Total runs:
118.1K
Run Growth:
71.8K
Growth Rate:
60.84%
Sponsored by
APIMart
amd / stable-diffusion-1.5_io16_amdgpu
Model's Last Updated: April 04 2025
text-to-image
Introduction of stable-diffusion-1.5_io16_amdgpu
Model Details of stable-diffusion-1.5_io16_amdgpu
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
Runs of amd stable-diffusion-1.5_io16_amdgpu on huggingface.co
10
Total runs
-1
24-hour runs
-1
3-day runs
-7
7-day runs
-12
30-day runs
More Information About stable-diffusion-1.5_io16_amdgpu huggingface.co Model
Url of stable-diffusion-1.5_io16_amdgpu
stable-diffusion-1.5_io16_amdgpu huggingface.co Url
Provider of stable-diffusion-1.5_io16_amdgpu huggingface.co
Other API from amd
Updated:April 14 2026
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39.9K
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17.25%
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35.7K
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27.1K
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76.05%
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6.25%
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92.69%
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92.77%
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90.64%
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25.07%
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15.89%
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8.9K
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2.1K
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23.88%
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8.7K
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22.75%
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3.6K
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46.73%
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98.82%
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67.86%
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3.8K
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99.42%
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80.60%
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36.49%
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99.33%
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2.65%
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831
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99.88%
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831
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Growth Rate:
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759
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759
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100.00%
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Run Growth:
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28.87%
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56.63%
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20.60%
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230
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55
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23.91%
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Run Growth:
-694
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-333.65%
Total runs:
207
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192
Growth Rate:
92.75%
Total runs:
156
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38
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24.36%
Total runs:
151
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50
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33.11%
Total runs:
149
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65
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43.62%
Total runs:
148
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46
Growth Rate:
31.08%
Total runs:
141
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25
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17.73%
Total runs:
136
Run Growth:
-394
Growth Rate:
-262.67%
Total runs:
132
Run Growth:
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Growth Rate:
-202.27%
Total runs:
119
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112
Growth Rate:
94.12%
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103
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91.15%
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101
Run Growth:
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11
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11.46%
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16
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17.20%
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27.50%
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53.75%
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96.61%
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Growth Rate:
40.68%