Model card auto-generated by SimpleTuner

README.md

@@ -18,7 +18,7 @@ widget:
     negative_prompt: 'blurry, cropped, ugly'
   output:
     url: ./assets/image_0_0.png
-- text: 'This vibrant photograph captures a woman standing on a wooden deck, gazing out at a stunning, clear, turquoise ocean below. The woman is a tall, slim blonde with long, straight hair flowing down her back. She wears a bright turquoise bikini that contrasts with the vivid blue water and highlights her tanned skin. The wooden deck is sturdy and well-built, with a railing that she leans on for support. The deck is positioned on a cliff that overlooks a breathtaking seascape. The water is a gradient of vibrant turquoise and deep blue, with a sandy beach visible at the bottom of the image. The beach is pristine white, with gentle waves lapping against it. Three sailboats are anchored in the water, adding a sense of scale and perspective. The cliffs are lush with green vegetation, contrasting with the blue water and sky. The sky is a clear, bright blue, with no clouds. The overall mood is one of serene, tranquil relaxation, with the bright colors and clear, sharp details adding to the vividness and clarity of the scene.'
+- text: 'a women laughing with short hair'
   parameters:
     negative_prompt: 'blurry, cropped, ugly'
   output:
@@ -32,7 +32,7 @@ This is a standard PEFT LoRA derived from [black-forest-labs/FLUX.1-dev](https:/
 
 The main validation prompt used during training was:
 ```
-This vibrant photograph captures a woman standing on a wooden deck, gazing out at a stunning, clear, turquoise ocean below. The woman is a tall, slim blonde with long, straight hair flowing down her back. She wears a bright turquoise bikini that contrasts with the vivid blue water and highlights her tanned skin. The wooden deck is sturdy and well-built, with a railing that she leans on for support. The deck is positioned on a cliff that overlooks a breathtaking seascape. The water is a gradient of vibrant turquoise and deep blue, with a sandy beach visible at the bottom of the image. The beach is pristine white, with gentle waves lapping against it. Three sailboats are anchored in the water, adding a sense of scale and perspective. The cliffs are lush with green vegetation, contrasting with the blue water and sky. The sky is a clear, bright blue, with no clouds. The overall mood is one of serene, tranquil relaxation, with the bright colors and clear, sharp details adding to the vividness and clarity of the scene.
+a women laughing with short hair
 ```
 
 
@@ -42,7 +42,7 @@ This vibrant photograph captures a woman standing on a wooden deck, gazing out a
 - Steps: `20`
 - Sampler: `FlowMatchEulerDiscreteScheduler`
 - Seed: `42`
-- Resolution: `1344x768`
+- Resolution: `1024x1024`
 - Skip-layer guidance: 
 
 Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
@@ -58,12 +58,12 @@ You may reuse the base model text encoder for inference.
 
 ## Training settings
 
-- Training epochs: 7
-- Training steps: 3500
+- Training epochs: 1
+- Training steps: 500
 - Learning rate: 0.0004
   - Learning rate schedule: polynomial
   - Warmup steps: 100
-- Max grad norm: 1.0
+- Max grad norm: 2.0
 - Effective batch size: 1
   - Micro-batch size: 1
   - Gradient accumulation steps: 1
@@ -83,27 +83,27 @@ You may reuse the base model text encoder for inference.
 
 ## Datasets
 
-### deephouse-512
+### nobel-512
 - Repeats: 10
-- Total number of images: 15
+- Total number of images: 11
 - Total number of aspect buckets: 1
 - Resolution: 0.262144 megapixels
 - Cropped: False
 - Crop style: None
 - Crop aspect: None
 - Used for regularisation data: No
-### deephouse-768
+### nobel-768
 - Repeats: 10
-- Total number of images: 15
+- Total number of images: 11
 - Total number of aspect buckets: 1
 - Resolution: 0.589824 megapixels
 - Cropped: False
 - Crop style: None
 - Crop aspect: None
 - Used for regularisation data: No
-### deephouse-1024
+### nobel-1024
 - Repeats: 10
-- Total number of images: 15
+- Total number of images: 11
 - Total number of aspect buckets: 1
 - Resolution: 1.048576 megapixels
 - Cropped: False
@@ -124,22 +124,22 @@ adapter_id = 'linhqyy/deephouse-st-2911'
 pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
 pipeline.load_lora_weights(adapter_id)
 
-prompt = "This vibrant photograph captures a woman standing on a wooden deck, gazing out at a stunning, clear, turquoise ocean below. The woman is a tall, slim blonde with long, straight hair flowing down her back. She wears a bright turquoise bikini that contrasts with the vivid blue water and highlights her tanned skin. The wooden deck is sturdy and well-built, with a railing that she leans on for support. The deck is positioned on a cliff that overlooks a breathtaking seascape. The water is a gradient of vibrant turquoise and deep blue, with a sandy beach visible at the bottom of the image. The beach is pristine white, with gentle waves lapping against it. Three sailboats are anchored in the water, adding a sense of scale and perspective. The cliffs are lush with green vegetation, contrasting with the blue water and sky. The sky is a clear, bright blue, with no clouds. The overall mood is one of serene, tranquil relaxation, with the bright colors and clear, sharp details adding to the vividness and clarity of the scene."
+prompt = "a women laughing with short hair"
 
 
 ## 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)
+## Note: The model was not quantised during training, so it is not necessary to quantise it 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
 image = pipeline(
     prompt=prompt,
     num_inference_steps=20,
     generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
-    width=1344,
-    height=768,
+    width=1024,
+    height=1024,
     guidance_scale=3.0,
 ).images[0]
 image.save("output.png", format="PNG")