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Zero
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import random
import unittest
import numpy as np
import torch
from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, FluxImg2ImgPipeline, FluxTransformer2DModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
torch_device,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class FluxImg2ImgPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
pipeline_class = FluxImg2ImgPipeline
params = frozenset(["prompt", "height", "width", "guidance_scale", "prompt_embeds", "pooled_prompt_embeds"])
batch_params = frozenset(["prompt"])
test_xformers_attention = False
def get_dummy_components(self):
torch.manual_seed(0)
transformer = FluxTransformer2DModel(
patch_size=1,
in_channels=4,
num_layers=1,
num_single_layers=1,
attention_head_dim=16,
num_attention_heads=2,
joint_attention_dim=32,
pooled_projection_dim=32,
axes_dims_rope=[4, 4, 8],
)
clip_text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
hidden_act="gelu",
projection_dim=32,
)
torch.manual_seed(0)
text_encoder = CLIPTextModel(clip_text_encoder_config)
torch.manual_seed(0)
text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
torch.manual_seed(0)
vae = AutoencoderKL(
sample_size=32,
in_channels=3,
out_channels=3,
block_out_channels=(4,),
layers_per_block=1,
latent_channels=1,
norm_num_groups=1,
use_quant_conv=False,
use_post_quant_conv=False,
shift_factor=0.0609,
scaling_factor=1.5035,
)
scheduler = FlowMatchEulerDiscreteScheduler()
return {
"scheduler": scheduler,
"text_encoder": text_encoder,
"text_encoder_2": text_encoder_2,
"tokenizer": tokenizer,
"tokenizer_2": tokenizer_2,
"transformer": transformer,
"vae": vae,
}
def get_dummy_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device="cpu").manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"height": 8,
"width": 8,
"max_sequence_length": 48,
"strength": 0.8,
"output_type": "np",
}
return inputs
def test_flux_different_prompts(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
output_same_prompt = pipe(**inputs).images[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = "a different prompt"
output_different_prompts = pipe(**inputs).images[0]
max_diff = np.abs(output_same_prompt - output_different_prompts).max()
# Outputs should be different here
# For some reasons, they don't show large differences
assert max_diff > 1e-6
def test_flux_prompt_embeds(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
output_with_prompt = pipe(**inputs).images[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = inputs.pop("prompt")
(prompt_embeds, pooled_prompt_embeds, text_ids) = pipe.encode_prompt(
prompt,
prompt_2=None,
device=torch_device,
max_sequence_length=inputs["max_sequence_length"],
)
output_with_embeds = pipe(
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
**inputs,
).images[0]
max_diff = np.abs(output_with_prompt - output_with_embeds).max()
assert max_diff < 1e-4
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