlaxControlNetModel.from_pretrained(
         'lllyasviel/sd-controlnet-canny'      ,							from_pt=UpperCamelCase      ,							dtype=jnp.bfloataa      )
     __a	,       __a			=					FlaxStableDiffusionControlNetPipeline.from_pretrained(
         'runwayml/stable-diffusion-v1-5'      ,							controlnet=UpperCamelCase      ,							from_pt=UpperCamelCase      ,							dtype=jnp.bfloataa      )
     __a			=					controlnet_params

     __a			=					'bird'
     __a			=					jax.device_count()
     __a			=					pipe.prepare_text_inputs([prompts] * num_samples      )

     __a			=					load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'      )
     __a			=					pipe.prepare_image_inputs([canny_image] * num_samples      )

     __a			=					jax.random.PRNGKey(0      )
     __a			=					jax.random.split(UpperCamelCase      ,							jax.device_count()      )

     __a			=					replicate(UpperCamelCase      )
     __a			=					shard(UpperCamelCase      )
     __a			=					shard(UpperCamelCase      )

     __a			=					pipe(
         prompt_ids=UpperCamelCase      ,							image=UpperCamelCase      ,							params=UpperCamelCase      ,							prng_seed=UpperCamelCase      ,							num_inference_steps=50      ,							jit=UpperCamelCase      ,							).images
     assert images.shape == (jax.device_count(), 1, 768, 512, 3)

     __a			=					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
     __a			=					images[0, 253:256, 253:256, -1]

     __a			=					jnp.asarray(jax.device_get(image_slice.flatten()      )      )
     __a			=					jnp.array(
         [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]      )
     print(f"output_slice: {output_slice}"      )
     assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a	,       __a			=					FlaxControlNetModel.from_pretrained(
         'lllyasviel/sd-controlnet-openpose'      ,							from_pt=UpperCamelCase      ,							dtype=jnp.bfloataa      )
     __a	,       __a			=					FlaxStableDiffusionControlNetPipeline.from_pretrained(
         'runwayml/stable-diffusion-v1-5'      ,							controlnet=UpperCamelCase      ,							from_pt=UpperCamelCase      ,							dtype=jnp.bfloataa      )
     __a			=					controlnet_params

     __a			=					'Chef in the kitchen'
     __a			=					jax.device_count()
     __a			=					pipe.prepare_text_inputs([prompts] * num_samples      )

     __a			=					load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'      )
     __a			=					pipe.prepare_image_inputs([pose_image] * num_samples      )

     __a			=					jax.random.PRNGKey(0      )
     __a			=					jax.random.split(UpperCamelCase      ,							jax.device_count()      )

     __a			=					replicate(UpperCamelCase      )
     __a			=					shard(UpperCamelCase      )
     __a			=					shard(UpperCamelCase      )

     __a			=					pipe(
         prompt_ids=UpperCamelCase      ,							image=UpperCamelCase      ,							params=UpperCamelCase      ,							prng_seed=UpperCamelCase      ,							num_inference_steps=50      ,							jit=UpperCamelCase      ,							).images
     assert images.shape == (jax.device_count(), 1, 768, 512, 3)

     __a			=					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
     __a			=					images[0, 253:256, 253:256, -1]

     __a			=					jnp.asarray(jax.device_get(image_slice.flatten()      )      )
     __a			=					jnp.array(
         [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]      )
     print(f"output_slice: {output_slice}"      )
     assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2









'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


UpperCAmelCase_ 		=      "path-to-your-trained-model"
UpperCAmelCase_ 		=      StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

UpperCAmelCase_ 		=      "A photo of sks dog in a bucket"
UpperCAmelCase_ 		=      pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")



