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Qi Cai
2025-04-07 15:31:48 +08:00
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commit 9bf3ac0e4f
6 changed files with 340 additions and 90 deletions
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.gitignore
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__pycache__
tmp
*_local.py
*.jpg
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README.md
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# HiDream-I1
`HiDream-I1` is a series of state-of-the-art open-source image generation models featuring a 16 billion parameter rectified flow transformer with Mixture of Experts architecture, designed to create high-quality images from text prompts.
`HiDream-I1` is a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds.
## Project Updates
- ```2025/4/7```: We've open-sourced the text-to-image model **HiDream-I1**.
@@ -13,8 +13,8 @@ We offer both the full version and distilled models. For more information about
| Name | Script | Inference Steps | HuggingFace repo |
| --------------- | -------------------------------------------------- | --------------- | ---------------------- |
| HiDream-I1-Full | [inference.py](./inference.py) | 50 | 🤗 [HiDream-I1-Full](https://huggingface.co/HiDream-ai/HiDream-I1-Full) |
| HiDream-I1-Dev | [inference_distilled.py](./inference_distilled.py) | 28 | 🤗 [HiDream-I1-Dev](https://huggingface.co/HiDream-ai/HiDream-I1-Dev) |
| HiDream-I1-Fast | [inference_distilled.py](./inference_distilled.py) | 16 | 🤗 [HiDream-I1-Fast](https://huggingface.co/HiDream-ai/HiDream-I1-Fast) |
| HiDream-I1-Dev | [inference.py](./inference_distilled.py) | 28 | 🤗 [HiDream-I1-Dev](https://huggingface.co/HiDream-ai/HiDream-I1-Dev) |
| HiDream-I1-Fast | [inference.py](./inference_distilled.py) | 16 | 🤗 [HiDream-I1-Fast](https://huggingface.co/HiDream-ai/HiDream-I1-Fast) |
## Quick Start
@@ -26,19 +26,26 @@ pip install -r requirements.txt
Then you can run the inference scripts to generate images:
``` python
# For full model inference
python ./inference.py
python ./inference.py --model_type full
# For distilled dev model inference
INFERENCE_STEP=28 PRETRAINED_MODEL_NAME_OR_PATH=HiDream-ai/HiDream-I1-Dev python inference_distilled.py
python ./inference.py --model_type dev
# For distilled fast model inference
INFERENCE_STEP=16 PRETRAINED_MODEL_NAME_OR_PATH=HiDream-ai/HiDream-I1-Fast python inference_distilled.py
python ./inference.py --model_type fast
```
> **Note:** The inference script will automatically download `meta-llama/Meta-Llama-3.1-8B-Instruct` model files. If you encounter network issues, you can download these files ahead of time and place them in the appropriate cache directory to avoid download failures during inference.
## Gradio Demo
We also provide a Gradio demo for interactive image generation. You can run the demo with:
``` python
python gradio_demo.py
```
## Evaluation Metrics
gradio_demo.py
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import torch
import gradio as gr
from hi_diffusers import HiDreamImagePipeline
from hi_diffusers import HiDreamImageTransformer2DModel
from hi_diffusers.schedulers.fm_solvers_unipc import FlowUniPCMultistepScheduler
from hi_diffusers.schedulers.flash_flow_match import FlashFlowMatchEulerDiscreteScheduler
from transformers import LlamaForCausalLM, PreTrainedTokenizerFast
