photodoodel added

Author: Your Name

src/diffusers/pipelines/__init__.py

@@ -30,6 +30,7 @@
     "ledits_pp": [],
     "marigold": [],
     "pag": [],
+    "photodoodle": [],
     "stable_diffusion": [],
     "stable_diffusion_xl": [],
 }
@@ -53,6 +54,7 @@
     _import_structure["ddpm"] = ["DDPMPipeline"]
     _import_structure["dit"] = ["DiTPipeline"]
     _import_structure["latent_diffusion"].extend(["LDMSuperResolutionPipeline"])
+    _import_structure["photodoodle"].extend(["PhotoDoodlePipeline"])
     _import_structure["pipeline_utils"] = [
         "AudioPipelineOutput",
         "DiffusionPipeline",
@@ -286,6 +288,7 @@
     _import_structure["mochi"] = ["MochiPipeline"]
     _import_structure["musicldm"] = ["MusicLDMPipeline"]
     _import_structure["omnigen"] = ["OmniGenPipeline"]
+    _import_structure["photodoodle"].extend(["PhotoDoodlePipeline"])
     _import_structure["visualcloze"] = ["VisualClozePipeline", "VisualClozeGenerationPipeline"]
     _import_structure["paint_by_example"] = ["PaintByExamplePipeline"]
     _import_structure["pia"] = ["PIAPipeline"]
@@ -492,6 +495,7 @@
         from .deprecated import KarrasVePipeline, LDMPipeline, PNDMPipeline, RePaintPipeline, ScoreSdeVePipeline
         from .dit import DiTPipeline
         from .latent_diffusion import LDMSuperResolutionPipeline
+        from .photodoodle import PhotoDoodlePipeline
         from .pipeline_utils import (
             AudioPipelineOutput,
             DiffusionPipeline,

src/diffusers/pipelines/photodoodle/README.md

@@ -0,0 +1,66 @@
+# PhotoDoodle Pipeline
+
+The PhotoDoodle pipeline is designed for image generation with conditional image input. It uses a combination of text and image conditioning to generate high-quality images.
+
+## Model Architecture
+
+The pipeline uses the following components:
+
+1. **Transformer**: A FluxTransformer2DModel for denoising image latents
+2. **VAE**: An AutoencoderKL for encoding/decoding images
+3. **Text Encoders**: 
+   - CLIP text encoder for initial text embedding
+   - T5 encoder for additional text understanding
+4. **Scheduler**: FlowMatchEulerDiscreteScheduler for the diffusion process
+
+## Usage
+
+```python
+from diffusers import PhotoDoodlePipeline
+import torch
+
+pipeline = PhotoDoodlePipeline.from_pretrained("black-forest-labs/FLUX.1-dev")
+pipeline = pipeline.to("cuda")
+# Load initial model weights
+pipeline.load_lora_weights("nicolaus-huang/PhotoDoodle", weight_name="pretrain.safetensors")
+pipeline.fuse_lora()
+pipeline.unload_lora_weights()
+
+pipeline.load_lora_weights("nicolaus-huang/PhotoDoodle",weight_name="sksmagiceffects.safetensors")
+
+# Generate image with text prompt and condition image
+prompt = "add a halo and wings for the cat by sksmagiceffects"
+condition_image = load_image("path/to/condition.jpg")  # PIL Image
+output = pipeline(
+    prompt=prompt,
+    condition_image=condition_image,
+    num_inference_steps=28,
+    guidance_scale=3.5
+)
+
+# Save the generated image
+output.images[0].save("generated_image.png")
+```
+
+## Parameters
+
+- `prompt`: Text prompt for image generation
+- `prompt_2`: Optional secondary prompt for T5 encoder
+- `condition_image`: Input image for conditioning
+- `height`: Output image height (default: 512)
+- `width`: Output image width (default: 512)
+- `num_inference_steps`: Number of denoising steps (default: 28)
+- `guidance_scale`: Classifier-free guidance scale (default: 3.5)
+- `num_images_per_prompt`: Number of images to generate per prompt
+- `generator`: Random number generator for reproducibility
+- `output_type`: Output format ("pil", "latent", or "pt")
+
+## Features
+
+- Dual text encoder architecture (CLIP + T5)
+- Image conditioning support
+- Position encoding for better spatial understanding
+- Support for LoRA fine-tuning
+- VAE slicing and tiling for memory efficiency
+- Progress bar during generation
+- Callback support for step-by-step monitoring 

src/diffusers/pipelines/photodoodle/__init__.py

@@ -0,0 +1,39 @@
+"""
+PhotoDoodle pipeline for image generation.
+"""
+
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_torch_available,
+    is_transformers_available,
+)
+
+_dummy_objects = {}
+_import_structure = {
+    "pipeline_photodoodle": ["PhotoDoodlePipeline"],
+}
+
+try:
+    if not (is_torch_available() and is_transformers_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
+else:
+    from .pipeline_photodoodle import PhotoDoodlePipeline
+
+if TYPE_CHECKING:
+    from .pipeline_photodoodle import PhotoDoodlePipeline
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )