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Update handle single blocks on _convert_xlabs_flux_lora_to_diffusers #9915

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Update handle single blocks on _convert_xlabs_flux_lora_to_diffusers #9915

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d5ed3df
Update handle single blocks on _convert_xlabs_flux_lora_to_diffusers …
Nov 12, 2024
d595416
Merge branch 'main' into fix/load_xlab_flux_lora
sayakpaul Nov 13, 2024
5bdbd5b
Remove comments and add test
Nov 14, 2024
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10 changes: 6 additions & 4 deletions src/diffusers/loaders/lora_conversion_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -636,10 +636,12 @@ def handle_qkv(sds_sd, ait_sd, sds_key, ait_keys, dims=None):
block_num = re.search(r"single_blocks\.(\d+)", old_key).group(1)
new_key = f"transformer.single_transformer_blocks.{block_num}"

if "proj_lora1" in old_key or "proj_lora2" in old_key:
if "proj_lora" in old_key:
new_key += ".proj_out"
elif "qkv_lora1" in old_key or "qkv_lora2" in old_key:
new_key += ".norm.linear"
elif "qkv_lora" in old_key and "up" not in old_key:
handle_qkv(old_state_dict, new_state_dict, old_key, [
f"transformer.single_transformer_blocks.{block_num}.norm.linear"
])

if "down" in old_key:
new_key += ".lora_A.weight"
Expand All @@ -657,4 +659,4 @@ def handle_qkv(sds_sd, ait_sd, sds_key, ait_keys, dims=None):
if len(old_state_dict) > 0:
raise ValueError(f"`old_state_dict` should be at this point but has: {list(old_state_dict.keys())}.")

return new_state_dict
return new_state_dict
22 changes: 22 additions & 0 deletions tests/lora/test_lora_layers_flux.py
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Original file line number Diff line number Diff line change
Expand Up @@ -282,3 +282,25 @@ def test_flux_xlabs(self):
max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), out_slice)

assert max_diff < 1e-3

def test_flux_xlabs_load_lora_with_single_blocks(self):
self.pipeline.load_lora_weights("salinasr/test_xlabs_flux_lora_with_singleblocks",
weight_name="lora.safetensors")
self.pipeline.fuse_lora()
self.pipeline.unload_lora_weights()
self.pipeline.enable_model_cpu_offload()

prompt = "a wizard mouse playing chess"

out = self.pipeline(
prompt,
num_inference_steps=self.num_inference_steps,
guidance_scale=3.5,
output_type="np",
generator=torch.manual_seed(self.seed),
).images
out_slice = out[0, -3:, -3:, -1].flatten()
expected_slice = np.array([0.04882812, 0.04101562, 0.04882812, 0.03710938, 0.02929688, 0.02734375, 0.0234375, 0.01757812, 0.0390625])
max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), out_slice)

assert max_diff < 1e-3
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