Update README.md

README.md

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----
-license: other
-base_model: "Qwen/Qwen-Image"
-tags:
-  - qwen_image
-  - qwen_image-diffusers
-  - text-to-image
-  - image-to-image
-  - diffusers
-  - simpletuner
-  - safe-for-work
-  - lora
-
-  - template:sd-lora
-  - lycoris
-pipeline_tag: text-to-image
-inference: true
-widget:
-- text: 'Graffito Mixed-Media Stop-Motion — MEDIUM SHOT of Tony, a puppet with a PHOTOGRAPHIC CUTOUT face and PAINTED PAPER CUTOUT body, running energetically towards the camera in a dimly lit street scene at night. Tony wears a light blue shirt with a red collar, his arms are outstretched, and his PHOTOGRAPHIC CUTOUT face shows an urgent expression. Behind him, a PAINTED CARDBOARD vehicle features a single bright headlight casting a harsh glare and illuminating Tony.'
-  parameters:
-    negative_prompt: 'blurry, cropped, ugly'
-  output:
-    url: ./assets/image_0_0.png
----
-
-# gr4f1tt0_v1_qwen
-
-This is a LyCORIS adapter derived from [Qwen/Qwen-Image](https://huggingface.co/Qwen/Qwen-Image).
-
-The main validation prompt used during training was:
-```
-Graffito Mixed-Media Stop-Motion — MEDIUM SHOT of Tony, a puppet with a PHOTOGRAPHIC CUTOUT face and PAINTED PAPER CUTOUT body, running energetically towards the camera in a dimly lit street scene at night. Tony wears a light blue shirt with a red collar, his arms are outstretched, and his PHOTOGRAPHIC CUTOUT face shows an urgent expression. Behind him, a PAINTED CARDBOARD vehicle features a single bright headlight casting a harsh glare and illuminating Tony.
-```
-
-
-## Validation settings
-- CFG: `3.5`
-- CFG Rescale: `0.0`
-- Steps: `30`
-- Sampler: `FlowMatchEulerDiscreteScheduler`
-- Seed: `None`
-- Resolution: `1536x640`
-
-
-Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
-
-You can find some example images in the following gallery:
-
-
-<Gallery />
-
-The text encoder **was not** trained.
-You may reuse the base model text encoder for inference.
-
-
-## Training settings
-
-- Training epochs: 297
-- Training steps: 8250
-- Learning rate: 0.0001
-  - Learning rate schedule: constant_with_warmup
-  - Warmup steps: 100
-- Max grad value: 0.01
-- Effective batch size: 4
-  - Micro-batch size: 1
-  - Gradient accumulation steps: 4
-  - Number of GPUs: 1
-- Gradient checkpointing: False
-- Prediction type: flow_matching[]
-- Optimizer: adamw_bf16
-- Trainable parameter precision: Pure BF16
-- Base model precision: `fp8-torchao`
-- Caption dropout probability: 0.0%
-
-
-
-### LyCORIS Config:
-```json
-{
-    "algo": "lokr",
-    "multiplier": 1.0,
-    "linear_dim": 10000,
-    "linear_alpha": 1,
-    "factor": 16,
-    "apply_preset": {
-        "target_module": [
-            "Attention",
-            "FeedForward"
-        ],
-        "module_algo_map": {
-            "Attention": {
-                "factor": 16
-            },
-            "FeedForward": {
-                "factor": 8
-            }
-        }
-    }
-}
-```
-
-
-## Datasets
-
-### graffito
-- Repeats: 0
-- Total number of images: 111
-- Total number of aspect buckets: 1
-- Resolution: 1.048576 megapixels
-- Cropped: False
-- Crop style: None
-- Crop aspect: None
-- Used for regularisation data: No
-
-
-## Inference
-
-
-```python
-import torch
-from diffusers import DiffusionPipeline
-from lycoris import create_lycoris_from_weights
-
-
-def download_adapter(repo_id: str):
-    import os
-    from huggingface_hub import hf_hub_download
-    adapter_filename = "pytorch_lora_weights.safetensors"
-    cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
-    cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
-    path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
-    path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
-    os.makedirs(path_to_adapter, exist_ok=True)
-    hf_hub_download(
-        repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
-    )
-
-    return path_to_adapter_file
-    
-model_id = 'Qwen/Qwen-Image'
-adapter_repo_id = 'davidrd123/gr4f1tt0_v1_qwen'
-adapter_filename = 'pytorch_lora_weights.safetensors'
-adapter_file_path = download_adapter(repo_id=adapter_repo_id)
-pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
-lora_scale = 1.0
-wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
-wrapper.merge_to()
-
-prompt = "Graffito Mixed-Media Stop-Motion — MEDIUM SHOT of Tony, a puppet with a PHOTOGRAPHIC CUTOUT face and PAINTED PAPER CUTOUT body, running energetically towards the camera in a dimly lit street scene at night. Tony wears a light blue shirt with a red collar, his arms are outstretched, and his PHOTOGRAPHIC CUTOUT face shows an urgent expression. Behind him, a PAINTED CARDBOARD vehicle features a single bright headlight casting a harsh glare and illuminating Tony."
-negative_prompt = 'blurry, cropped, ugly'
-
-## Optional: quantise the model to save on vram.
-## Note: The model was quantised during training, and so it is recommended to do the same during inference time.
-from optimum.quanto import quantize, freeze, qint8
-quantize(pipeline.transformer, weights=qint8)
-freeze(pipeline.transformer)
-    
-pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
-model_output = pipeline(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    num_inference_steps=30,
-    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
-    width=1536,
-    height=640,
-    guidance_scale=3.5,
-).images[0]
-
-model_output.save("output.png", format="PNG")
-
-```
-
-
-