MODEL_PREFIX = "HiDream-ai"
LLAMA_MODEL_NAME = "meta-llama/Meta-Llama-3.1-8B-Instruct"
# Model configurations
MODEL_CONFIGS = {
"dev": {
"path": f"{MODEL_PREFIX}/HiDream-I1-Dev",
"guidance_scale": 0.0,
"num_inference_steps": 28,
"shift": 6.0,
"scheduler": FlashFlowMatchEulerDiscreteScheduler
},
"full": {
"path": f"{MODEL_PREFIX}/HiDream-I1-Full",
"guidance_scale": 5.0,
"num_inference_steps": 50,
"shift": 3.0,
"scheduler": FlowUniPCMultistepScheduler
},
"fast": {
"path": f"{MODEL_PREFIX}/HiDream-I1-Fast",
"guidance_scale": 0.0,
"num_inference_steps": 16,
"shift": 3.0,
"scheduler": FlashFlowMatchEulerDiscreteScheduler
}
}
# Resolution options
RESOLUTION_OPTIONS = [
"1024 × 1024 (Square)",
"768 × 1360 (Portrait)",
"1360 × 768 (Landscape)",
"880 × 1168 (Portrait)",
"1168 × 880 (Landscape)",
"1248 × 832 (Landscape)",
"832 × 1248 (Portrait)"
]
# Load models
def load_models(model_type):
config = MODEL_CONFIGS[model_type]
pretrained_model_name_or_path = config["path"]
scheduler = FlowUniPCMultistepScheduler(num_train_timesteps=1000, shift=config["shift"], use_dynamic_shifting=False)
tokenizer_4 = PreTrainedTokenizerFast.from_pretrained(
LLAMA_MODEL_NAME,
use_fast=False)
text_encoder_4 = LlamaForCausalLM.from_pretrained(
LLAMA_MODEL_NAME,
output_hidden_states=True,
output_attentions=True,
torch_dtype=torch.bfloat16).to("cuda")
transformer = HiDreamImageTransformer2DModel.from_pretrained(
pretrained_model_name_or_path,
subfolder="transformer",
torch_dtype=torch.bfloat16).to("cuda")
pipe = HiDreamImagePipeline.from_pretrained(
pretrained_model_name_or_path,
scheduler=scheduler,
tokenizer_4=tokenizer_4,
text_encoder_4=text_encoder_4,
torch_dtype=torch.bfloat16
).to("cuda", torch.bfloat16)
pipe.transformer = transformer
return pipe, config
# Parse resolution string to get height and width
def parse_resolution(resolution_str):
if "1024 × 1024" in resolution_str:
return 1024, 1024
elif "768 × 1360" in resolution_str:
return 768, 1360
elif "1360 × 768" in resolution_str:
return 1360, 768
elif "880 × 1168" in resolution_str:
return 880, 1168
elif "1168 × 880" in resolution_str:
return 1168, 880
elif "1248 × 832" in resolution_str:
return 1248, 832
elif "832 × 1248" in resolution_str:
return 832, 1248
else:
return 1024, 1024 # Default fallback
# Generate image function
def generate_image(model_type, prompt, resolution, seed):
global pipe, current_model
# Reload model if needed
if model_type != current_model:
del pipe
torch.cuda.empty_cache()
print(f"Loading {model_type} model...")
pipe, config = load_models(model_type)
current_model = model_type
print(f"{model_type} model loaded successfully!")
# Get configuration for current model
config = MODEL_CONFIGS[model_type]
guidance_scale = config["guidance_scale"]
num_inference_steps = config["num_inference_steps"]
# Parse resolution
height, width = parse_resolution(resolution)
# Handle seed
if seed == -1:
seed = torch.randint(0, 1000000, (1,)).item()
generator = torch.Generator("cuda").manual_seed(seed)
images = pipe(
prompt,
height=height,
width=width,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
num_images_per_prompt=1,
generator=generator
).images
return images[0], seed
# Initialize with default model
print("Loading default model (full)...")
current_model = "full"
pipe, _ = load_models(current_model)
print("Model loaded successfully!")
