Code cleaning and removed redundant code-2

Signed-off-by: Amit Raj <[email protected]>

Author: Amit Raj

QEfficient/diffusers/models/transformers/__init__.py

@@ -0,0 +1,6 @@
+# -----------------------------------------------------------------------------
+#
+# Copyright (c) Qualcomm Technologies, Inc. and/or its subsidiaries.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# ----------------------------------------------------------------------------

QEfficient/utils/_utils.py

@@ -532,7 +532,7 @@ def create_model_params(qeff_model, **kwargs) -> Dict:
     """
     model_params = copy.deepcopy(kwargs)
     model_params = {k: v for k, v in model_params.items() if k in KWARGS_INCLUSION_LIST}
-    model_params["config"] = qeff_model.model.config
+    model_params["config"] = qeff_model.model.config.to_diff_dict()
     model_params["peft_config"] = getattr(qeff_model.model, "active_peft_config", None)
     model_params["applied_transform_names"] = qeff_model._transform_names()
     return model_params

examples/diffusers/flux/flux_1_schnell.py

@@ -4,20 +4,48 @@
 # SPDX-License-Identifier: BSD-3-Clause
 #
 # -----------------------------------------------------------------------------
+
+"""
+FLUX.1-schnell Image Generation Example
+
+This example demonstrates how to use the QEFFFluxPipeline to generate images
+using the FLUX.1-schnell model from Black Forest Labs. FLUX.1-schnell is a
+fast, distilled version of the FLUX.1 text-to-image model optimized for
+speed with minimal quality loss.
+
+Key Features:
+- Fast inference with only 4 steps
+- High-quality image generation from text prompts
+- Optimized for Qualcomm Cloud AI 100 using ONNX runtime
+- Deterministic output using fixed random seed
+
+Output:
+- Generates an image based on the text prompt
+- Saves the image as 'cat_with_sign.png' in the current directory
+"""
+
 import torch
 
 from QEfficient import QEFFFluxPipeline
 
-pipeline = QEFFFluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", use_onnx_function=True)
+# Initialize the FLUX.1-schnell pipeline from pretrained weights
+# use_onnx_function=True enables ONNX-based optimizations for faster compilation
+pipeline = QEFFFluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", use_onnx_function=False)
 
+# Generate an image from a text prompt
 output = pipeline(
     prompt="A cat holding a sign that says hello world",
     guidance_scale=0.0,
     num_inference_steps=4,
     max_sequence_length=256,
     generator=torch.manual_seed(42),
 )
+
+# Extract the generated image from the output
 image = output.images[0]
+
+# Save the generated image to disk
 image.save("cat_with_sign.png")
 
+# Print the output object (contains perf info)
 print(output)

examples/diffusers/flux/flux_1_shnell_custom.py

@@ -37,17 +37,17 @@
 # Option 1: Basic initialization with custom image dimensions
 pipeline = QEFFFluxPipeline.from_pretrained(
     "black-forest-labs/FLUX.1-schnell",
-    height=256,
-    width=256,
+    height=512,
+    width=512,
 )
 
 # Option 2: Advanced initialization with custom modules
 # Uncomment and modify to use your own custom components:
 #
 # pipeline = QEFFFluxPipeline.from_pretrained(
 #     "black-forest-labs/FLUX.1-schnell",
-#     height=256,
-#     width=256,
+#     height=512,
+#     width=512,
 #     text_encoder=custom_text_encoder,      # Your custom CLIP text encoder
 #     transformer=custom_transformer,         # Your custom transformer model
 #     tokenizer=custom_tokenizer,             # Your custom tokenizer

scripts/Jenkinsfile

@@ -22,6 +22,7 @@ pipeline {
                    . preflight_qeff/bin/activate &&
                    pip install --upgrade pip setuptools &&
                    pip install .[test] &&
+                   pip install .[diffusers] &&
                    pip install junitparser pytest-xdist &&
                    pip install librosa==0.10.2 soundfile==0.13.1 && #packages needed to load example for whisper testing
                    pip install --extra-index-url https://download.pytorch.org/whl/cpu timm==1.0.14 torchvision==0.22.0+cpu einops==0.8.1 && #packages to load VLMs