Update README.md
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README.md
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@@ -38,7 +38,7 @@ Example: `python -m deepcompressor.app.diffusion.ptq examples/diffusion/configs/
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Folder Structure
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- refer [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)
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- refer [black-forest-labs/FLUX.1-Kontext-dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev/tree/main)
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@@ -51,11 +51,7 @@ potential fix: app.diffusion.pipeline.config.py
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```python
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@staticmethod
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def _default_build(
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name: str,
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path: str,
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dtype: str | torch.dtype,
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device: str | torch.device,
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shift_activations: bool
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) -> DiffusionPipeline:
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if not path:
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if name == "sdxl":
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@@ -64,6 +60,8 @@ potential fix: app.diffusion.pipeline.config.py
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path = "stabilityai/sdxl-turbo"
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elif name == "pixart-sigma":
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path = "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS"
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elif name == "flux.1-dev":
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path = "black-forest-labs/FLUX.1-dev"
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elif name == "flux.1-canny-dev":
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@@ -76,24 +74,22 @@ potential fix: app.diffusion.pipeline.config.py
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path = "black-forest-labs/FLUX.1-schnell"
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else:
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raise ValueError(f"Path for {name} is not specified.")
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pipeline = FluxControlPipeline.from_pretrained(path, torch_dtype=dtype)
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elif name == "flux.1-fill-dev":
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pipeline = FluxFillPipeline.from_pretrained(path, torch_dtype=dtype)
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elif name.startswith("sana-"):
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if dtype == torch.bfloat16:
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pipeline = SanaPipeline.from_pretrained(
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path, variant="bf16", torch_dtype=dtype, use_safetensors=True
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)
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pipeline.vae.to(dtype)
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pipeline.text_encoder.to(dtype)
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else:
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pipeline = SanaPipeline.from_pretrained(path, torch_dtype=dtype)
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else:
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pipeline = AutoPipelineForText2Image.from_pretrained(path, torch_dtype=dtype)
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# Debug output
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print(">>> DEVICE:", device)
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print(">>> PIPELINE TYPE:", type(pipeline))
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@@ -128,14 +124,14 @@ potential fix: app.diffusion.pipeline.config.py
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Debug Log
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```
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Loading
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Loading pipeline components...: 57%|████████████████████████████████████████████████████████████████ | 4/7 [00:00<00:00, 9.47it/s]
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You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers
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Loading
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>>> DEVICE: cuda
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>>> PIPELINE TYPE: <class 'diffusers.pipelines.flux.
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>>> Moving transformer to cuda using to_empty()
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>>> WARNING: transformer.to_empty(cuda) failed: Module.to_empty() takes 1 positional argument but 2 were given
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>>> Falling back to transformer.to(cuda)
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@@ -146,16 +142,16 @@ Loading pipeline components...: 100%|██████████████
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>>> Moving text_encoder to cuda using to_empty()
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>>> WARNING: text_encoder.to_empty(cuda) failed: Module.to_empty() takes 1 positional argument but 2 were given
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>>> Falling back to text_encoder.to(cuda)
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```
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2) KeyError: <class 'diffusers.models.transformers.transformer_flux.FluxAttention'>
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Folder Structure
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- refer [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev/tree/main)
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- refer [black-forest-labs/FLUX.1-Kontext-dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev/tree/main)
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```python
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@staticmethod
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def _default_build(
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name: str, path: str, dtype: str | torch.dtype, device: str | torch.device, shift_activations: bool
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) -> DiffusionPipeline:
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if not path:
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if name == "sdxl":
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path = "stabilityai/sdxl-turbo"
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elif name == "pixart-sigma":
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path = "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS"
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elif name == "flux.1-kontext-dev":
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path = "black-forest-labs/FLUX.1-Kontext-dev"
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elif name == "flux.1-dev":
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path = "black-forest-labs/FLUX.1-dev"
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elif name == "flux.1-canny-dev":
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path = "black-forest-labs/FLUX.1-schnell"
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else:
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raise ValueError(f"Path for {name} is not specified.")
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if name in ["flux.1-kontext-dev"]:
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pipeline = FluxKontextPipeline.from_pretrained(path, torch_dtype=dtype)
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elif name in ["flux.1-canny-dev", "flux.1-depth-dev"]:
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pipeline = FluxControlPipeline.from_pretrained(path, torch_dtype=dtype)
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elif name == "flux.1-fill-dev":
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pipeline = FluxFillPipeline.from_pretrained(path, torch_dtype=dtype)
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elif name.startswith("sana-"):
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if dtype == torch.bfloat16:
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pipeline = SanaPipeline.from_pretrained(path, variant="bf16", torch_dtype=dtype, use_safetensors=True)
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pipeline.vae.to(dtype)
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pipeline.text_encoder.to(dtype)
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else:
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pipeline = SanaPipeline.from_pretrained(path, torch_dtype=dtype)
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else:
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pipeline = AutoPipelineForText2Image.from_pretrained(path, torch_dtype=dtype)
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# Debug output
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print(">>> DEVICE:", device)
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print(">>> PIPELINE TYPE:", type(pipeline))
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Debug Log
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```
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25-07-22 20:11:56 | I | === Start Evaluating ===
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25-07-22 20:11:56 | I | * Building diffusion model pipeline
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Loading pipeline components...: 0%| | 0/7 [00:00<?, ?it/s]
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You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers
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Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 18.92it/s]
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Loading pipeline components...: 100%|█████████████████████████████████████████████████████| 7/7 [00:00<00:00, 9.50it/s]
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>>> DEVICE: cuda
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>>> PIPELINE TYPE: <class 'diffusers.pipelines.flux.pipeline_flux_kontext.FluxKontextPipeline'>
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>>> Moving transformer to cuda using to_empty()
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>>> WARNING: transformer.to_empty(cuda) failed: Module.to_empty() takes 1 positional argument but 2 were given
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>>> Falling back to transformer.to(cuda)
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>>> Moving text_encoder to cuda using to_empty()
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>>> WARNING: text_encoder.to_empty(cuda) failed: Module.to_empty() takes 1 positional argument but 2 were given
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>>> Falling back to text_encoder.to(cuda)
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25-07-22 20:11:59 | I | Replacing fused Linear with ConcatLinear.
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25-07-22 20:11:59 | I | + Replacing fused Linear in single_transformer_blocks.0 with ConcatLinear.
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25-07-22 20:11:59 | I | - in_features = 3072/15360
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25-07-22 20:11:59 | I | - out_features = 3072
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25-07-22 20:11:59 | I | + Replacing fused Linear in single_transformer_blocks.1 with ConcatLinear.
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25-07-22 20:11:59 | I | - in_features = 3072/15360
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25-07-22 20:11:59 | I | - out_features = 3072
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25-07-22 20:11:59 | I | + Replacing fused Linear in single_transformer_blocks.2 with ConcatLinear.
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25-07-22 20:11:59 | I | - in_features = 3072/15360
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25-07-22 20:11:59 | I | - out_features = 3072
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```
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2) KeyError: <class 'diffusers.models.transformers.transformer_flux.FluxAttention'>
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