Nitro-T-0.6B

Name: Nitro-T-0.6B
Author: amd

157

600M

1 language

license:apache-2.0

amd

Image Model

OTHER

0.6B params

New

157 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

2GB+ RAM

Mobile

Laptop

Server

Quick Summary

Introduction Nitro-T is a family of text-to-image diffusion models focused on highly efficient training.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

1GB+ RAM

Code Examples

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

Quickstartpythontransformers

import torch
from diffusers import DiffusionPipeline
from transformers import AutoModelForCausalLM

torch.set_grad_enabled(False)

device = torch.device('cuda:0')
dtype = torch.bfloat16
resolution = 512
MODEL_NAME = "amd/Nitro-T-0.6B"

text_encoder = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    MODEL_NAME,
    text_encoder=text_encoder,
    torch_dtype=dtype, 
    trust_remote_code=True,
)
pipe.to(device)

image = pipe(
    prompt="The image is a close-up portrait of a scientist in a modern laboratory. He has short, neatly styled black hair and wears thin, stylish eyeglasses. The lighting is soft and warm, highlighting his facial features against a backdrop of lab equipment and glowing screens.",
    height=resolution, width=resolution,
    num_inference_steps=20,
    guidance_scale=4.0,
).images[0]

image.save("output.png")

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