docs/source/en/api/pipelines/hunyuanimage21.md
HunyuanImage-2.1 is a 17B text-to-image model that is capable of generating 2K (2048 x 2048) resolution images
HunyuanImage-2.1 comes in the following variants:
| model type | model id |
|---|---|
| HunyuanImage-2.1 | hunyuanvideo-community/HunyuanImage-2.1-Diffusers |
| HunyuanImage-2.1-Distilled | hunyuanvideo-community/HunyuanImage-2.1-Distilled-Diffusers |
| HunyuanImage-2.1-Refiner | hunyuanvideo-community/HunyuanImage-2.1-Refiner-Diffusers |
[!TIP] Caching may also speed up inference by storing and reusing intermediate outputs.
HunyuanImage-2.1 applies Adaptive Projected Guidance (APG) combined with Classifier-Free Guidance (CFG) in the denoising loop. HunyuanImagePipeline has a guider component (read more about Guider) and does not take a guidance_scale parameter at runtime. To change guider-related parameters, e.g., guidance_scale, you can update the guider configuration instead.
import torch
from diffusers import HunyuanImagePipeline
pipe = HunyuanImagePipeline.from_pretrained(
"hunyuanvideo-community/HunyuanImage-2.1-Diffusers",
torch_dtype=torch.bfloat16
)
pipe = pipe.to("cuda")
You can inspect the guider object:
>>> pipe.guider
AdaptiveProjectedMixGuidance {
"_class_name": "AdaptiveProjectedMixGuidance",
"_diffusers_version": "0.36.0.dev0",
"adaptive_projected_guidance_momentum": -0.5,
"adaptive_projected_guidance_rescale": 10.0,
"adaptive_projected_guidance_scale": 10.0,
"adaptive_projected_guidance_start_step": 5,
"enabled": true,
"eta": 0.0,
"guidance_rescale": 0.0,
"guidance_scale": 3.5,
"start": 0.0,
"stop": 1.0,
"use_original_formulation": false
}
State:
step: None
num_inference_steps: None
timestep: None
count_prepared: 0
enabled: True
num_conditions: 2
momentum_buffer: None
is_apg_enabled: False
is_cfg_enabled: True
To update the guider with a different configuration, use the new() method. For example, to generate an image with guidance_scale=5.0 while keeping all other default guidance parameters:
import torch
from diffusers import HunyuanImagePipeline
pipe = HunyuanImagePipeline.from_pretrained(
"hunyuanvideo-community/HunyuanImage-2.1-Diffusers",
torch_dtype=torch.bfloat16
)
pipe = pipe.to("cuda")
# Update the guider configuration
pipe.guider = pipe.guider.new(guidance_scale=5.0)
prompt = (
"A cute, cartoon-style anthropomorphic penguin plush toy with fluffy fur, standing in a painting studio, "
"wearing a red knitted scarf and a red beret with the word 'Tencent' on it, holding a paintbrush with a "
"focused expression as it paints an oil painting of the Mona Lisa, rendered in a photorealistic photographic style."
)
image = pipe(
prompt=prompt,
num_inference_steps=50,
height=2048,
width=2048,
).images[0]
image.save("image.png")
use distilled_guidance_scale with the guidance-distilled checkpoint,
import torch
from diffusers import HunyuanImagePipeline
pipe = HunyuanImagePipeline.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Distilled-Diffusers", torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")
prompt = (
"A cute, cartoon-style anthropomorphic penguin plush toy with fluffy fur, standing in a painting studio, "
"wearing a red knitted scarf and a red beret with the word 'Tencent' on it, holding a paintbrush with a "
"focused expression as it paints an oil painting of the Mona Lisa, rendered in a photorealistic photographic style."
)
out = pipe(
prompt,
num_inference_steps=8,
distilled_guidance_scale=3.25,
height=2048,
width=2048,
generator=generator,
).images[0]
[[autodoc]] HunyuanImagePipeline
[[autodoc]] HunyuanImageRefinerPipeline
[[autodoc]] pipelines.hunyuan_image.pipeline_output.HunyuanImagePipelineOutput