# Create Gradio interface
with gr.Blocks(title="HiDream Image Generator") as demo:
gr.Markdown("# HiDream Image Generator")
with gr.Row():
with gr.Column():
model_type = gr.Radio(
choices=list(MODEL_CONFIGS.keys()),
value="full",
label="Model Type",
info="Select model variant"
)
prompt = gr.Textbox(
label="Prompt",
placeholder="A cat holding a sign that says \"Hi-Dreams.ai\".",
lines=3
)
resolution = gr.Radio(
choices=RESOLUTION_OPTIONS,
value=RESOLUTION_OPTIONS[0],
label="Resolution",
info="Select image resolution"
)
seed = gr.Number(
label="Seed (use -1 for random)",
value=-1,
precision=0
)
generate_btn = gr.Button("Generate Image")
seed_used = gr.Number(label="Seed Used", interactive=False)
with gr.Column():
output_image = gr.Image(label="Generated Image", type="pil")
generate_btn.click(
fn=generate_image,
inputs=[model_type, prompt, resolution, seed],
outputs=[output_image, seed_used]
)
# Launch app
if __name__ == "__main__":
demo.launch()
hi_diffusers/models/transformers/transformer_hidream_image.py
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return x
def patchify(self, x, max_seq, img_sizes=None):
pz2 = self.patch_size * self.patch_size
pz2 = self.config.patch_size * self.config.patch_size
if isinstance(x, torch.Tensor):
B, C = x.shape[0], x.shape[1]
device = x.device
inference.py
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import torch
import argparse
from hi_diffusers import HiDreamImagePipeline
from hi_diffusers import HiDreamImageTransformer2DModel
from hi_diffusers.schedulers.fm_solvers_unipc import FlowUniPCMultistepScheduler
from hi_diffusers.schedulers.flash_flow_match import FlashFlowMatchEulerDiscreteScheduler
from transformers import LlamaForCausalLM, PreTrainedTokenizerFast
pretrained_model_name_or_path = "HiDream-ai/HiDream-I1-Full"
scheduler = FlowUniPCMultistepScheduler(num_train_timesteps=1000, shift=1, use_dynamic_shifting=False)
tokenizer_4 = PreTrainedTokenizerFast.from_pretrained(
"meta-llama/Meta-Llama-3.1-8B-Instruct",
use_fast=False)
text_encoder_4 = LlamaForCausalLM.from_pretrained(
"meta-llama/Meta-Llama-3.1-8B-Instruct",
output_hidden_states=True,
output_attentions=True,
torch_dtype=torch.bfloat16).to("cuda")
parser = argparse.ArgumentParser()
parser.add_argument("--model_type", type=str, default="dev")
args = parser.parse_args()
model_type = args.model_type
MODEL_PREFIX = "HiDream-ai"
LLAMA_MODEL_NAME = "meta-llama/Meta-Llama-3.1-8B-Instruct"
transformer = HiDreamImageTransformer2DModel.from_pretrained(
pretrained_model_name_or_path,
subfolder="transformer",
torch_dtype=torch.bfloat16).to("cuda")
# Model configurations
MODEL_CONFIGS = {
"dev": {
"path": f"{MODEL_PREFIX}/HiDream-I1-Dev",
"guidance_scale": 0.0,
"num_inference_steps": 28,
"shift": 6.0,
"scheduler": FlashFlowMatchEulerDiscreteScheduler
},
"full": {
"path": f"{MODEL_PREFIX}/HiDream-I1-Full",
"guidance_scale": 5.0,
"num_inference_steps": 50,
"shift": 3.0,
"scheduler": FlowUniPCMultistepScheduler
},
"fast": {
"path": f"{MODEL_PREFIX}/HiDream-I1-Fast",
"guidance_scale": 0.0,
"num_inference_steps": 16,
"shift": 3.0,
"scheduler": FlashFlowMatchEulerDiscreteScheduler
}
}
pipe = HiDreamImagePipeline.from_pretrained(
pretrained_model_name_or_path,
scheduler=scheduler,
tokenizer_4=tokenizer_4,
text_encoder_4=text_encoder_4,
torch_dtype=torch.bfloat16
).to("cuda")
pipe.transformer = transformer
# Resolution options
RESOLUTION_OPTIONS = [
"1024 × 1024 (Square)",
"768 × 1360 (Portrait)",
"1360 × 768 (Landscape)",
"880 × 1168 (Portrait)",
"1168 × 880 (Landscape)",
"1248 × 832 (Landscape)",
"832 × 1248 (Portrait)"
]
# Load models
def load_models(model_type):
config = MODEL_CONFIGS[model_type]
pretrained_model_name_or_path = config["path"]
scheduler = FlowUniPCMultistepScheduler(num_train_timesteps=1000, shift=config["shift"], use_dynamic_shifting=False)
tokenizer_4 = PreTrainedTokenizerFast.from_pretrained(
LLAMA_MODEL_NAME,
