saddam213 commited on Mar 9

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Icon.png +3 -0
README.md +7 -0
Sample.png +3 -0
Sample2.png +3 -0
Sample3.png +3 -0
Sample4.png +3 -0
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README.md ADDED Viewed

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+# PhotographerAlpha LCM - Onnx DirectML Optimized
+## Original Model
+https://civitai.com/models/253141/photographer-alpha7lcm
+## Amuse
+https://www.amuse-ai.com/

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+    "FileVersion": "1",
+    "Created": "2025-03-08T00:00:00",
+    "IsProtected": false,
+    "Name": "Photographer Alpha",
+    "ImageIcon": "https://huggingface.co/TensorStack/PhotographerAlpha-LCM-amuse/resolve/main/Icon.png",
+    "Author": "dimonapatrick243",
+    "Description": "Latent Consistency Models (LCMs) enhance Latent Diffusion Models (LDMs) by reducing iterative steps, enabling faster, high-quality image generation. Efficiently trained, LCMs achieve state-of-the-art performance and can be fine-tuned for specific datasets.",
+    "Rank": 3000,
+    "Group": "Online",
+    "Template": "LCM",
+    "Category": "StableDiffusion",
+    "StableDiffusionTemplate": {
+        "PipelineType": "LatentConsistency",
+        "ModelType": "Base",
+        "SampleSize": 512,
+        "TokenizerLength": 768,
+        "Tokenizer2Limit": 77,
+        "DiffuserTypes": [
+            "TextToImage",
+            "ImageToImage",
+            "ImageInpaintLegacy",
+            "ControlNet",
+            "ControlNetImage"
+        ],
+        "SchedulerDefaults": {
+            "SchedulerType": "LCM",
+            "Steps": 6,
+            "StepsMin": 1,
+            "StepsMax": 50,
+            "Guidance": 1,
+            "GuidanceMin": 1,
+            "GuidanceMax": 1,
+            "TimestepSpacing": "Linspace",
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+            "BetaEnd": 0.012
+        }
+    },
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+    "MemoryMax": 4.6,
+    "DownloadSize": 4.2,
+    "Website": "https://civitai.com/models/253141/photographer-alpha7lcm",
+    "Licence": "https://huggingface.co/spaces/CompVis/stable-diffusion-license",
+    "LicenceType": "NonCommercial",
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+        "https://huggingface.co/TensorStack/PhotographerAlpha-LCM-amuse/resolve/main/README.md"
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+    "Tags": []
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