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