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--- |
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pipeline_tag: text-to-image |
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license: creativeml-openrail-m |
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base_model: |
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- stable-diffusion-v1-5/stable-diffusion-v1-5 |
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library_name: diffusers |
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--- |
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# stable-diffusion-1.5 optimized for AMD GPU |
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## Original Model |
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https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5 |
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## _io32/16 |
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_io32: model input is fp32, model will convert the input to fp16, perform ops in fp16 and write the final result in fp32 |
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_io16: model input is fp16, perform ops in fp16 and write the final result in fp16 |
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## Running |
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### 1. Using Amuse GUI Application |
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Use Amuse GUI application to run it: https://www.amuse-ai.com/ |
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use _io32 model to run with Amuse application |
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### 2. Inference Demo |
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Use the code below to get started with the model. |
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With Python using Diffusers OnnxStableDiffusionPipeline |
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Required Modules |
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``` |
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accelerate |
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numpy==1.26.4 # Due to newer version of numpy changing dtype when multiplying |
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diffusers |
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torch |
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transformers |
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onnxruntime-directml |
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``` |
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Python Script |
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``` |
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import onnxruntime as ort |
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from diffusers import OnnxStableDiffusionPipeline |
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model_dir = "D:\\Models\\stable-diffusion-v1-5_io32" |
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batch_size = 1 |
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num_inference_steps = 30 |
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image_size = 512 |
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guidance_scale = 7.5 |
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prompt = "a beautiful cabin in the mountains of Lake Tahoe" |
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ort.set_default_logger_severity(3) |
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sess_options = ort.SessionOptions() |
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sess_options.enable_mem_pattern = False |
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sess_options.add_free_dimension_override_by_name("unet_sample_batch", batch_size * 2) |
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sess_options.add_free_dimension_override_by_name("unet_sample_channels", 4) |
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sess_options.add_free_dimension_override_by_name("unet_sample_height", image_size // 8) |
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sess_options.add_free_dimension_override_by_name("unet_sample_width", image_size // 8) |
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sess_options.add_free_dimension_override_by_name("unet_time_batch", batch_size) |
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sess_options.add_free_dimension_override_by_name("unet_hidden_batch", batch_size * 2) |
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sess_options.add_free_dimension_override_by_name("unet_hidden_sequence", 77) |
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pipeline = OnnxStableDiffusionPipeline.from_pretrained( |
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model_dir, provider="DmlExecutionProvider", sess_options=sess_options |
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) |
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result = pipeline( |
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[prompt] * batch_size, |
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num_inference_steps=num_inference_steps, |
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callback=None, |
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height=image_size, |
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width=image_size, |
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guidance_scale=guidance_scale, |
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generator=None |
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) |
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output_path = "output.png" |
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result.images[0].save(output_path) |
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print(f"Generated {output_path}") |
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``` |
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### Inference Results |
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