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---
library_name: diffusers
---
# yujiepan/FLUX.1-dev-tiny-random
This pipeline is intended for debugging. It is adapted from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) with smaller size and randomly initialized parameters.
## Usage
```python
import torch
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained("yujiepan/FLUX.1-dev-tiny-random", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=3.5,
num_inference_steps=50,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0)
).images[0]
# image.save("flux-dev.png")
```
## Codes
```python
import importlib
import torch
import transformers
import diffusers
import rich
def get_original_model_configs(
pipeline_cls: type[diffusers.FluxPipeline],
pipeline_id: str
):
pipeline_config: dict[str, list[str]] = \
pipeline_cls.load_config(pipeline_id)
model_configs = {}
for subfolder, import_strings in pipeline_config.items():
if subfolder.startswith("_"):
continue
module = importlib.import_module(".".join(import_strings[:-1]))
cls = getattr(module, import_strings[-1])
if issubclass(cls, transformers.PreTrainedModel):
config_class: transformers.PretrainedConfig = cls.config_class
config = config_class.from_pretrained(
pipeline_id, subfolder=subfolder)
model_configs[subfolder] = config
elif issubclass(cls, diffusers.ModelMixin) and issubclass(cls, diffusers.ConfigMixin):
config = cls.load_config(pipeline_id, subfolder=subfolder)
model_configs[subfolder] = config
elif subfolder in ['scheduler', 'tokenizer', 'tokenizer_2', 'tokenizer_3']:
pass
else:
raise NotImplementedError(f"unknown {subfolder}: {import_strings}")
return model_configs
def load_pipeline(pipeline_cls: type[diffusers.DiffusionPipeline], pipeline_id: str, model_configs: dict[str, dict]):
pipeline_config: dict[str, list[str]
] = pipeline_cls.load_config(pipeline_id)
components = {}
for subfolder, import_strings in pipeline_config.items():
if subfolder.startswith("_"):
continue
module = importlib.import_module(".".join(import_strings[:-1]))
cls = getattr(module, import_strings[-1])
print(f"Loading:", ".".join(import_strings))
if issubclass(cls, transformers.PreTrainedModel):
config = model_configs[subfolder]
component = cls(config)
elif issubclass(cls, transformers.PreTrainedTokenizerBase):
component = cls.from_pretrained(pipeline_id, subfolder=subfolder)
elif issubclass(cls, diffusers.ModelMixin) and issubclass(cls, diffusers.ConfigMixin):
config = model_configs[subfolder]
component = cls.from_config(config)
elif issubclass(cls, diffusers.SchedulerMixin) and issubclass(cls, diffusers.ConfigMixin):
component = cls.from_pretrained(pipeline_id, subfolder=subfolder)
else:
raise (f"unknown {subfolder}: {import_strings}")
components[subfolder] = component
if 'transformer' in component.__class__.__name__.lower():
print(component)
pipeline = pipeline_cls(**components)
return pipeline
def get_pipeline():
torch.manual_seed(42)
pipeline_id = "black-forest-labs/FLUX.1-dev"
pipeline_cls = diffusers.FluxPipeline
model_configs = get_original_model_configs(pipeline_cls, pipeline_id)
HIDDEN_SIZE = 8
model_configs["text_encoder"].hidden_size = HIDDEN_SIZE
model_configs["text_encoder"].intermediate_size = HIDDEN_SIZE * 2
model_configs["text_encoder"].num_attention_heads = 2
model_configs["text_encoder"].num_hidden_layers = 2
model_configs["text_encoder"].projection_dim = HIDDEN_SIZE
model_configs["text_encoder_2"].d_model = HIDDEN_SIZE
model_configs["text_encoder_2"].d_ff = HIDDEN_SIZE * 2
model_configs["text_encoder_2"].d_kv = HIDDEN_SIZE // 2
model_configs["text_encoder_2"].num_heads = 2
model_configs["text_encoder_2"].num_layers = 2
model_configs["transformer"]["num_layers"] = 2
model_configs["transformer"]["num_single_layers"] = 4
model_configs["transformer"]["num_attention_heads"] = 2
model_configs["transformer"]["attention_head_dim"] = HIDDEN_SIZE
model_configs["transformer"]["pooled_projection_dim"] = HIDDEN_SIZE
model_configs["transformer"]["joint_attention_dim"] = HIDDEN_SIZE
model_configs["transformer"]["axes_dims_rope"] = (4, 2, 2)
# model_configs["transformer"]["caption_projection_dim"] = HIDDEN_SIZE
model_configs["vae"]["layers_per_block"] = 1
model_configs["vae"]["block_out_channels"] = [HIDDEN_SIZE] * 4
model_configs["vae"]["norm_num_groups"] = 2
model_configs["vae"]["latent_channels"] = 16
pipeline = load_pipeline(pipeline_cls, pipeline_id, model_configs)
return pipeline
pipe = get_pipeline()
pipe = pipe.to(torch.bfloat16)
from pathlib import Path
save_folder = '/tmp/yujiepan/FLUX.1-dev-tiny-random'
Path(save_folder).mkdir(parents=True, exist_ok=True)
pipe.save_pretrained(save_folder)
pipe = diffusers.FluxPipeline.from_pretrained(save_folder, torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=3.5,
num_inference_steps=50,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0)
).images[0]
configs = get_original_model_configs(diffusers.FluxPipeline, save_folder)
rich.print(configs)
pipe.push_to_hub(save_folder.removeprefix('/tmp/'))
```
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