sd high res

app.py

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+import gradio as gr
+import jax
+import numpy as np
+import jax.numpy as jnp
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+from PIL import Image
+from diffusers import FlaxStableDiffusionPipeline
+
+def create_key(seed=0):
+    return jax.random.PRNGKey(seed)
+
+
+pipe, params = FlaxStableDiffusionPipeline.from_pretrained(
+    "MuhammadHanif/stable-diffusion-v1-5-high-res", 
+    controlnet=controlnet, revision="flax", 
+    dtype=jnp.bfloat16, 
+    use_memory_efficient_attention=True
+)
+
+def infer(prompts, negative_prompts):
+    
+    num_samples = 1 #jax.device_count()
+    rng = create_key(0)
+    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)
+    
+    p_params = replicate(params)
+    prompt_ids = shard(prompt_ids)
+    negative_prompt_ids = shard(negative_prompt_ids)
+    
+    output = pipe(
+        prompt_ids=prompt_ids,
+        params=p_params,
+        height=1088,
+        width=1088,
+        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:])))
+    return output_images
+
+gr.Interface(infer, inputs=["text", "text"], outputs="gallery").launch()