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```python |
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#!/usr/bin/env python3 |
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from diffusers import FlaxStableDiffusionPipeline |
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from jax import pmap |
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import numpy as np |
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import jax |
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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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prng_seed = jax.random.PRNGKey(0) |
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num_inference_steps = 50 |
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pipeline, params = FlaxStableDiffusionPipeline.from_pretrained("fusing/stable-diffusion-flax-new", use_auth_token=True) |
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del params["safety_checker"] |
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# pmap |
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p_sample = pmap(pipeline.__call__, static_broadcasted_argnums=(3,)) |
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# prep prompts |
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prompt = "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of field, close up, split lighting, cinematic" |
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num_samples = jax.device_count() |
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prompt = num_samples * [prompt] |
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prompt_ids = pipeline.prepare_inputs(prompt) |
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# replicate |
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params = replicate(params) |
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prng_seed = jax.random.split(prng_seed, 8) |
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prompt_ids = shard(prompt_ids) |
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# run |
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images = p_sample(prompt_ids, params, prng_seed, num_inference_steps).images |
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# get pil images |
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images_pil = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) |
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import ipdb; ipdb.set_trace() |
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print("Images should be good") |
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# images_pil[0].save(...) |
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``` |