use_fast=False)
text_encoder_4 = LlamaForCausalLM.from_pretrained(
LLAMA_MODEL_NAME,
output_hidden_states=True,
output_attentions=True,
torch_dtype=torch.bfloat16).to("cuda")
transformer = HiDreamImageTransformer2DModel.from_pretrained(
pretrained_model_name_or_path,
subfolder="transformer",
torch_dtype=torch.bfloat16).to("cuda")
pipe = HiDreamImagePipeline.from_pretrained(
pretrained_model_name_or_path,
scheduler=scheduler,
tokenizer_4=tokenizer_4,
text_encoder_4=text_encoder_4,
torch_dtype=torch.bfloat16
).to("cuda", torch.bfloat16)
pipe.transformer = transformer
return pipe, config
# Parse resolution string to get height and width
def parse_resolution(resolution_str):
if "1024 × 1024" in resolution_str:
return 1024, 1024
elif "768 × 1360" in resolution_str:
return 768, 1360
elif "1360 × 768" in resolution_str:
return 1360, 768
elif "880 × 1168" in resolution_str:
return 880, 1168
elif "1168 × 880" in resolution_str:
return 1168, 880
elif "1248 × 832" in resolution_str:
return 1248, 832
elif "832 × 1248" in resolution_str:
return 832, 1248
else:
return 1024, 1024 # Default fallback
# Generate image function
def generate_image(pipe, model_type, prompt, resolution, seed):
# Get configuration for current model
config = MODEL_CONFIGS[model_type]
guidance_scale = config["guidance_scale"]
num_inference_steps = config["num_inference_steps"]
# Parse resolution
height, width = parse_resolution(resolution)
# Handle seed
if seed == -1:
seed = torch.randint(0, 1000000, (1,)).item()
generator = torch.Generator("cuda").manual_seed(seed)
images = pipe(
prompt,
height=height,
width=width,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
num_images_per_prompt=1,
generator=generator
).images
return images[0], seed
# Initialize with default model
print("Loading default model (full)...")
pipe, _ = load_models(model_type)
print("Model loaded successfully!")
prompt = "A cat holding a sign that says \"Hi-Dreams.ai\"."
images = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=5.0,
num_inference_steps=50,
num_images_per_prompt=1,
generator=torch.Generator("cuda").manual_seed(42)
).images
for i, image in enumerate(images):
image.save(f"{i}.jpg")
resolution = "1024 × 1024 (Square)"
seed = -1
image, seed = generate_image(pipe, model_type, prompt, resolution, seed)
image.save("output.png")
inference_distilled.py
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import os
import torch
from hi_diffusers import HiDreamImagePipeline
from hi_diffusers import HiDreamImageTransformer2DModel
from hi_diffusers.schedulers.flash_flow_match import FlashFlowMatchEulerDiscreteScheduler
from transformers import LlamaForCausalLM, PreTrainedTokenizerFast
INFERENCE_STEP = int(os.getenv("INFERENCE_STEP", "28"))
PRETRAINED_MODEL_NAME_OR_PATH = os.getenv("PRETRAINED_MODEL_NAME_OR_PATH", "HiDream-I1-Dev")
scheduler = FlashFlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=6.0, use_dynamic_shifting=False)
tokenizer_4 = PreTrainedTokenizerFast.from_pretrained(
"meta-llama/Meta-Llama-3.1-8B-Instruct",
use_fast=False)
text_encoder_4 = LlamaForCausalLM.from_pretrained(
"meta-llama/Meta-Llama-3.1-8B-Instruct",
output_hidden_states=True,
output_attentions=True,
torch_dtype=torch.bfloat16).to("cuda")
transformer = HiDreamImageTransformer2DModel.from_pretrained(
PRETRAINED_MODEL_NAME_OR_PATH,
subfolder="transformer",
torch_dtype=torch.bfloat16).to("cuda")
pipe = HiDreamImagePipeline.from_pretrained(
PRETRAINED_MODEL_NAME_OR_PATH,
scheduler=scheduler,
tokenizer_4=tokenizer_4,
text_encoder_4=text_encoder_4,
torch_dtype=torch.bfloat16
).to("cuda")
pipe.transformer = transformer
prompt = "A cat holding a sign that says \"Hi-Dreams.ai\"."
images = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=0.0,
num_inference_steps=INFERENCE_STEP,
num_images_per_prompt=1,
generator=torch.Generator("cuda").manual_seed(42)
).images
for i, image in enumerate(images):
image.save(f"{i}.jpg")