Add comprehensive model card for training compatibility

README.md

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+---
+license: other
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+  - flux
+  - flux-dev
+  - text-to-image
+  - diffusers
+  - safetensors
+  - stable-diffusion
+  - image-generation
+base_model: black-forest-labs/FLUX.1-dev
+---
+
+# Real Dream Flux V1 V4 (RDV4)
+
+A fine-tuned FLUX.1-dev transformer model optimized for high-quality image generation with improved realism and detail.
+
+## Model Description
+
+**Real Dream Flux V1 V4** is a specialized version of FLUX.1-dev trained to produce photorealistic and detailed imagery. This model contains the transformer component in safetensors format, making it compatible with various training and inference tools including Ostris AI Toolkit.
+
+### Model Details
+
+- **Model Type:** FLUX Transformer (FluxTransformer2DModel)
+- **Base Model:** black-forest-labs/FLUX.1-dev
+- **Format:** safetensors (15.91 GB)
+- **Architecture:** 
+  - 19 dual attention layers
+  - 38 single attention layers
+  - 24 attention heads with 128 dims per head
+  - Joint attention dimension: 4096
+
+## Usage
+
+### With Diffusers
+
+```python
+from diffusers import FluxPipeline, FluxTransformer2DModel
+import torch
+
+# Load the custom transformer
+transformer = FluxTransformer2DModel.from_pretrained(
+    "SD2704/RDV4",
+    subfolder="",
+    torch_dtype=torch.bfloat16
+)
+
+# Create pipeline with custom transformer
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    transformer=transformer,
+    torch_dtype=torch.bfloat16
+)
+pipe.to("cuda")
+
+prompt = "your prompt here"
+image = pipe(prompt, num_inference_steps=50, guidance_scale=3.5).images[0]
+image.save("output.png")
+```
+
+### For Training with Ostris AI Toolkit
+
+This model is fully compatible with Ostris AI Toolkit GUI for fine-tuning and LoRA training:
+
+1. In Ostris AI Toolkit, select "Load Model from Hugging Face"
+2. Enter model ID: `SD2704/RDV4`
+3. The toolkit will automatically download the transformer and config
+4. Configure your training parameters and dataset
+5. Start training!
+
+### Training Configuration
+
+Recommended settings for fine-tuning:
+- Learning rate: 1e-5 to 5e-6
+- Batch size: 1-2 (depending on VRAM)
+- Gradient accumulation: 4-8 steps
+- Mixed precision: bf16
+- Optimizer: AdamW or AdamW8bit
+
+## Training Data
+
+This model was trained on curated datasets focusing on photorealistic imagery and detailed compositions.
+
+## Intended Use
+
+- High-quality image generation
+- Fine-tuning for specialized domains
+- LoRA training for style adaptation
+- Research and creative applications
+
+## Limitations
+
+- Requires significant VRAM for inference (minimum 16GB recommended)
+- Based on FLUX.1-dev architecture limitations
+- May require prompt engineering for optimal results
+
+## License
+
+This model follows the FLUX.1-dev licensing terms. Please refer to the base model license for usage restrictions.
+
+## Citation
+
+```
+@misc{rdv4,
+  author = {SD2704},
+  title = {Real Dream Flux V1 V4},
+  year = {2024},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/SD2704/RDV4}
+}
+```
+
+## Acknowledgments
+
+Based on [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) by Black Forest Labs.